This document provides a tutorial on how to use the CEOP-AEGIS Data Portal. It introduces the main components of the portal, including the map window, dataset window, and variable window. It then guides the user through navigating datasets and variables, visualizing and mapping data, performing queries, and exporting results. The tutorial is presented in five parts and includes some exercises to help users learn how to operate the portal.
1) O documento discute como a mídia influencia a sexualidade dos adolescentes, promovendo a banalização do sexo e aumentando a gravidez precoce e DSTs.
2) Foi realizada uma pesquisa com estudantes para investigar seus hábitos e percepções sobre sexualidade e a influência da mídia.
3) Os resultados mostraram que a mídia exerce grande influência na sexualidade dos jovens, apontando para a necessidade de educação sexual na família e escola.
This very short document appears to be in an unknown language or code and does not provide any discernible information to summarize in 3 sentences or less. It contains unrecognizable characters and words.
The document describes a house including rooms and furniture. Fifth grade students created the drawings of the house, rooms, and furniture. The house was drawn by fifth grade students from Visconde Juromenha.
This short document wishes the recipient a Merry Christmas and a hand full of joy and happiness. It is signed by Visconde Juromenha and references a Christmas HandTree, suggesting it is a holiday card or message. The document keeps its message simple by focusing on spreading holiday cheer and goodwill.
1) The document discusses the key concepts and objectives of an investment grade solar system feasibility study, including quantifying demand, balancing the value stack against the cost stack, and harvesting system design.
2) It outlines a 12-step process for conducting a feasibility study, from identifying the host customer and site to risk factors, capitalization, permitting, construction, and operations.
3) The goals are to get to feasible projects faster by leveraging tax incentives, technology, and customer needs, and to build projects that are right-sized and capitalized for success.
El documento contiene preguntas sobre gráficas de rutas metabólicas de enzimas, incluyendo su localización y relación con procesos biotecnológicos. También contiene preguntas sobre dos ciclos celulares, la división mitótica y meiótica, así como sobre la fermentación alcohólica y láctica.
A witch is flying on her broomstick with her cat when a storm blows away her hat, bow, and wand. The helpful animals find the items and hop on the broom to return them. The overloaded broom breaks but the witch and animals are happy together on the ground.
1) O documento discute como a mídia influencia a sexualidade dos adolescentes, promovendo a banalização do sexo e aumentando a gravidez precoce e DSTs.
2) Foi realizada uma pesquisa com estudantes para investigar seus hábitos e percepções sobre sexualidade e a influência da mídia.
3) Os resultados mostraram que a mídia exerce grande influência na sexualidade dos jovens, apontando para a necessidade de educação sexual na família e escola.
This very short document appears to be in an unknown language or code and does not provide any discernible information to summarize in 3 sentences or less. It contains unrecognizable characters and words.
The document describes a house including rooms and furniture. Fifth grade students created the drawings of the house, rooms, and furniture. The house was drawn by fifth grade students from Visconde Juromenha.
This short document wishes the recipient a Merry Christmas and a hand full of joy and happiness. It is signed by Visconde Juromenha and references a Christmas HandTree, suggesting it is a holiday card or message. The document keeps its message simple by focusing on spreading holiday cheer and goodwill.
1) The document discusses the key concepts and objectives of an investment grade solar system feasibility study, including quantifying demand, balancing the value stack against the cost stack, and harvesting system design.
2) It outlines a 12-step process for conducting a feasibility study, from identifying the host customer and site to risk factors, capitalization, permitting, construction, and operations.
3) The goals are to get to feasible projects faster by leveraging tax incentives, technology, and customer needs, and to build projects that are right-sized and capitalized for success.
El documento contiene preguntas sobre gráficas de rutas metabólicas de enzimas, incluyendo su localización y relación con procesos biotecnológicos. También contiene preguntas sobre dos ciclos celulares, la división mitótica y meiótica, así como sobre la fermentación alcohólica y láctica.
A witch is flying on her broomstick with her cat when a storm blows away her hat, bow, and wand. The helpful animals find the items and hop on the broom to return them. The overloaded broom breaks but the witch and animals are happy together on the ground.
3.- Integrating environmental data in ModestR (Version ModestR v5.3 or higher) modestrsoftware
This document provides a step-by-step tutorial for integrating and using environmental data in the ModestR software. It describes how to import environmental data in ASCII grid format from sources like the Bio-Oracle dataset. It then demonstrates how to visualize the data on maps in ModestR, clip the data for a specific area, and export the clipped raster files for further use. The tutorial also explains how integrated environmental data can be overlaid on distribution maps and exported in images.
Bringing GEOSS services into Practice for Beginners: GeoNode TutorialKudos S.A.S
Bringing GEOSS services into Practice for Beginners: GeoNode Tutorial
Archivo original: ftp://orion.grid.unep.ch/GEOSS_services/geonode/Geonode_tutorial.pdf
This document provides an introduction and user guide for LogMan 8.0, a geotechnical borehole logging software. It outlines the key features and functions of the software, including its ability to [1] quickly create, edit, and print borehole logs and cross sections; [2] store all project data in a single file; and [3] automatically join soil layers in cross sections. The document provides instructions on installation, registration, and getting started with the software. It describes the main interface areas and windows for inputting lithology, borehole, and cross section data. Finally, it outlines the recommended order for inputting data and creating drawings with LogMan 8.0.
AGENDA
Curator considerations for germplasm images
--Include a color calibration chart
--Include a measure tape
--Include a barcode label
--Include a watermark and a standard background color
--Draft guidelines for germplasm images
Data manager considerations for germplasm images
--CIP policy for images
--Update file image properties (metadata) with ExifTool: Authors and Copyright
--Upload image metadata on Genesys: Using a quick guide
Exploring Halifax Attractions using the Esri Runtime SDK for AndroidCOGS Presentations
This document summarizes the steps taken to create an Android app for exploring attractions in Halifax using the Esri Runtime SDK. It describes setting up the development environment, adding maps and basemaps, accessing feature layer data from the cloud, and adding geocoding capabilities. It also details how various layers for trails, breweries, parks, shopping centers and hospitals were created and symbolized for use in the app. The document outlines how each of these layers can be queried, turned on/off, and highlighted through buttons in the Android app interface. It notes some problems encountered like Android Studio updates and projects not syncing correctly from GitHub.
1. The document describes the process for inputting data and calculating drought indices using the DMAP V2.0 tool. It involves importing data via Excel files or NetCDF files, selecting stations and variables, then calculating drought indices like SPI, PDSI, and KBDI.
2. The tool allows importing time series data for rainfall, temperature, soil moisture, and other variables to compute multiple drought indices. Data can be imported from Excel or NetCDF files by selecting stations, variables, and specifying formatting.
3. After inputting data, drought indices are calculated and can be visualized in plots. Severity thresholds can be customized, and drought start dates, durations, and magnitudes are outputted in a
This document provides a user manual for DUALEM Log software. It describes how to install the software, set up data acquisition, connect to DUALEM sensors and GPS devices, collect and download data, and understand the status indicators. The software allows users to log sensor measurements, GPS data, and other fields in real-time and transfer logs to a computer for further processing and analysis.
The document provides instructions for creating forms in Geopaparazzi using the HortonMachine application. It describes how to:
1. Create sections, tabs, and widgets like text fields, checkboxes, and dropdowns within the form builder application to design a custom survey form for university buildings.
2. Add fields for general information like name, faculty, and number of enrolled students.
3. Add additional tabs for structural details and images.
4. Populate dropdowns by specifying options in the form builder.
5. Designate certain fields like name as mandatory for the survey.
The form can then be exported and used to collect geospatial data on university buildings using the
Detection of medical instruments project- PART 1Sairam Adithya
this presentation is about a project done by me and my colleague related to computer vision. This project is used to classify the uploaded images of biomedical instruments into prominent ones like ECG, EEG, x-ray machine, CT, MRI, and so on. A website has been developed on which the user can upload any image he is unknown of and the model will tell what instrument it is along with a paragraph explaining the instrument in a crisp manner
Consuming and Publishing Ordnance Survey Open Data with Open Source SoftwareJoanne Cook
The document outlines steps to consume Ordnance Survey open data using open source software in 2 hours. It describes downloading sample OS data and mastermap data, preparing and loading the data into a PostgreSQL/PostGIS database using Python scripts. It then explains configuring a MapServer mapfile to serve the data, and viewing the results in a web browser using OpenLayers and in Quantum GIS desktop GIS software. The goal is to go from raw OS data to viewing it in a web and desktop GIS using only open source tools.
An optical satellite tracking system for undergraduate research bruski, jon...Srinivas Naidu
This document describes an optical satellite tracking system developed for undergraduate research using commercial off-the-shelf components. The system includes a camera, lens, tracking mount, laptop computer, and software. It has been used to successfully image unresolved satellites as dim as 6th magnitude. Future plans include establishing a permanent assembly with networked control and automated data collection.
una buena y una alternativa mas de cada uno de los elementos del clima, asi sucesivbamente, tales como se muestra en el siguiente documento mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm juuh kkk jjhhh mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj hyhhhh dsjsjjs s jdkdk s uuwuwuwuw owowow uuwuwuwuw wowowowowow owowowow owwowowowo
Cutter et al-2007-Systematic Conservation Planning ModulePeter Cutter
This document provides an introduction to using decision support tools for conservation planning. It outlines the goals of gaining experience with conservation planning and tools like ArcGIS, Marxan, and CLUZ. The document describes the landscape and data for a case study in Oregon, including planning units, species occurrence data, and cost data. It provides overviews of the ArcGIS, Marxan, and CLUZ software, and includes step-by-step instructions for opening and navigating ArcGIS and initializing the CLUZ extension for conservation planning analyses.
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal
This document summarizes research on object detection and recognition using the Single Shot Multi-Box Detector (SSD) deep learning model. SSD improves on existing object detection systems by eliminating the need for generating object proposals and resampling pixels or features, thereby making detection faster and encapsulating all computation in a single neural network. The researchers applied SSD to standard datasets like PASCAL VOC, COCO, and ILSVRC and achieved competitive accuracy compared to methods using additional proposal steps, with SSD running significantly faster at 59 FPS. Experimental results on PASCAL VOC using SSD achieved a mean average precision of 74.3%, outperforming a comparable Faster R-CNN model.
CopanMobile is a software program for Windows Mobile devices that allows users to perform coordinate geometry calculations. It was created by Underhill Geomatics Ltd. for surveying tasks. Key features include traverse calculations, coordinate transformations, area calculations, map checking, and GPS navigation. The software also allows users to store coordinate data in files and share files across sessions.
This document describes how to perform maximum likelihood classification on Landsat imagery to identify wetlands using ENVI 4.7. It discusses:
1. Loading and calibrating pre- and post-Hurricane Katrina Landsat datasets for a region in Louisiana.
2. Creating regions of interest (ROIs) through polygon selection on image data and scatter plot methods to identify water and land pixels for classification training.
3. Performing atmospheric correction using dark subtraction and QUAC methods.
4. Running maximum likelihood classification using the ROIs as training data to produce classifications identifying water and land, and evaluating the results.
The document summarizes the Geohazards TEP (GEP) service, which provides on-demand and systematic processing of Earth observation (EO) data to support geohazards analysis. The GEP offers access to Copernicus and other satellite data, massive cloud computing power, and processing services via a web portal, APIs, and command line tools. It processes data for applications like earthquake response, landslide mapping, and regularly monitors the Alps with Sentinel-1 data. Documentation and tutorials are available on the GEP website.
This document discusses using anomaly detection and visual analytics to improve smart product performance by identifying abnormal sensor or software events. It presents a case study using unsupervised auto-encoder models in Keras and TensorFlow to detect anomalies in drone event log data. Specifically, it finds controller signal loss events and then uses visual analysis of flight paths in Python, Tableau and Plotly to determine where and why errors occurred, such as from environmental obstructions. The goal is to resolve issues and improve product performance by understanding anomalous events.
This document provides a tutorial for using Princeton's Maximum Entropy (MaxEnt) software to create an environmental suitability map. The 11 step process includes: 1) converting species occurrence data to a CSV file, 2) downloading and installing MaxEnt, 3) importing environmental layers into GIS software, 4) standardizing the layers, 5) running MaxEnt to model species distributions based on the environmental variables, and 6) interpreting the results, which predict habitat suitability as a map with red areas being most suitable. The example application is using MaxEnt to predict where ancient Chacoan heritage ruins are likely to exist in the San Juan Basin.
This document provides details on algorithms for determining snow and ice properties from MODIS satellite data. It discusses algorithms for mapping snow cover, fractional snow cover, and estimating snow water equivalent. For snow cover mapping, it describes calculating surface reflectance from MODIS bands, adjusting the Normalized Difference Snow Index threshold, and using additional algorithms and image fusion. Validation will involve comparing algorithm outputs to in situ measurements at field sites. The snow water equivalent algorithm is based on theoretical relationships between satellite observations and snow properties and will be prototyped and validated using field data.
This document reports on snow cover area retrieval using MODIS data over the Qinghai-Tibet Plateau. It uses MODIS surface reflectance and temperature data, as well as DEM and Landsat data, to develop an algorithm for mapping snow cover. The algorithm is validated using high resolution Landsat images and existing MODIS snow cover products. Results show the algorithm can accurately retrieve snow cover area from MODIS data over the plateau.
3.- Integrating environmental data in ModestR (Version ModestR v5.3 or higher) modestrsoftware
This document provides a step-by-step tutorial for integrating and using environmental data in the ModestR software. It describes how to import environmental data in ASCII grid format from sources like the Bio-Oracle dataset. It then demonstrates how to visualize the data on maps in ModestR, clip the data for a specific area, and export the clipped raster files for further use. The tutorial also explains how integrated environmental data can be overlaid on distribution maps and exported in images.
Bringing GEOSS services into Practice for Beginners: GeoNode TutorialKudos S.A.S
Bringing GEOSS services into Practice for Beginners: GeoNode Tutorial
Archivo original: ftp://orion.grid.unep.ch/GEOSS_services/geonode/Geonode_tutorial.pdf
This document provides an introduction and user guide for LogMan 8.0, a geotechnical borehole logging software. It outlines the key features and functions of the software, including its ability to [1] quickly create, edit, and print borehole logs and cross sections; [2] store all project data in a single file; and [3] automatically join soil layers in cross sections. The document provides instructions on installation, registration, and getting started with the software. It describes the main interface areas and windows for inputting lithology, borehole, and cross section data. Finally, it outlines the recommended order for inputting data and creating drawings with LogMan 8.0.
AGENDA
Curator considerations for germplasm images
--Include a color calibration chart
--Include a measure tape
--Include a barcode label
--Include a watermark and a standard background color
--Draft guidelines for germplasm images
Data manager considerations for germplasm images
--CIP policy for images
--Update file image properties (metadata) with ExifTool: Authors and Copyright
--Upload image metadata on Genesys: Using a quick guide
Exploring Halifax Attractions using the Esri Runtime SDK for AndroidCOGS Presentations
This document summarizes the steps taken to create an Android app for exploring attractions in Halifax using the Esri Runtime SDK. It describes setting up the development environment, adding maps and basemaps, accessing feature layer data from the cloud, and adding geocoding capabilities. It also details how various layers for trails, breweries, parks, shopping centers and hospitals were created and symbolized for use in the app. The document outlines how each of these layers can be queried, turned on/off, and highlighted through buttons in the Android app interface. It notes some problems encountered like Android Studio updates and projects not syncing correctly from GitHub.
1. The document describes the process for inputting data and calculating drought indices using the DMAP V2.0 tool. It involves importing data via Excel files or NetCDF files, selecting stations and variables, then calculating drought indices like SPI, PDSI, and KBDI.
2. The tool allows importing time series data for rainfall, temperature, soil moisture, and other variables to compute multiple drought indices. Data can be imported from Excel or NetCDF files by selecting stations, variables, and specifying formatting.
3. After inputting data, drought indices are calculated and can be visualized in plots. Severity thresholds can be customized, and drought start dates, durations, and magnitudes are outputted in a
This document provides a user manual for DUALEM Log software. It describes how to install the software, set up data acquisition, connect to DUALEM sensors and GPS devices, collect and download data, and understand the status indicators. The software allows users to log sensor measurements, GPS data, and other fields in real-time and transfer logs to a computer for further processing and analysis.
The document provides instructions for creating forms in Geopaparazzi using the HortonMachine application. It describes how to:
1. Create sections, tabs, and widgets like text fields, checkboxes, and dropdowns within the form builder application to design a custom survey form for university buildings.
2. Add fields for general information like name, faculty, and number of enrolled students.
3. Add additional tabs for structural details and images.
4. Populate dropdowns by specifying options in the form builder.
5. Designate certain fields like name as mandatory for the survey.
The form can then be exported and used to collect geospatial data on university buildings using the
Detection of medical instruments project- PART 1Sairam Adithya
this presentation is about a project done by me and my colleague related to computer vision. This project is used to classify the uploaded images of biomedical instruments into prominent ones like ECG, EEG, x-ray machine, CT, MRI, and so on. A website has been developed on which the user can upload any image he is unknown of and the model will tell what instrument it is along with a paragraph explaining the instrument in a crisp manner
Consuming and Publishing Ordnance Survey Open Data with Open Source SoftwareJoanne Cook
The document outlines steps to consume Ordnance Survey open data using open source software in 2 hours. It describes downloading sample OS data and mastermap data, preparing and loading the data into a PostgreSQL/PostGIS database using Python scripts. It then explains configuring a MapServer mapfile to serve the data, and viewing the results in a web browser using OpenLayers and in Quantum GIS desktop GIS software. The goal is to go from raw OS data to viewing it in a web and desktop GIS using only open source tools.
An optical satellite tracking system for undergraduate research bruski, jon...Srinivas Naidu
This document describes an optical satellite tracking system developed for undergraduate research using commercial off-the-shelf components. The system includes a camera, lens, tracking mount, laptop computer, and software. It has been used to successfully image unresolved satellites as dim as 6th magnitude. Future plans include establishing a permanent assembly with networked control and automated data collection.
una buena y una alternativa mas de cada uno de los elementos del clima, asi sucesivbamente, tales como se muestra en el siguiente documento mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm juuh kkk jjhhh mmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmm jjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj hyhhhh dsjsjjs s jdkdk s uuwuwuwuw owowow uuwuwuwuw wowowowowow owowowow owwowowowo
Cutter et al-2007-Systematic Conservation Planning ModulePeter Cutter
This document provides an introduction to using decision support tools for conservation planning. It outlines the goals of gaining experience with conservation planning and tools like ArcGIS, Marxan, and CLUZ. The document describes the landscape and data for a case study in Oregon, including planning units, species occurrence data, and cost data. It provides overviews of the ArcGIS, Marxan, and CLUZ software, and includes step-by-step instructions for opening and navigating ArcGIS and initializing the CLUZ extension for conservation planning analyses.
IRJET- Object Detection and Recognition using Single Shot Multi-Box DetectorIRJET Journal
This document summarizes research on object detection and recognition using the Single Shot Multi-Box Detector (SSD) deep learning model. SSD improves on existing object detection systems by eliminating the need for generating object proposals and resampling pixels or features, thereby making detection faster and encapsulating all computation in a single neural network. The researchers applied SSD to standard datasets like PASCAL VOC, COCO, and ILSVRC and achieved competitive accuracy compared to methods using additional proposal steps, with SSD running significantly faster at 59 FPS. Experimental results on PASCAL VOC using SSD achieved a mean average precision of 74.3%, outperforming a comparable Faster R-CNN model.
CopanMobile is a software program for Windows Mobile devices that allows users to perform coordinate geometry calculations. It was created by Underhill Geomatics Ltd. for surveying tasks. Key features include traverse calculations, coordinate transformations, area calculations, map checking, and GPS navigation. The software also allows users to store coordinate data in files and share files across sessions.
This document describes how to perform maximum likelihood classification on Landsat imagery to identify wetlands using ENVI 4.7. It discusses:
1. Loading and calibrating pre- and post-Hurricane Katrina Landsat datasets for a region in Louisiana.
2. Creating regions of interest (ROIs) through polygon selection on image data and scatter plot methods to identify water and land pixels for classification training.
3. Performing atmospheric correction using dark subtraction and QUAC methods.
4. Running maximum likelihood classification using the ROIs as training data to produce classifications identifying water and land, and evaluating the results.
The document summarizes the Geohazards TEP (GEP) service, which provides on-demand and systematic processing of Earth observation (EO) data to support geohazards analysis. The GEP offers access to Copernicus and other satellite data, massive cloud computing power, and processing services via a web portal, APIs, and command line tools. It processes data for applications like earthquake response, landslide mapping, and regularly monitors the Alps with Sentinel-1 data. Documentation and tutorials are available on the GEP website.
This document discusses using anomaly detection and visual analytics to improve smart product performance by identifying abnormal sensor or software events. It presents a case study using unsupervised auto-encoder models in Keras and TensorFlow to detect anomalies in drone event log data. Specifically, it finds controller signal loss events and then uses visual analysis of flight paths in Python, Tableau and Plotly to determine where and why errors occurred, such as from environmental obstructions. The goal is to resolve issues and improve product performance by understanding anomalous events.
This document provides a tutorial for using Princeton's Maximum Entropy (MaxEnt) software to create an environmental suitability map. The 11 step process includes: 1) converting species occurrence data to a CSV file, 2) downloading and installing MaxEnt, 3) importing environmental layers into GIS software, 4) standardizing the layers, 5) running MaxEnt to model species distributions based on the environmental variables, and 6) interpreting the results, which predict habitat suitability as a map with red areas being most suitable. The example application is using MaxEnt to predict where ancient Chacoan heritage ruins are likely to exist in the San Juan Basin.
This document provides details on algorithms for determining snow and ice properties from MODIS satellite data. It discusses algorithms for mapping snow cover, fractional snow cover, and estimating snow water equivalent. For snow cover mapping, it describes calculating surface reflectance from MODIS bands, adjusting the Normalized Difference Snow Index threshold, and using additional algorithms and image fusion. Validation will involve comparing algorithm outputs to in situ measurements at field sites. The snow water equivalent algorithm is based on theoretical relationships between satellite observations and snow properties and will be prototyped and validated using field data.
This document reports on snow cover area retrieval using MODIS data over the Qinghai-Tibet Plateau. It uses MODIS surface reflectance and temperature data, as well as DEM and Landsat data, to develop an algorithm for mapping snow cover. The algorithm is validated using high resolution Landsat images and existing MODIS snow cover products. Results show the algorithm can accurately retrieve snow cover area from MODIS data over the plateau.
This document describes algorithms for quantitative precipitation estimation (QPE), 3D mosaic, and hydrometeor classification using polarimetric radar data in China. It details the radar network and scan strategies used, as well as algorithms for radar data quality control, remapping raw data to a Cartesian grid, and mosaicking reflectivity from multiple radars. It also describes a fuzzy logic method for hydrometeor classification based on polarimetric radar measurements, and plans for algorithm prototyping and validation.
Ceop aegis2011 training course-daily-programme_v11_05_12jrgcolin
The document provides details about an advanced training program hosted by CEOP-AEGIS from May 17-19, 2011 in Beijing, China. The objective is to introduce stakeholders from China and other Asian countries to CEOP-AEGIS project results related to in-situ earth observation, retrievals and modeling of land surface processes and land-atmosphere interactions with emphasis on the Tibetan Plateau. The 3-day program includes lectures, demonstrations and hands-on exercises using collected data. Target participants include technical staff, students and postdocs from meteorological and water agencies in China and other Asian countries.
The document outlines the agenda for a 3-day training course covering 10 working packages (WPs) on monitoring water resources in a plateau region using ground-based and satellite observations. Each day focuses on several WPs, with presentations on the theoretical background, datasets, and preliminary results of each. Presenters are assigned from the organizations leading each WP. Day 1 covers WPs 1-4, Day 2 covers WPs 5-8, and Day 3 includes WPs 9-10 as well as roundtable discussions on research topics and implementing results.
This project aims to improve knowledge of hydrology and meteorology in Asia and Europe through international cooperation. It constructs an observing system using ground and satellite data to monitor water resources in major river basins impacted by the Tibetan Plateau. The project will analyze links between snow/vegetation on the Plateau and monsoons/extreme precipitation. It contributes to GEO by integrating data to assess vulnerability of watersheds to climate change and supporting climate change adaptation efforts through hydrological observations, predictions, and identifying causes of systems' vulnerabilities.
This document discusses water resources, agriculture, ecosystems, and biodiversity as they relate to climate change. It summarizes several European projects focused on using observations from space and on the ground to monitor water balances, adapt to climate change impacts, and ensure food and water security for populations that rely on water from glacial regions in Asia. Opportunities for collaboration between these projects are also discussed.
This document provides information on the CEOP-AEGIS contribution to the GEOSS Data CORE. It discusses how CEOP-AEGIS will integrate hydrometeorological data from large transnational river basins, such as the Qinghai-Tibet Plateau and major rivers in Southeast Asia. The document outlines how CEOP-AEGIS will make this data freely available through its portal and contribute to the GEOSS water theme by providing documents, datasets, and services related to topics like drought monitoring, flood forecasting, and water balance modeling.
This project aims to improve knowledge of hydrology and meteorology in Asia and Europe through international cooperation. It constructs an observing system using ground and satellite data to monitor water resources in major river basins influenced by the Tibetan Plateau. The project also analyzes linkages between snow/vegetation on the Plateau and monsoon precipitation. It contributes to GEO by integrating data to assess vulnerability of watersheds to climate change and supporting climate change adaptation efforts through hydrological observations, analyses and predictions.
The CEOP-AEGIS project aims to construct an observing system using ground and satellite data to monitor water resources in Asia, specifically the water yield from the Tibetan Plateau into major rivers. In the first 18 months of the project, the partners defined their roles and contributions, established communication methods, and held two meetings. The kick-off meeting had 65 participants and focused on project planning. The annual progress meeting included a joint workshop and business meeting to review the work. The objectives are on track to using monitoring of snow, vegetation and surface fluxes as precursors to improve precipitation forecasts in Southeast Asia.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
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CEOP-AEGIS Data Portal Tutorial
1. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
The
CEOP-‐AEGIS
Data
Portal
tutorial
http://dataportal.ceop-‐aegis.org
J.COLIN
Image
Sciences,
Computer
Sciences
and
Remote
Sensing
Laboratory,
CNRS
/
University
of
Strasbourg,
France,
j.colin@unistra.fr
Version
:
1.0.2
Introduction
During
this
tutorial,
you
will
be
guided
step
by
step
to
discover
the
portal,
display
data,
get
information,
perform
queries
and
export
results.
To
complete
this
tutorial,
you
will
need
to
have
an
Internet
browser.
We
recommend
you
to
use
Firefox
for
better
compatibility.
Also
ensure
that
you
have
a
good
Internet
access.
This
tutorial
is
organized
in
five
parts.
The
first
part
will
explain
you
how
to
access
the
portal,
and
will
let
you
discover
the
interface.
The
second
part
will
introduce
navigating
through
products,
variables
and
meta-‐data.
The
third
part
will
focus
on
visualizing
and
mapping
a
product,
animating
time
series,
visualizing
in
situ
data.
The
fourth
part
will
explain
you
have
to
perform
advanced
queries
in
space
and
time.
The
last
part
will
present
you
how
to
export
datasets
and
subsets
to
your
computer
for
further
analysis.
This
tutorial
ends
with
a
few
exercises
to
allow
you
to
test
your
understanding
of
the
use
of
the
portal.
Username: dchart
Important:
The
access
to
the
portal
may
require
a
Password: #dchart!
username
and
password.
Please
ensure
to
have
this
information
with
you
before
you
start.
!
2. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
The
Map
component
When
you
enter
the
data
portal,
you
will
access
a
web
page
containing
various
components.
The
first
component
is
the
map
window.
By
default,
the
map
window
shows
you
a
view
of
the
entire
world.
On
the
left
side
of
the
map,
you
can
see
a
group
of
buttons.
If
you
move
your
mouse
cursor
above
a
button,
you
will
see
some
explanations
showing
up.
The
map
window
allows
performing
to
different
operations.
First,
it
will
show
you
the
actual
extent
of
a
dataset,
or
the
location
of
a
ground
station.
Second,
it
will
allow
you
to
define
your
own
area
of
interest.
!
3. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
To
select
an
area
of
interest,
use
the
“set
region”
mode
by
click
on
the
following
button:
To
drag
the
map,
use
the
other
“hand”
button:
When
you
use
the
map
window,
you
have
two
modes.
They
can
be
activated
using
the
two
buttons
on
top
left.
Using
the
square
button,
you
can
select
an
area
of
interest
on
the
map.
Using
the
hand
button,
you
can
drag
the
map
with
the
mouse.
The
arrows
buttons
are
used
to
navigate
in
the
map.
The
plus
and
minus
button
are
used
to
zoom
in
and
zoom
out.
The
Dataset
component
The
second
component
is
the
dataset
window.
A
dataset
is
a
registered
source
of
data.
Each
dataset
is
belonging
to
a
category.
You
will
find
datasets
belonging
to
atmosphere,
hydrology,
land
and
in
situ.
!
4. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
The
atmosphere
category
groups
numerical
weather
prediction
model
outputs.
You
will
mainly
find
data
provided
by
the
grapes
model
of
the
Chinese
Meteorological
Administration.
The
hydrology
category
groups
both
hydrological
model
outputs
and
remote
sensing
products
on
early
warning.
Early
warning
products
concern
either
drought
or
flood.
The
land
category
groups
remote
sensing
based
products.
These
products
are
time
series
of
vegetation
properties,
land
surface
albedo
and
temperature.
The
in
situ
category
groups
any
ground
observation
data.
To
work
with
a
given
dataset,
simply
click
on
one
of
them.
When
a
dataset
is
selected,
the
available
variables
will
appear
in
the
variable
window
on
the
right
end
side
of
the
portal.
To
obtain
some
information
about
a
dataset,
you
can
click
on
its
name.
Information
will
be
displayed
in
a
new
navigator
window.
Once
you
have
consulted
this
information,
feel
free
to
close
this
window.
The
variable
component
displays
all
the
variables
contained
in
the
selected
dataset.
A
dataset
may
only
contain
one
variable,
or
dozens.
!
5. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
Display
and
animate
GRAPES
data
Let’s
make
an
example.
To
see
some
atmosphere
data,
you
can
simply
select
the
atmosphere
category.
In
the
variable
panel,
select
the
air
temperature
at
the
height
of
the
boundary
layer.
Then
click
on
the
button
called
plot
selected.
You
can
find
this
button
on
top
of
the
datasets
window,
and
on
the
toolbar
in
the
bottom
of
your
web
browser
window.
The
result
will
appear
in
the
figure
component,
below
the
map
component.
The
figure
displays
the
dataset
name,
the
date
and
time,
as
well
as
the
variable
name,
unit
and
colour
ramp.
!
6. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
Remember
than
each
dataset
is
a
time
series
of
a
given
variable.
Therefore,
you
can
visualize
the
evolution
of
a
variable
with
time.
Below
the
figure
window,
you
will
see
three
blue
buttons.
They
allow
you
to
mover
backward,
play,
and
move
forward
in
time.
For
an
example,
click
on
the
play
button
to
see
how
the
air
temperature
behaves
in
time.
The
time
is
displayed
in
the
title
of
the
figure.
For
an
example,
the
grapes
data
are
stored
every
30
minutes.
To
stop
the
animation,
click
on
the
same
button.
The
next
part
of
the
tutorial
will
introduce
you
to
more
advances
features
of
the
portal.
Customize
2D
maps
Let’s
move
to
the
other
components
of
the
portal.
On
the
right
of
the
figure
window,
you
will
see
a
plot
type
component.
This
allows
you
to
make
various
kinds
of
plot.
!
7. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
The
shaded
2D
plot
is
the
standard
map
representation.
If
you
check
filled
2D
contour
plot,
you
need
to
refresh
the
figure
by
clicking
on
the
plot
selected
button
again.
Plot
2D
subset
Each
dataset
has
its
own
coverage.
The
extent
of
each
data
may
vary
a
little.
When
you
select
a
dataset,
the
extent
is
displayed
on
the
map.
Click
on
each
dataset
to
see
the
actual
coverage.
You
may
well
be
interested
to
see
only
a
subset
of
the
available
area.
To
do
so,
you
have
two
possibilities.
The
first
is
to
select
your
own
area
of
interest
by
dragging
the
mouse
on
the
map.
Remember
that
you
must
stay
within
the
actual
coverage
of
the
dataset.
!
8. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
For
an
example,
select
the
second
dataset
in
the
hydrology
category.
The
variable
stored
in
this
dataset
is
the
routed
river
discharge.
In
other
words,
it
displays
the
flow
of
rivers
in
cubic
meters
per
seconds
as
computed
by
the
hydrological
model.
Click
on
the
plot
selected
button.
You
will
see
the
entire
area
covered
by
the
data
by
default.
Now
make
a
subset
over
the
eastern
part
of
the
Himalaya,
and
click
on
the
plot
selected
button
to
refresh
again.
You
now
have
a
close
up
view
of
your
data.
You
can
make
an
animation
to
see
how
it
behaves
with
time.
The
second
way
to
make
a
spatial
subset
is
to
use
the
ranges
component
on
the
bottom
right
of
the
portal.
The
ranges
component
allow
you
to
manually
refine
the
range
in
latitude
and
longitude.
There
you
can
manually
enter
the
coordinates
of
your
area
of
interest,
or
use
the
sliders.
The
same
way,
you
can
navigate
in
time
by
either
defining
manually
the
dates
and
hours,
or
use
the
slider.
Be
aware
that
the
date
and
time
syntax
must
fit
with
the
template.
Otherwise,
you
would
get
an
error.
It
is
often
simpler
to
use
the
slider.
1D
plot
over
latitude
and
longitude
These
two
2D
map
functions
make
traditional
map
views.
Now,
let’s
make
one-‐
dimensional
plots.
A
one-‐dimensional
plot
allows
you
to
view
a
variable
in
a
latitude-‐
time
or
longitude-‐time
representation.
This
is
particularly
useful
to
see
the
time
evolution
over
a
range
of
latitude
or
longitude.
Please
note
that
when
you
select
a
one-‐dimensional
plot,
the
selected
area
will
change
on
the
map
to
reflect
the
location
of
the
selection.
If
you
wish
to
change
it,
simply
use
the
set
region
button
and
drag
your
mouse
over
the
area
of
interest.
!
9. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
Let’s
make
an
example.
Select
the
first
dataset
of
the
hydrology
category.
This
dataset
contains
specific
runoff
data.
Now
let’s
make
a
plot
showing
the
variation
of
the
specific
runoff
from
West
to
East
of
the
Tibetan
plateau
at
a
latitude
of
28
degrees
North.
Select
the
1D
line
plot
as
plot
type,
then
select
make
an
East-‐West
selection
on
the
southern
part
of
the
plateau.
You
area
of
interest
will
look
like
a
line.
Now
if
you
click
on
the
plot
selected
button
to
refresh
the
figure,
you
will
obtain
a
line
plot
of
the
specific
runoff
from
West
to
East.
If
you
use
the
blue
play
button,
you
will
see
the
evolution
of
the
plot
with
time.
!
10. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
Take
some
time
to
make
some
tests.
And
remember,
each
time
you
change
the
type
of
plot,
the
area
of
interest
or
the
variable,
you
have
to
click
on
the
plot
selected
button
to
refresh
the
figure.
1D
plot
of
LAI
In
the
plot
view
component,
you
will
notice
that,
below
latitude
and
longitude,
you
can
also
select
time.
To
try
this,
let’s
first
select
the
leaf
area
index
product
of
the
land
category.
Then
we
select
the
one-‐dimensional
line
plot,
then
the
time
plot
view.
!"
11. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
You
will
notice
that
the
area
of
interest
has
become
smaller
on
the
map.
The
selection
actually
covers
one
grid
of
data.
Therefore,
in
this
configuration,
the
plot
will
show
you
the
time
evolution
of
the
leaf
area
index
at
the
location
on
the
map.
Click
on
the
map
to
define
a
location,
then
use
the
plot
selected
button
to
refresh
What
you
see
now
is
the
line
plot
showing
the
evolution
of
the
vegetation
for
the
entire
period
of
time
recorded
in
the
dataset.
Plot
of
in
situ
data
Now
let’s
visualize
some
in
situ
data.
In
situ
data
are
ground
measurements
performed
at
one
location.
These
data
offer
fewer
representation
options,
because
they
are
one-‐
dimensional
by
nature.
!!
12. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
If
you
select
the
in
situ
dataset
call
Lhasa
ground
station
data
2010,
you
will
see
that
the
plot
types
proposed
are
limited
to
one-‐dimensional
plots.
The
2D
plots
are
not
proposed
anymore.
In
the
variables
component,
you
can
see
four
variables.
These
are
longwave
and
shortwave
radiation
incoming
and
outgoing
from
the
land
surface.
Let’s
select
the
longwave
downward
variable
and
click
on
the
plot
selected.
In
the
figure,
you
now
see
the
entire
time
series
of
this
measurement.
!"
13. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
To
identify
the
location
of
this
station,
have
a
look
at
the
map.
You
should
see
a
red
dot
with
a
yellow
circle.
You
can
also
get
the
actual
coordinates
of
this
station
below
the
map.
The
text
shows
the
station
name,
as
well
as
the
latitude,
longitude,
altitude
in
meter,
and
starting
date.
Subsets
in
time
You
may
wish
to
see
a
variable
for
a
given
range
of
dates.
For
an
example,
select
the
shortwave
downward
variable
and
plot
it.
!"
14. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
Each
pick
is
a
day,
starting
from
April
third.
Let’s
say
that
we
want
to
display
only
a
few
days
around
April
seventh.
On
the
right
of
the
plot
type
component,
you
can
see
a
station
filter
component.
The
latitude,
longitude
and
altitude
filters
are
fixed
to
the
station.
But
the
time
filter
gives
you
a
range
of
dates.
You
can
use
the
sliders
to
change
the
interval.
Now
let’s
set
the
time
range
from
April
seventh
to
April
ten.
Then
click
on
the
plot
selected
button.
You
should
now
see
three
days
of
data,
with
three
picks.
Download
the
sample
data
Now
let’s
say
that
we
want
to
export
these
data
to
make
further
analysis.
In
the
plot
type
component,
select
the
download
data
entry.
!"
15. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
Once
selected,
you
will
see
a
new
component
on
the
bottom
right.
This
component
is
called
data
download
format.
Select
the
comma
separated
option,
then
click
on
the
usual
plot
selected
button.
The
download
window
of
your
Internet
browser
will
pop
up.
Save
this
file
on
your
desktop.
The
file
you
got
is
a
text
file.
The
data
are
stored
in
comma
separated
format.
You
can
open
this
file
in
Microsoft
Excel,
and
make
your
own
statistics
and
plots.
!"
16. The
CEOP-‐AEGIS
Data
Portal
Tutorial
Version
1.0.2
Jérôme
Colin
Exercises
Exercise
1:
Make
a
map
of
specific
runoff
for
the
1st
of
June
2008
over
the
area
ranging
from
90
to
100
East,
and
26
to
33
North.
Exercise
2:
Make
a
plot
of
routed
river
discharge
values
at
the
point
location
(26.00N-‐97.70E)
for
the
entire
year
2009.
Exercise
3:
What
is
the
highest
value
of
specific
humidity
at
point
location
(32.2N-‐92.0E)
between
October
12th
and
October
14th
2008
?
Exercise
4:
Export
values
of
shortwave
and
longwave
incoming
radiation
at
Lhasa
station
for
the
month
of
May
2010
and
make
your
own
plot
in
Microsoft
Excel.
!"