Abstract Ternary Silver Indium selenide Sulfur AgInSe1.8S0.2 in pure form and with a 0.2 ratio of Sulfur were fabricated via thermal evaporation under vacuum 3*10-6 torr on glasses substrates with a thickness of (550) nm. These films were investigated to understand their structural, optical, and Hall Characteristics. X-ray diffraction analysis was employed to examine the impact of varying Sulfur ratios on the structural properties. The results revealed that the AgInSe1.8S0.2 thin films in their pure form and with a 0.2 Sulfur ratio, both at room temperature and after annealing at 500 K, exhibited a polycrystalline nature with a tetragonal structure and a predominant orientation along the (112) plane, indicating an enhanced degree of crystallinity. The Atomic Force Microscopy (AFM) was utilized to explore how Sulfur affects roughness of surfaces and sampls Grain Size . Furthermore, optical parameters, such as the optical gap and absorption coefficient, were calculated to assess the influence of Sulfur on the optical properties of the AgInSe1.8S0.2 thin films. The UV/Visible measurements indicated a reduction in the energy band gap to 1.78 eV for AgInSe1.8S0.2 at 500 K, making these films potentially suitable for photovoltaic applications. These thin films exhibited donor characteristics, with an increase in electron concentration observed with higher Sulfur content and annealing temperature
Optical burst switching (OBS) network is a new generation optical communication technology. In an OBS network, an edge node first sends a control packet, called burst header packet (BHP) which reserves the necessary resources for the upcoming data burst (DB). Once the reservation is complete, the DB starts travelling to its destination through the reserved path. A notable attack on OBS network is BHP flooding attack where an edge node sends BHPs to reserve resources, but never actually sends the associated DB. As a result the reserved resources are wasted and when this happen in sufficiently large scale, a denial of service (DoS) may take place. In this study, we propose a semi-supervised machine learning approach using k-means algorithm
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