Main contractor Damen and more than a hundred companies contribute to combat support ship

On the same subject

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  March 17, 2023 | International, Other Defence

  L'Union europénne se dote d'une stratégie spatiale pour la défense

  La Commission et le service européen d'action extérieure ont dévoilé leur première feuille de route pour assumer plus de responsabilité en matière de sécurité dans l'espace. Une loi européenne sur l'espace est promise pour 2024.

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  October 31, 2019 | International, Aerospace, Naval, Land, C4ISR, Security

  Contract Awards by US Department of Defense - October 30, 2019

  ARMY General Dynamics Land Systems Inc., Sterling Heights, Michigan, was awarded a $162,403,915 hybrid (cost-plus-fixed-fee and firm-fixed-price) contract to procure small multipurpose equipment transport systems; support hardware including authorized stockage list kits and prescribed load list kits; and services for refurbishment, user training, field service representative, system technical support, program management support for pre-production meetings, and storage. Four bids were solicited with four received. Work locations and funding will be determined with each order, with an estimated completion date of Oct. 29, 2024. U.S. Army Contracting Command, Warren, Michigan, is the contracting activity (W56HZV-20-D-0002). The Boeing Co., Mesa, Arizona, was awarded a $62,294,566 modification (PZ0005) to contract W58RGZ-19-C-0024 for performance-based logistics support for the AH-64D/E Apache Attack helicopter. Work will be performed in Mesa, Arizona, with an estimated completion date of April 30, 2024. Fiscal 2019 Army working capital funds in the amount of $62,294,566 were obligated at the time of the award. U.S. Army Contracting Command, Redstone Arsenal, Alabama, is the contracting activity. AGCM Inc.,* Corpus Christi, Texas (W912DY-20-D-0002); Alliance Consulting Group Inc.,* Alexandria, Virginia (W912DY-20-D-0003); PCS and MOCA JV LLC,* Decatur, Georgia (W912DY-20-D-0004); Professional Project Services Inc., Oak Ridge, Tennessee (W912DY-20-D-0005); Project Time and Cost LLC, Atlanta, Georgia (W912DY-20-D-0006); and Michael Baker International Inc., Alexandria, Virginia (W912DY-20-D-0007), will compete for each order of the $49,000,000 firm-fixed-price contract for architect-engineering services. Bids were solicited via the internet with seven received. Work locations and funding will be determined with each order, with an estimated completion date of Nov. 18, 2024. U.S. Army Corps of Engineers, Huntsville, Alabama, is the contracting activity. NAVY BAE Systems Technology Solutions & Services Inc., Rockville, Maryland, is awarded a $69,247,177 cost-plus-fixed-fee, indefinite-delivery/indefinite-quantity contract for up to 931,200 man hours of installation and certification technical support to the Combat Integration and Identification Systems Division, Naval Air Warfare Center, Aircraft Division (NAWCAD) Webster Outlying Field and Patuxent River in support of the Navy and the governments of Japan, South Korea and Australia. Work will be performed in St. Inigoes, Maryland (80%); and Rockville, Maryland (20%), and is expected to be completed in April 2025. No funds will be obligated at time of award; funds will be obligated on individual task orders as they are issued. This contract was competitively procured via an electronic request for proposals; one offer was received. The NAWCAD, Patuxent River, Maryland, is the contracting activity (N00421-20-D-0003). Aircraft Readiness Alliance LLC,* Anchorage, Alaska, is awarded a $55,170,944 modification (P00012) to a previously awarded cost-plus-fixed-fee contract (N68936-17-C-0081). This modification exercises an option to provide depot level maintenance services in support of the Fleet Readiness Center Southwest mission. Work will be performed in San Diego, California (79.5%); Lemoore, California (8.5%) Camp Pendleton, California (3.4%); Yuma, Arizona (2.4%); Miramar, California (2.2%); Whidbey Island, Washington (1.7%); Kaneohe Bay, Hawaii (1%); Nellis, Nevada (1%); and Fallon, Nevada (0.3%), and is expected to be completed in October 2020. Fiscal 2020 working capital (Navy) funds in the amount of $19,062,893 will be obligated at time of award, none of which will expire at the end of the current fiscal year. The Naval Air Warfare Center, Weapons Division, China Lake, California, is the contracting activity. AUSTAL USA LLC, Mobile, Alabama, is awarded a $21,529,121 cost-plus-fixed-fee task order N69316-20-F-4000 against a previously awarded basic ordering agreement N00024-19-G-2318 to accomplish advance planning, material procurement and work in support of the post shakedown availability (PSA) of the littoral combat ship USS Charleston (LCS 18). This effort encompasses all of the manpower, support services, material, non-standard equipment and associated technical data and documentation required to prepare for and accomplish the USS Charleston (LCS 18) PSA. The work to be performed will include correction of government responsible trial card deficiencies, new work identified between custody transfer and the time of PSA, and incorporation of approved engineering changes that were not incorporated during the construction period which are not otherwise the building yard's responsibility under the ship construction contract. Work will be performed in Seattle, Washington, and is expected to be completed by September 2020. Fiscal 2020 operation and maintenance (Navy); fiscal 2014 shipbuilding and conversion (Navy); and fiscal 2019 other procurement (Navy) funding in the amount of $14,700,000 will be obligated at time of award and will not expire at the end of the current fiscal year. Funding: fiscal 2020 operation and maintenance, Navy (75%); fiscal 2014 shipbuilding and conversion (20%); and fiscal 2019 other procurement, Navy (5%). This contract was not competitively procured in accordance with 10 U.S. Code 2304(c)(1); only one responsible source and no other supplies or services will satisfy agency requirements. The Supervisor of Shipbuilding, Conversion, and Repair Gulf Coast, Pascagoula, Mississippi, is the contracting activity. The Boeing Co., Seattle, Washington, is awarded a $17,630,211 modification (05) to a cost-plus-fixed-fee delivery order (N00019-17-F-2017) against a previously issued basic ordering agreement (N00019-16-G-0001). This modification exercises an option to perform 27 modifications in support of the Increment 3 Block 1 retrofit requirement for P-8A aircraft for the Navy and the government of Australia. Work will be performed in Seattle, Washington (64.9%); Edinburgh, Australia (33.8%); and Meza, Arizona (1.3%), and is expected to be completed in September 2021. Fiscal 2020 aircraft procurement (Navy) funds in the amount of $11,362,276; and cooperative engagement agreement funds in the amount of $6,267,935 are being obligated on this award, none of which will expire at the end of the current fiscal year. The Naval Air Systems Command, Patuxent River, Maryland, is the contracting activity. Lockheed Martin Aeronautics Co., Fort Worth, Texas, is awarded a $10,571,178 modification (P00015) to a previously awarded cost-plus-incentive-fee contract (N00019-14-C-0040). This modification provides for the development and delivery of an enhanced simulator database and project management support for the F-35 aircraft in support of the government of Japan. Work will be performed in Orlando, Florida (70%); and Fort Worth, Texas (30%), and is expected to be completed in July 2021. Foreign Military Sales funds in the amount of $10,311,534 will be obligated at time of award, none of which will expire at the end of the current fiscal year. The Naval Air Systems Command, Patuxent River, Maryland is the contracting activity. DEFENSE LOGISTICS AGENCY Allison Transmission, Indianapolis, Indiana, has been awarded a maximum $35,266,682 firm-fixed-price contract for transmissions. This was a sole-source acquisition using justification 10 U.S. Code 2304 (c)(1), as stated in Federal Acquisition Regulation 6.302-1. This is a one-year base contract with one, one-year option period being awarded at the time of award. Location of performance is Indiana, with a March 31, 2022, performance completion date. Using military service is Army. Type of appropriation is fiscal 2020 Army working capital funds. The contracting activity is the Defense Logistics Agency Land and Maritime, Warren, Michigan (SPRDL1-20-C-0023). Alliant Healthcare, Grand Rapids, Michigan, has been awarded a maximum $30,000,000 fixed-price with economic-price-adjustment, indefinite-delivery/indefinite-quantity contract for medical/surgical supplies. This was a competitive acquisition with 16 responses received. This is a five-year contract with no options. Location of performance is Michigan, with an Oct. 29, 2024, performance completion date. Using customers are Army, Navy, Air Force, Marine Corps and federal civilian agencies. Type of appropriation is fiscal 2019 through 2024 defense working capital funds. The contracting activity is the Defense Logistics Agency Troop Support, Philadelphia, Pennsylvania (SPE2DE-20-D-0004). Propper International, Cabo Rojo, Puerto Rico, has been awarded a maximum $13,099,478 firm-fixed-price, indefinite-quantity contract for Improved Combat Vehicle Crewmen's coveralls with the operational camouflage pattern. This is a one-year base contract with four one-year option periods. This was a competitive acquisition with two responses received. Location of performance is Puerto Rico, with an Oct. 29, 2020, performance completion date. Using military service is Army. Type of appropriation is fiscal 2020 through 2021 defense working capital funds. The contracting activity is the Defense Logistics Agency Troop Support, Philadelphia, Pennsylvania (SPE1C1-20-D-1205). AIR FORCE Lockheed Martin Co., Space Systems Co., King of Prussia, Pennsylvania, has been awarded a $7,325,831 contract modification (P00031) to previously awarded contract FA8823-17-C-0003 for the AN/UMQ-13 Meteorological Data Station MARK IV-B System sustainment. The MARK IV-B is a web enabled client-server system that receives, processes, disseminates and stores real time imagery and mission sensor data from polar orbiting and geostationary satellites. Data from multiple satellites can be processed simultaneously. Using approved network protocols the system disseminates environmental imagery and sensor data to internal/external modeling systems and provides stored environmental imagery and sensor data to forecaster users. The MARK IV-B Forecaster client software is fully interrogatable and enables weather personnel to manipulate and transform environmental data. The MARK IV-B provides both automatic and semiautomatic dissemination of products to other approved external systems. Work will be performed at King of Prussia, Pennsylvania, and is expected to be completed by Oct. 31, 2020. The total cumulative face value is $27,313,202. Fiscal 2020 operations and maintenance funds in the amount of $3,438,207; and spectrum relocation funds in the amount of $644,391 are being obligated at the time of option award. The Space and Missile Systems Center, Peterson Air Force Base, Colorado, is the contracting activity. *Small Business

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  September 19, 2018 | International, C4ISR

  Differentiating a port from a shipyard is a new kind of problem for AI

  By: Daniel Cebul It's well known that satellites and other intelligence, surveillance and reconnaissance platforms collect more data than is possible for humans to analyze. To tackle this problem, the Intelligence Advanced Research Projects Activity, or IARPA, conducted the Functional Map of the World (fMoW) TopCoder challenge from July 2017 through February 2018, inviting researchers in industry and academia to develop deep learning algorithms capable of scanning and identifying different classes of objects in satellite imagery. IARPA curated a dataset of 1 million annotated, high-resolution satellite images aggregated using automated algorithms and crowd sourced images for competitors to train their algorithms to classify objects into 63 classes, such as airports, schools, oil wells, shipyards, or ports. Researchers powered their deep learning algorithms by combining large neural networks, known as convolutional neural networks (CNNs), and computers with large amounts of processing power. The result was a network that, when fed massive amounts of training data, can learn to identify and classify various objects from satellite imagery. By combining a number of these networks into what is called an ensemble, the algorithm can judge the results from each CNN to produce a final, improved result that is more robust than any single CNN. This is how a team from Lockheed Martin, led by Mark Pritt, designed their deep learning algorithm for the challenge. Pritt explained to C4ISRNET, that he and his team developed their CNN using machine learning software and framework from online open source software libraries, such as Tensor Flow. Earning a top five finish, the algorithm designed by Pritt's team achieved a total accuracy of 83 percent, and was able to classify 100 objects per second. Pritt said that with fully functioning algorithm, this software could take an image recognition task that takes a human an hour to complete and reduce the process to a few seconds. The team's algorithm excelled at identifying classes with distinctive features, and successfully matched nuclear power plants, tunnel openings, runways, tool booths, and wind farms with accuracies greater than 95 percent, but struggled with more indiscreet classes such as shipyards and ports, hospitals, office buildings, and police stations. “Usually when you develop an algorithm its nice to see where it succeeds, but you actually learn the most where you look at where the algorithm fails or it doesn't do well,” Pritt said. In trying to decipher why the algorithms struggled, Pritt said the competitors suggested that some objects simply don't have any distinguishing features from the point of view of a satellite image for the algorithms to recognize. “Maybe the most important ingredient you need for these new types of algorithm to work is the dataset because these algorithms require a great amount of data to train on,” Pritt explained. “It's kind of analogous to the way a human will learn in childhood how to recognize things. You need lots of examples of what those things are and then you can start to generalize and make your own judgments,” he said. But even with large amounts of training data that is correctly labeled, it is also possible the deep learning technology of today cannot reach the higher levels of intelligence to recognize nuanced differences. For example, Lockheed Martin's algorithm confused shipyards and ports 56 percent of the time. Pritt said that people “look at an image and they can tell that it's a port or a shipyard, they are usually looking at very subtle things such as if there is a ship in dry dock or if there is a certain type of crane present. They are looking for details in the image that are maybe higher level or more complicated than what these deep learning algorithms can do right now.” However, the fact that these algorithms cannot do everything should not dismiss the significant contribution they could provide to the defense and intelligence community. Hakjae Kim, IARPA's program manager for the fMoW challenge, said the benefits of this technology could extend far beyond faster image processing. “I want to look at it more in the perspective that we can do things we weren't able to do before,” Kim said. “Because its technology that we are now able to do x, y and z, there are more applications you can create because with the human power it is just impossible to do before.” Kim and Pritt stressed managing expectations for CNN-based artificial intelligence. “This is a real technology that will work, but it also has limitations. I don't want to express this technology as a magic box that will just solve everything magically,” Kim said. “I don't want the users in the field to get disappointed by the initial delivery of this technology and say 'Oh, this is another technology that was oversold and this is not something we can use," he added. Part of managing our expectations for AI requires recognizing that although intelligence is in the name, this technology does not think and reason like humans. “A lot of the time we think that because we use the term AI, we tend to think these algorithms are like us, they are intelligent like us,” Pritt said. “And in someways they seem to mimic our intelligence, but when they fail we realize ‘Oh, this algorithm doesn't really know anything, [it] doesn't have any common sense.'” So how are IARPA and Lockheed Martin working to improve their algorithms? For IARPA, Kim's team is working on updating and maintaining their dataset to ensure algorithms have the most up to date information to train on, ultimately making the CNN-based algorithms easier to trust. “[S]ubtle changes in the area mess up the brains of the system and that system will give you a totally wrong answer,” Kim explained. “So we have planned to continuously look over the area and make sure the algorithm we are developing and reassessing for the government to test on and use to be robust enough for their application," he furthered. Work is also underway at American universities. Kim described how a team of researchers at Boston University are using the fMoW dataset and tested algorithms to create heat maps that visualize what part of the image algorithms are using to classify objects. They've found that sometimes it is not the object itself, but clues surrounding the object that aid most in classification. For example a “windmill that actually shows a shadow gives a really good indicator of what that object is,” Kim said. “Shadows show a better view of the object. A shadow is casting the side view of the object over on the ground, so [BU's heat map algorithm] actually points out the shadow is really important and the key feature to make the object identified as a windmill.” But don't expect these algorithms to take away the jobs of analysts any time soon. “I think you still need a human doing the important judgments and kind of higher level thinking,” Pritt said. “I don't think AI will take away our jobs and replace humans, but I think what we have to do is figure out how to use them as a tool and how to use them efficiently, and that of course requires understanding what they do well and what they do poorly," he concluded. https://www.c4isrnet.com/intel-geoint/2018/09/18/differentiating-a-port-from-a-shipyard-is-a-new-kind-of-problem-for-ai