In computer security, jailbreaking is defined as the act of removing limitations that a vendor attempted to hard-code or hard-wire into its hardware and/or software. It is a form of privilege escalation. The term may have originated with the use of toolsets to break out of a chroot or jail in UNIX-like operating systems. This allowed the user to see files outside of the file system that the administrator intended to make available to the application or user in question. The term was first used in its modern meaning in the iPhone/iOS jailbreaking community and has also been used as a term for PlayStation Portable hacking; these devices have repeatedly been subject to jailbreaks, allowing the execution of arbitrary code, and sometimes have had those jailbreaks disabled by vendor updates, especially in the case of iOS devices. == iOS jailbreaking == iOS systems including the iPhone, iPad, and iPod Touch have been subject to iOS jailbreaking efforts since they were released, and continuing with each firmware update. iOS jailbreaking tools have included the option to install package frontends such as Cydia and Installer.app, third-party alternatives to the App Store, as a way to find and install system tweaks and binaries. To prevent iOS jailbreaking, Apple has made the device boot ROM execute checks for SHSH blobs in order to disallow uploads of custom kernels and prevent software downgrades to earlier, jailbreakable firmware. In an "untethered" jailbreak, the iBoot environment is changed to execute a boot ROM exploit and allow submission of a patched low level bootloader or hack the kernel to submit the jailbroken kernel after the SHSH check. == Other phones == A similar method of jailbreaking exists for S60 Platform smartphones, where utilities such as HelloOX allow the execution of unsigned code and full access to system files. or edited firmware (similar to the M33 hacked firmware used for the PlayStation Portable) to circumvent restrictions on unsigned code. Nokia has since issued updates to curb unauthorized jailbreaking, in a manner similar to Apple. Rooting is the equivalent concept for Android phones and other devices. == Console jailbreaking == In the case of gaming consoles, jailbreaking is often used to execute homebrew games. In 2011, Sony, with assistance from law firm Kilpatrick Stockton, sued 21-year-old George Hotz and associates of the group fail0verflow for jailbreaking the PlayStation 3 (see Sony Computer Entertainment America v. George Hotz and PlayStation Jailbreak). == AI jailbreaks == Jailbreaking can also occur in systems and software that use generative artificial intelligence models, such as ChatGPT. In jailbreaking attacks on artificial intelligence systems, users are able to manipulate the system to behave differently than it was intended, making it possible to reveal information about how the model was instructed by the vendor (the "system prompt") or to induce it to respond in an anomalous or harmful way. These attacks typically simply require prompting the AIs with specific phrasal templates - no software is typically required, although software could theoretically be used to "industrialise" such exploits, and some research has been done in this direction. In 2024, a consortium of AI firms founded HackAPrompt.com, a competition to encourage users to find new and effective AI jailbreaking techniques. These and other findings from "ethical hackers" have been used by AI model providers to try to improve AI safety.
Plotly
Plotly is a technical computing company headquartered in Montreal, Quebec, that develops online data analytics and visualization tools. Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, JavaScript and REST. == History == Plotly was founded by Alex Johnson, Jack Parmer, Chris Parmer, and Matthew Sundquist. The founders' backgrounds are in science, energy, and data analysis and visualization. Early employees include Christophe Viau, a Canadian software engineer and Ben Postlethwaite, a Canadian geophysicist. Plotly was named one of the Top 20 Hottest Innovative Companies in Canada by the Canadian Innovation Exchange. Plotly was featured in "startup row" at PyCon 2013, and sponsored the SciPy 2018 conference. Plotly raised $5.5 million during its Series A funding, led by MHS Capital, Siemens Venture Capital, Rho Ventures, Real Ventures, and Silicon Valley Bank. The Boston Globe and Washington Post newsrooms have produced data journalism using Plotly. In 2020, Plotly was named a Best Place to Work by the Canadian SME National Business Awards, and nominated as Business of the Year. == Products == Plotly offers open-source and enterprise products. Dash is an open-source Python, R, and Julia framework for building web-based analytic applications. Many specialized open-source Dash libraries exist that are tailored for building domain-specific Dash components and applications. Some examples are Dash DAQ, for building data acquisition GUIs to use with scientific instruments, and Dash Bio, which enables users to build custom chart types, sequence analysis tools, and 3D rendering tools for bioinformatics applications. Dash Enterprise is Plotly's paid product for building, testing, deploying, managing and scaling Dash applications organization-wide. Chart Studio Cloud is a free, online tool for creating interactive graphs. It has a point-and-click graphical user interface for importing and analyzing data into a grid and using stats tools. Graphs can be embedded or downloaded. Chart Studio Enterprise is a paid product that allows teams to create, style, and share interactive graphs on a single platform. It offers expanded authentication and file export options, and does not limit sharing and viewing. Data visualization libraries Plotly.js is an open-source JavaScript library for creating graphs and powers Plotly.py for Python, as well as Plotly.R for R, MATLAB, Node.js, Julia, and Arduino and a REST API. Plotly can also be used to style interactive graphs with Jupyter notebook. Figure converters which convert matplotlib, ggplot2, and IGOR Pro graphs into interactive, online graphs. == Data visualization libraries == Plotly provides a collection of supported chart types across several programming languages: == Dash == Dash is a Python framework built on top of React, a JavaScript library. Dash also works for R, and most recently supports Julia. While still described as a Python framework, Python isn't used for the other languages: "... describing Dash as a Python framework misses a key feature of its design: the Python side (the back end/server) of Dash was built to be lightweight and stateless [allowing] multiple back-end languages to coexist on an equal footing". It is possible to integrate D3.js charts as Dash components. Dash provides the default CSS (plus HTML and JavaScript), but for custom styling Dash applications, CSS can be added, or Dash Enterprise used. === Dash Enterprise === Dash Enterprise is Plotly's paid product for building, testing, deploying, managing and scaling Dash applications organization-wide. The product integrates with enterprise IT systems to enable organizations to build, deploy and scale low-code Dash applications. With open-source Dash, analytic applications can be run from a local machine, but cannot be easily accessed by others in the organization. ==== Enterprise IT integration ==== Dash Enterprise installs on cloud environments and on-premises. Amazon Web Services, Google Cloud Platform, and Microsoft Azure are supported, as are multiple Linux on-premises servers. Authentication integrations include LDAP, AD, PKI, Okta, SAML, OAuth2, SSO, and email authentication, and Dash application access is managed through a GUI rather than code. Dash Enterprise connects to major big data backends, including Salesforce, PostgreSQL, Databricks via PySpark, Snowflake, Dask, Datashader, and Vaex. In 2020, Plotly partnered with NVIDIA to integrate Dash with RAPIDS, and NVIDIA participated in Plotly's Series C funding round. ==== Low-code capabilities ==== Dash Enterprise enables low-code development of Dash applications, which is not possible with open-source Dash. Enterprise users can write applications in multiple development environments, including Jupyter Notebook. Dash Enterprise ships with several “development engines” for drag-and-drop application editing, application design, and automated reporting, as well as dozens of artificial intelligence and machine learning application templates. ==== Deployment and scaling ==== Dash application code is deployed to Dash Enterprise using the git-push command. Dash application deployments are containerized to avoid dependency conflicts, and can be embedded in existing web platforms without iframes. Deployed applications can be managed and accessed in a single portal called App Manager, where administrators can control user authentication and view usage analytics. Dash Enterprise scales horizontally with Kubernetes. Jobs queuing, GPU acceleration, and CPU parallelization support high performance computing requirements. Plotly also offers professional services for application development and workshop training.
Web performance
Web performance refers to the speed in which web pages are downloaded and displayed on the user's web browser. Web performance optimization (WPO), or website optimization is the field of knowledge about increasing web performance. Faster website download speeds have been shown to increase visitor retention and loyalty and user satisfaction, especially for users with slow internet connections and those on mobile devices. Web performance also leads to less data travelling across the web, which in turn lowers a website's power consumption and environmental impact. Some aspects which can affect the speed of page load include browser/server cache, image optimization, and encryption (for example SSL), which can affect the time it takes for pages to render. The performance of the web page can be improved through techniques such as multi-layered cache, light weight design of presentation layer components and asynchronous communication with server side components. == History == In the first decade or so of the web's existence, web performance improvement was focused mainly on optimizing website code and pushing hardware limitations. According to the 2002 book Web Performance Tuning by Patrick Killelea, some of the early techniques used were to use simple servlets or CGI, increase server memory, and look for packet loss and retransmission. Although these principles now comprise much of the optimized foundation of internet applications, they differ from current optimization theory in that there was much less of an attempt to improve the browser display speed. Steve Souders coined the term "web performance optimization" in 2004. At that time Souders made several predictions regarding the impact that WPO as an "emerging industry" would bring to the web, such as websites being fast by default, consolidation, web standards for performance, environmental impacts of optimization, and speed as a differentiator. One major point that Souders made in 2007 is that at least 80% of the time that it takes to download and view a website is controlled by the front-end structure. This lag time can be decreased through awareness of typical browser behavior, as well as of how HTTP works. == Optimization techniques == Web performance optimization improves user experience (UX) when visiting a website and therefore is highly desired by web designers and web developers. They employ several techniques that streamline web optimization tasks to decrease web page load times. This process is known as front end optimization (FEO) or content optimization. FEO concentrates on reducing file sizes and "minimizing the number of requests needed for a given page to load." In addition to the techniques listed below, the use of a content delivery network—a group of proxy servers spread across various locations around the globe—is an efficient delivery system that chooses a server for a specific user based on network proximity. Typically the server with the quickest response time is selected. The following techniques are commonly used web optimization tasks and are widely used by web developers: Web browsers open separate Transmission Control Protocol (TCP) connections for each Hypertext Transfer Protocol (HTTP) request submitted when downloading a web page. These requests total the number of page elements required for download. However, a browser is limited to opening only a certain number of simultaneous connections to a single host. To prevent bottlenecks, the number of individual page elements are reduced using resource consolidation whereby smaller files (such as images) are bundled together into one file. This reduces HTTP requests and the number of "round trips" required to load a web page. Web pages are constructed from code files such JavaScript and Hypertext Markup Language (HTML). As web pages grow in complexity, so do their code files and subsequently their load times. File compression can reduce code files by about 40 percent, thereby improving site responsiveness. Web Caching Optimization reduces server load, bandwidth usage and latency. CDNs use dedicated web caching software to store copies of documents passing through their system. Many website platforms, such as SiteGround, IONOS, Wix, and Hostinger, rely on global CDNs and caching technologies to deliver faster page loads across different geographical regions. Subsequent requests from the cache may be fulfilled should certain conditions apply. Web caches are located on either the client side (forward position) or web-server side (reverse position) of a CDN. Web browsers are also able to store content for re-use through the HTTP cache or web cache. Requests web browsers make are typically routed to the HTTP cache to validate if a cached response may be used to fulfill a request. If such a match is made, the response is fulfilled from the cache. This can be helpful for reducing network latency and costs associated with data-transfer. The HTTP cache is configured using request and response headers. Code minification distinguishes discrepancies between codes written by web developers and how network elements interpret code. Minification removes comments and extra spaces as well as crunch variable names in order to minimize code, decreasing files sizes by as much as 60%. In addition to caching and compression, lossy compression techniques (similar to those used with audio files) remove non-essential header information and lower original image quality on many high resolution images. These changes, such as pixel complexity or color gradations, are transparent to the end-user and do not noticeably affect perception of the image. Another technique is the replacement of raster graphics with resolution-independent vector graphics. Vector substitution is best suited for simple geometric images. Lazy loading of images and video reduces initial page load time, initial page weight, and system resource usage, all of which have positive impacts on website performance. It is used to defer initialization of an object right until the point at which it is needed. The browser loads the images in a page or post when they are needed such as when the user scrolls down the page and not all images at once, which is the default behavior, and naturally, takes more time. == HTTP/1.x and HTTP/2 == Since web browsers use multiple TCP connections for parallel user requests, congestion and browser monopolization of network resources may occur. Because HTTP/1 requests come with associated overhead, web performance is impacted by limited bandwidth and increased usage. Compared to HTTP/1, HTTP/2 is binary instead of textual is fully multiplexed instead of ordered and blocked can therefore use one connection for parallelism uses header compression to reduce overhead allows servers to "push" responses proactively into client caches Instead of a website's hosting server, CDNs are used in tandem with HTTP/2 in order to better serve the end-user with web resources such as images, JavaScript files and Cascading Style Sheet (CSS) files since a CDN's location is usually in closer proximity to the end-user. == Metrics == In recent years, several metrics have been introduced that help developers measure various aspects of the performance of their websites. In 2019, Google introduced metrics such as Time to First Byte (TTFB), First Contentful Paint (FCP), First Paint (FP), First Input Delay (FID), Cumulative Layout Shift (CLS) and Largest Contentful Paint (LCP) allow for website owner to gain insights into issues that might hurt the performance of their websites making it seem sluggish or slow to the user. Other metrics including Request Count (number of requests required to load a page), DOMContentLoaded (time when HTML document is completely loaded and parsed excluding CSS style sheets, images, etc.), Above The Fold Time (content that is visible without scrolling), Round Trip Time, number of Render Blocking Resources (such as scripts, stylesheets), Onload Time, Connection Time, Total Page Size help provide an accurate picture of latencies and slowdowns occurring at the networking level which might slow down a site. Modules to measure metrics such as TTFB, FCP, LCP, FP etc are provided with major frontend JavaScript libraries such as React, NuxtJS and Vue. Google publishes a library, the core-web-vitals library that allows for easy measurement of these metrics in frontend applications. In addition to this, Google also provides the Lighthouse, a Chrome dev-tools component and PageSpeed Insight a site that allows developers to measure and compare the performance of their website with Google's recommended minimums and maximums. In addition to this, tools such as the Network Monitor by Mozilla Firefox help provide insight into network-level slowdowns that might occur during transmission of data.
Cloud9 (service provider)
Cloud9 is a mobile network operator focussed on providing mobile subscriptions over the air to programmable SIM cards, SoftSIMs and eSIMs. Their service is used in both smartphones and IoT devices. The company is privately held with headquarters in the United Kingdom. == History == Cloud9, originally owned by Wire9 Telecom Plc, funded and established by investor and telecom specialist, Lee Jones, before being sold for an undisclosed sum by Jones to billionaire Romain Zaleski. It established in the UK, Gibraltar, and Isle of Man as a domestic Mobile Network Operator. Cloud9 obtained spectrum licenses in the Isle of Man in 2007 and Gibraltar in 2010. Around 2011, Cloud9 decided to focus on supplying global SIM cards to save roaming charges. The Gibraltar spectrum licence was sold to another company. The business relocated its core network to Telehouse in London and became a subsidiary of BlueMango Technologies Ltd. Later the company was acquired by Wireless Logic Ltd. In 2013, Cloud9 acquired the IPR of Zynetix Ltd. Through this acquisition, the company achieved sales as an MVNE. In 2014, the company was voted as a Red Herring Top 100 Europe finalist. == Features == Cloud9 has shipped several million 'Travel SIMs'; all SIM cards have been branded with the logo of these resellers. Additionally, the company provides the digital signatures ('profiles' or 'IMSIs') that provide a SIM card with the ability to register with a network and function. These can be provisioned over the air to dynamic SIM cards such as programmable removable UICCs, SoftSIMs and eSIMs. They are members of the GSM Association and are involved in the GSMA remote SIM provisioning standard for eSIMs that will be released soon. The Cloud9 core network also supports 4G (HSS/PDG). Its Mobile Country Code is 234 and its Mobile Network Code is 18. TADIG code is GBRC9. The company has been allocated the following UK number ranges by Ofcom: 4478722, 4477000, 4474409, 4479782, 4479783 and 4475588 The core network is hosted on Cloud9 servers at Telehouse near Canary Wharf in London. Additional components are hosted in Amazon Web Services facilities around the world in order to minimise latency and provide scalability.
Technology company
A technology company, or tech company, is a company that focuses primarily on the manufacturing, support, research and development of—most commonly computing, telecommunication and consumer electronics–based—technology-intensive products and services, which include businesses relating to digital electronics, software, optics, new energy, and Internet-related services such as cloud storage and e-commerce services. Big Tech refers to the 6 largest companies, both in the United States and globally, symbolized by the metonym 'Silicon Valley', where 4 of them are based. == Details == According to Fortune, as of 2020, the ten largest technology companies by revenue are: Apple Inc., Samsung, Foxconn, Alphabet Inc., Microsoft, Huawei, Dell Technologies, Hitachi, IBM, and Sony. Amazon has higher revenue than Apple, but is classified by Fortune in the retail sector. The most profitable listed in 2020 are Apple Inc., Microsoft, Alphabet Inc., Intel, Meta Platforms, Samsung, and Tencent. Apple Inc., Alphabet Inc. (owner of Google), Meta Platforms (owner of Facebook), Microsoft, and Amazon.com, Inc. are often referred to as the Big Five multinational technology companies based in the United States. These five technology companies dominate major functions, e-commerce channels, and information of the entire Internet ecosystem. As of 2017, the Big Five had a combined valuation of over $3.3 trillion and make up more than 40 percent of the value of the Nasdaq-100 index. Many large tech companies have a reputation for innovation, spending large sums of money annually on research and development. According to PwC's 2017 Global Innovation 1000 ranking, tech companies made up nine of the 20 most innovative companies in the world, with the top R&D spender (as measured by expenditure) being Amazon, followed by Alphabet Inc., and then Intel. As a result of numerous influential tech companies and tech startups opening offices in proximity to one another, a number of technology districts have developed in various areas across the globe. These include: Silicon Valley in the San Francisco Bay Area, Silicon Wadi in Israel, Silicon Docks in Dublin, Silicon Hills in Austin, Tech City in London; Digital Media City in Seoul, Zhongguancun in Beijing, Cyberjaya in Malaysia and Cyberabad in Hyderabad, India. As of 2026, Europe has six of the world's 100 most valuable tech companies, compared with 56 in the United States and 16 in China.
ISO/IEC JTC 1/SC 24
ISO/IEC JTC 1/SC 24 Computer graphics, image processing and environmental data representation is a standardization subcommittee of the joint subcommittee ISO/IEC JTC 1 of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), which develops and facilitates standards within the field of computer graphics, image processing, and environmental data representation. The international secretariat of ISO/IEC JTC 1/SC 24 is the British Standards Institute (BSI) located in the United Kingdom. == History == ISO/IEC JTC 1/SC 24 was formed in 1987 from ISO/TC 97 as a result of Resolution 21 at the ISO/IEC JTC 1 plenary. The group's origins began in computer graphics, the standardization of which was originally under ISO/IEC JTC 1/SC 21/WG 2. However, when ISO/IEC JTC 1/SC 24 was created, the standardization activity of ISO/IEC JTC 1/SC 21/WG 2 was carried over to the new subcommittee. The initial five working groups of ISO/IEC JTC 1/SC 24 were titled, “Architecture,” “Application programming interfaces,” “Metafiles and interfaces,” “Language bindings,” and “Validation, testing and registration.” The work of ISO/IEC JTC 1/SC 24 began with the Graphical Kernel System (GKS), which was adopted from ISO/IEC JTC 1/SC 21/WG 2. However, since GKS only addressed 2D functionality, attention turned to the standardization of 3D functionality. This resulted in two standards being published: GKS-3D in 1988 and PHIGS in 1989, both of which addressed 3D functionality. Since 1991, ISO/IEC JTC 1/SC 24 has held plenaries in a number of countries, including the Netherlands, Germany, United States, France, Canada, Japan, Sweden, Korea, United Kingdom, Australia, and Czech Republic. == Scope == The scope of ISO/IEC JTC 1/SC 24 is the “Standardization of interfaces for information technology based applications relating to”: Computer graphics Image processing Environmental data representation Support for the Mixed and Augmented Reality (MAR) Interaction with, and visual representation of, information Included are the following related areas: Modeling and simulation and related reference models Virtual reality with accompanying augmented reality/augmented virtuality aspects and related reference models Application program interfaces Functional specifications Representation models Interchange formats, encodings and their specifications, including metafiles Device interfaces Testing methods Registration procedures Presentation and support for creation of multimedia, hypermedia, and mixed reality documents Excluded are the following areas: Character and image coding Coding of multimedia, hypermedia, and mixed reality document interchange formats JTC 1 work in user system interfaces and document presentation ISO/TC 207 work on ISO 14000 environment management, ISO/TC 211 work on geographic information and geomatics Software environments as described by ISO/IEC JTC 1/SC 22 == Structure == ISO/IEC JTC 1/SC 24 is made up of four active working groups, each of which carries out specific tasks in standards development within the field of computer graphics, image processing and environmental data representation, together with ITU-T Study Group 16. As a response to changing standardization needs, working groups of ISO/IEC JTC 1/SC 24 can be disbanded if their area of work is no longer applicable, or established if new working areas arise. The focus of each working group is described in the group's terms of reference. Active working groups of ISO/IEC JTC 1/SC 24 are: == Collaborations == ISO/IEC JTC 1/SC 24 works in close collaboration with a number of other organizations or subcommittees, both internal and external to ISO or IEC, in order to avoid conflicting or duplicative work. Organizations internal to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: ISO/IEC JTC 1/WG 7, Sensor Networks ISO/IEC JTC 1/SC 29, Coding of audio, picture, multimedia and hypermedia information ISO/IEC JTC 1/SC 32, Data management and interchange ISO/TAG 14, Imagery and technology ISO/TC 130, Graphic Technology ISO/TC 184/SC 4, Industrial data ISO/TC 211, Geographic information/Geomatics ISO/TC 215, Health informatics IEC TC 100, Audio, video and multimedia system and equipment Some organizations external to ISO or IEC that collaborate with or are in liaison to ISO/IEC JTC 1/SC 24 include: Defence Geospatial Information Working Group (DGIWG) Digital Imaging and Communications in Medicine (DICOM) International Hydrographic Organization (IHO) The Khronos Group NATO - Joint Intelligence Surveillance and Reconnaissance Capability Group (JISRCG) OMG Robotics DTF Open CGM Open Geospatial Consortium (OGC) SEDRIS Organization Simulation Interoperability Standards Organization (SISO) US National Imagery Transmission Format Standard (NITFS) Technical Board (US NTB) Web3D Consortium World Intellectual Property Organization (WIPO) World Wide Web Consortium (W3C) == Member countries == Countries pay a fee to ISO to be members of subcommittees. The 11 "P" (participating) members of ISO/IEC JTC 1/SC 24 are: Australia, China, Egypt, France, India, Japan, Republic of Korea, Portugal, Russian Federation, United Kingdom, and United States. The 22 "O" (observer) members of ISO/IEC JTC 1/SC 24 are: Argentina, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Canada, Cuba, Czech Republic, Finland, Ghana, Hungary, Iceland, Indonesia, Islamic Republic of Iran, Italy, Kazakhstan, Malaysia, Poland, Romania, Serbia, Slovakia, Switzerland, and Thailand. == Published standards == ISO/IEC JTC 1/SC 24 currently has 80 published standards under their direct responsibility within the field of computer graphics, image processing, and environmental data representation, including:
MADI
Multichannel Audio Digital Interface (MADI) standardized as AES10 by the Audio Engineering Society (AES) defines the data format and electrical characteristics of an interface that carries multiple channels of digital audio. The AES first documented the MADI standard in AES10-1991 and updated it in AES10-2003 and AES10-2008. The MADI standard includes a bit-level description and has features in common with the two-channel AES3 interface. MADI supports serial digital transmission over coaxial cable or fibre-optic lines of 28, 56, 32, or 64 channels; and sampling rates to 96 kHz and beyond with an audio bit depth of up to 24 bits per channel. Like AES3 and ADAT Lightpipe, it is a unidirectional interface from one sender to one receiver. == Development and applications == MADI was developed by AMS Neve, Solid State Logic, Sony and Mitsubishi and is widely used in the audio industry, especially in the professional audio sector. It provides advantages over other audio digital interface protocols and standards such as AES3, ADAT Lightpipe, TDIF (Tascam Digital Interface), and S/PDIF (Sony/Philips Digital Interface). These advantages include: Support for a greater number of channels per line Use of coaxial and optical fiber media that support transmission of audio signals over 100 meters, up to 3000 meters over multi-mode and 40,000 meters over single-mode optical fiber The original specification (AES10-1991) defined the MADI link as a 56-channel transport for linking large-format mixing consoles to digital multitrack recording devices. Large broadcast studios also adopted it for routing multi-channel audio throughout their facilities. The 2003 revision (AES10-2003) adds a 64-channel capability by removing varispeed operation and supports 96 kHz sampling frequency with reduced channel capacity. The latest AES10-2008 standard includes minor clarifications and updates to correspond to the current AES3 standard. Audio over Ethernet of various types is the primary alternative to MADI for transport of many channels of professional digital audio. == Transmission format == MADI links use a transmission format similar to Fiber Distributed Data Interface (FDDI) networking. Since MADI is most often transmitted on copper links via 75-ohm coaxial cables, it more closely compares to the FDDI specification for copper-based links, called CDDI. AES10-2003 recommends using BNC connectors with coaxial cables and SC connectors with optic fibers. MADI over fibre can support a range of up to 2 km. The basic data rate is 100 Mbit/s of data using 4B5B encoding to produce a 125 MHz physical baud rate. Unlike AES3, this clock is not synchronized to the audio sample rate, and the audio data payload is padded using JK sync symbols. Sync symbols may be inserted at any subframe boundary, and must occur at least once per frame. Though the standard disassociates the transmission clock from the audio sample rate, and thus requires a separate word clock connection to maintain synchronization, some vendors do give the option of locking to parts of the transmission timing information for purposes of deriving a word clock. The audio data is almost identical to the AES3 payload, though with more channels. Rather than letters, MADI assigns channel numbers from 0–63. Frame synchronization is provided by sync symbols outside the data itself, rather than an embedded preamble sequence, and the first four time slots of each sub-channel are encoded as normal data, used for sub-channel identification: Bit 0: Set to 1 to mark channel 0, the first channel in each frame Bit 1: Set to 1 to indicate that this channel is active (contains interesting data) Bit 2: notA/B channel marker, used to mark left (0) and right (1) channels. Generally, even channels are A and odd channels are B. Bit 3: Set to 1 to mark the beginning of a 192-sample data block == Sampling frequency == The original AES10-1991 specification allowed 56 channels at sample rates from 32 to 48 kHz with an additional vari-speed range of ± 12.5%. This leads to a total range of 28 to 54 kHz. At the highest frequency, this produces a total of 56 × 32 × 54 = 96768 kbit/s, leaving 3.232% of the channel for synchronization marks and transmit clock error. The 2003 revision specifies different relations between sampling frequency and number of channels. 32 kHz to 48 kHz ± 12.5%, 56 channels; 32 kHz to 48 kHz nominal, 64 channels; 64 kHz to 96 kHz ± 12.5%, 28 channels. With a 48 kHz sampling frequency, 64 channels take 64 × 32 × 48000 = 98.304 Mbit/s. Adding the minimum 8 × 58 kbit/s of framing produces 98688 bit/s, leaving 1.312% free for timing variation and other overhead. Both versions of the standard accommodate higher sampling frequencies (for example, 96 kHz or 192 kHz) by using two or more channels per audio sample on the link.