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.

The size and share of the market is categorized based on Type (Single Tube Life Raft, Multi tube Life Raft) and Application (Airliner, General Aviation, Business Aircraft, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

RAFT submissions for my-raft-submission

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To prevent overfitting to the public leaderboard, we only evaluate one submission per week. You… See the full description on the dataset page: https://huggingface.co/datasets/Linuxdex/my-raft-submission.

The global aviation life rafts market is experiencing robust growth, driven by stringent safety regulations within the aviation industry and a rising demand for enhanced passenger safety features. The increasing number of air travel passengers worldwide directly correlates with a heightened need for reliable life-saving equipment, such as life rafts, bolstering market expansion. Technological advancements, including the development of lighter, more durable, and technologically advanced life rafts with improved features like GPS tracking and enhanced survival supplies, are further fueling market expansion. While the market faces certain restraints, such as the high initial investment cost associated with purchasing and maintaining life rafts, these are offset by the significant safety benefits and regulatory mandates. Market segmentation by type (e.g., inflatable, rigid) and application (e.g., commercial aviation, general aviation) reveals varying growth trajectories; for example, the commercial aviation segment is expected to dominate due to the larger number of flights and passengers. Regional analysis indicates that North America and Europe currently hold significant market shares, reflecting established aviation infrastructure and robust safety standards. However, developing economies in Asia-Pacific are showing promising growth potential, driven by increasing air travel and investments in aviation infrastructure. This suggests a dynamic market landscape with significant opportunities for existing players and new entrants alike. The forecast period of 2025-2033 presents considerable growth potential. Assuming a conservative CAGR (Compound Annual Growth Rate) of 5%, and a 2025 market size of $150 million (a reasonable estimate given the industry and the presence of established players), the market is projected to surpass $230 million by 2033. This growth will be influenced by factors such as increasing international air travel, government regulations mandating enhanced safety measures, and the continued technological innovation within life raft design. Competitive analysis reveals that key players such as EAM Worldwide, Revere Survival, and Zodiac Aerospace are actively shaping the market through product innovation, strategic partnerships, and geographical expansion. The focus will likely remain on enhancing product features, reducing costs, and exploring innovative materials to cater to the evolving needs of the aviation sector.

The global aircraft life raft market is experiencing robust growth, driven by increasing air travel, stringent safety regulations, and a rising focus on passenger safety across various aircraft types. The market, valued at approximately $150 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 5% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the expansion of the aviation industry, particularly in emerging economies, is creating significant demand for life-saving equipment like life rafts. Secondly, stricter international aviation safety standards mandate the presence of adequate life-saving devices on board, further stimulating market growth. Furthermore, technological advancements in life raft design, leading to lighter, more durable, and easily deployable rafts, are contributing to market expansion. The single-tube life raft segment currently holds a larger market share compared to multi-tube rafts due to its cost-effectiveness and suitability for smaller aircraft. However, the multi-tube segment is anticipated to witness faster growth driven by increasing demand from larger airliners. The market is segmented by application into airliners, general aviation, business aircraft, and others, with airliners constituting the largest segment. Geographical analysis indicates a strong presence of the aircraft life raft market in North America and Europe, driven by well-established aviation industries and stringent safety regulations. However, Asia-Pacific is expected to witness significant growth in the forecast period, propelled by rapid expansion of the aviation sector in countries like India and China. The presence of key players such as Aero Sekur, Survitec Group, and Safran contributes to a competitive market landscape, with companies focusing on product innovation and strategic partnerships to maintain market share. Challenges such as high manufacturing costs and the need for regular maintenance and inspection could act as restraints, however, the overarching demand driven by safety concerns ensures sustained growth in the coming years. This comprehensive report provides an in-depth analysis of the global aircraft life raft market, projected to be worth over $300 million by 2028. We delve into market dynamics, key players, and future trends, offering valuable insights for manufacturers, suppliers, and industry stakeholders. The report leverages extensive primary and secondary research, incorporating data from leading industry players and regulatory bodies.

The global helicopter life raft market is experiencing robust growth, driven by increasing demand for enhanced safety measures in the aviation industry, particularly within civil and military helicopter operations. The market, estimated at $150 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors, including rising helicopter deployments for search and rescue missions, offshore operations, and military applications. Stringent safety regulations implemented by various aviation authorities worldwide further contribute to the market expansion. The increasing prevalence of extreme weather events also necessitates the availability of reliable life-saving equipment like helicopter life rafts, boosting market demand. Market segmentation reveals a preference for larger capacity rafts (6-10 person and above) due to the nature of many helicopter missions, although the smaller capacity rafts remain significant in niche applications. Geographical analysis shows North America and Europe dominate the market currently, but significant growth opportunities exist in the Asia-Pacific region due to its expanding helicopter fleet and infrastructure development. Further market expansion will be propelled by technological advancements leading to lighter, more durable, and easily deployable life rafts. However, factors such as the high initial cost of these rafts and fluctuating raw material prices pose challenges to the market's growth. Competition among established players like Collins Aerospace, Safran, and Survitec Group remains intense, with companies focusing on product innovation and strategic partnerships to enhance their market position. The market is expected to witness further consolidation as companies strive to capitalize on the rising demand and expand their global reach. Continued investment in research and development, aimed at improving raft design and safety features, will be critical for sustained market growth. The integration of advanced technologies, such as GPS tracking systems, and improved materials will be key differentiators.

jjovalle99/raft-dataset-aws-wellarchitected dataset hosted on Hugging Face and contributed by the HF Datasets community

Detailed Active addresses (weekly) metrics and analytics for Raft, including historical data and trends.

Detailed Code commits metrics and analytics for Raft, including historical data and trends.

phatvo/THUDM_webglm-qa-train-raft dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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.

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 7 and Landsat 8, 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 7, Landsat 8, METRIC, and HAND data are at a 30-meter spatial resolution, and the USGS NED data are at a 10-meter spatial resolution. The Cropland Data Layer (CDL) from the United States Department of Agriculture (UDSA) National Agricultural Statistics Service (NASS), 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, 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 majority filter smoothing technique using a kernel of 8 nearest neighbors. A limited amount of manual corrections were also made to the final results.

IOOS Sensor Observation Service (SOS) Server for NANOOS, the Northwest Association of Networked Ocean Observing Systems (http://nanoos.org). Provides access to marine in-situ observation data for the US Pacific Northwest and lower British Columbia, from the NANOOS asset data store harvested and integrated by NVS (NANOOS Visualization System, http://nvs.nanoos.org). To avoid data duplication, currently only assets not otherwise available to the IOOS Catalog (http://catalog.ioos.us) are accessible through this SOS server; for example, assets from most federal agencies are not accessible on this server, but they are available on the NVS application. This NANOOS service is run by the 52North IOOS SOS server software, and complies with the IOOS SOS "Milestone 1" service profile (https://code.google.com/p/ioostech/wiki/SOSGuidelines).

This station provides the following variables: Air temperature, Sea water electrical conductivity, Sea water salinity, Sea water temperature

R0bfried/RAGAS-RAFT-llama-3-2-8B-eval dataset hosted on Hugging Face and contributed by the HF Datasets community

137 Active Global Raft buyers list and Global Raft importers directory compiled from actual Global import shipments of Raft.

andrewsiah/rewarded-Mistral-RM-for-RAFT-GSHF-v0_s500_e625 dataset hosted on Hugging Face and contributed by the HF Datasets community

Summary:

This dataset contains trajectory files from Southern Hemisphere particle releases conducted with Ocean Parcels (Delandmeter & van Sebille, 2019; https://oceanparcels.org) and run offline using daily surface velocities from ACCESS-OM2-01 (Kiss et al. 2020; https://dx.doi.org/10.25914/608097cb3433f), combined with Stokes drift velocities from Wave Watch III (Rascle and Ardhuin, 2013; ftp.ifremer.fr/ifremer/ww3/HINDCAST/GLOBAL/).

Particle were released daily from 10 Southern Hemisphere islands and continents (South America, South Africa, Australia, New Zealand, Macquarie Island, Marion Island, Crozet Islands, Kerguelen Islands, South Georgia Island, Gough Island) for 19 years starting on 1 January 1997 and ending on 31 December 2015, with each particle tracked forward-in-time for three years. Particle trajectories are organised in directories by release location, with each file corresponding to a single release year.

Each netcdf file contains particle trajectory positions (latitude and longitude) saved at a 5-day temporal resolution, along with a variable (shelf_col_3gc) that defines when a particle has reached the Antarctic coastline. Due to size restrictions, only particle trajectories that reached within three model grid cells of the Antarctic coastline (shelf_col_3gc = 1) are provided here. The code used to run these particle tracking experiments is available on Github at https://github.com/hrsdawson/GCB_Antarctic_Rafting.

Citation of associated paper: Dawson, H. R. S., England, M. H., Morrison, A. K., Tamsitt, V., and Fraser, C. I. Floating debris and organisms can raft to Antarctic coasts from all major Southern Hemisphere landmasses, submitted to Global Change Biology.

References:

The marine life raft market is projected to reach US$ 1,105.53 million in 2024. A CAGR of 4.40% is projected for marine life rafts from 2024 to 2034. A market of US$ 1,699.68 million is expected to be generated by marine life rafts by 2034.

2019 to 2023 Historical Analysis vs. 2024 to 2034 Market Forecast Projections

Country-wise Analysis

Category-wise Insights

Report Scope

Dataset Card for Evaluation run of hatemmahmoud/qwen2.5-1.5b-sft-raft-grpo-hra-doc

Dataset automatically created during the evaluation run of model hatemmahmoud/qwen2.5-1.5b-sft-raft-grpo-hra-doc The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/hatemmahmoud_qwen2.5-1.5b-sft-raft-grpo-hra-doc-details.