Report on the Operations of the Environmental Data Collection and Processing Facility (EDCPF)

1970 ◽  
Author(s):  
T. J. Flahie ◽  
H. F. Warren ◽  
D. M. Carnine ◽  
R. F. Dolan ◽  
E. W. Smith
Keyword(s):  
Data Collection ◽  
Environmental Data ◽  
Processing Facility

scholarly journals Spatially and temporally distributed data foraging decisions in disciplinary field science

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Cristina G. Wilson ◽  
Feifei Qian ◽  
Douglas J. Jerolmack ◽  
Sonia Roberts ◽  
Jonathan Ham ◽  
...  
Keyword(s):  
Data Collection ◽  
Simulated Data ◽  
Environmental Data ◽  
Distributed Data ◽  
Field Science ◽  
In Situ Data ◽  
Disciplinary Field ◽  
Variable Space ◽  
Query Selection ◽  
Fitting Error

AbstractHow do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions.

Unmanned vehicles for environmental data collection

2010 ◽  
Vol 13 (2) ◽  
pp. 369-380 ◽  
Author(s):  
J. Borges de Sousa ◽  
G. Andrade Gonçalves
Keyword(s):  
Data Collection ◽  
Unmanned Vehicles ◽  
Environmental Data

scholarly journals A Practical and Adaptive Approach to Predicting Indoor CO2

2021 ◽  
Vol 11 (22) ◽  
pp. 10771
Author(s):  
Giacomo Segala ◽  
Roberto Doriguzzi-Corin ◽  
Claudio Peroni ◽  
Tommaso Gazzini ◽  
Domenico Siracusa
Keyword(s):  
Data Collection ◽  
Prediction Accuracy ◽  
Network Models ◽  
Window Size ◽  
Environmental Data ◽  
Neural Network Models ◽  
Using Data ◽  
Carbon Dioxide Co2 ◽  
Long Term Performance ◽  
Term Performance

COVID-19 has underlined the importance of monitoring indoor air quality (IAQ) to guarantee safe conditions in enclosed environments. Due to its strict correlation with human presence, carbon dioxide (CO2) represents one of the pollutants that most affects environmental health. Therefore, forecasting future indoor CO2 plays a central role in taking preventive measures to keep CO2 level as low as possible. Unlike other research that aims to maximize the prediction accuracy, typically using data collected over many days, in this work we propose a practical approach for predicting indoor CO2 using a limited window of recent environmental data (i.e., temperature; humidity; CO2 of, e.g., a room, office or shop) for training neural network models, without the need for any kind of model pre-training. After just a week of data collection, the error of predictions was around 15 parts per million (ppm), which should enable the system to regulate heating, ventilation and air conditioning (HVAC) systems accurately. After a month of data we reduced the error to about 10 ppm, thereby achieving a high prediction accuracy in a short time from the beginning of the data collection. Once the desired mobile window size is reached, the model can be continuously updated by sliding the window over time, in order to guarantee long-term performance.

scholarly journals An Energy Efficient and Lifetime Ratio Improvement Methods Based on Energy Balancing

2019 ◽  
Vol 9 (1S6) ◽  
pp. 52-61
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Energy Efficient ◽  
Residual Energy ◽  
Environmental Data ◽  
Energy Balancing ◽  
Control Centre

WSN consist of set of Sensing points which are responsible for collecting the detected information and then send the packets towards control centre which is responsible for processing of data. The applications of WSN include environmental data analysis, defence data collection and information. The survey of algorithms is done for the improvement of lifetime ratio. Four different algorithms namely Random, Random-CGT, EGT-Random and GTEB algorithms. The four algorithms are compared and then it is proved GTEB exhibits best behaviour with respect to energy consumed, number of non-holes, number of holes, Non-Hole to Hole ratio, residual energy, overhead and throughput.

scholarly journals Assessment of riparian environments through semi-automated procedures for the computation of eco-morphological indicators: Preliminary results of the WEQUAL project

2019 ◽  
Vol 70 (3) ◽  
pp. 131-145 ◽  
Author(s):  
Raimondo Gallo ◽  
Gianluca Ristorto ◽  
Alex Bojeri ◽  
Nadia Zorzi ◽  
Gabriele Daglio ◽  
...  
Keyword(s):  
Data Collection ◽  
Unmanned Aircraft ◽  
Environmental Data ◽  
Shared Environment ◽  
Surface Model ◽  
Riparian Areas ◽  
Raw Data ◽  
Ecological Criteria ◽  
Web Platform ◽  
The Web

Summary The aim of WEQUAL project (WEb service centre for QUALity multidimensional design and tele-operated monitoring of Green Infrastructures) is the development of a system that is able to support a quick environmental monitoring of riparian areas subjected to the realization of new green infrastructures (GI). The Wequal’s idea is to organize a service center able to manage both the Web Platform and the whole data collection and analysis processes. Through a personal account, the final user (designer, technician, researcher) can get access to the service and requires the evaluation of alternatives GI projects. On the Web Platform, a set of algorithms runs in order to calculate, through automatic procedures, all the ecological criteria required to evaluate a quality environmental index that describes the eco-morphological value of the monitored riparian areas. For this aim, the WEQUI index was developed, which uses 15 indicators that are easy to monitor. In this paper, the approach for environmental data collection and the procedures to perform the automatic assessment of two of the ecological criteria are described. For the computation, the implemented algorithms use data including the vegetation indexes, Digital Terrain Model (DTM), Digital Surface Model (DSM) and a 3D point cloud classification. All the raw data are collected by UAVs (Unmanned Aircraft Vehicle) equipped with a 3D Lidar, multispectral camera and RGB camera. Interpreting all the raw data collected by these sensors, using a multi-attribute approach, the WEQUI index is assessed. The computed ecological index is then used to assess the riparian environmental quality at ex-ante and ex-post river stabilization works. This index, integrated with additional not-technical or not-ecological indicators such as investment required, maintenance costs or social acceptance, can be used in multicriteria analyses in order to evaluate the intervention from a wider point of view. The platform is expected to be attractive for GI designers and policy makers by providing a shared environment, which is able to integrate the method of detection and evaluation of complex indexes and a multidimensional evaluation supported by an expert guide.

scholarly journals Gestation at completion of prenatal questionnaires in ALSPAC

2020 ◽  
Vol 5 ◽  
pp. 100
Author(s):  
Yasmin Iles-Caven ◽  
Kate Northstone ◽  
Jean Golding
Keyword(s):  
Data Collection ◽  
Pregnant Women ◽  
First Trimester ◽  
Environmental Data ◽  
Parents And Children ◽  
Using Data ◽  
Time Critical ◽  
Avon Longitudinal Study ◽  
In Pregnancy ◽  
Made In

Enrolling a cohort in pregnancy can be methodologically difficult in terms of structuring data collection. For example, some exposures of interest may be time-critical while other (often retrospective) data can be collected at any point during pregnancy.  The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prime example of a cohort where certain data were collected at specific time points and others at variable times depending on the gestation at contact.  ALSPAC aimed to enrol as many pregnant women as possible in a geographically defined area with an expected date of delivery between April 1991 and December 1992. The ideal was to enrol women as early in pregnancy as possible, and to collect information, when possible, at two fixed gestational periods (18 and 32 weeks). A variety of methods were used to enrol participants.   Approximately 80% of eligible women resident in the study area were enrolled. Gestation at enrolment ranged from 4-41 (median = 14) weeks of pregnancy. Given this variation in gestation we describe the various decisions that were made in regard to the timing of questionnaires to ensure that appropriate data were obtained from the pregnant women.  45% of women provided data during the first trimester, this is less than ideal but reflects the fact that many women do not acknowledge their pregnancy until the first trimester is safely completed. Data collection from women at specific gestations (18 and 32 weeks) was much more successful (80-85%). Unfortunately, it was difficult to obtain environmental data during the first trimester. Given the time critical nature of exposures during this trimester, researchers must take the gestational age at which environmental data was collected into account. This is particularly important for data collected using the questionnaire named ‘Your Environment’ (using data known as the A files).

Comparison of Vendor Supplied Environmental Data Collection Mechanisms

2015 ◽  
Author(s):  
Sean Wallace ◽  
Venkatram Vishwanath ◽  
Susan Coghlan ◽  
Zhiling Lan ◽  
Michael E. Papka
Keyword(s):  
Data Collection ◽  
Environmental Data

scholarly journals Data Gathering from a Multimodal Dense Underwater Acoustic Sensor Network Deployed in Shallow Fresh Water Scenarios

2019 ◽  
Vol 8 (4) ◽  
pp. 55 ◽  
Author(s):  
Alberto Signori ◽  
Filippo Campagnaro ◽  
Fabian Steinmetz ◽  
Bernd-Christian Renner ◽  
Michele Zorzi
Keyword(s):  
Data Collection ◽  
Low Cost ◽  
World Ocean ◽  
Environmental Data ◽  
Sensor Nodes ◽  
Real Field ◽  
Acoustic Sensor ◽  
Adaptive Mechanism ◽  
Underwater Acoustic ◽  
Inspection Service

The Robotic Vessels as-a-Service (RoboVaaS) project intends to exploit the most advanced communication and marine vehicle technologies to revolutionize shipping and near-shore operations, offering on-demand and cost-effective robotic-aided services. In particular, the RoboVaaS vision includes a ship hull inspection service, a quay walls inspection service, an antigrounding service, and an environmental and bathymetry data collection service. In this paper, we present a study of the underwater environmental data collection service, performed by a low-cost autonomous vehicle equipped with both a commercial modem and a very low-cost acoustic modem prototype, the smartPORT Acoustic Underwater Modem (AHOI). The vehicle mules the data from a network of low cost submerged acoustic sensor nodes to a surface sink. To this end, an underwater acoustic network composed by both static and moving nodes has been implemented and simulated with the DESERT Underwater Framework, where the performance of the AHOI modem has been mapped in the form of lookup tables. The performance of the AHOI modem has been measured near the Port of Hamburg, where the RoboVaaS concept will be demonstrated with a real field evaluation. The transmission with the commercial modem, instead, has been simulated with the Bellhop ray tracer thanks to the World Ocean Simulation System (WOSS), by considering both the bathymetry and the sound speed profile of the Port of Hamburg. The set up of the polling-based MAC protocol parameters, such as the maximum backoff time of the sensor nodes, appears to be crucial for the network performance, in particular for the low-cost low-rate modems. In this work, to tune the maximum backoff time during the data collection mission, an adaptive mechanism has been implemented. Specifically, the maximum backoff time is updated based on the network density. This adaptive mechanism results in an 8% improvement of the network throughput.

Environmental Conditions in TT 209, Luxor. The Case of a Theban Tomb Subject to Periodic Flooding

2019 ◽  
Vol 105 (1) ◽  
pp. 115-125
Author(s):  
Miguel Ángel Molinero Polo ◽  
Vicente Soler Javaloyes
Keyword(s):  
Relative Humidity ◽  
Data Collection ◽  
Environmental Conditions ◽  
Flash Floods ◽  
Environmental Data ◽  
Natural Conditions ◽  
Archaeological Work ◽  
Ancient Times ◽  
Collection Temperature ◽  
Periodic Flooding

TT 209 was built in a wadi, a location that means it has been affected by flash floods since ancient times. The team in charge of its study and conservation has initiated a systematic programme of environmental data collection (temperature and relative humidity) in order to understand the natural conditions of the tomb and any transformations caused by archaeological work in its underground chambers, as well as to inform future conservation. These statistics can also be useful for excavation and conservation programmes in nearby tombs whose architectonic structure is similar and have also suffered from exposure to water damage.

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