Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation.

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers' workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology's applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community.

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning.

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR .

[Influence of Artificial Root Exudates and Actual Root Exudates on the Microbial Community in Pyrene-contaminated Soil].

The high-throughput Illumina NovaSeq sequencing method was adopted to study the effect of artificial root exudates and Lolium perenne L. root exudates on the community structure, α and ß diversity, and gene function of the bacterial communities in pyrene-contaminated soils to understand the impact of root exudates on microbial communities. The results showed that root exudates did not significantly change the composition of pyrene-contaminated soil bacterial communities. The main dominant bacterial phyla were Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, etc. The main dominant bacterial genera were Sphingomonas, Lactobacillus, Bacillus, etc. Root exudates changed the relative abundance of dominant species to a different extent and resulted in discriminating bacteria. The genus Lachnospiraceae belonging to Proteobacteria and Ruminiclostridium belonging to Firmicutes were the biomarkers in the artificial root exudates group and the actual root exudate group, respectively. The common discriminating bacteria in both root exudate groups compared to those in the control group were polycyclic aromatic hydrocarbon (PAHs)-degrading bacteria. Root exudates selectively promoted the growth of PAHs-degrading bacteria. Root exudates had little effect on the richness and diversity of the bacterial communities in pyrene-contaminated soil. However, they significantly influenced the soil bacterial community structure, which resulted from significant changes in low-abundance species. The bacterial community structures of the two root exudate groups were similar. Root exudates decreased pyrene concentration in the soil by 14.0% (artificial root exudates) and 8.7% (actual root exudates). The promotion of pyrene degradation affected by root exudates was due to the growth promotion of PAHs-degrading bacteria and the significant increase in the abundance of some functional genes. This research can supply data for the exploration of a rhizoremediation mechanism in PAHs-contaminated soils.