A comparative study of ordinary and mineralised Portland cement clinker from two different production units: Part I: Composition and hydration of the clinkers

Portland cement clinkers from two production units were investigated; Plant 1: ordinary clinker (P1) and clinker mineralised with CaF₂+CaSO₄ (P1m); Plant 2: ordinary clinker (P2) and two clinkers mineralised with CaF₂+CaSO₄ (P2m, low SO₃ and P2m′, high SO₃). The chemical composition of the clinkers was determined by X-ray fluorescence, ICP analysis, titration (free lime) and ion selective electrode measurements (F). Observed clinker parameters (LSF, SR, AR, R, wt.% MgO, F, SO₃, free lime): P1 (0.96, 2.72, 1.27, 1.04, 0.78, 0.06, 0.64, 0.71); P1m (1.03, 2.21, 1.58, 2.18, 0.87, 0.23, 1.95, 0.69); P2 (1.00, 2.66, 1.72, 0.75, 4.06, 0.20, 1.38, 1.51); P2m (1.01, 2.91, 1.96, 0.90, 3.21, 0.39, 1.72, 2.06); P2m′ (0.97, 2.70, 1.84, 1.15, 3.86, 0.42, 2.48, 0.89). The qualitative and quantitative phase compositions were characterised using X-ray powder diffraction, backscattered electron imaging, X-ray microanalysis and elemental mapping, plus optical reflection microscopy. Phases observed in all clinkers were: alite, β-belite, cubic aluminate, ferrite and free lime. Additional phases observed were: aphthitalite (P1, P2, P2m, P2m′), calcium langbeinite (P1m) and periclase (P2, P2m, P2m′). The clinker composition and texture differ more between the two plants, than between ordinary and mineralised clinker from the same production unit. Laboratory cements were prepared by mixing ground clinker with CaSO₄·2H₂O. The cements were hydrated in an isothermal calorimeter at 20 °C (water/cement weight ratio=0.5) during 33 h. After 12 h, the laboratory cement based on P1m reached a higher level of reaction than the one based on P1. The P2m and P2m′ laboratory cements had a slower reaction than the P2 cement.     

Modes in lossy stratified media with application to underground propagation of radio waves

The problem considered is that of determining the complex propagation constant of a plane wave on a flat layer of lossy dielectric with a loss-tangent of less than one, and of uniform thickness, surrounded on both sides by identical layers having a loss-tangent greater than one, and thicknesses much larger than the skin depth. A formula given, relates this propagation constant to the path loss between two antennas provided their excitation efficiencies are known. As an example, curves showing attenuation and wavelength are given for the following properties of the layers: Middle layer =sigma = 10^{-6}(ohm meter)^{-1},epsilon_{r} = 10, mu_{r} = 1. Surrounding layers =simga = 10^{-2}(ohm meter)^{-1},epsilon_{r} = 10,mu_{r} = 1. Frequencies between 1 kc and 10 Mc are covered, as well as values on thickness of the meddle layers ranging from 10 m-200 m. (The computations were performed under the direction of John Nihen).     

Surface Reflectance Estimation Using Prior Spatial and Spectral Information

Surface prior-information reflectance estimation (SPIRE) algorithms estimate changes in spectral reflectance using imperfect prior spatial and spectral information. This paper combines spectral and spatial processing to estimate local changes in spectral reflectance between pairs of spectral images under spatially and spectrally varying multiplicative and additive noise, which arise from variations in illumination and atmospheric effects. This approach extends the spatial SPIRE algorithms that were described earlier and utilizes only a prior reflectance image cube and ensembles of typical multiplicative and additive illumination noise spectral vectors that are deduced from images cubes of similar scenes. The method minimizes the impact of environmental noise by replacing with their prior equivalents low-spatial-frequency content and low-order principal components that are known to be noisy based on prior noise spectra. This filtering and substitution process occurs in log space when minimizing the effects of multiplicative noise. Tests on Hyperspectral Digital Imagery Collection Experiment visible near-infrared-shortwave infrared data demonstrated the algorithm's superior ability to estimate absolute reflectance changes under varying illumination conditions. SPIRE performance was nearly identical to the empirical line method (ELM) ground-truth-based atmospheric compensation results and was better than the physics-based Atmospheric removal (ATREM) code overall, particularly, under high clouds and haze. A ldquoSelective SPIRErdquo technique that chooses between combined-spatial/spectral and spectral-only SPIRE reflectance estimates was developed; it maximizes estimation performance on both changed and unchanged pixels. Minimum-distance classification experiments demonstrated Selective SPIRE's superior performance relative to both ATREM and ELM in cross-image supervised classification applications.