Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.
