Planck is a scientific satellite that represents the next milestone in space-based research related to the cosmic microwave background, and in many other astrophysical fields. Planck was launched on 14 May of 2009 and is now operational. The uncertainty in the optical response of its detectors is a key factor allowing Planck to achieve its scientific objectives. More than a decade of analysis and measurements have gone into achieving the required performances. In this paper, we describe the main aspects of the Planck optics that are relevant to science, and the estimated in-flight performance, based on the knowledge available at the time of launch. We also briefly describe the impact of the major systematic effects of optical origin, and the concept of in-flight optical calibration. Detailed discussions of related areas are provided in accompanying papers.

Golpour I., Parian J.A., Chayjan R.A. (2014): Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech J. Food Sci., 32: 280-287.We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

Thin layer drying characteristics of high moisture corn under fixed, semi fluidized and fluidized bed conditions with high initial moisture content (66.82% wb) in a laboratory fluidized bed convective dryer was studied at air temperatures of 50, 65, 80 and 95°C. In order to find a suitable drying curve, seven thin layer-drying models were fitted to the experimental data of moisture ratio. Among the applied mathematical models, Midilli et al. model was the best for drying behavior prediction in corn thin layer drying. This model presented high values for correlation coefficient (R 2 ). Fick´s second law was used to compute moisture diffusivity with some simplifications. Computed values of moisture diffusivity varied at the boundary of 4.87 × 10 -11 -2.90 × 10 -10 m 2 s -1 and 1.02 × 10 -10 -1.29 × 10 -9 m 2 s -1 during the first and second drying falling-rate, respectively. Values of effective moisture diffusivity for corn were also increased as input air temperature was increased. Value of activation energy varied from a minimum of 18.57 to a maximum of 50.74 kJ mol -1 from 50 to 95°C with drying conditions of fixed to fluidized bed. Specific energy consumption (SEC) for thin-drying of high moisture corn was found to be in the range of 0.33 × 10 6 -1.52 × 10 6 kJ kg -1 from 50 to 95°C with drying condition of fluidized and fixed bed, respectively. Increase in air temperature in each air velocity caused decrease in SEC value. These corn properties would be necessary to design the best dryer system and to determine the best point of drying process.Additional key words: drying; maize; Midilli et al. model; semi fluidized. ResumenModelización de la difusividad de la humedad, la energía de activación y el consumo específico de energía para el grano de maíz húmedo en un secador convectivo de lecho fijo y fluidizado Se estudiaron las características del secado en capa delgada del grano de maíz húmedo en condiciones de lecho fijo, semi-fluidizado y fluidizado con alto contenido de humedad inicial (66,82%), en un secador de convección de lecho fluidizado de laboratorio a las temperaturas del aire de 50, 65, 80 y 95°C. Con el fin de encontrar una curva de secado apropiada, se ajustaron siete modelos matemáticos de secado en capa delgada a los datos experimentales de la ratio de humedad. Entre los modelos aplicados, el de Midilli et al., con un alto coeficiente de correlación (R 2 ), fue el mejor para predecir el secado del maíz en capa delgada. Se utilizó la segunda ley de Fick para calcular, con algunas simplificaciones, la difusividad de la humedad, que dio unos valores entre 4,87 × 10 -11 -2.90 y 1,02 × 10 -11 -1.29 m 2 s -1 durante la primera y segunda fase de secado de rapidez decreciente, respectivamente. Los valores de la difusividad efectiva de la humedad para el maíz también aumentaron al aumentar la temperatura de entrada del aire. El valor de la energía de activación varió desde un mínimo de 18,57 a un máximo de 50,74 kJ mol -1 entre 50 y 95°C, con condiciones de secado del lecho fijo a fluidizado. El c...

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