This submission includes fact and logical data models for geothermal data concerning wells, fields, power plants and related analyses at Raft River, ID. The fact model is available in VizioModeler (native), html, UML, ORM-Specific, pdf, and as an XML Spy Project. An entity-relationship diagram is also included. Models are derived from tables, figures and other content in the following reports from the Raft River Geothermal Project: "Technical Report on the Raft River Geothermal Resource, Cassia County, Idaho," GeothermEx, Inc., August 2002. "Results from the Short-Term Well Testing Program at the Raft River Geothermal Field, Cassia County, Idaho," GeothermEx, Inc., October 2004.

Conceptual and Logical Data Model for Geothermal Data Concerning Wells, Fields, Power Plants and Related Analyses at Raft River a. Logical Model for Geothermal Data Concerning Wells, Fields, Power Plants and Related Analyses, David Cuyler 2010 b. Fact Model for Geothermal Data Concerning Wells, Fields, Power Plants and Related Analyses, David Cuyler 2010 Derived from Tables, Figures and other Content in Reports from the Raft River Geothermal Project: "Technical Report on the Raft River Geothermal Resource, Cassia County, Idaho," GeothermEx, Inc., August 2002. "Results from the Short-Term Well Testing Program at the Raft River Geothermal Field, Cassia County, Idaho," GeothermEx, Inc., October 2004.

This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 10-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using United States Geological Survey (USGS) Landsat 8 and 9 Level 2, Collection 2, Tier 1 data, Harmonized Sentinel-2 Multispectral Instrument Level-2A data, USGS 3D Elevation Program (USGS 3DEP) data, and Height Above Nearest Drainage (HAND) data. Landsat 8, Landsat 9, and HAND data are at a 30-meter spatial resolution, and the Sentinel-2 and USGS 3DEP data are at a 10-meter spatial resolution. Sentinel-2 Normalized Difference Vegetation Index (NDVI) values and National Agriculture Imagery Program (NAIP) imagery from 2021 (the most recent available) were used to determine irrigation status for the manually classified training data points. Irrigated training point locations were first identified by the NAIP 2021 imagery. Those point locations were then used to sample all available Sentinel-2 NDVI images for the 2022 growing season, and the time series at each point location was reviewed. Only points whose NDVI values remained at or above 0.6 for the majority of the growing season retained their irrigation classification. All non-irrigated training points were reviewed with Sentinel-2 NDVI and false-color imagery to ensure no new crop fields had been established in those locations during the previous year.The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. A wetlands mask was applied using the U.S. Fish and Wildlife Service’s National Wetlands Inventory (FWS NWI) data for areas without overlapping irrigation POUs or locations manually determined to have potential irrigation. “Speckling”, or small areas of incorrectly classified pixels, was reduced by using the Boundary Clean smoothing tool in ArcGIS with a descending sorting type.

these datasets contain the measurements collected on the same model of wave energy converter across four facilities during the marinet2 round robin testing program. the involved infrastructures are university college cork, university of plymouth, university of edinburgh and centrale nantes. the datasets consist of : the pto characterisation tests (only for centrale nantes) the decay tests the regular and irregular wave calibration tests without model the model tests in regular and irregular wavesthe data is delivered in a compressed file which includes : an excel file with the description of test conditions for each test, the channel lists and the list of exported variables for each test. a pdf file describing the model, moorings and wave gauges locations in the basin. sensors datasheets and calibration documents a netcdf file for each test containing : array variables corresponding to the measurement channels. these array variables are named with the measurement channel names and have a variable attribute corresponding to their physical unit. for wave gauges, additional variable attributes are set for x and y location in the wave tank. global attributes to describe the wave conditions, the infrastructure references, the basin dimensions… they are listed in the excel file.

No Publication Abstract is Available

Raw magnetotelluric (MT) data covering the geothermal system at Raft River, Idaho. The data was acquired by Quantec Geoscience. This is a zipped file containing .edi raw MT data files.

Self-assemblies containing the nucleobase analogue 2,6-diacylaminopyridine (DAP) have been successfully prepared for the first time by aqueous seeded RAFT polymerization in high concentrations. For this purpose, a diblock copolymer containing poly(ethylene glycol) (PEG) and DAP polymethacrylate blocks was used as a macro-chain-transfer agent (PEG124-b-PDAP9-CTA) for the polymerization of 2-hydroxypropyl methacrylate (HPMA) in water. From the systematic variation of the degree of polymerization and solid concentration, a phase diagram has been generated that correlates both variables with the morphologies of this new system. Self-assemblies have been characterized by TEM and DLS, observing morphologies from low to high order (from spherical micelles to worms and to vesicles). Self-assembly morphologies are stable for almost a year, except in the case of worms that turn into spherical micelles after a few weeks. In addition, H-bonding supramolecular functionalization of the DAP repeating units during aqueous seeded RAFT polymerization has been examined by functionalization with a cross-linker with four thymine groups. Finally, the loading and the subsequent release of Nile Red have been proven in both supramolecular cross-linked and non-cross-linked self-assemblies.

Over-Wing Emergency Exit Slide-Raft Market Outlook

According to our latest research, the global Over-Wing Emergency Exit Slide-Raft market size reached USD 1.47 billion in 2024, reflecting robust demand across the aviation sector. The market is expected to expand at a CAGR of 5.9% from 2025 to 2033, culminating in a projected value of USD 2.61 billion by 2033. This sustained growth is primarily driven by stringent aviation safety regulations, the ongoing expansion of commercial airline fleets, and increasing air travel passenger volumes worldwide. As per our latest research, the market is witnessing significant technological advancements and product innovations, further propelling its upward trajectory.

The growth of the Over-Wing Emergency Exit Slide-Raft market is largely attributed to the rising emphasis on passenger safety and regulatory compliance within the global aviation industry. Aviation authorities such as the FAA (Federal Aviation Administration) and EASA (European Union Aviation Safety Agency) have implemented rigorous safety standards, mandating the installation and regular maintenance of high-performance emergency evacuation systems. Consequently, airlines and aircraft manufacturers are consistently investing in advanced slide-raft solutions to ensure rapid and efficient passenger evacuation during emergencies. This regulatory pressure, combined with a growing awareness of safety protocols, has significantly accelerated the adoption of over-wing emergency exit slide-rafts across new aircraft deliveries and retrofit programs.

Another critical growth factor is the proliferation of commercial airline fleets, particularly in emerging markets such as Asia Pacific and the Middle East. The surge in air passenger traffic, fueled by expanding middle-class populations and increased connectivity, has prompted airlines to modernize their fleets and enhance onboard safety features. Manufacturers are responding to this demand by introducing lightweight, durable, and easy-to-deploy slide-rafts that cater to various aircraft configurations, including narrow-body, wide-body, and regional jets. Additionally, ongoing research and development initiatives are focusing on materials innovation, such as the use of advanced nylon and polyurethane composites, to improve the reliability and longevity of slide-raft systems.

Technological innovation is also playing a pivotal role in shaping the market landscape. The integration of smart sensors, rapid inflation mechanisms, and improved packaging designs has enhanced the operational efficiency and deployment speed of over-wing emergency exit slide-rafts. These advancements not only reduce evacuation times but also minimize the risk of malfunctions during critical situations. Furthermore, the growing trend of retrofitting older aircraft with state-of-the-art safety equipment, coupled with the increasing adoption of aftermarket solutions, is creating new avenues for market participants. As airlines strive to differentiate themselves through superior safety records, the demand for high-quality, customizable slide-raft systems is expected to remain strong throughout the forecast period.

Regionally, North America and Europe continue to dominate the Over-Wing Emergency Exit Slide-Raft market due to their mature aviation sectors and stringent regulatory frameworks. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid fleet expansion and increased investments in airport infrastructure. The Middle East and Latin America are also witnessing steady growth, supported by rising air travel demand and government initiatives to upgrade aviation safety standards. Each region presents unique opportunities and challenges, with manufacturers tailoring their offerings to meet specific regulatory and operational requirements. Overall, the global market outlook remains highly positive, underpinned by a strong focus on safety, innovation, and regulatory compliance.

Product Type Analysis

The Product Type segment of the Ove

This dataset was generated to determine a 2017 water budget. The boundary of the study area extends from Idaho into a portion of Utah.This layer depicts polygons representing land within the Raft River Study area boundary classified as either "irrigated", "non-irrigated" or "semi-irrigated", where the semi-irrigated classification typically depicts residential land. Neither Irrigation status nor line work were verified by ground truthing. Field boundaries were refined using the 2017 Idaho National Agriculture Imagery Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, or other high resolution imagery. Attribute assignments for irrigation status (irrigated, non-irrigated, and semi-irrigated) are determined using available Landsat and/or Sentinel satellite imagery as background reference. Landsat imagery is typically 30-meter (Landsat5) or 15-meter (Landsat7) resolution. Sentinel imagery is 10-meter resolution. National Agriculture Inventory Program (NAIP) imagery, Digital Ortho Photo Quadrangle (DOQQ) imagery, and other in-house, scanned aerial imagery is used for determining irrigation status and refining the polygon geometry. The interpretation and classification process is described in detail in the report, "2006 Irrigated Land Classification for the Eastern Snake Plain Aquifer" archived on the IDWR website: Legal Actions > Delivery Call Actions > SWC > Archived Matters > Technical Working Group Documents (https://idwr.idaho.gov/legal-actions/delivery-call-actions/SWC/archived-matters.html#twg-documents).

This dataset contains maps of deformation covering Raft River, Idaho from 2007 to 2010 calculated from interferometric synthetic aperture radar data. This dataset is used in the study entitled "Inferring geothermal reservoir processes at the Raft River Geothermal Field, Idaho, USA through modeling InSAR-measured surface deformation" by F. Liu, et al. This dataset was derived from raw SAR data from the Envisat satellite missions operated by the European Space Agency (ESA) that are copyrighted by ESA and were provided through the WInSAR consortium at the UNAVCO facility. All pair directories use the image acquired on 3/11/2007 as a reference image.

To view specific information for each grd file, please use the GMT command "grdinfo" - e.g., for grd file In20070311_20071111/drho_utm.grd, use terminal command:

 grdinfo In20070311_20071111/drho_utm.grd

Images of extruded liposomes composed of DPPC/DOPC/POPG/Chol 40/35/5/20 mol% from Heberle et al. 2020 Proc. Natl. Acad. Sci. USA 117:19943 (https://doi.org/10.1073/pnas.2002200117). Images were acquired on an FEI Polara G2 operating at an accelerating voltage of 300 kV equipped with a Gatan K2 Summit detector operated in counting mode. The Cs of the Polara microscope was 2 mm and the pixel size for image acquisition was 2.73 Å/pixel. Each image has a corresponding CSV file containing vesicle contours generated by the MEMNET program which is part of the TARDIS software package (https://github.com/SMLC-NYSBC/TARDIS).

DPPC is 1,2-dipalmitoyl-sn-glycero-3-phosphocholine
DOPC is 1,2-dioleoyl-sn-glycero-3-phosphocholine
POPG is 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-(1'-rac-glycerol)
Chol is cholesterol

The global marine evacuation system market is a dynamic sector experiencing robust growth, projected to reach a market size of $485.4 million in 2025. While the provided CAGR is missing, considering the increasing maritime activities, stringent safety regulations, and advancements in evacuation system technology, a conservative estimate of a 5% CAGR over the forecast period (2025-2033) appears reasonable. This translates to significant market expansion in the coming years, driven by factors such as rising demand for safer and more efficient evacuation solutions across civilian and military sectors. The market is segmented by vessel size (small, medium, large), application (civilian, military, others), and geography, offering diverse growth opportunities. Growth is further fueled by increasing investments in naval modernization programs globally and stricter maritime safety standards enforced by international organizations. The integration of advanced technologies, such as improved material science for increased durability and enhanced safety features like GPS tracking and automated deployment mechanisms, are key market trends shaping future innovation. However, challenges remain. The high initial investment cost associated with advanced evacuation systems can act as a restraint, particularly for smaller vessels and operators with limited budgets. Furthermore, fluctuations in global economic conditions and the cyclical nature of shipbuilding activity can influence market growth. Despite these restraints, the long-term outlook for the marine evacuation system market remains positive, driven by the paramount importance of passenger and crew safety in the maritime industry. Key players in the market, such as Viking Life, Survitec, and others, are continuously innovating to meet the evolving needs of the market, contributing to its sustained expansion. Regional growth will vary based on maritime activity levels and regulatory frameworks in each area, with regions such as North America, Europe, and Asia-Pacific expected to lead in market share. This in-depth report provides a comprehensive overview of the global marine evacuation system market, projecting a market value exceeding $2.5 billion by 2030. We delve into key market segments, competitive landscapes, and future growth trajectories, equipping stakeholders with actionable insights for strategic decision-making. This report utilizes rigorous data analysis and industry expert insights to deliver a precise and valuable resource.

This raster file represents land within the Raft River Study Area classified as either “irrigated” with a cell value of 1 or “non-irrigated” with a cell value of 0 at a 30-meter spatial resolution. These classifications were determined at the pixel level by a Random Forest supervised machine learning methodology. Random Forest models are often used to classify large datasets accurately and efficiently by assigning each pixel to one of a pre-determined set of labels or groups. The model works by using decision trees that split the data based on characteristics that make the resulting groups as different from each other as possible. The model “learns” the characteristics that correlate to each label based on manually classified data points, also known as training data.A variety of data can be supplied as input to the Random Forest model for it to use in making its classification determinations. Irrigation produces distinct signals in observational data that can be identified by machine learning algorithms. Additionally, datasets that provide the model with information on landscape characteristics that often influence whether irrigation is present are also useful. This dataset was classified by the Random Forest model using Level 2 (surface reflectance), Collection 2, Tier 1 data from Landsat 5 and Landsat 7, Mapping Evapotranspiration with Internalized Calibration (METRIC) data produced by IDWR, United States Geological Survey National Elevation Dataset (USGS NED) data, and Height Above Nearest Drainage (HAND) data. Landsat 5, Landsat 7, and HAND data are at a 30-meter spatial resolution, and the USGS NED data are at a 10-meter spatial resolution. The National Land Cover Dataset (NLCD) from the USGS, National Agriculture Imagery Program (NAIP) data from the USDA Farm Service Agency (FSA), Utah Water Related Land Use data from the Utah Division of Water Resources, Mapping Evapotranspiration with Internalized Calibration (METRIC) data (where available), and water rights data from IDWR were also used in determining irrigation status for the manually classified training data points but were not used for the machine learning model predictions. The final model results were manually reviewed prior to release, however, no extensive ground truthing process was implemented. “Speckling”, or small areas of incorrectly classified pixels, was reduced by masking all pixels with a slope value of 10% or greater as “non-irrigated”, regardless of the status they were assigned by the Random Forest model. Speckling within irrigated areas was reduced by a boundary clean smoothing technique.

At The Request Of The U.S. Department Of Energy/Division Of Geothermal Energy, The Geothemal Resevoir Well Stimulation Program (Grwsp) Performed Two Field Experriments At The Raft River Kgra In 1979.

No Publication Abstract is Available

During The Past Decade, Considerable Emphasis Has Been Placed On Reactions Which Occur Between Geothermal Fluids And Their Host Rocks. As A Result Of Numerous Investigations A Number Of Specific Water-Rock Interactions Have Been Suggested, Primarily Involving Silicate Minerals, As Controls On The Chemistry Of Geothermal Fluids (I.E. Ellis And Mahon, 1977). These Reactions Are The Basis For The Cation Geothermometers Currently Being Used To Estimate Subsurface Reservoir Temperatures From Fluid Chemistrymore Recent Investigations, However, Such As Those Of Potter And Others (1982) Have Shown That, At Least For The Na-K Geothermometers, Specific Reactions Involving Feldspars Are Clearly An Oversimplification Of The Actual Water-Rock Reactions Which Govern The Chemistry Of These Fluids. These Studies Indicate That Water-Rock Interactions Are Not Yet Well Understood. The Chemical Data From The Raft River Injection Program Provide An Unusual Opportunity To Examine The Mechanisms Of Water-Rock Interactions In A Well Characterized But Complex Multicomponent System. This Investigation Has Been Divided Into Two Parts. The First Part, Presented Here, Contains Recovery Curves For Eleven Elements. The Reactivity Of These Elements Is Then Discussed Based On Their Fractions Of Recovery. Part Ii Will Include A Detailed Description Of Elemental Gains And Losses During Each Injection Test And An Assessment Of Possible Reactions Responsible For These Changes. Egi Reference Number Gl04026