Secure Internet Servers for the Federated States of Micronesia was 182.57536 Number per 1 Mil. People in January of 2020, according to the United States Federal Reserve. Historically, Secure Internet Servers for the Federated States of Micronesia reached a record high of 210.87593 in January of 2019 and a record low of 9.38289 in January of 2004. Trading Economics provides the current actual value, an historical data chart and related indicators for Secure Internet Servers for the Federated States of Micronesia - last updated from the United States Federal Reserve on April of 2025.

The Russian data processing server market fell slightly to $2B in 2024, waning by -3.1% against the previous year. Over the period under review, consumption, however, continues to indicate a relatively flat trend pattern. Over the period under review, the market reached the maximum level at $2.2B in 2021; however, from 2022 to 2024, consumption remained at a lower figure.

Secure Internet Servers for the Russian Federation was 13344.75748 Number per 1 Mil. People in January of 2020, according to the United States Federal Reserve. Historically, Secure Internet Servers for the Russian Federation reached a record high of 13344.75748 in January of 2020 and a record low of 1.61080 in January of 2003. Trading Economics provides the current actual value, an historical data chart and related indicators for Secure Internet Servers for the Russian Federation - last updated from the United States Federal Reserve on April of 2025.

This repository contains the different outputs generated for the paper "Blockchain-enabled Server-less Federated Learning", submitted to Computer Networks (COMNET) journal. Two types of outputs are provided:

Blockchain queue simulator (output_queue_simulator): results of the simulations done in the batch-service queue simulator (https://github.com/fwilhelmi/batch_service_queue_simulator) to characterize the queue latency of blockchain applications.

Tensorflow (output_tensorflow): results of the simulations done in Tensorflow Federated (TFF), resulting from the application of different models to the federated EMNIST dataset.

Each folder also includes the scripts used to execute the corresponding simulations. For more details, see the repository in https://github.com/fwilhelmi/blockchain_enabled_federated_learning

IMPORTANT information regarding proper legend rendering in ArcMap: Due to the limitations of Graphical Device Interface (GDI) resources per application on Windows, ArcMap does not display the legend in the Table of Contents for the ArcGIS Server service layer if the legend has more than 100 items. As of December 2011, there are 968 unique legend items in the Level IV Ecoregion Polygon legend. Follow this link (http://support.esri.com/en/knowledgebase/techarticles/detail/33741) for instructions about how to increase the maximum number of ArcGIS Server service layer legend items allowed for display in ArcMap. Note the instructions at this link provide a slightly incorrect path to "Maximum Legend Count". The correct path is HKEY_CURRENT_USER > Software > ESRI > ArcMap > Server > MapServerLayer > Maximum Legend Count. When editing the "Maximum Legend Count", update the field, "Value data" to 1000. To download a PDF version of the Level IV ecoregion map and legend, go to ftp://ftp.epa.gov/wed/ecoregions/us/Eco_Level_IV_US_pg.pdf. Please read the remainder of this layer description for general information about Level IV Omernik Ecoregions. This layer represents Level IV Omernik Ecoregions. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. Compilation of the level IV maps, performed at 1:250,000 scale, has been a part of collaborative projects between US Environmental Protection Agency, National Health and Environmental Effects Laboratory (NHEERL)--Corvallis, OR, the US Forest Service, Natural Resources Conservation Service, and a variety of other state and federal resource agencies. The ecoregions and subregions are designed to serve as a spatial framework for environmental resource management. The most immediate needs by the states are for developing regional biological criteria and water resource standards, and for setting management goals for nonpoint-source pollution. Level IV ecoregions are intended for large geographic extents (i.e. states, multiple counties, or river basins). Use for smaller areas, such as individual counties or a 1:24,000 scale map boundary, is not recommended. Explanation of the methods used to delineate the ecoregions are given in Omernik (1995), Griffith et al. (1994), and Gallant et al. (1989). For more information about Omernik ecoregions or to download ecoregion maps and GIS data, go to: http://www.epa.gov/wed/pages/ecoregions.htm.

In the United States, the federal government manages lands in significant parts of the country. These lands include 193 million acres managed by the US Forest Service in the nation's 154 National Forests and 20 National Grasslands, Bureau of Land Management lands that cover 247 million acres in Alaska and the Western United States, 150 million acres managed for wildlife conservation by the US Fish and Wildlife Service, 84 million acres of National Parks and other lands managed by the National Park Service and over 30 million acres managed by the Department of Defense. The Bureau of Reclamation manages a much smaller land base than the other agencies included in this layer but plays a critical role in managing the country's water resources.The agencies included in this layer are:Bureau of Land ManagementBureau of ReclamationDepartment of DefenseNational Park ServiceUS Fish and Wildlife ServiceUS Forest ServiceDataset SummaryPhenomenon Mapped: United States lands managed by six federal agenciesUnits: MetersCell Size: 30.92208102 metersSource Type: DiscretePixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, US Virgin Islands, Guam, American Samoa, and Northern Mariana Islands. The layer also includes National Monuments and Wildlife Refuges in the Pacific Ocean, Atlantic Ocean, and the Caribbean Sea.Source: BLM, DoD, USFS, USFWS, NPS, PADUS 3.0. Rasterized by Esri from features April 2024.Publication Date: Various - Esri compiled and published this layer in April 2024. See individual agency views for data vintage.ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/The service behind this layer was published with 6 functions allowing the user to select different views of the data. Other layers created from this service using functions include:USA Bureau of Land Management LandsUSA Bureau of ReclamationUSA Department of Defense LandsUSA National Park Service LandsUSA Fish and Wildlife Service LandsUSA Forest Service LandsA hosted feature service created from these data is also available.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "federal lands" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "federal lands" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

The rapid upswing in interest in federated learning (FL) and federated analytics (FA) architectures has corresponded with the rapid increase in commercial AI software products, ranging from face detection and language translation to connected IOT devices, smartphones, and autonomous vehicles equipped with high-resolution sensors. However, the traditional client-server model does not readily address questions of data ownership, privacy, and data location in the context of the multiple datasets required for machine learning. In this paper, we report on a pilot distributed ledger and smart contract network model, designed to track analytic jobs in an HPC supercomputing environment. The test system design integrates the FL/FA model into a blockchain-based network architecture, wherein the test system records interactions with the global server and blockchain network. The design goal is to create a secure audit trail of supercomputer analytic operations and the ability to securely federate those operations across multiple supercomputer deployments. As there are still relatively few real-world applications of FL/FA models and blockchain networks in use, our system design, test deployment, and sample code are intended to provide interested researchers with exploratory tools for future research.

Graph and download economic data for Producer Price Index by Commodity: Data Processing and Related Services: Hosting, Active Server Pages (ASP) and Other Information Technology (IT) Infrastructure Provisioning Services (WPU38110101) from Dec 2008 to Apr 2025 about information technology, infrastructure, processed, services, commodities, PPI, inflation, price index, indexes, price, and USA.

United States - Producer Price Index by Industry: Data Processing, Hosting and Related Services: Hosting, Active Server Pages (ASP), and Other Information Technology (IT) Infrastructure Provisioning Services was 116.99600 Index Dec 2006=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Data Processing, Hosting and Related Services: Hosting, Active Server Pages (ASP), and Other Information Technology (IT) Infrastructure Provisioning Services reached a record high of 118.50200 in January of 2025 and a record low of 99.30000 in February of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Data Processing, Hosting and Related Services: Hosting, Active Server Pages (ASP), and Other Information Technology (IT) Infrastructure Provisioning Services - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Secure Internet Servers for the Sudan (ITNETSECRP6SDN) from 2010 to 2023 about servers, Sudan, and internet.

This data is a web map service (WMS) from directly connected to FEMA's GIS server. Any edits, changes, data updates are processed at their site and are reflected here as they update their web services.

United States - Secure Internet Servers for Pacific Island Small States was 262.95415 Number per 1 Mil. People in January of 2020, according to the United States Federal Reserve. Historically, United States - Secure Internet Servers for Pacific Island Small States reached a record high of 262.95415 in January of 2020 and a record low of 15.42658 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Secure Internet Servers for Pacific Island Small States - last updated from the United States Federal Reserve on May of 2025.

United States - Secure Internet Servers for OECD Members was 57528.24489 Number per 1 Mil. People in January of 2020, according to the United States Federal Reserve. Historically, United States - Secure Internet Servers for OECD Members reached a record high of 57528.24489 in January of 2020 and a record low of 99.44324 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Secure Internet Servers for OECD Members - last updated from the United States Federal Reserve on April of 2025.

Graph and download economic data for Secure Internet Servers for Ireland (ITNETSECRP6IRL) from 2010 to 2023 about servers, internet, and Ireland.

The amount of water in soil is based on rainfall amount, what proportion of rain infiltrates into the soil, and the soil"s storage capacity. Available water storage is the maximum amount of plant available water a soil can provide. It is an indicator of a soil’s ability to retain water and make it sufficiently available for plant use. Available Water Storage is a capacity estimate for the top 150 centimeters of soil. It is calculated from the difference between soil water content at field capacity and the permanent wilting point adjusted for salinity and fragments. Available water storage is used to develop water budgets, predict droughtiness, design and operate irrigation systems, design drainage systems, protect water resources, and predict yields. Available water storage is an important input into hydrologic models including the Soil and Water Assessment Tool (SWAT) - a water quality model that is designed to assess non-point and point source pollution at the river basin scale. Available water storagecan also be used as an indication of a soil"s drought susceptibility, for water recharge modeling, to assess a soil"s ability to support crops, and for many other purposes. Dataset SummaryPhenomenon Mapped: Amount of water a soil can hold that is available to plantsGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands, Republic of Palau, Republic of the Marshall Islands, Federated States of Micronesia, and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Units: MillimetersCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerSource:Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for available water storage is derived from the gSSURGO map unit aggregated attribute table field: Available Water Storage 0-150cm Weighted Average (aws0150wta). What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "available water storage" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "available water storage" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Secure Internet Servers for the Commonwealth of the Northern Mariana Islands was 1615.78957 Number per 1 Mil. People in January of 2020, according to the United States Federal Reserve. Historically, Secure Internet Servers for the Commonwealth of the Northern Mariana Islands reached a record high of 1615.78957 in January of 2020 and a record low of 28.82343 in January of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for Secure Internet Servers for the Commonwealth of the Northern Mariana Islands - last updated from the United States Federal Reserve on May of 2025.

United States - Producer Price Index by Commodity: Data Processing and Related Services: Hosting, Active Server Pages (ASP) and Other Information Technology (IT) Infrastructure Provisioning Services was 114.54200 Index Dec 2008=100 in April of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Data Processing and Related Services: Hosting, Active Server Pages (ASP) and Other Information Technology (IT) Infrastructure Provisioning Services reached a record high of 115.91200 in January of 2025 and a record low of 98.00000 in October of 2015. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Data Processing and Related Services: Hosting, Active Server Pages (ASP) and Other Information Technology (IT) Infrastructure Provisioning Services - last updated from the United States Federal Reserve on May of 2025.

Graph and download economic data for Secure Internet Servers: All Income Levels for East Asia and Pacific (ITNETSECRP6EAS) from 2010 to 2023 about servers, East Asia, Pacific, internet, securities, and income.

Data composition and characteristics of included studies.

Federated learning ensures that data can be trained globally across clients without leaving the local environment, making it suitable for fields involving privacy data such as healthcare and finance. The knowledge graph technology provides a way to express the knowledge of the Internet into a form more similar to the human cognitive world. The training of the knowledge graph embedding model is similar to that of many models, which requires a large amount of data for learning to achieve the purpose of model development. The security of data has always been a focus of public attention, and driven by this situation, knowledge graphs have begun to be combined with federated learning. However, the combination of the two often faces the problem of federated data statistical heterogeneity, which can affect the performance of the training model. Therefore, An Algorithm for Heterogeneous Federated Knowledge Graph (HFKG) is proposed to solve this problem by limiting model drift through comparative learning. In addition, during the training process, it was found that both the server aggregation algorithm and the client knowledge graph embedding model performance can affect the overall performance of the algorithm.Therefore, a new server aggregation algorithm and knowledge graph embedding model RFE are proposed. This paper uses the DDB14, WN18RR, and NELL datasets and two methods of dataset partitioning to construct data heterogeneity scenarios for extensive experiments. The experimental results show a stable improvement, proving the effectiveness of the federated knowledge graph embedding aggregation algorithm HFKG-RFE, the knowledge graph embedding model RFE and the federated knowledge graph relationship embedding aggregation algorithm HFKG-RFE formed by the combination of the two.