Indonesian women have emerged as global consumers, and various studies are needed to expand the global fashion market targeting their needs. In this study, a survey was conducted to determine the fashion design preferences of Indonesian Muslim women according to demographic characteristics and religious variables. In this study, Indonesian women aged from their teens to their 40s were surveyed by questionnaire during June 2020. This yielded 301 survey responses, which were analyzed using χ2-test, t-test, ANOVA, and Duncan's multiple range test using SPSS23. The results of the study are as follows: Analysis of the demographic characteristics of Indonesian Muslim women and the degree of acceptance of fashion trends according to religious variables showed significant differences according to age, monthly income, and the extent to which they wore a hijab. Analysis of the demographic characteristics of the women and the degree of acceptance of the fitted garment according to religious variables showed significant differences depending on their age, whether they were married, their monthly income, religious faithfulness, the extent to which they wore a hijab, and the degree of acceptance of Islamic discipline. After examining the difference in Muslim women’s preferred fashion images (according to demographic characteristics and religious variables), significant differences were identified according to their age, income, extent to which they wore a hijab, and the degree of Islamic discipline with regard to clothing.

In this study, the precise orbit determination (POD) software is developed for optical observation. To improve the performance of the estimation algorithm, a nonlinear batch filter, based on the unscented transform (UT) that overcomes the disadvantages of the least-squares (LS) batch filter, is utilized. The LS and UT batch filter algorithms are verified through numerical simulation analysis using artificial optical measurements. We use the real optical observation data of a low Earth orbit (LEO) satellite, Cryosat-2, observed from optical wide-field patrol network (OWL-Net), to verify the performance of the POD software developed. The effects of light travel time, annual aberration, and diurnal aberration are considered as error models to correct OWL-Net data. As a result of POD, measurement residual and estimated state vector of the LS batch filter converge to the local minimum when the initial orbit error is large or the initial covariance matrix is smaller than the initial error level. However, UT batch filter converges to the global minimum, irrespective of the initial orbit error and the initial covariance matrix.