Security Attributes Based Digital Rights Management

Most real-life systems delegate responsibilities to different authorities. We apply this model to a digital rights management system, to achieve flexible security. In our model a hierarchy of authorities issues certificates that are linked by cryptographic means. This linkage establishes a chain of control, identity-attribute-rights, and allows flexible rights control over content. Typical security objectives, such as identification, authentication, authorization and access control can be realised. Content keys are personalised to detect illegal super distribution. We describe a working prototype, which we develop using standard techniques, such as standard certificates, XML and Java. We present experimental results to evaluate the scalability of the system. A formal analysis demonstrates that our design is able to detect a form of illegal super distribution

A PBW basis for Lusztig's form of untwisted affine quantum groups

Let $\mathfrak{g}$ be an untwisted affine Kac-Moody algebra over the field $K \,$, and let $U_q(\mathfrak{g})$ be the associated quantum enveloping algebra; let $\mathfrak{U}_q(g)$ be the Lusztig's integer form of $U_q(\mathfrak{g}) \,$, generated by $q$-divided powers of Chevalley generators over a suitable subring $R$ of $K(q) \,$. We prove a Poincar\'e-Birkhoff-Witt like theorem for $\mathfrak{U}_q(\mathfrak{g}) \,$, yielding a basis over $R$ made of ordered products of $q$-divided powers of suitable quantum root vectors.Comment: 22 pages, AMS-TeX C, Version 2.1c. This is the author's final version, corresponding to the printed journal versio

Marshall Space Flight Center Faculty Fellowship Program

The 2017 Marshall Faculty Fellowship Program involved 21 faculty in the laboratories and departments at Marshall Space Flight Center. These faculty engineers and scientists worked with NASA collaborators on NASA projects, bringing new perspectives and solutions to bear. This Technical Memorandum is a compilation of the research reports of the 2017 Marshall Faculty Fellowship program, along with the Program Announcement (Appendix A) and the Program Description (Appendix B). The research affected the following six areas: (1) Materials (2) Propulsion (3) Instrumentation (4) Spacecraft systems (5) Vehicle systems (6) Space science The materials investigations included composite structures, printing electronic circuits, degradation of materials by energetic particles, friction stir welding, Martian and Lunar regolith for in-situ construction, and polymers for additive manufacturing. Propulsion studies were completed on electric sails and low-power arcjets for use with green propellants. Instrumentation research involved heat pipes, neutrino detectors, and remote sensing. Spacecraft systems research was conducted on wireless technologies, layered pressure vessels, and two-phase flow. Vehicle systems studies were performed on life support-biofilm buildup and landing systems. In the space science area, the excitation of electromagnetic ion-cyclotron waves observed by the Magnetospheric Multiscale Mission provided insight regarding the propagation of these waves. Our goal is to continue the Marshall Faculty Fellowship Program funded by Center internal project offices. Faculty Fellows in this 2017 program represented the following minority-serving institutions: Alabama A&M University and Oglala Lakota College

Convex Bases of PBW type for Quantum Affine Algebras

This note has two purposes. First we establish that the map defined in [L, $\S 40.2.5$ (a)] is an isomorphism for certain admissible sequences. Second we show the map gives rise to a convex basis of Poincar\'e--Birkhoff--Witt (PBW) type for \bup, an affine untwisted quantized enveloping algebra of Drinfel$'$d and Jimbo. The computations in this paper are made possible by extending the usual braid group action by certain outer automorphisms of the algebra.Comment: 7 pages, to appear in Comm. Math. Phy

Cyclic response of masonry piers retrofitted with timber frames and boards

The quasi-static in-plane cyclic response of two single-leaf calcium silicate unreinforced masonry piers was investigated to show the effectiveness of an innovative timber retrofit solution. The aim of the intervention is to increase the pier in-plane and out-of-plane strength and displacement capacity, thus reducing the seismic vulnerability of this typology of unreinforced masonry construction with a light, cost-effective, sustainable and reversible approach. The retrofit technique consists of a timber frame mechanically connected by means of steel fasteners to the masonry pier and building floors. Oriented strand timber boards are then nailed to the frame. In-plane quasi-static shear-compression cyclic tests were performed on two single-leaf calcium silicate brick piers with identical geometry and masonry mechanical properties: one was tested unstrengthened while the other was tested in the retrofitted configuration. The experimental results showed evident improvements in the lateral force-displacement response of the retrofitted specimen. More specifically, compared with the bare masonry pier, the retrofitted pier exhibited slightly higher stiffness, larger strength and significantly greater displacement capacity

A Deep Learning Approach to Radio Signal Denoising

This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy

Using autoencoders for radio signal denoising

We investigated the use of a Deep Learning approach to radio signal de-noising. This data-driven approach has does not require explicit use of expert knowledge to set up the parameters of the denoising procedure and grants great flexibility across many channel conditions. The core component used in this work is a Convolutional De-noising AutoEncoder, known to be very effective in image processing. The key of our approach consists in transforming the radio signal into a representation suitable to the CDAE: we transform the time-domain signal into a 2D signal using the Short Time Fourier Transform. We report about the performance of the approach in preamble denoising across protocols of the IEEE 802.11 family, studied using simulation data. This approach could be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A perspective advantage of using the AutoEncoders in that pipeline is that they can be co-trained with the downstream classifier, to optimize the classification accuracy

Results from DROXO. III. Observation, source list and X-ray properties of sources detected in the "Deep Rho Ophiuchi XMM-Newton Observation"

X-rays from very young stars are powerful probes to investigate the mechanisms at work in the very first stages of the star formation and the origin of X-ray emission in very young stars. We present results from a 500 ks long observation of the Rho Ophiuchi cloud with a XMM-Newton large program named DROXO, aiming at studying the X-ray emission of deeply embedded Young Stellar Objects (YSOs). The data acquired during the DROXO program were reduced with SAS software, and filtered in time and energy to improve the signal to noise of detected sources; light curves and spectra were obtained. We detected 111 sources, 61 of them associated with rho Ophiuchi YSOs as identified from infrared observations with ISOCAM. Specifically, we detected 9 out of 11 Class I, 31 out of 48 Class II and 15 out 16 Class III objects. Six objects out of 21 classified Class III candidates are also detected. At the same time we suggest that 15 Class III candidates that remain undetected at log Lx < 28.3 are not related to the cloud population. The global detection rate is ~64%. We have achieved a flux sensitivity of ~5 x 10^{-15} erg s^{-1} cm^{-2}. The Lx to L_bol ratio shows saturation at a value of ~ -3.5 for stars with T_eff <= 5000 K or 0.7 M_sun as observed in the Orion Nebula. The plasma temperatures and the spectrum absorption show a decline with YSO class, with Class I YSOs being hotter and more absorbed than Class II and III YSOs. In one star (GY 266) with infrared counterpart in 2MASS and Spitzer catalogs we have detected a soft excess in the X-ray spectrum which is best fitted by a cold thermal component less absorbed than the main thermal component of the plasma. Such a soft component hints to the presence of plasma heated by shocks due to jets outside the dense circumstellar material.Comment: Accepted for publication on Astronomy & Astrophysics journa