The software code SKYEAD.pack for retrieval of aerosol size distribution and optical thickness from data of direct and diffuse solar radiation is described; measurements are carried out with sky radiometers in the wavelength range 0.369-1.048 µm. The treatment of the radiative transfer problem concerning the optical quantities is mainly based on the IMS (improved multiple and single scattering) method, which uses the delta-M approximation for the truncation of the aerosol phase function and corrects the solution for the first- and second-order scattering. Both linear and nonlinear inversion methods can be used for retrieving the size distribution. Improved calibration methods for both direct and diffuse radiation, the data-analysis procedure, the results from the proposed code, and several connected problems are discussed. The results can be summarized as follows: (a) the SKYRAD.pack code can retrieve the columnar aerosol features with accuracy and efficiency in several environmental situations, provided the input parameters are correctly given; (b) when data of both direct and diffuse solar radiation are used, the detectable radius interval for aerosol particles is approximately from 0.03 to 10 µm; (c) besides the retrieval of the aerosol features, the data-analysis procedure also permits the determination of average values for three input parameters (real and imaginary aerosol refractive index, ground albedo) from the optical data; (d) absolute calibrations for the sky radiometer are not needed, and calibrations for direct and diffuse radiation can be carried out with field data; (e) the nonlinear inversion gives satisfactory results in a larger radius interval, without the unrealistic humps that occur with the linear inversion, but the results strongly depend on the first-guess spectrum; (f) aerosol features retrieved from simulated data showed a better agreement with the given data for the linear inversion than for the nonlinear inversion.

The problems encountered in the elaboration of measurements of direct and sky diffuse solar irradiance are the following: (1) to carry out the calibration for the direct irradiance, which consists in determining the direct irradiance at the upper limit of the atmosphere; (2) to carry out the calibration for the diffuse irradiance, which consists in determining the solid viewing angle of the sky radiometer; (3) to determine the input parameters, namely, ground albedo, real and imaginary parts of the aerosol refractive index, and aerosol radius range; and (4) to determine from the optical data the columnar aerosol optical depth and volume radius distribution. With experimental data and numerical simulations a procedure is shown that enables one to carry out the two calibrations needed for the sky radiometer, to determine a best estimate of the input parameters, and, finally, to obtain the average features of the atmospheric aerosols. An interesting finding is that inversion of only data of diffuse irradiance yields the same accuracy of result as data of both diffuse and direct irradiance; in this case, only calibration of the solid viewing angle of the sky radiometer is needed, thus shortening the elaboration procedure. Measurements were carried out in the Western Mediterranean Sea (Italy), in Tokyo (Japan), and in Ushuaia (Tierra del Fuego, Argentina); data were elaborated with a new software package, the Skyrad code, based on an efficient radiative transfer scheme.

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Eye tracking is one of the most exploited techniques in literature for finding usability problems in web-based user interfaces (UIs). However, it is usually employed in a laboratory setting, considering that an eye-tracker is not commonly used in web browsing. In contrast, web application providers usually exploit remote techniques for large-scale user studies (e.g. A/B testing), tracking low-level interactions such as mouse clicks and movements. In this article, we discuss a method for predicting whether the user is looking at the content pointed by the cursor, exploiting the mouse movement data and a segmentation of the contents in a web page. We propose an automatic method for segmenting content groups inside a web page that, applying both image and code analysis techniques, identifies the user-perceived group of contents with a mean pixel-based error around the 20%. In addition, we show through a user study that such segmentation information enhances the precision and the accuracy in predicting the correlation between between the user’s gaze and the mouse position at the content level, without relaying on user-specific features.