This paper introduces a novel concept of KM-single valued neutrosophic Hausdorff space and KM-single valued neutrosophic manifold space. This study generalizes the concept of KM-single valued neu-trosophic manifold space to union and product of KM-single valued neu-trosophic manifold space and in this regard investigates some product of KM-single valued neutrosophic manifold spaces. Indeed, this study analyses the notation of KM-single valued neutrosophic manifold based on a valued-level subset.

In this paper we introduce entire sequence spaces defined by a sequence of modulus functions F=(¦k) We study some topological properties of these spaces and prove some inclusion relations.

In this article, we study the Hausdorff operator, defined via a general linear mapping AA, on weighted Herz spaces in the setting of the Heisenberg group. Under some assumptions on the mapping AA, we establish its sharp boundedness on power-weighted Herz spaces and power-weighted Lebesgue spaces in the Heisenberg group. Our proof is heavily based on the block decomposition of the Herz space, which is quite different from any other function spaces. Our results extend and improve some existing theorems.

In this paper, the relations between separation axioms and (quasi)topological MV-algebras are studied. It is proved that T0-spaces and (T1) Hausdorff spaces are equivalent in (quasi) topological MV-algebras. Also, some topologies on MV-algebras are generated by ideals, filters and prefilters. It is shown that the MV-algebras equipped with these topologies are (para)topological MV-algebras and (T0) normal spaces. In addition, some conditions are derived for locally compact Hausdorff MV-algebras to make them into normal paratopological MV-algebras. Finally, quotient MV-algebras are studied to get a Hausdorff topological quotient MV-algebra.

One of the best attitude sensors for space applications is the star sensor. This sensor determines the attitude using stars in the field of view. One of the main advantages of this sensor is attitude initialization using lost-in-space star identification algorithms. This paper presents a new lost-in-space star identification algorithm based on Hausdorff distance. Using Hausdorff distance, two different identification algorithms have been proposed, and their results have been compared. The first approach is designed based on pivot star, and the second approach uses the segmentation of the celestial sphere. The performance of these two approaches has been investigated using the simulation of 200 random directions of the star sensor in different magnitudes. The results show the approach of the pivot star has a better performance, and its identification rate is 93.5% at the magnitude of 6. Also, the identification time of the Hausdorff algorithm has been compared with the pyramid algorithm and some geometric algorithms. The results show that the Hausdorff identification algorithm has the shortest identification time which makes it suitable for real-time applications