A catalogue service that conforms to the HTTP protocol binding of the OpenGIS Catalogue Service ISO Metadata Application Profile specification (version 2.0.2)

By implementing the Torrent extension, CKAN instances gain the ability to distribute data much more effectively. Users benefit from accelerated download speeds, especially for large files. Institutions can reduce server bandwidth costs and enhance the availability of datasets to wider audiences through peer-to-peer technology.

The ckanext-localimp extension for CKAN enhances the platform's data ingestion capabilities by enabling users to import resources directly from a server's local filesystem, bypassing the standard HTTP upload method. This approach is particularly useful when dealing with large files or when the application server has direct access to users' home directories. By introducing an alternative to the conventional file upload mechanism, the extension helps address limitations related to file size, cache issues, and worker blocking associated with HTTP-based uploads. Key Features: Local File Import: Provides an alternative method for uploading resources to CKAN datasets by specifying a file path relative to the user's home directory on the server. Upload Method Selection: Allows users to choose between two upload methods: upload_remote (standard CKAN functionality using FieldStorage) and upload_local (new functionality importing from local file path). X-Accel-Redirect Support: Seamlessly integrates with nginx and the ckanext-iauth extension to serve large files efficiently by leveraging X-Accel-Redirect, mitigating potential performance bottlenecks on the application server. User Home Directory Access: Requires the application server to have access to user home directories, which simplifies large file uploads without relying on traditional web server-based file handling. LDAP Integration: Recommends the use of ckanext-ldap for user authentication, facilitating server access to LDAP (PosixAccount) details for user management within the CKAN environment. SFTP/FTPS Support: Suggests using a dedicated input server with chrooted SFTP and FTPS access allowing users to securely upload files to their home directories, ensuring compatibility with the local import feature. Configuration Settings: Provides flexible configuration options and encourages the documentation of these settings, which can enhance the customization and performance of the extension. Technical Integration: This extension fundamentally alters the resource creation process in CKAN. Instead of solely relying on the upload parameter (FieldStorage using multipart/form-data), it introduces the upload_local parameter, that lets the user specify a path on a local filesystem. The extension leverages reverse proxying and X-Accel-Redirect capabilities of nginx (with the aid of the ckanext-iauth extension) to serve large files directly, reducing the application server load and optimizing performance, especially when resources are sizable. Benefits & Impact: By allowing users to upload data directly from the server's filesystem, the ckanext-localimp extension offers significant benefits in terms of efficiency and resource management. It is most suitable for environments where the CKAN application server can access user home directories (for example via shared filesystem or network file share). The extension addresses the limitations of HTTP uploads by bypassing the application server for large file serving, which prevents cache saturation and avoids blocking worker processes. This ultimately results in improved CKAN performance, particularly when dealing with substantial datasets.

The NIST BGP Secure Routing Extension (BGP-SRx) prototype is an open source reference implementation and research platform for investigating emerging BGP security extensions and supporting protocols such as RPKI Origin Validation and BGPSec Path Validation. BGP-SRx is designed in such to minimize the dependencies on and the impact on specific router implementations. As a result, much functionality is provided by the stand-alone SRx server module. The prototype is also designed to support experimentation with various deployment architectures. As a result, the SRx module can run on the router, the validating cache, or on a completely separate platform.

The Annotations extension for CKAN adds functionality related to annotations, though the details are sparse in the provided documentation. It appears to rely on a Mosaics server and integrates with CKAN through configuration settings. The extension likely allows users to add and manage annotations related to datasets or resources within CKAN, enhancing metadata or providing additional context. Key Features: Mosaics Server Integration: Leverages a Mosaics server, implying the extension utilizes or displays mosaic-based visualizations or annotation interfaces. The exact function associated with Mosiac is unknown. Configuration-Based Setup: Requires a URL pointing to the Mosaics server to be configured in the CKAN ini file, allowing customization and integration with the existing CKAN instance. Technical Integration: This extension integrates into CKAN through configuration settings defined in the development.ini or similar CKAN configuration file. Specifically, the mosaics server URL must be specified, suggesting the extension utilizes this URL to interact with the specified Mosaics service - this interaction will more than likely be centered around data sets within a typical CKAN deployment. Benefits & Impact: Based on the limited information, the primary benefit is likely enhancing CKAN's ability to create and manage annotations, enriching datasets with additional contextual information.

RISC-V Vector Extension Processor Market Outlook

According to our latest research, the global RISC-V Vector Extension Processor market size in 2024 stands at USD 1.2 billion, reflecting robust adoption across multiple industries. The market is experiencing a notable growth trajectory, registering a CAGR of 26.4% from 2025 to 2033. By 2033, the RISC-V Vector Extension Processor market is forecasted to reach USD 10.2 billion, driven by increasing demand for open-source processor architectures, expanding applications in artificial intelligence (AI), and the growing need for high-performance computing solutions. The rapid rise in edge computing and the proliferation of AI workloads are significant contributors to this impressive growth, as per the latest research findings.

The escalating demand for scalable and cost-efficient processor architectures is a primary growth factor for the RISC-V Vector Extension Processor market. Enterprises and developers are increasingly shifting towards open-source alternatives to avoid vendor lock-in and reduce licensing costs, which has historically been a limitation with proprietary instruction set architectures (ISAs). The RISC-V Vector Extension offers immense flexibility, allowing organizations to tailor processor designs for specialized workloads, ranging from AI inference to real-time data analytics. Furthermore, the surge in edge computing applications necessitates processors that can handle complex computations efficiently while maintaining low power consumption. This trend is particularly pronounced in sectors such as IoT, automotive, and industrial automation, where real-time processing and adaptability are paramount. The ability of RISC-V Vector Extension Processors to deliver high throughput, scalability, and customization is fueling their adoption across both established and emerging industries.

Another significant driver for the RISC-V Vector Extension Processor market is the accelerating integration of AI and machine learning (ML) into mainstream applications. As AI algorithms become more sophisticated and computationally demanding, the need for processors capable of parallel data processing and vectorized operations has intensified. RISC-V Vector Extension Processors are uniquely suited to these requirements, offering a modular and extensible architecture that can efficiently execute AI workloads, including deep learning, computer vision, and natural language processing. The open-source nature of the RISC-V ecosystem encourages collaboration and rapid innovation, enabling the development of tailored solutions for specific AI applications. This has led to increased investments from semiconductor companies and technology giants seeking to develop next-generation AI accelerators and high-performance computing platforms based on RISC-V Vector Extensions. The robust ecosystem, combined with the growing availability of development tools and software support, is expected to further accelerate market expansion over the forecast period.

The global push towards digital transformation and Industry 4.0 initiatives is also propelling the RISC-V Vector Extension Processor market. Industrial automation, smart manufacturing, and connected devices require processors that can deliver real-time performance, energy efficiency, and adaptability to evolving workloads. RISC-V Vector Extension Processors are increasingly being integrated into embedded systems, robotics, and industrial control units, providing the computational power necessary for advanced automation and predictive maintenance solutions. Additionally, the automotive sector is embracing RISC-V Vector Extension Processors for applications such as autonomous driving, advanced driver-assistance systems (ADAS), and in-vehicle infotainment, where high-speed data processing and safety are critical. The convergence of these trends is establishing RISC-V Vector Extension Processors as a cornerstone technology for the next wave of digital innovation.

The emergence of RISC-V Server solutions is transforming the landscape of enterprise computing, offering a compelling alternative to traditional server architectures. With the growing emphasis on open-source technologies, RISC-V Servers are gaining traction for their ability to provide customizable, efficient, and cost-effective computing power. These servers are particularly appealing to organizations seeking to optimize their data center

This data set contains the files produced by UNRES server with the new features described in related article.Included archives contain NMR-data-assisted modeling with UNRES enhanced with the time-averaging feature of UNRES of the conformational ensemble of the 2LWA multistate proteins and modeling of the conformational ensemble of the CysZ4 tetramer.See readme.txt file for details.

Scholarometer (beta) is a social tool to facilitate citation analysis and help evaluate the impact of an author''s publications. It is a social (crowdsourcing) application that leverages the wisdom of the crowds. Scholarometer makes visualization of author and discipline networks available on the web site. It requires users to tag their queries with one or more discipline names, choosing from predefined ISI subject categories or arbitrary tags. This generates annotations that go into a database, which collects statistics about the various disciplines, such as average number of citations per paper, average number of papers per authors, etc. This data is publicly available. Scholarometer users can save the finding into formats appropriate for local reference management software (e.g., EndNote), or for social publication sharing systems (e.g., BibSonomy). Currently, our system supports the following export formats: BibTex (BIB), RefMan (RIS), EndNote (ENW), comma-separated values (CSV), tab-separated values (XLS), and BibJSON. Export data is dynamically generated in response to any filter, merge or delete actions performed by the user. Since Scholarometer is a browser extension that provides a smart interface for Google Scholar, it does not have the limitations of server based citation analysis tools that sit between the user and Google Scholar. At the same time Scholarometer is not an application, such as Publish or Perish, and therefore it is platform independent and runs on every system that supports the Firefox or the Chrome browser. Still, Scholarometer uses Google Scholar, which provides the most comprehensive source of citation data across the sciences and social sciences. Scholarometer provides a RESTful web API so that other developers can make use of our crowdsourced data. Select the method on the left panel to see corresponding documentation. The extension/add-on code is available in the Mozilla Firefox Add-ons and Google Chrome Extensions repositories. Additional server-side code is not available at this time.

ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The primary ENC data in this web service was downloaded from NOAA's public site. Other test datasets from IHO are also included. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. To learn more about this product visit ArcGIS Maritime.

The ckanext-jena extension enriches CKAN's capabilities by integrating semantic data management using Apache Jena. This extension enables users to store semantic resources, such as RDF, TTL, and OWL files, within an Apache Jena instance. It supports conducting SPARQL queries against these semantic resources, providing a mechanism for advanced data exploration and integration within the CKAN ecosystem. Key Features: Semantic Resource Storage: Allows users to upload and store semantic resources (RDF, TTL, OWL) in a designated Apache Jena instance, expanding CKAN’s capacity to manage and expose semantically rich data. SPARQL Query Support: Enables the execution of SPARQL queries against the stored semantic resources, facilitating semantic data retrieval and sophisticated data analysis directly within CKAN. jenasearchsparql API Endpoint: Provides a specific API endpoint (jenasearchsparql) that accepts resource_id and q parameters to execute semantic queries, simplifying programmatic interaction with the Jena knowledge base. Integration with Apache Jena: Requires a running Apache Jena and Fuseki server to provide the underlying semantic data storage and query processing capabilities. Technical Integration: The ckanext-jena extension integrates with CKAN by providing API endpoints that interact with a configured Apache Jena and Fuseki server. The extension requires configuration within CKAN's INI file, specifying the necessary settings for Jena connectivity. Users access the extension’s features through the jenasearchsparql API, connecting CKAN's resource management with Jena's semantic reasoning capabilities. This is called with resource_id and q parameters for semantic queries, according to the readme. Benefits & Impact: Implementing the ckanext-jena extension allows CKAN to manage not just data, but also the relationships and context surrounding that data. This leads to improved data discovery, more powerful data analytics, and richer data integration possibilities by leveraging semantic web technologies within the CKAN platform. This benefits users in a variety of knowledge-based applications.

The study of molecular dynamics simulations is largely facilitated by analysis and visualization toolsets. However, these toolsets are often designed for specific use cases and those only, while scripting extensions to such toolsets is often exceedingly complicated. To overcome this problem, we designed a software application called AViS which focuses on the extensibility of analysis. By utilizing the dataflow programming (DFP) paradigm, algorithms can be defined by execution graphs, and arbitrary data can be transferred between nodes using visual connectors. Extension nodes can be implemented in either Python, C++, and Fortran, and combined in the same algorithm. AViS offers a comprehensive collection of nodes for sophisticated visualization state modifications, thus greatly simplifying the rules for writing extensions. Input files can also be read from the server automatically, and data is fetched automatically to improve memory usage. In addition, the visualization system of AViS uses physically-based rendering techniques, improving the 3D perception of molecular structures for interactive visualization. By performing two case studies on complex molecular systems, we show that the DFP workflow offers a much higher level of flexibility and extensibility when compared to legacy workflows. The software source code and binaries for Windows, MacOS, and Linux are freely available at https://avis-md.github.io/.

This sample application was built with the latest Maritime Widgets using Web AppBuilder for ArcGIS. The ENC web service is from ArcGIS Maritime server extension.You can download the sample Maritime widgets from ArcGIS Maritime product support by clicking here.ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The base data for this web service is sample AML data download from here. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. To learn more about this product visit ArcGIS Maritime.

The dataset consists of data from three different sources; flow records collected from the university backbone network, log entries from the two university DHCP (Dynamic Host Configuration Protocol) servers and a single RADIUS (Remote Authentication Dial In User Service) accounting server. The data was collected from 2019-07-12 00:00 to 2019-07-16 23:59 with a few hours overhead on both sides of the interval for the log entries to cover long connection sessions overlapping to and from the time frame. We measured the flow data from the university uplink to the Internet. In the dataset, we kept only flows with source IP addresses from university wireless networks (Eduroam). The flow data was then enriched with information from DHCP and RADIUS servers to contain ID of the RADIUS session and operating system od the transmitting device as derived from DHCP logs. The dataset is in the form of CSV file with the following information fields important for OS identification: Basic flow features Date flow start - timestamp of flow start Date flow end - timestamp of flow end Src IPv4 - source IPv4 address sPort - source L4 port Dst IPv4 - destination IPv4 address dPort - destination L4 port Extended TCP/IP parameters SYN size - the size of the initial SYN packet of a TCP connection (in bytes) TCP win - value of TCP Window size parameter TCP SYN TTL - observed TTL value HTTP parameters HTTP Host - hostname from the HTTP request HTTP UA OS - OS identification based on user-agent HTTP UA OS MAJ - OS identification based on user-agent HTTP UA OS MIN - OS identification based on user-agent HTTP UA OS BLD - OS identification based on user-agent TLS parameters TLS SNI - Server Name Indication field TLS SNI length - length of SNI in bytes TLS Client Version - TLS client hello Version field Client Cipher Suites - list of supported cipher suites TLS Extension Types - list of extension IDs TLS Extension Lengths - list of extension lengths TLS Elliptic Curves - list of supported curves (or supported groups in TLS1.3) TLS EC Point Formats - list of EC formats Log based extensions Session ID - ID of the session to match flows from one device Ground Truth OS - OS name derived from log data The observed network traffic contains privacy-sensitive information. Hereby, we declare that the monitored data used for our research were processed in accordance with the EU General Data Protection Regulation 2016/679. The published dataset was anonymized with cryptographic means using Crypto-PAn algorithm to preserve both the scientific value and user privacy. When using this dataset, please cite the original work as follows: @inproceedings{lastovicka2020using, title={Using TLS Fingerprints for OS Identification in Encrypted Traffic}, author={La{\v{s}}tovi{\v{c}}ka, Martin and {\v{S}}pa{\v{c}}ek, Stanislav and Velan, Petr and {\v{C}}eleda, Pavel}, booktitle = {2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)}, doi = {http://dx.doi.org/10.1109/NOMS47738.2020.9110319}, keywords = {OS fingerprinting;passive monitoring;IPFIX;TLS}, isbn = {978-1-7281-4973-8}, pages = {1-6}, publisher = {IEEE Xplore Digital Library}, year = {2020} }

The global market for Custom Full Modular Power Extension Cords is projected for robust expansion, driven by an estimated market size of approximately $1.2 billion in 2025, with a projected Compound Annual Growth Rate (CAGR) of around 11% through 2033. This substantial growth is fueled by the increasing demand for personalized and efficient power solutions in the rapidly evolving technology landscape. The primary drivers include the burgeoning popularity of high-performance gaming setups, the proliferation of custom-built PCs requiring specialized cable management for aesthetics and airflow, and the expanding online retail sector that facilitates direct-to-consumer sales of tailored components. Furthermore, the growing adoption of advanced computing infrastructure in data centers and professional workstations also contributes significantly, as these environments necessitate reliable and adaptable power delivery systems. The market is characterized by a strong preference for custom lines, reflecting a clear trend towards user-defined configurations that precisely match system requirements and build aesthetics. The market is poised to witness sustained momentum, with key segments like Computer City and Online Retail leading the charge. While Sata and VGA lines represent established product categories, the increasing complexity of modern PC builds and server racks is elevating the importance of custom and modular solutions. The "Others" category for types, likely encompassing specialized industrial or enterprise-grade extension cords, is also expected to see considerable growth. Key restraints, such as the potential for higher manufacturing costs associated with custom solutions and a lack of widespread standardization in certain niche applications, are being mitigated by advancements in manufacturing technology and an increasing consumer willingness to invest in premium, tailored components. Geographically, the Asia Pacific region, particularly China and India, is anticipated to emerge as a dominant force due to its vast manufacturing capabilities and a rapidly expanding consumer base for electronics and gaming. North America and Europe will remain significant markets, driven by early adoption of advanced technology and a strong DIY PC building culture. This report provides a comprehensive analysis of the global Custom Full Modular Power Extension Cord market, encompassing a detailed examination of market dynamics, trends, opportunities, and challenges from 2019 to 2033. The study period covers historical data from 2019-2024 and a forecast period from 2025-2033, with 2025 serving as both the base and estimated year. We project a significant market valuation reaching hundreds of millions of units by the end of the forecast period, driven by technological advancements, increasing demand for personalized computing solutions, and evolving consumer preferences. This in-depth research will empower stakeholders with actionable insights to navigate the competitive landscape and capitalize on emerging growth avenues.

The Pakistan Data Portal extension configures CKAN to serve as a data portal, likely tailored for Pakistani datasets. Based on the limited information, it involves specific server configurations and potentially custom themes or functionalities beneficial to government agencies or organizations involved in publishing and managing datasets related to Pakistan. The configuration includes modifications to Tomcat and Solr presumably optimized for CKAN 2.0. Key Features: Server Configuration: Designed for specific server environments (s136 and s124), implying curated settings for optimal performance, potentially addressing challenges related to database and SOLR server setup. Tomcat/Solr Configuration Management: Simplifies modifications to the Tomcat and Solr configurations, which can be complex, by centralizing these changes within a defined directory (/usr/share/solr/ckan-2.0/conf). Potential Theme Customization: Implies a customized user interface tailored for accessing Pakistan-related government datasets, which might improve usability and user satisfaction for regional users. Technical Integration: The extension seems to primarily configure the CKAN instance with adjustments to server settings (Tomcat, Solr) and likely front-end modifications. This ensures that all the necessary components are working well together for effective data handling and management. The configuration might involve customized API endpoints, tailored themes, or unique dataset schema/validation rules. Benefits & Impact: The Pakistan Data Portal extension streamlines the process of establishing and maintaining a CKAN-based data catalog for datasets focused on Pakistan. By offering pre-configured server settings, the extension reduces time and resources spent on setting up and optimizing a CKAN data portal. This enables organizations to efficiently publish and manage datasets, improve data accessibility, and support data-driven decision-making in Pakistan.

Global, Latin America, Spain Hosting And Domain Market size was valued at USD 89,979.54 Million in 2023 and is expected to reach USD 1,91,624.43 Million by the end of 2031 with a CAGR of 10.16% during the forecast period 2024-2031.

Global, Latin America, Spain Hosting And Domain Market Overview

The Global, Latin America, and Spain Hosting and Domain Market is a crucial aspect of the internet infrastructure, serving as the backbone for websites and online services. Hosting refers to the provision of storage space and computing resources on servers that are connected to the internet. Websites and applications are hosted on these servers, making them accessible to users worldwide. Domains are the unique addresses that users type into their browsers to access specific websites. Multiple companies offer hosting services, providing different plans tailored to the needs of various clients, ranging from individual bloggers to large corporations.

Timeseries data from 'LEVEE 31 NORTH EXTENSION AT 1 MILE NR WEST MIAMI,' (ism-secoora-gov_usgs_waterdata_0-297) cdm_data_type=TimeSeries cdm_timeseries_variables=station,longitude,latitude contributor_email=feedback@axiomdatascience.com,feedback@axiomdatascience.com contributor_name=Axiom Data Science,Axiom Data Science contributor_role=contributor,processor contributor_role_vocabulary=NERC contributor_url=https://www.axiomdatascience.com,https://www.axiomdatascience.com Conventions=IOOS-1.2, CF-1.6, ACDD-1.3, NCCSV-1.2 defaultDataQuery=river_discharge,height_above_station_datum,z,time&time>=max(time)-3days Easternmost_Easting=-80.497639 featureType=TimeSeries geospatial_lat_max=25.746333 geospatial_lat_min=25.746333 geospatial_lat_units=degrees_north geospatial_lon_max=-80.497639 geospatial_lon_min=-80.497639 geospatial_lon_units=degrees_east geospatial_vertical_max=0.0 geospatial_vertical_min=0.0 geospatial_vertical_positive=up geospatial_vertical_units=m history=Downloaded from USGS National Water Information System (NWIS) at id=107676 infoUrl=https://sensors.ioos.us/#metadata/107676/station institution=USGS National Water Information System (NWIS) naming_authority=com.axiomdatascience Northernmost_Northing=25.746333 platform=fixed platform_name=LEVEE 31 NORTH EXTENSION AT 1 MILE NR WEST MIAMI, platform_vocabulary=http://mmisw.org/ont/ioos/platform processing_level=Level 2 references=https://erddap.secoora.org/erddap/tabledap/gov_usgs_waterdata_022907647,, sourceUrl=http://erddap.secoora.org/erddap/tabledap/gov_usgs_waterdata_022907647 Southernmost_Northing=25.746333 standard_name_vocabulary=CF Standard Name Table v72 station_id=107676 time_coverage_end=2023-12-08T07:00:00Z time_coverage_start=2021-07-29T18:00:00Z Westernmost_Easting=-80.497639

This operation view contains services with shipping, maritime boundaries, and weather information for the west coast of the United States. The services in this web map are powered by ArcGIS GeoEvent Extension for Server and contain alerts for ships in certain boundaries, such as nature preserves, or inclement weather.Some of the widgets contained in this operation view are lists that sort the most important data such as those in geofences and those reporting with hazardous cargo. Data contained in this operation view includes:Maritime Boundaries and Port Information:Maritime Boundaries - Various maritime boundaries information provided by the National Oceanic and Atmospheric Administration (NOAAShipping Information:Proximity Alert - Generated buffer information created from an ArcGIS for GeoEvent Extension for Server processor of military vessels.Ship Position- Simulated shipping information obtained from the US Coast Guard (USCG).Weather Information:Meteorological Service of Environment Canada - Web map service with forecast, analysis, and observation layersforunderstanding current meteorological or oceanographic data.NOAA Lightning Strike Density - Time-enabled map service providing maps of experimental lightning strike density data.NOAA Weather Observations - Time-enabled map service providing map depicting the latest surface weather and marine weather observations.NOAA Weather Radar Mosaic - Time-enabled map service providing maps depicting mosaics of base reflectivity images across the United States.NOAA Weather Satellite Information - Time-enabled map service providing maps depicting visible, infrared, and water vapor imagery.

Dividends-Paid Time Series for AIC Inc. AIC Inc. provides OEM/ODM, commercial off-the-shelf, and server and storage solutions in the United States, Asia, and Europe. The company offers rackmount, multi-node, GPU, and network security servers; storage, just a bunch of disks, just a bunch of extensions, high availability, and all-flash NVMe servers; industrial PC and server chassis products; accessories, such as canisters and front bezels; designing services with max I/O; and AMD EPYC systems. It also provides cloud/data center, network appliance, media entertainment, industrial PC, video surveillance, and VMware ready solutions. The company was formerly known as T-win Systems, Inc. and changed its name to AIC Inc. in June 2011. AIC Inc. was founded in 1996 and is headquartered in Taoyuan City, Taiwan.

ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The base data for this web service is sample AML data download from here. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. To learn more about this product visit ArcGIS Maritime.