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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

DSS-13: Using an OSI Process Control Standard for Monitor and Control

The flexibility and robustness of a monitor and control (M&C) system are a direct result of the underlying inter-processor communications architecture. A new architecture for M&C at the Deep Space Communications Complexes (DSCCs) has been developed based on the Manufacturing Message Specification (MMS) process control standard of the Open System Interconnection (OSI) suite of protocols. This architecture has been tested both in a laboratory environment and under operational conditions at the Deep Space Network (DSN) experimental station (DSS-13). The DSS-13 experience in the application of OSI standards to support M&C has been extremely successful. MMS meets the functional needs of the station and provides a level of flexibility and responsiveness previously unknown in that environment.

Stockett, Michael H.↗
NASA NTRS1993

Using Manufacturing Message Specification for Monitor and Control at Venus

The flexibility and robustness of a monitor and control (M&C) system are a direct result of the underlying inter-processor communications architecture. A new architecture for M&C at the Deep Space Communications Complexes (DSCCs) has been developed based on the Manufacturing Message Specification (MMS) process control standard of the Open System Interconnection (OSI) suite of protocols. This architecture has been tested both in a laboratory environment and under operational conditions at the Deep Space Network (DSN) experimental Venus station (DSS-13). The Venus experience in the application of OSI standards to support M&C has been extremely successful. MMS meets the functional needs of the station and provides a level of flexibility and responsiveness previously unknown in that environment...

Stockett, Michael H.↗
DOE OSTI2017

Application of an in-situ soil sampler for assessing subsurface biogeochemical dynamics in a diesel-contaminated coastal site during soil flushing operations

Subsurface biogeochemistry and contaminant dynamics during the remediation of diesel-contamination by in-situ soil flushing were investigated at a site located in a coastal region. An in-situ sampler containing diesel-contaminated soils separated into two size fractions (<0.063- and <2-mm) was utilized in two monitoring wells: DH1 (located close to the injection and extraction wells for in-situ soil flushing) and DH2 (located beyond sheet piles placed to block the transport of leaked diesel). Total petroleum hydrocarbon (TPH) concentrations and biogeochemical properties were monitored both in soil and groundwater for six months. A shift occurred in the groundwater type from Ca-HCO 3 to Na-CI due to seawater intrusion during intense pumping, while the concentrations of Ni, Cu, Co, V, Cr, and Se increased substantially following surfactant (TWEEN 80) injection. The in-situ sampler with fine particles was more sensitive to variations in conditions during the remedial soil flushing process. In both wells, soil TPH concentrations in the <0.063-mm fraction were much higher than those in the <2-mm fraction. Increases in soil TPH in DH1 were consistent with the expected outcomes following well pumping and surfactant injection used to enhance TPH extraction. However, the number of diesel-degrading microorganisms decreased after surfactant injection. 16S-rRNA gene-based analysis also showed that the community composition and diversity depended on both particle size and diesel contamination. The multidisciplinary approach to the contaminated site assessments showed that soil flushing with surfactant enhanced diesel extraction, but negatively impacted in-situ diesel biodegradation as well as groundwater quality. In conclusion, the results also suggest that the in-situ sampler can be an effective monitoring tool for subsurface biogeochemistry as well as contaminant dynamics.

02 PETROLEUM↗
NASA NTRS2005

Optically Stimulated Electron Emission Contamination Monitor and Method

An apparatus and method for performing quality inspections on a test surface based on optically stimulated emission of electrons. In one embodiment, the apparatus comprises a device for producing optical radiation having a plurality of different spectrum lines, selecting at least one of the spectrum lines, and directing the selected spectrum line to the test surface, and circuitry for detecting a current of photoelectrons emitted from the test surface, generating a signal indicative of photoelectron current, and for indicating a condition of quality based on the generated signal indicative of the photoelectron current. In one embodiment, the method comprises producing optical radiation having a plurality of different spectrum lines, selecting at least one of the spectrum lines and directing the selected spectrum line to the test surface, detecting a current of photoelectrons emitted from the test surface and generating a signal indicative of photoelectron current, and indicating a condition of quality based on the generated signal indicative of the photoelectron current.

Christopher S Welch↗
DOE OSTI2023

Machine-learning-based, online estimation of ceramic’s microstructure upon the laser spot brightness during laser sintering

The ceramic microstructure strongly influences its properties. During manufacturing, the online monitoring of microstructure is critical to ensure the desired material properties. So far, the microstructure on the relevant scale is usually characterized offline using scanning electron microscopy (SEM), which is time and cost-consuming. In this work, we demonstrate a cost-effective, machine learning (ML)-based approach to simulate the SEM micrographs in real-time from the laser spot brightness. We experimentally observed a strong correlation between the laser spot brightness and the corresponding microstructure at the exact locations. The brightness values obtained from thermal emission images and the corresponding SEM micrographs were used in the training datasets. The ML algorithm was a style-based conditional generative adversarial network (CGAN). After training, the ML model could generate high-fidelity microstructure images within 0.1 seconds based on in-situ captured brightness at the laser sintering spot. We used the average grain sizes as the metric to evaluate the accuracy of the ML-predicted micrographs. Here, the ML-predicted microstructures were in good agreement, with less than 5% in difference from the real SEM images. In conclusion, we demonstrate the cost-effective, online microstructure estimation during laser sintering with a simple setup (a camera, a regular computer, and the ML model).

08 HYDROGEN↗
DOE OSTI2022

Development of fast-running eighth core nodal solution

A fast-running, eighth-core solution technique has been developed for the quick core calculations in ANC, the Westinghouse core analysis code based on the nodal expansion method, and the BEACON{sup TM} Core Monitoring System (BEACON). This technique, referred to as 'Fast Eighth' (Fast8), enables users to obtain satisfactory macroscopic core calculation results such as reactivity, axial offset and assembly power much more quickly than calculations performed in the standard full-core or quarter-core geometry. Fast8 uses three techniques: folding the radial geometry into eighth-core geometry, reducing the axial geometry into a coarse axial mesh geometry, and smearing the spacer grids into nodes on-the-fly after the first calculation in the standard geometry. These geometry adjustments in Fast8 contribute to the reduction of the number of neutronic and material nodes while maintaining the reaction rates in each node. The axial mesh condition dependency in Fast8 is investigated in a xenon transient calculation in single assembly geometry, and the Fast8 maximum axial mesh size is determined based on these results. Fast8 is then applied to the load swing calculation, and the results are compared with the reference, full-core geometry, solution. The critical boron concentration and axial offset of Fast8 show good agreement with the reference solutions, and that Fast8 is a good technique for obtaining macroscopic core calculation results in significantly shorter time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

42 ENGINEERING↗
DOE OSTI2024

Enhancing Data Quality Monitoring at CMS with Interactive Visualization Tools and Automated Reference Run Selection

Current data quality monitoring (DQM) tools at CMS offer granularity limited to per-run analysis. Consequently, issues manifesting at the per-lumisection level can go unnoticed or, even if detectable, often lead to the classification of the whole run as bad, resulting in unnecessary data loss. Additionally, shifters have to evaluate a large set of monitoring elements during their long shifts, increasing the probability of human errors or overlooked problems. In this contribution, we present ongoing work on the development of tools that will provide shifters with an accessible, granularity-enhanced view of DQM data through interactive and dynamic visualizations. Furthermore, we introduce a reference run selection tool currently under development, which will automate the selection based on data-taking conditions and will offer a curated set of training data for machine learning models that will be used for the partial automation of the offline data certification process. These endeavors will be integrated into the DIALS website, enabling enhancements in data certification accuracy and improving the accessibility of DQM at CMS.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1974

Preliminary results of ERTS-investigations by W-German investigations

Results are presented of West German investigations into multidisciplinary geoscientific experiments in central Germany and the Alps, and hydrogeological investigations in the Pampa of Argentina based on ERTS-1 data. The main goals of the investigation were achieved. The studies have given a good idea of the possibilities and limitations of ERTS imagery depending on the objectives in question and on the geographical conditions of the areas under investigation. Even in the well known region of central Europe, ERTS has proven its ability of improving present knowledge. In fields such as pollution monitoring and regional planning the satellite techniques should have distinct practical value. For any regional study of less known areas, the value of ERTS imagery can hardly be overestimated.

Muehlfeld, R.↗
NASA NTRS1976

Microbiology studies in the Space Shuttle

Past space microbiology studies have evaluated three general areas: microbe detection in extraterrestrial materials; monitoring of autoflora and medically important species on crewmembers, equipment, and cabin air; and in vitro evaluations of isolated terrestrial species carried on manned and unmanned spaceflights. These areas are briefly reviewed to establish a basis for presenting probable experiment subjects applicable to the Space Shuttle era. Most extraterrestrial life detection studies involve visitations to other heavenly bodies. Although this is not applicable to the first series of Shuttle flights, attempts to capture meteors and spores in space could be important. Human pathogen and autoflora monitoring will become more important with increased variety among crewmembers. Inclusion of contaminated animal and plant specimens in the space lab will necessitate inflight evaluation of cross-contamination and infection potentials. The majority of Shuttle microbiology studies will doubtless fall into the third study area. Presence of a space lab will permit a whole range of experimentation under conditions similar to these experienced in earth-based laboratories. The recommendations of various study groups are analyzed, and probable inflight microbiological experiment areas are identified for the Life Sciences Shuttle Laboratory.

Taylor, G. R.↗
NASA NTRS1976

Display/control requirements for automated VTOL aircraft

A systematic design methodology for pilot displays in advanced commercial VTOL aircraft was developed and refined. The analyst is provided with a step-by-step procedure for conducting conceptual display/control configurations evaluations for simultaneous monitoring and control pilot tasks. The approach consists of three phases: formulation of information requirements, configuration evaluation, and system selection. Both the monitoring and control performance models are based upon the optimal control model of the human operator. Extensions to the conventional optimal control model required in the display design methodology include explicit optimization of control/monitoring attention; simultaneous monitoring and control performance predictions; and indifference threshold effects. The methodology was applied to NASA's experimental CH-47 helicopter in support of the VALT program. The CH-47 application examined the system performance of six flight conditions. Four candidate configurations are suggested for evaluation in pilot-in-the-loop simulations and eventual flight tests.

Hoffman, W. C.↗
NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗
NASA NTRS1988

Hierarchical representation and machine learning from faulty jet engine behavioral examples to detect real time abnormal conditions

The theoretical basis and operation of LEBEX, a machine-learning system for jet-engine performance monitoring, are described. The behavior of the engine is modeled in terms of four parameters (the rotational speeds of the high- and low-speed sections and the exhaust and combustion temperatures), and parameter variations indicating malfunction are transformed into structural representations involving instances and events. LEBEX extracts descriptors from a set of training data on normal and faulty engines, represents them hierarchically in a knowledge base, and uses them to diagnose and predict faults on a real-time basis. Diagrams of the system architecture and printouts of typical results are shown.

Gupta, U. K.↗
DOE OSTI2019

Thermally Conductive Backsheets (TCB) for Reduced Operating Temperatures

The performance of photovoltaic (PV) modules is primarily influenced by the plane of array (POA) irradiance and their operating temperature. The lower the module operating temperature, the higher is its performance. Increasing a module’s operating temperature reduces its power output due to an increase in the semiconductor’s intrinsic carrier density, leading to a reduction in the open-circuit voltage and, subsequently, reducing the maximum power point voltage. Reducing the operating temperature lowers the levelized cost of energy (LCOE) via increased energy production (i.e., more kilowatt hour over the life of the asset) and increased service lifetime (years in the field). The module operating temperature is dictated by the weather parameters and the thermal properties of module construction materials. The primary weather parameters influencing the PV module temperature are: the ambient temperature (directly related), irradiance (directly related), and wind speed (inversely related). The primary thermal properties of module construction materials influencing the PV module temperature are: the optical transmissivity and thermal conductivity of superstrate, the thermal conductivity of substrate, the thermal conductivity of encapsulant and the efficiency of the encapsulated solar cells. The primary goal of this study is to reduce the operating temperature of PV modules using thermally conductive backsheet (TCB) substrates developed by the industry. The temperature of PV modules installed in a large multi-MW plant can also be influenced by their physical locations in the plants due to the influence of wind direction. A thermal mapping of PV plants has also been investigated and presented in this study. The most commonly used PV backsheet material has been polyvinyl fluoride (DuPont trade name “Tedlar”)-based backsheet, which typically has three layers: Tedlar–polyethylene terephthalate (PET)–Tedlar (TPT). The primary goal of this project is to reduce the module operating temperature below the benchmark TPT backsheet temperature using newly developed TCBs by the industry. The overall approach of this project involves six focus areas: i) select and procure one TPT and thirteen promising TCBs available in the industry; ii) construct a large number of 1-cell and 9-cell modules using benchmark TPT and selected TCBs [all other construction materials are fixed and they are: glass superstrate, EVA encapsulant, mono-Si cell and junction box]; iii) to identify best performing TCBs, monitor the long-term field operating temperatures of the constructed modules which are installed in three climatic conditions (hot-low wind [Mesa, AZ], hot-high wind [Maricopa County, AZ] and temperate [Cumberland County, NC]; iv) to identify the most durable TCB, perform extended accelerated reliability testing of modules constructed with best three TCBs and benchmark TPT; v) to predict module temperature, develop physical and empirical thermal models with and without thermal properties of all construction materials in the modules, respectively; and vi) to determine the influence of wind speed and direction on the module operating temperature in a plant, and perform thermal mapping of PV modules in two large PV plants [Maricopa County, AZ and Cumberland County, NC]. The key achievements of this project are listed below: Backsheet selection: 14 backsheets were procured after an extensive literature and industry search. Finally, based on the thermal and other selection criteria, 1 TPT and 4 thermally conductive backsheets were selected for this project. Fabrication of mini modules: 50 one-cell modules were fabricated in year 1 for thermal performance evaluation. The module design was changed to nine-cell module at start of year 2 to mimic commercial design. 20 nine-cell modules were fabricated in year 2 for thermal performance evaluation. 28 four-cell modules were fabricated in year 3 for accelerated stress-testing. Thermal conductivity of backsheets: Year 1 TCBs showed at least 8% higher thermal conductivities than TPT. Year 2 and 3 TCBs showed at least 20% higher thermal conductivities than TPT. Installation of mini PV modules at three sites: 6 one-cell modules and 8 nine-cell modules were installed at AZ-1 site [Mesa, AZ]. 12 one-cell modules and 6 nine-cell modules were installed at each of two powerplants (AZ-2 [Maricopa County, AZ] and NC sites). Nominal operating cell temperatures (NOCT) of mini PV modules: NOCT of TCBs were observed to be lower than TPT with differences as high as 2°C. Comparison of thermal performance: The median operating temperature of one-cell modules was found to be reduced by 2°C by using TCB_A modules compared to TPT modules on several days in a month for few months in a year. The median temperature improvement of 4oC was achieved at several instances on a few days, but not all days, in a year using multiple TCBs compared to TPT. The final project milestone of achieving median temperature improvement of 5oC was achieved at several instances on a few days, but not all days, in a year using TCB_A compared to TPT. Empirical thermal model (without the need of construction material properties): Empirical thermal model for one-cell module was developed based on the input data of irradiance, wind speed and ambient temperatures. The primary input data differentiator for temperature differences between TPT and TCBs appears to be the irradiance level (solar gain). Accelerated durability testing: 20 four-cell modules using 4 backsheets (1 TPT and 3 TCBs) were exposed to Damp Heat 1000 hours (8 modules), UV/Thermal cycling (50 cycles)/Humidity freeze (10 cycles) test sequence (8 modules) and UV Weathering for qualification (8 modules). All the modules with four selected backsheets passed the stress tests per the retest guidelines of IEC 61215 standard for the backsheet change. Simplified comparative LCOE analysis: Using backsheet-specific power degradation levels in the accelerated tests (for TCB_A and TPT only), efficiency, service lifetime and assuming same cost for all backsheets, the LCOE of modules using TCB_A is determined to be lower than TPT. Due to lower operating temperature of TCB_A-based modules compared to TPT-based modules, the operating efficiency and service life of TCB_A-based modules are calculated to be higher than TPT-based modules. Temperature mapping of PV power plants: A distinct temperature difference (as high as 4°C) has been observed between the modules located in various parts of a single PV plant.

14 SOLAR ENERGY↗
DOE OSTI2024

Remote Sensing Detection of Growing Season Freeze-Induced Defoliation of Montane Quaking Aspen (Populus tremuloides) in Southern Utah, USA

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗
NASA NTRS1988

Integration of a computerized two-finger gripper for robot workstation safety

A microprocessor-based controller has been developed that continuously monitors and adjusts the gripping force applied by a special two-finger gripper. This computerized force sensing gripper system enables the endeffector gripping action to be independently detected and corrected. The gripping force applied to a manipulated object is real-time monitored for problem situations, situations which can occur during both planned and errant robot arm manipulation. When unspecified force conditions occur at the gripper, the gripping force controller initiates specific reactions to cause dynamic corrections to the continuously variable gripping action. The force controller for this intelligent gripper has been interfaced to the controller of an industrial robot. The gripper and robot controllers communicate to accomplish the successful completion of normal gripper operations as well as unexpected hazardous situations. An example of an unexpected gripping condition would be the sudden deformation of the object being manipulated by the robot. The capabilities of the interfaced gripper-robot system to apply workstation safety measures (e.g., stop the robot) when these unexpected gripping effects occur have been assessed.

Sneckenberger, John E.↗
NASA NTRS2000

The Visibility of Earth Transits

The recent photometric detection of planetary transits of the solar-like star HD 209458 at a distance of 47 parsecs suggest that transits can reveal the presence of Jupiter-size planetary companions in the solar neighborhood. Recent space-based transit searches have achieved photometric precision within an order of magnitude of that required to detect the much smaller transit signal of an earth-size planet across a solar-size star. Laboratory experiments in the presence of realistic noise sources have shown that CCDs can achieve photometric precision adequate to detect the 9.6 E-5 dimming of the Sun due to a transit of the Earth. Space-based solar irradiance monitoring has shown that the intrinsic variability of the Sun would not preclude such a detection. Transits of the Sun by the Earth would be detectable by observers that reside within a narrow band of sky positions near the ecliptic plane, if the observers possess current Earth epoch levels of technology and astronomical expertise. A catalog of solar-like stars that satisfy the geometric condition for Earth transit visibility are presented.

Castellano, Timothy P.↗