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At least 109 records · Page 6

GPS-supported smartphone app-based integrated travel diary and time-use data collection: challenges and lessons learned

Travel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

GPS-supported smartphone apps↗

Partial reflection data collection and processing using a small computer

Online data collection of the amplitudes of circularly polarized radio waves, partially reflected from the D region of the earth's ionosphere, has enabled the calculation of an electron-density profile in the height region 60-90 km. A PDP 15/30 digital computer with an analog to digital converter and magnetic tape as an intermediary storage device are used. The computer configuration, the software developed, and the preliminary results are described.

Birley, M. H.↗

A seismic signal processor suitable for use with the NOAA/GOES satellite data collection system

Because of the high data-rate requirements, a practical system capable of collecting seismic information in the field and relaying it, via satellite, to a central collection point is not yet available. A seismic signal processor has been developed and tested for use with the NOAA/GOES satellite data collection system. Performance tests on recorded, as well as real time, short period signals indicate that the event recognition technique used is nearly perfect in its rejection of environmental noise and other non-seismic signals and that, with the use of solid state buffer memories, data can be acquired in many swarm situations. The design of a complete field data collection platform is discussed based on the prototype evaluation.

Webster, W. J., Jr.↗

Performance of the ERTS data collection system in a total system context

The author has identified the following significant results. Approximately 70 percent of the DCP's being installed by Department of the Interior investigators have been successfully operated. These include DCP's operating in Iceland, Central America, Hawaii, and the Continental United States. The Data Collection System specifications are being met for frequency of data collection.

Paulson, R. W.↗

Towards a knowledge-based system to assist the Brazilian data-collecting system operation

A study is reported which was carried out to show how a knowledge-based approach would lead to a flexible tool to assist the operation task in a satellite-based environmental data collection system. Some characteristics of a hypothesized system comprised of a satellite and a network of Interrogable Data Collecting Platforms (IDCPs) are pointed out. The Knowledge-Based Planning Assistant System (KBPAS) and some aspects about how knowledge is organized in the IDCP's domain are briefly described.

Rodrigues, Valter↗

AuroraGPT Data Collection Interface

SF-25-043 This software package facilitates data collection for the purpose of assessing the performance of LLMs on scientific topics

Underwood, Robert [Argonne National Laboratory (AN↗

The evolution of location and data collection systems in the United States.

Satellite location and data collection systems development began in the early 1960's in NASA and the French CNES. These systems were initially developed for application to meteorology and oceanography as a means of tracking moving platforms on a global scale. Additional applications such as geology, hydrology, and ecology have since evolved. To date, five successful missions have been completed. With each successive launch, systems improved in accordance with user requirements - particularly reduction in cost and complexity of platform equipment. With planned launches, facilities will be available to the user community through 1980; NASA is currently forecasting needs beyond 1980.

Morakis, J. C.↗

Use of the LANDSAT-2 Data Collection System in the Colorado River Basin Weather Modification Program

The author has identified the following significant results. The LANDSAT data collection system has proven itself to be a valuable tool for control of cloud seeding operations and for verification of weather forecasts. These platforms have proven to be reliable weather resistant units suitable for the collection of hydrometeorological data from remote severe weather environments. The detailed design of the wind speed and direction system and the wire-wrapping of the logic boards were completed.

Kahan, A. M.↗

Hydrologic applications of ERTS-1 data collection system in central Arizona

The Earth Resources Technology Satellite (ERTS-1) Data Collection System (DCS) was used to relay hydrologic data (streamflow rates, precipitation amounts, soil and air temperature, and snow-moisture content) from remote sites in central Arizona to those responsible for reservoir management. The ERTS-DCS was utilized to furnish near-real time information on snow-moisture content and streamflow rates to the Salt River Project for use in the management and operation of reservoirs on the Salt and Verde Rivers. The Salt River Project, aided by near-real time hydrologic data furnished by both microwave and ERTS-telemetry, was successful in predicting the volume of runoff into the reservoirs. Serious flooding in the downstream Phoenix metropolitan area was prevented by prudent water management.

Schumann, H. H.↗

Parallel Plate System for Collecting Data Used to Determine Viscosity

A parallel-plate system collects data used to determine viscosity. A first plate is coupled to a translator so that the first plate can be moved along a first direction. A second plate has a pendulum device coupled thereto such that the second plate is suspended above and parallel to the first plate. The pendulum device constrains movement of the second plate to a second direction that is aligned with the first direction and is substantially parallel thereto. A force measuring device is coupled to the second plate for measuring force along the second direction caused by movement of the second plate.

Kaukler, William↗

Kinematic Sensors Evaluation for Spaceflight Exercise Data Collections

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

S. Faragalla↗