Non-terrestrial network (NTN) is the next generation access technology between terminals and satellites, based on new air interface technology developed by the 3rd Generation Partnership Project(3GPP) in the Release 17. While 3GPP integrates 5G NR's Physical Random Access Channel (PRACH) into NTN scenarios, it faces an issue of insufficient preambles. Currently, numerous studies have combined M sequences or Golden sequences with Zadoff-Chu (ZC) sequences to design PRACH formats to address this challenge. This paper introduces a novel PRACH format based on Golden sequences and ZC sequences. Golden sequences and ZC sequences are individually detected, resulting in the introduction of two detection methods: the Golden priority detection algorithm and the ZC priority detection algorithm. This innovative design resolves the problem of massive detection calculations found in current common detection algorithms and also increases the quantity of preambles. Finally, the simulation results show that the new scheme can obtain the accurate results with less calculation.

MOTIVATION: Identifying cis-acting genetic variants associated with gene expression levels-an analysis commonly referred to as expression quantitative trait loci (eQTLs) mapping-is an important first step toward understanding the genetic determinant of gene expression variation. Successful eQTL mapping requires effective control of confounding factors. A common method for confounding effects control in eQTL mapping studies is the probabilistic estimation of expression residual (PEER) analysis. PEER analysis extracts PEER factors to serve as surrogates for confounding factors, which is further included in the subsequent eQTL mapping analysis. However, it is computationally challenging to determine the optimal number of PEER factors used for eQTL mapping. In particular, the standard approach to determine the optimal number of PEER factors examines one number at a time and chooses a number that optimizes eQTLs discovery. Unfortunately, this standard approach involves multiple repetitive eQTL mapping procedures that are computationally expensive, restricting its use in large-scale eQTL mapping studies that being collected today. RESULTS: Here, we present a simple and computationally scalable alternative, Effect size Correlation for COnfounding determination (ECCO), to determine the optimal number of PEER factors used for eQTL mapping studies. Instead of performing repetitive eQTL mapping, ECCO jointly applies differential expression analysis and Mendelian randomization analysis, leading to substantial computational savings. In simulations and real data applications, we show that ECCO identifies a similar number of PEER factors required for eQTL mapping analysis as the standard approach but is two orders of magnitude faster. The computational scalability of ECCO allows for optimized eQTL discovery across 48 GTEx tissues for the first time, yielding an overall 5.89% power gain on the number of eQTL harboring genes (eGenes) discovered as compared to the previous GTEx recommendation that does not attempt to determine tissue-specific optimal number of PEER factors. AVAILABILITYAND IMPLEMENTATION: Our method is implemented in the ECCO software, which, along with its GTEx mapping results, is freely available at www.xzlab.org/software.html. All R scripts used in this study are also available at this site. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.