DoD 2012.B SBIR Solicitation

OBJECTIVE: Develop reliable methods for making statistically valid conclusions from test data sets that do not lend themselves to traditional statistical techniques. DESCRIPTION: There are times when it is necessary to compare functional performance between subsystems where the base platform is not identical. A common form of this is when a subsystem upgrade is tested many years after testing of the original subsystem was performed. In such a case, the objective is to demonstrate that the new subsystem is at least as good as the old. Although the follow-on test for the new subsystem is likely to be tested on a vehicle of the same type and model (a similar vehicle), it is often not feasible (or not possible) to configure the base platform so as to duplicate all parameters affecting the particular subsystem under test (thus requiring a disparate vehicle). Further, even when performing an isolated sequence of tests there are statistically unfriendly aspects of the data. For example, the data may have significant non-linearities or the data cannot be correlated directly (particularly in time). Logistical difficulties do not allow tests to be randomized. For example, tests are often performed on vehicles that are part of the test fleet, whereas the fundamental question is how new subsystems perform on vehicles that are part of the operational fleet. Standard techniques do not allow for extrapolating test conclusions from one subset of vehicles to the other. Contributions are sought to significantly advance the theoretical foundations of comparing data sets from similar tests that have disparate data parameters and for extrapolating conclusions from tests on a subset of test articles to other subsets. Potential topics for consideration include, but are not limited to, the following: - Statistical theory to allow comparison and extrapolation of data sets as described above. - Algorithmic or computational methods for analysis of such data sets - Methods for designing tests taking into consideration logistical constraints, existing related data sets, knowledge of various test article subsets, and known test objectives. - Development of tools for implementing the statistical theory and associated methods. - Methods for mining data sets for the information needed to validate these theories and methods. PHASE I: Identify foundational theories and associated methods for comparing disparate but similar data sets and for extrapolating conclusions from tests on a subset of test articles to other subsets. Establish the types of conclusions that can be drawn using these methods and associated measures of validity. Mature specific theories and identify methods to be developed into tools for test analysis. PHASE II: Mature the theoretical foundations identified in Phase 1 and develop specific reliable methods and processes based on this theory. Develop tools that implement the methods and allow for test design based on these methods and appropriate knowledge about the test assets and objectives. Develop tools to help automate data set analysis based on these methods and demonstrate their uses. Provide statistical documentation as to the degree to which derived conclusions are valid. PHASE III: Integrate developed tools and methods into DoD test centers. Generalize tools for other applications, both military and commercial. Generalized tools should be useful to testing of commercial aircraft, other vehicles, medical and scientific devices, software systems, and other complex systems. REFERENCES: 1. Bjorkman, E. A., & Brownlow, J. (2011). Statistically Defensible Test and Evaluation: The Air Force Flight Test Center Perspective. International Test and Evaluation Association Journal, 32(3), 261-266, Sept. 2011. 2. Deaconu, S., & Coleman, H. W. (2000). Limitations of Statistical Design of Experiments Approaches in Engineering Testing. Journal of Fluids Engineering, 122(2). 3. Gallagher, M. A, Weir, J. D., & True, W. D. (1997). Relating Weapon System Test Sizes to Warfighting Capability. Military Operations Research, 3(3). 4. Hahn, G. J., & Meeker, W.Q. (1993). Assumptions for Statistical Inference. The American Statistician, 47(1).

OBJECTIVE: Conceive and develop methods and techniques for optimizing signal detection and noise reduction for passive Signature Exploitation of lambertian scattered light modulated by vibrating surfaces with intended application from space. DESCRIPTION: Achieving space-based surveillance requires detecting not only with a limited number of photons but also with potentially small fractional modulation detectable from orbit, including geosynchronous. Present understanding requires direct coupled sensors within a reasonable cost, and detection of small fractional photon modulation levels requires sensitive detectors with minimal added detector noise, which can severely limit detection. This requires a detailed understanding of the physics of the detected signal on orbit and the small modulation of the detected signal contained therein, while simultaneously minimizing all noise sources, especially in the first few stages of signal amplification. Metadata such as sensor calibration information, sensor characteristics, sensor geometry corresponding to each data collection, and so forth, are relevant to the observational description. As modulation information from sources with small fractional modulation is sought, the detection physics becomes increasingly relevant. The Air Force Research Laboratory has a limited information database on these issues, and needs a more complete understanding of how to proceed. This STTR topic solicits innovative approaches for optimizing this type of detector for orbits from LEO to GEO for lambertian scattered light with small fractional modulation, and especially for minimizing sources of noise. PHASE I: The contractor shall define, model, and design innovative methods and techniques for optimizing signal detection and noise reduction for passive Signature Exploitation of Lambertian scattered light modulated by vibrating surfaces with intended application from space, but with other applications Air Force wide. PHASE II: In Phase II of this STTR effort, the contractor will design, verify, construct, test, and demonstrate a prototype detector to collect and assemble various types of satellite relevant data. The contractor shall implement or simulate a prototype sensor design that shall improve substantially on current state of the art detection. PHASE III: This technology can remotely monitor ground and surface vibrations from a distance, with a goal of detection from space, autonomous robotic vehicles, unpiloted aerial vehicles, and objects or vehicles that are well-characterized prior to use. REFERENCES: 1. Pereira, W., Clark, F., Jeong, L., Noyes, B., Noah, P., Pacleb, C., Dalrymple, S., Westphal, A., Hypertemporal Imaging Diffuse Modulation (HTI-DM) Experiment, AFRL-RV-HA-TR-2011-1010, 28 February 2011. 2. Hay, J.R., Kielkopf, J.F., Clark, F.O., Non-Contact Stand-off Optical Sensing of Cable Vibrations for Monitoring Structural Health of the William H. Harsah Bridge, CSX Eastern Parkway Overpass, and The Sherman-Minton Ohio River Bridge at Louisville, KY, USA, Proceedings of the 15th International Conference on Experimental Mechanics (ICEM15) (Porto, Portugal) July 2012.

OBJECTIVE: Human-engineered imaging sensors are anthropomorphic and in some respects very limited in capability. Develop an advanced imaging sensor concept that samples all of the information in the radiation field, taking inspiration from biological systems. DESCRIPTION: Develop advanced imaging sensors specifically designed to utilize most if not all of the information in the light field (spectral, temporal, polarization, detailed object shape) for applications enabling autonomous behavior, including egomotion determination, to aid in navigation; as well as target detection, recognition, ranging and tracking. Develop an integrated design that will include information processing at a fundamentally integrated level with the optics and transduction. Take inspiration from biological systems which are designed this way. Arthropods (insects, crustacea, and arachnids) have developed a variety of systems to exploit the information in the radiation field that are worth consideration. Egomotion determination involves local motion detection which enables global motion detection (optic flow). Target detection involves target-background discrimination which would involve motion detection for moving targets but could also involve spectral, shape, and polarization discrimination. Camouflage-breaking techniques are particularly interesting for static targets. Direction sensing relative to the celestial polarization pattern is fairly well understood in insects, using the ommatidia in the Dorsal Rim Area of the compound eye. Wide field of view (at least pi steradians) systems and ability to conform to shapes with smooth contours, such as airframes, would be especially valuable considerations. PHASE I: Develop a design for a prototype system; analyze the design to demonstrate functionality and feasibility. PHASE II: Produce a deliverable functional prototype with preliminary contractor testing, amenable to further in-depth testing by the sponsor. PHASE III: Commercial applications include surveillance sensors, and sensors for search and rescue. Military applications include ISR sensors and sensors for autonomous vehicles. REFERENCES: 1. M.F. Land and D.E. Nilsson, Animal Eyes, Oxford, 2002. 2. E. Warrant and D.E. Nilsson, Invertebrate Vision, Cambridge, 2006. 3. F. G. Barth, J. A. C. Humphrey, T. W. Secomb, sensors and sensing in biology and engineering, Springer, 2003. 4. Harland, D. P. and R. R. Jackson (2004)."Portia Perceptions: The Umwelt of an Araneophagic Jumping Spider."in Complex Worlds from Simpler Nervous Systems. F. R. Prete. Cambridge MA, Bradford, MIT Press. 5. Chiou, T.H., S. Kleinlogel, et al. (2008)."Circular Polarization Vision in a Stomatopod Crustacean."Current Biology 18: 429-434. 6. Homberg, U., S. Heinze, et al. (2011)."Central neural coding of sky polarization in insects"Philosophical Transactions of the Royal Society of London B 366 (Theme Issue"New directions in biological research on polarized light"): 680-687.

OBJECTIVE: Develop ultra high performance concrete (UHPC) materials and processes needed to produce large, high-strength test structures. DESCRIPTION: Concrete materials science has experienced a revolutionary advance in terms of the aggregates, matrix, bonding agents, accelerators, plasticizers, and other additives employed to produce high-strength forms and structures capable of withstanding harsh and sometimes extreme environmental conditions. With these new formulations and techniques, compressive strengths exceeding 15,000 psi are realized and strengths in excess of 40,000 psi have been reported. Nanotechnology applications are also an active area of UHPC research and they could result in additional improvements in strength and crack resistance. While the Air Force is interested in UHPC for buildings, command centers, and runways, the role that UHPC might play in protecting potential adversaries and their installations must also be considered. The Air Force"s capability to ground test against realistic targets at our test centers needs to keep pace with UHPC development. We are looking for UHPC formulations and methods that can be readily implemented at our test centers. Although high strength and impact resistance are of primary concern, the ability to use materials that are readily available in the region of test activity is also an important concern. PHASE I: Identify UHPC materials, techniques, and capabilities suitable for use at Air Force test centers. Demonstrate laboratory scale fabrication of test articles with compressive strengths in excess of 30,000 psi. Develop a plan that identifies the hardware, processes, and materials required to construct full size test structures at test centers using local materials. PHASE II: Increase demonstration unit scale sizes and evaluate construction processes. Cylinder and/or core load test scaled and full size units to demonstrate that the desired strength has been obtained. Develop requirements identified in small scale unit testing to allow proceeding to construction of 22"x22"x4"full scale unit. PHASE III: Construct full scale test structures with the desired strength at Air Force test centers. Formulations and techniques developed will have broad commercial application in the construction industry. REFERENCES: 1. HIGH PERFORMANCE CONCRETE STRUCTURAL DESIGNERS"GUIDE http://www.google.com/url?sa=t & rct=j & q=ultra%20high%20performance%20concrete%20pdf & source=web & cd=1 & ved=0CCcQFjAA & url=http%3A%2F%2Fknowledge.fhwa.dot.gov%2Fcops%2Fhpcx.nsf%2FAll%2BDocuments%2FA10B9708BF2C9D3D85256FD2007403A5%2F%24FILE%2FFinal%2520HPC%2520Structural%2520Designers%2520Guide.pdf & ei=wQ1eT7f-MImH0QG365ED & usg=AFQjCNHHjMHhLnXhT68DccVcpm5NWIFxhw 2. http://www.economist.com/node/21548918. 3. http://www.fhwa.dot.gov/publications/research/infrastructure/structures/11038/index.cfm 4. http://www.wired.com/dangerroom/2007/04/irans_superconc/

OBJECTIVE: To develop a process for real-time radar location of targets in cluttered environments, specifically air traffic targets in environments complicated by complex natural (e.g., hills and valleys) and manmade (e.g., wind turbines) features. DESCRIPTION: Our nation is developing a diverse range of renewable energy projects. Some of these energy production sites are located near existing military bases or military test ranges. Furthermore, it has been observed that operation of these energy production sites can confound military equipment or otherwise negatively impact training or operational readiness. A prime example is the impact of interference to air traffic radar operation caused by the reflection of the air traffic radar signal from rotating wind turbine blades. Two broad classes of error can occur: one is false positives or targets which are produced by the scatter of radar signals from rotating blades and can be misinterpreted by radar systems as weather or aircraft; the other is false negatives or dropped targets which corresponds to a loss of radar signal strength and can cause the masking of actual air traffic radar returns. Note that the engineering standard for probability of target detection is 80%. Additional complications arise because of static clutter (examples of which are terrain and topography features, wind turbine masts, etc.) and dynamic clutter (examples of which are the spinning blades attached to these wind turbine masts together with the extra complication of slight vibrations, various rotation speeds, and orientation changes exhibited by the spinning blades). A first step in addressing this problem is to characterize and define the clutter. The second step would be to be able to extract information and signals from the radar reflections provided by moving targets in the clutter environment. To this end a modeling and simulation undertaking wherein the Maxwell's equations are solved for the scattering (from moving objects with correct Doppler shifts) of varioius radar signals is solicited. This undertaking should include careful error analysis of the numerical approach so that a third broad class of error in addition to the two mentioned above isn't introduced. Because the target could be masked during its track by terrain or wind turbines, both line-of-sight and non-line-of-sight methods are appropriate where it is to be understood that the latter is a source of multipathing. PHASE I: Efforts should concentrate on the development of a mathematical construct for the characterization of the clutter environment. Fixed features will result in static clutter terms, but the construct should account for dynamic (e.g., spinning turbine) terms. Some consideration for inclusion of moving targets would be useful. PHASE II: Efforts should expand the methodology of Phase I to address the detection of targets in the clutter field. This should account for targets within appropriate ranges of altitude, speed, and track trajectories. The probability of detection as demonstrated by simulation or actual flights should exceed the 80% standard. PHASE III: Adoption into military air traffic control radar systems is anticipated. Transition to civil airports or high-traffic corridors serviced by current radar systems should be a goal. REFERENCES: 1. Steinhoff, J., Chitta, S., (2010)"Long distance wave computation using nonlinear solitary waves", Journal of Computational & Applied Mathematics, 234(6), 1826-1833. 2. Steinhoff, J. and Chitta, S. (2010)"Long-time solution of the wave equation using nonlinear dissipative structures", Chapter 32 in Integral Methods in Science and Engineering, Volume 2, pages 339-349. 3. S. Chitta, P. Sanematsu, J. Steinhoff, (2011)"Nonlinear Localized Dissipative Structures for Solving Wave Equations over Long Distances", Chapter 33 in Integral Methods in Science and Engineering: Computational Methods and Analytic Aspects.

OBJECTIVE: This topic seeks innovative methods for deriving a sparse set of physical target features that can be used for exploitation of air to ground signature data collected from electro-optic measurement systems including EO, IR and LADAR. DESCRIPTION: Current methods for exploiting EO signature data include statistical pattern recognition techniques and model based approaches. Model-based approaches use simulated data in combination with measurement data to define features for potential exploitation. Current approaches designing algorithms based on feature extraction do not guarantee robustness because the extracted features are not linked to reliably known causal physics. Second, limitations of current approaches also include the lack of linkage to a systems theory model for disciplined trade space design of algorithm features. Innovative methods for extracting salient physics based features that are associated with robust physical mechanisms are needed. Objects of interest for this topic include civilian vehicles including passenger vehicles and sport utility vehicles and dismounts. Although some exploitable features may change with background, this topics seeks methods that identify the dominant causal physics that both characterize the object and underlay the optimal inference solution. Associating the exploitable information content with the underlying causal physics based observables that each sensor can provide is a first step towards the development of new exploitation concepts and algorithms. Analysis methods are needed to efficiently produce the sparse cueing of salient physical features critical to the exploitation potential which exists within the full signature data. In addition, methods of modeling realistic sources of signature uncertainty within a systems theory prototype are required for exploitation concepts and algorithm development. New analytical approaches are specifically sought for identifying observable physical features that can be reliably exploited. Methods for discovering salient features that contribute to exploitation should also account for and model uncertainties in the measurement process such that they are robust to realistic sensor measurement effects. Realistic uncertainties in civilian vehicles and dismounts should also be modeled. Feature robustness and persistence should be assessed using metrics tied to systems theory such that methods proposed have a theoretical basis. Example system theories that have application to discrimination could include Sparse Bayesian Learning Theory and Information Theory. 1. Sufficiently accounting for, or eliminating uncertainty in sensor feature measurements. 2. Sufficiently accounting for uncertainty sources in the object of interest. 3. Reliance on a priori information. If the proposed analysis methods rely on a database that is developed offline, sources of measured or methods of simulating data should be specifically identified in the proposal. 4. Target Feature Exploitation. Enhanced methods of deriving object physical properties from sensed observables that account for realistic sensor limitations should be specifically addressed. PHASE I: Develop and conduct proof-of-concept demonstration of innovative physical feature based discrimination approaches using simulated data that model generic sensor characteristics. Characterize physical features with exploitation potential using metrics theoretically linked to system theory. Establish a mathematical foundation for cueing to underlying physical mechanisms responsible for inference. PHASE II: Mature algorithms and methods developed on Phase I results and demonstrate technology using controlled measurement data. PHASE III: Mature algorithms and methods developed on Phase II. Develop user friendly software tool and demonstrate technology using realistic sensor data. REFERENCES: 1. Keinosuke Fukanaga, Introduction to Statistical Pattern Recognition, 2nd Edition, Academic Press, 1990. 2. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, Wiley 2001. 3. T. Cover and J. Thomas, Elements of Information Theory, New York, Wiley, 1991. 4. Tipping, Michael E."Sparse Bayesian Learning and the Relevance Vector Machine,"Journal of Machine Learning Research, June 2001, 211-244. 5. M. Bell,"Information Theory and Radar: Mutual Information and the Design and Analysis of Radar Waveforms and Systems", Ph.D. Dissertation, Department of Electrical Engineering.

OBJECTIVE: Develop a miniaturized, power efficient C-band telemetry (TM) transmitter with performance comparable to current state of the art miniaturized S-band transmitters. DESCRIPTION: A miniaturized TM subsystem for an airborne transmitter is needed in the 1-2 cubic inch form factor to support existing efforts to miniaturize flight test instrumentation. C-band transmitter RF devices are steadily improving, but their gain and efficiency are several years behind L-band and S-band devices that are available commercially. The challenge of designing test instrumentation with C-Band capable RF devices encompasses a trade space that includes size, power, and link margin requirements. Power consumption is a critical factor, and adequate reserves must be available to the actual equipment being tested. The most difficult challenge is recovering the lost link margin. Moving from 2.2 GHz to 4.4 GHz costs 6 dB in path loss. In theory, the gain of the receiving antenna should increase by 6 dB to compensate for this loss, but in practice, the antenna gain will not improve that much. Any misalignment of the feed or slight shift in dish parabola accuracy will keep the antenna gain from getting the full 6 dB, and telemetry experts estimate that the gain is closer to 3 dB. This leaves the instrument platforms to manage a link margin 3 dB less capable than current operations in S-Band during times when the platforms need more data transmitted to the ground. This may require the transmitter to increase power output from current specification to a 10-watts specification, which will add a new level of complexity to the internal system design and impact to systems power sources. Power consumption and management are thus a critical considerations in unit development. Miniaturized C-Band Transmitter Specific Needs: Size: less than 2 cubic inches Modulation Schemes: All ARTM waveforms (PCM/FM, SOQPSK, CPM) Input Power: Less than 40 Watts RF Output Power: At least 10 Watts Operating Frequency: Selectable between 4400-4940, 5091-5150, and 5925-6700 MHz PHASE I: Design C-band transmitter and develop concepts for telemetry support infrastructure in compliance with Mil-Std-461 EMI/EMC guidelines. Designed unit must be able to operate reliably and safely in concert with other test range electronic devices, including munitions. Designed unit must be able to maintain C-band link margins comparable to those of other TM subsystems operating in S-band. PHASE II: Build a prototype TM transmitter unit and demonstrate that meets the stated performance objectives. Demonstrate capabilities in a test range environment and show that it can operate reliably and safely in concert with other test range electronic devices, including munitions. Demonstrate the ability to maintain C-band link margins comparable to those of other TM subsystems operating in S-band. PHASE III: Integrate new C-band telemetry capabilities into test range environments and show that the unit reliably operates meeting stated objectives. To be successful, the device must comply with IRIG Std RCC106-09, Mil-Std-461 and environmental specification. REFERENCES: 1. Federal Communications Commission (2011). National Broadband Plan accessed at http://www.broadband.gov/download-plan/ on 10 Nov 2011. 2. G. F. Guimares et al.,"S-Band Amplification and S- to C/L-Band Wavelength Conversion Using a TDFA/FOPA Hybrid Amplifier", Annals of Optics (2006). 3. J.F.L. Freitas et al.,"Raman Enhanced Parametric Amplifier Based SC Band Wavelength Converter: Experiment and Simulations", Optics Communications 255, 314318 (2005). 4. Peebles, Peyton Z. Jr, (1998), Radar Principles, John Wiley and Sons, Inc., p 20. 5."Experiment and Simulations", Optics Communications 255, 314318 (2005).

OBJECTIVE: Develop a compact, low-cost test system with integrated control of temperature, electric field, and magnetic field for non-destructive characterization of novel electronic materials and devices at THz frequencies. DESCRIPTION: The region from 0.1 THz (1011 Hz) to 10 THz (1013 Hz) is a largely unexplored region of the electromagnetic spectrum. The lower end of this region, 94 GHz, is now being developed for radar and communications applications, and the upper end can bridge the gap with long-wavelength infrared. To explore the THz region, it is important to know which materials and devices will work effectively as sources, detectors, interconnects, and other passive components at such frequencies. Presently, very few commercial test systems are available in the THz region and those available tend to be bulky and costly. In addition, there is no test system that can be easily used to characterize materials and devices over a broad range of THz frequencies with integrated control of temperature and magnetic field. Therefore, a compact and low-cost test system will greatly facilitate and further stimulate the exploration of the THz region. Ideally, such a test system should provide the same level of convenience as in microwave and infrared test systems. For best performance and convenience, THz sources and detectors should be placed in close proximity to materials and devices being tested. This may require THz generation and detection within cryogenic and magnetic field environments. PHASE I: Design a prototype system operating in the frequency range 0.1 3 THz with integrated control of temperature, electric field and magnetic field. Process for making measurements and deriving selected material and device properties from measured THz spectral data should be outlined. Trade-off between size, cost and precision should be discussed. PHASE II: Build the test system in commercial format and include software to allow convenient measurement and subsequent analysis of electronic properties. In particular, include algorithms to calculate optical mobility and optical concentration in order to provide a non-destructive determination of electrical material properties. PHASE III: Demonstrate methods for effective non-destructive testing of specific device structures to determine their performance at THz frequencies. This test system is useful for characterizing materials and devices for radar, communications, chemical biological sensing, and other security applications. REFERENCES: 1. D. M. Mittleman, J. Cunningham, M. C. Nuss, and M. Geva,"Noncontact semiconductor wafer characterization with the terahertz Hall effect,"Appl. Phys. Lett., vol. 71, pp. 16-18, 1997. 2. M. S. Sherwin, C. A. Schmuttenmaer, and P. H. Bucksbaum, Eds.,"DOE-NSF-NIH workshop opportunities in THz science,"[Online]. Available: http://www.sc.doe.gov/bes/reports/files/THz_rpt.pdf (2004). 3. M. Naftaly, and R. E. Miles,"Terahertz time-domain spectroscopy for material characterization,"Proc. Of IEEE, vol. 95, pp.1658-1665, 2007. 4. R. Singh, Z. Tian, J. Han, C. Rockstuhl, J. Gu, and W. Zhang,"Cryogenic temperatures as a path toward high-Q terahertz metamaterials,"Appl. Phys. Lett., vol. 96, p. 071114, 2010.

OBJECTIVE: Develop new game decision models and efficient computational algorithms for autonomous space systems with the capabilities for self defense when there are potential adversarial strikes. DESCRIPTION: Former Air Force Space Command (AFSPC) Commander General Lance Lord defined space situation awareness (SSA) in simple terms:"The foundation of Space Superiority is Space Situation Awareness, which means having a complete understanding of what is happening in space."What exactly does this mean? General Lord goes on to say in his 2005 article in High Frontier that"It is no longer sufficient to simply know where a satellite is in space. We must know what the satellite is capable of doing, what it is being used for and what it may be used for in the future."Today the United States has a tremendous investment in space, especially in military, intelligence, scientific, and commercial sectors. However, one of the most important space vulnerabilities is the lack of persistent situation awareness of the space operational environment to ensure freedom of action. Space can be an important battlefield in modern warfare because intelligence information from the space has become extremely vital for strategic decisions. The presence of adversaries in addition to real-time and hidden information constraints greatly complicates the decision making process. It becomes necessary to perform space defense analysis and mission trade studies. Although pursuit-evasion game theory is relevant to this problem, most results in the existing literature are from the pursuers"perspective and thus not applicable. Innovative solutions are sought for (a) proper game models and constructive game training for a space-based Low Earth Orbit (LEO) and/or near Geostationary Earth Orbit (GEO) defending scenario whereby multiple denying LEO/near GEO assets, defending LEO/near GEO assets and pursuing LEO/near GEO assets with either equal or unequal capabilities are assumed with imperfect, sporadic observations and jamming confrontations due to dynamic network topologies and inter-satellite links (ISLs); (b) possible constructive methods and approximate solution techniques on distributed learning under sparse communications and adverse environments due to orbital geometries, propagations and interferences; (c) efficient computational algorithms to determines real-time cooperative strategies for LEO/near GEO assets, neutral objects and threats in persistent area denial; and (d) assess the performance under technical failure inaccurate measurements and loss of communications. Proposed advances together with potential deliverables including novel mathematical developments, interaction modeling, performance metrics, advanced engagement concepts, and design principles shall set the foundations to enable assured operations of teams of autonomous defense systems to adapt to hostile and non-traditional environments which shall capitalize on effective utilization of modeling and analysis of uncertain systems as well as multi-level, multi-group, multi-agent control and decision analysis. PHASE I: Develop constructive methods and analysis tools for a proof-of-concept entailing orbital geometries of LEO and/or near GEO assets, antenna beamwidths, crosslink angular velocities, ISL interferences, Doppler shifts and adversarial engagements including co-orbital threat models, levels of deception and collateral damages, asymmetric sensing and actuation capabilities for LEO and/or near GEO assets. PHASE II: Refine Phase I system concept and algorithms of the proof-of-concept to include operational constraints of space-based visible/radar sensors, LEO/near GEO space platforms, maneuver capabilities, characteristics of orbital planes and space assets per plane on asset observability and reachability. Conduct 3D simulations and visualizations to characterize performance of decision support tools using NASA General Mission Analysis, OMNET++, Java programming, Service-Oriented-Architecture framework. PHASE III: Adversarial decision analysis and robust decision making tools from Phase II activities are applicable to protected tactical space communications with dynamic spectrum sharing, routing adaptation and interference mitigations. REFERENCES: 1. Gen Lance Lord,"Space Superiority,"High Frontier 1, No. 3 (2005):4. 2. D. Li, J.B. Cruz, Jr., and C. Schumacher,"Stochastic Multi-Player Pursuit Evasion Differential Games,"International Journal of Robust and Nonlinear Control, Vol. 18, pp. 218247, 2008. 3. K. D. Pham,"Risk-Averse Based Paradigms for Uncertainty Forecast and Management in Differential Games of Persistent Disruptions and Denials,"Proceedings of American Control Conference, pp. 842-849, Baltimore, MD, 2010. 4. D. Shen, K. D. Pham, G. Chen and E. P. Blasch,"Pursuit-Evasion Orbital Game for Satellite Interception and Collision Avoidance,"SPIE Defense and Security 2011: Sensors and Systems for Space Applications IV, Proceedings of SPIE, Vol. 8044, Orlando, FL, 2011.

OBJECTIVE: Develop materials and instruments for cryodeposit mitigation and removal in radiometric calibration chambers. DESCRIPTION: A better understanding of the cryodeposition process is required such that techniques can be developed to successfully remove cryodeposits that can be such a problem in test chamber performance. Water ice layers on the order of 100nm (and greater) can significantly affect the performance of an optical component. At some thickness (highly dependent upon temperature) there is a conversion from a transparent film to a highly scattering form. If the transition is thermally induced due to an increase or decrease of the substrate/film temperature, it may be correlated to a phase change in the ice from amorphous to a crystal state or from one crystal state to another which leads to a density gradient in the ice, while a non-thermally induced (i.e. thickness induced) transition could be the result of stresses from the changing density of the ice and the difference in elastic properties between ice films and the underlying substrate. Prevention of cryodeposits could possibly be accomplished by various means that minimize the sticking coefficients of critical surfaces (hydrophilic coatings, helium curtain, electromagnetic fields, etc.). Removal of water cryodeposits can be accomplished by desorption of the water molecules. There are different types of desorption including, among others, thermal and photo-induced (using various wavelength regions) that have been evaluated. Other techniques may exist that can also accomplish the removal of these cryodeposits. The removal of other, more complex contaminants shall also be investigated. A removal system is needed that will not contaminate the chamber directly through its use, and can be used in the chamber"s cryogenic environment. The removal process can be utilized during the cryo pumpdown, but only at non-test times when radiometric data are not being acquired. The system must be of such a size that it does not impact the optical or other chamber systems. It must not contaminate or otherwise damage the targeted optical element or the System Under Test (SUT). PHASE I: Identify cryodeposition mechanisms and phenomenology specific to optical and mechanical substrates used in cryo-vacuum test chambers. Investigate prevention mitigation, and removal techniques. Develop plan for prevention, mitigation, and removal. PHASE II: Develop materials and instruments to minimize and address cryodeposit formation in cryo-vacuum test chambers. Demonstrate technology in cryo-vacuum test chamber environment or facsimile (<77 K, 10-6 Torr) for water and complex molecules (hydrocarbon or silicone) deposition. PHASE III: Transition technology to Air Force cryo-vacuum test chambers. Other transition partners might include: NASA, Raytheon, Ball Aerospace, Kinetic Kill Vehicle-in-the-Loop Simulator (KHILS), Johns Hopkins University Applied Physics Lab, MIT Lincoln Labs, Alliant Techsystems, Inc (ATK). REFERENCES: 1. Andersson S. and van Dishoek E.F. Photodesorption of Water Ice [Journal]. - Leiden : Astronomy and Astrophysics, 2008. - Vol. 491. 2. Drobyshev A. [et al.] Thermal Desorption and Spectrometric Investigation of Polyamorphic and Polymorphic Transformations in Cryovacuum Condensates of Water [Journal]. - Almaty : Low Temperature Physics, 2007. - 5 : Vol. 33. 3. Focsa C., Chazallon B. and Destombes J.L. Resonant Desorption of Ice with a Tunable LiNbO3 Optical Parametric Oscillator [Journal]. - Lille : Surface Science, 2003. - Vol. 528. 4. Krasnopoler A. and George S.M. Infrared Resonant Desorption of H2O from Ice Multilayers [Journal]. - Boulder : Journal of Physical Chemistry B, 1998. - Vol. 102. 5. J. Labello,"Water Ice Films in Cryogenic Vacuum Chambers,"Ph.D. Dissertation, The University of Tennessee Space Institute, Dec. 2011.

OBJECTIVE: Develop a solar irradiance spectrum forecast toolset that can accurately determine current and future high-resolution solar extreme ultraviolet irradiance spectra using near real-time solar observations. DESCRIPTION: The solar spectral irradiance at the top of the atmosphere is the main energy input to Earth"s thermosphere. It excites, dissociates and ionizes the neutral constituents in the thermosphere. It is important to accurately determine the solar irradiance spectrum at very high-resolution to make it possible to compute the effects of radiation on the various absorbing species. Thermosphere density is a critical factor in determining orbital drag used for providing collision avoidance warnings for manned spaceflight and other high-value assets, accurately cataloging orbiting objects, predicting reentry times, and estimating satellite lifetimes, on-board fuel requirements, and attitude dynamics. Uncertainties in neutral density variations are the major limiting factor for precise low-Earth orbit determination at altitudes below about 700 km. Long-standing shortfalls in satellite drag prediction have been, in large part, due to inadequate prediction capability for solar EUV spectra. R & D effort in this area has recently been enhanced by an AFOSR-supported Multi-University Research Initiative entitled"Neutral Atmosphere Density Interdisciplinary Research."It has greatly improved the understanding of the neutral density profiles under various solar and geomagnetic conditions. While near real time data and indices including EUV data and solar flux are now becoming available, thermospheric models have not yet taken advantage of the data for improving neutral density modeling. Empirical solar irradiance specification models (e.g. HEUVAC and SOLAR2000 models) have provided proxy-based solar EUV for characterizing solar irradiance variability across the solar spectrum. Current EUV forecast models commonly rely on time series analysis of past solar measurements. Forecasts of the daily F10.7 cm solar radio data adjusted to 1 AU, the E10.7 EUV proxy index, and a Lyman-alpha index (from Mg II index) are now routinely available. Recent research has indicated the feasibility of forecasting the solar F10.7 index utilizing advanced predictions of the global solar magnetic field generated by a flux transport model. Other researchers have shown that daily solar irradiance spectra can be efficiently constructed based on a set of semi-empirical physical models of solar features and their emitted spectra as a function of viewing angle, combined with solar images. A challenging R & D effort beyond the current state of art is to be able to efficiently nowcast and forecast of high-resolution EUV spectra in the range of 0.1 to 100 nm using the available near real-time observations of solar features. This topic thus requests innovative R & D to develop a semi-empirical physical model of solar atmosphere that can be assimilated with near real-time solar observations. The objective of this STTR is to establish technical feasibility of specification and forecast of high-resolution EUV spectra that can be used to drive thermospheric neural density modeling for near real-time operations. This STTR will also provide motivation for improving EUV-driven ionospheric processes. Successful proposals will help develop innovative algorithms employing new physical models and near real time solar data and indices. The new algorithms will eventually be utilized in the modeling of satellite drag by the Air Force Space Command (AFSPC) and the Joint Space Operations Center (JSpOC). PHASE I: Develop and assess a solar irradiance spectrum modeling system of empirical and/or physical models of solar atmosphere using near real-time solar observations. Demonstrate that the proposed physical model is feasible to achieve the goal of accurately determining current and future high-resolution solar extreme ultraviolet irradiance spectra. PHASE II: Develop a system of tools that can assimilate semi-empirical physical model of solar atmosphere with the available near real-time solar observations. Develop innovative algorithms that forecast high-resolution EUV spectra. Demonstrate that the developed model can be used as input to improve thermosphere and ionospheric physical modeling. Deliverables will include at least the models, prediction algorithms, software system, and validation reports. PHASE III: Results of this work can be used to improve AF space catalog accuracy, a critical component for space situational awareness. The developed model can be utilized in DoD operational centers. New algorithms under this grant can be used in high accuracy collision avoidance in commercial applications. REFERENCES: 1. NOAA Solar Ultraviolet Spectral Irradiance website: http://www.ngdc.noaa.gov/stp/solar/solaruv.html 2. Henney, C. J., W. A. Toussaint, S. M. White, and C. N. Arge (2012), Forecasting F10.7 with solar magnetic flux transport modeling, Space Weather, 10, S02011, doi:10.1029/2011SW000748. 3. Fontenla, J. M., J. Harder, W. Livingston, M. Snow, and T. Woods (2011), High-resolution solar spectral irradiance from extreme ultraviolet to far infrared, J. Geophys. Res., 116, D20108, doi:10.1029/2011JD016032. 4. Richards, P. G., T. N. Woods and W. K. Peterson, HEUVAC: A new high resolution EUV proxy model, Adv. Space Res., 37, 315, 2006. 5. Codrescu, M. V., C. Negrea, M. Fedrizzi, T. J. Fuller-Rowell, A. Dobin, N. Jakowsky, H. Khalsa, T. Matsuo, and N. Maruyama (2012), A real-time run of the Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model, Space Weather, 10, S02001, doi:10.1029/2011SW000736.

OBJECTIVE: Develop a full aircraft (scaled fighter sized) test articles for transonic aeroelastic research. Collect test article wind tunnel and other performance data and demonstrate utility for CSE tool application and CFD validation. DESCRIPTION: Physical understanding and modeling of real world full aircraft dynamics is required in order to support test and evaluation of future aircraft and weapons systems. Improvements in testing time could be realized if enhanced decision data become available, potentially resolving issues and reducing uncertainty. The full value of aircraft dynamics modeling and simulation has not yet been realized, however, due to the paucity of appropriate validation data. Although there is often an abundance of data obtained from flight tests, these data do not always lend themselves to resolving model discrepancies due to uncertainties in instrument performance and local range conditions. In addition, flight test data are not conducive to the systematic variations of key parameters needed for computational fluid dynamics (CFD) model validation, especially when the conditions of interest compromise flight safety. The current publically available data sets, cited below, add little or no value to our ability to gain insight into relevant full aircraft performance during test and evaluation. In order to assist the acquisition cycle as a whole, high fidelity data sets must be utilized in conjunction with multi-physics computational science and engineering (CSE) tools as applied to these systems. Validation data are needed for CSE tool application, in particular, to address single discipline structure and multi-disciplinary design optimization issues. Ultimately these data sets will improve physical understanding and build confidence in the predictive capabilities of CSE tools with respect to aeroelastic instabilities, aerodynamic characteristics, etc. and help establish the relevant envelope boundary prior to open air flight tests. We seek to address this problem through the construction of aircraft physical models suitable for wind tunnel testing and capable of providing relevant validation data to assist in CFD model and CSE tool development. If successful, the data sets could significantly improve lifecycle costs and reduce the risk associated with the testing and certification of military and commercial aircraft. During Phase 1, the contractor shall develop test article design and fabrication techniques, design 3 or more test articles, and assess merits and deficiencies of each with respect to fabrication, testing, and validation applications. Detailed designs and design specifications for each model must be made available in the public domain. During Phase 2, the contractor shall fabricate one or more full aircraft (scaled fighter sized) aeroelastic configuration transonic aeroelastic research and test articles. Advanced model construction processes, such as 3D printing, additive manufacturing, or rapid prototyping should be employed, if feasible. Updated public domain designs with actual build specifications for each test article will be provided by the contractor. Sufficient wind tunnel test data should be collected during Phase 2 to demonstrate the utility of test article design and fabrication and the ability of test articles to provide relevant validation data. The contractor will also corroborate data and physical characteristics of both the fluid and structural components through the use of CSE tools. At the end of Phase 2, all test articles, designs, and design specifications shall be delivered to a test facility for additional testing and assessment. PHASE I: Develop test article design and fabrication techniques. Design 3 or more test articles and assess merits and deficiencies of each with respect to expected performance, fabrication, and testing. Although designs capable of advancing transonic aeroelastic research are of particular interest, designs that facilitate subsonic testing are also desired. PHASE II: Produce one or more test articles. Update designs with actual build specifications for each. Collect test article wind tunnel and other performance data. Demonstrate utility for CSE tool application and CFD modeling and simulation validation. Deliver test articles to test facility for additional testing and assessment. Although test articles capable of advancing transonic aeroelastic research are of particular interest, articles that facilitate subsonic testing are also desired. PHASE III: Produce new and more complex test articles on demand. Demonstrate repeatability of design structure, article measurement, and simulation objectives. Potential government customers include Air Force, Navy, and NASA. Commercial potential with Boeing, Lockheed Martin, and other primes. REFERENCES: 1. Yates, E.C., AGARD Standard Aeroelastic Configurations for Dynamic Response I-Wing 445.6,. AGARD Report No. 765. 2. Pototzky, A.S.,"Scaling Laws Applied to a Model Formulation of the Aeroservoelastic Equations,"AIAA paper 2002-1598. 3. Pak, C., and Lung, S.,"Reduced Uncertainties in the Robust Flutter Analysis of the Aerostructures Test Wing,"Proceedings of the 27th Congress of International Council of the Aeronautical Sciences, Nice, France, 2010. 4. Reimer, L., Braun, C., Chen, B.H., Ballmann, J.: Computational Aeroelastic Design and Analysis of the HIRENASD Wind Tunnel Wing Model and Tests. International Forum on Aeroelasticity and Structural Dynamics (IFASD) 2007, Stockholm, Sweden, Paper IF-071. 5. Ballmann, J., Dafnis, A., Korsch, H., Buxel, C., Reimerdes, H.-G., Brakhage, K.-H., Olivier, H., Braun, C., Baars, A., Boucke, A.: Experimental Analysis of High Reynolds Number Aero-Structural Dynamics in ETW, 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, January 7-10, AIAA paper 2008-841. 6. Lung, S., and Pak, C.,"Updating the Finite Element Model of the Aerostructures Test Wing Using Ground Vibration Test Data,"AIAA-2009-2528, Proceedings of the 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Palm Springs, California, 2009. 7. Goodrich, M., Gorham J.,"Wind Tunnels of the Western Hemisphere,"Library of Congress, 2008.

OBJECTIVE: Develop an adaptive optics system using a fiber laser array as the spatial phase correction system within the subaperture of an array of discrete telescopes. DESCRIPTION: Recent advances in laser array weapons (a system of discrete telescopes) may drive the subapertures to a diameter larger than Fried diameter. In addition to inter-subaperture phasing (between subapertures) this requires intra-subaperture (within a subaperture) phase conjugation to control the atmospheric phase distortions. An array of fiber lasers will feed the subaperture providing the opportunity to accomplish power scaling and spatial phase control given accurate wavefront data. Each subaperture is required to be phase stabilized near the exit pupil, canceling high frequency phase noise from both the remote amplifier bank and optical path from the master oscillator. The architecture should provide a means for beaconless intra-subaperture phase sensing and control, (inter-aperture phase sensing is not required for this research). Attention should be paid to transmit/receive isolation, viable controls design where conflicts may arise with slow and fast bandwidth control, and multi-input/multi-output sensing and correcting. The subaperture should accommodate 7-19 fibers in a high area-fill-factor arrangement. The closed-loop bandwidth should be scalable to accommodate aircraft boundary layer disturbances and moderate atmospheric disturbances. PHASE I: Develop an architecture to accomplish subaperture wavefront sensing and fiber-laser based phase conjugation in the context of a viable laser array weapon. PHASE II: Validate the theoretical approach by conducting tests in a laboratory environment. The validation should focus on fundamental measurement/correction limits in stressing environments with less emphasis placed on obtaining a bandwidth optimized product. PHASE III: Combining the laser-source-power-scaling with spatial phase control can be used for other monolithic applications such as compensated beacons for astronomy or free space optical communication. The inherently ultra-fast actuation rate may provide other uses. REFERENCES: 1. Marron, J., Kendrick, R., Thurman, S. T., Seldomridge, N., Grow, T. D., & Embry, C. (2010). Extended-range digital holographic imaging (Vol. Vol. 7684). Proc. Of SPIE. 2. Motes, R. A., & Berdine, R. W. (2009). Introduction to High Power Fiber Lasers. Albuquerque: Directed Energy Professional Society. 3. Sasiela, R. J. (2007). Electromagnetic Wave Propagation in Turbulence (Second ed.). Bellingham, WA: SPIE Press.

OBJECTIVE: Develop machine learning technology that can significantly improve warfighter wide area situational awareness based on multiple sensors. DESCRIPTION: Layered sensing enables situational awareness (SA) about an area of interest (AOI) by providing multiple high-resolution views of the area. SA in a wide area of operations is particularly challenging as the sensor resources have to be stretched to satisfy a large number of warfighter requests. Typical wide area sensor layering consists of a patchwork of high and low resolution sensor views. For example, SAR/GMTI radars may first provide low-revisit data over large areas and then high-resolution high-revisit data over pre-defined areas of where an activity of interest is suspected. EOIR and WAMI sensors can scan a large area of interest at low resolution and then provide high-resolution images over small areas of interest. Depending on the availability of the resources and time constraints, multiple sensors can be layered over the same activity or each sensor can cover different activities. AF and Army users then have the challenge of fusing this disparate data to uncover activities of interest. The objective of this topic is to develop machine learning technologies that can address two challenges in the wide area sensor layering: (1) improve analyst"s ability to detect activities of interest in wide area layered sensor data, (2) to deliver the sensor data to the AF and Army users in time to make a decision over a combination of satellite, airborne and mobile low-bandwidth networks. Machine learning technologies can improve the performance of activity detection methods by taking advantage of the training data arising during the operations. The activity detection system has an opportunity to learn from the detections provided by the users, and from ad hoc multi-sensor co-collects provided by the layered sensors. Traditional machine learning uses labeled examples, generated by analysts by analyzing the collected data, to train the learning algorithms; the training process produces a decision rule which can be applied to detect activities in the future data. In a typical scenario, however, more data is available; for instance each low-resolution may have corresponding high-resolution views and analysts might have enhanced parts of the data by describing the situation in detail. Such additional data from the high-resolution sensors or the analyst may not be available in the operational environment when activities of interest must be detected in real time. Advanced machine learning technology that can use additional information during training is desired. Furthermore, the machine learning technology should take into account the specifics of operational sensor data: high variability of observed activities and sensor observations, internal structure within the classes of interest; presence of large number of clutter classes, limited amount of learning samples, and the need to integrate machine learning into the human analytical process. Such adaptive activity detection system can significantly increase probability of detection of activities of interest and reduce the false alarm rate. The goal of the adaptive network management system is to ensure that the right information flows to the right users in time to provide situational awareness. The system will monitor network performance under varying sensor output and user requests, learn to predict future bottlenecks, and develop proactive network management and prioritization policies. PHASE I: Develop machine learning technologies which can operate on training data containing additional information not available during the test stage, and consider the complex structure of classes of interest and large number of clutter classes. Apply machine learning technology to detection of activities in sensor data and management of sensor networks. PHASE II: Apply developed technology to forensic datasets from radar, EOIR, WAMI sensors and to track the data/activities. Demonstrate detection and false alarm performance. Demonstrate benefit to the analyst. PHASE III: Integrate with ground stations. REFERENCES: 1. M. Bryant,P. Johnson, B. M. Kent, M. Nowak, S. Rogers, (2008)"LAYERED SENSING, Its Definition, Attributes, and Guiding Principles for AFRL Strategic Technology Development", Sensors Directorate, Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio. 2. D. Deptula, (2012),"New ISR Concepts for the 21st Century", Remarks by David A. Deptula, Lt Gen USAF (Ret), CEO, Mav6, LLC Mid East ISR Symposium, Abu Dhabi, UAE 5 Feb 2012 http://edgefighter.com/2012/02/12/new-isr-concepts- for-the-21st-century/ 3. M.S. Cromer, W.G. McDonough, J.A. Conway,(2009),"Leading the way in geospatial intellingence", Military Intelligence Professional Bulletin, http://findarticles.com/p/articles/mi_m0IBS/is_3_35/ai_n57942959/

OBJECTIVE: Develop new paradigms in high pressure combustion dynamics that can render conventional approaches obsolete. Explore innovative applications of emerging research in methods to extract key models and information from large data sets. DESCRIPTION: Advanced combustion systems are becoming increasingly dependent on factors which are controlled by the dynamics of the system. The combustion system dynamics are, in turn, controlled by interacting physiochemical processes such as chemical kinetics, turbulence, multiphase flows, and acoustic motions. In addition, combustion systems are designed to operate at the highest possible pressure in order to maximize thermodynamic efficiency. High pressures increase the energy release density and are known to exacerbate the problem. Modern computational and experimental capabilities are now making it possible to explore increasingly complex combustion dynamic behavior, but come at the expense of swamping analytical systems with vast amounts of data. The sheer amount of data can make it extremely challenging to extract the root causes of the behavior required to solve problems. The increasing geometrical complexity of advanced combustion systems contributes significantly to the problem. Rapid advances are being made in computational mathematics in recent times, however, in extracting key information from large data sets and in building efficient reduced numerical models which maintain the physical fidelity of the complete system to a high degree. Some examples of these innovative methods include the extraction of dynamically relevant modal information (Schmid, 2010), construction of"certifiable"reduced basis models for parameterized systems (see the review by Quarteroni et al, 2011) and the implementation of such techniques as the parameter space becomes very large (Bui-Thanh et al, 2008). To a large extent, integration of advances in these other fields into large scale simulations or experimental data sets has not been explored. There is therefore a broad opportunity for significant innovations leading to new paradigms that could render conventional approaches used today obsolete. Innovations are solicited here in the particular area of high pressure combustion dynamics. Significant interest exists in developing reduced order models (ROM) of complex physical phenomena through systematic model reduction. Interest also exists but is not limited to applying these techniques under conditions of geometric complexity, where component-wise analysis might be required leading to systems of ROMs or systems of ROMs that interact with high fidelity simulations or experiments. Reduced order models that are limited only to chemical kinetics schemes are excluded from this solicitation. PHASE I: Identify and demonstrate the feasibility of innovative applications of emerging research to extract key models and information from large data sets to large scale simulations and experimental data related to high pressure combustion dynamics. PHASE II: Develop the innovation or innovations identified in phase I into a workable framework and demonstrate the approach on a variety of cases. PHASE III: Combustion dynamics controls key factors affecting the performance of a large variety of military applications, including liquid rockets, solid rockets, gas turbines, and augmentors, and non-military applications, including large gas turbines for land based power. REFERENCES: 1. Bui-Thanh, T., Willcox, K., Ghattas, O.,"Model reduction for large-scale systems with high-dimensional parametric input space,"SIAM J. Sci. Compute., V. 30, No. 6, pp. 3270-3288, 2008. 2. Quarteroni, A., Rozza, G., Manzoni, A.,"Certified reduced basis approximation for parameterized partial differential equations and applications,"J. Mathematics in Industry, 1:3, doi:10.1186/2190-5983-1-3, June 2011. 3. Schmid P.J.,"Dynamic mode decomposition of numerical and experimental data,"J. Fluid Mech., V. 656, pp. 5-28, 2010.