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The Fearless Steps Apollo (FS-APOLLO) resource is a collection of over 150,000 hours of audio, associated meta-data, and supplemental technological toolkit intended to benefit the (i) speech processing technology, (ii) communication science, team-based psychology, and history, and (iii) education/STEM, preservation/archival communities. The FSAPOLLO initiative which started in 2014 has since resulted in the preservation of over 75,000 hours of NASA Apollo Missions audio. Systems created for this audio collection have led to the emergence of several new Speech and Language Technologies (SLT). This paper seeks to provide an overview of the latest advancements in the FS-Apollo effort and explore upcoming strategies in big-data deployment, outreach, and novel avenues of K-12 and STEM education facilitated through this resource. 

Fearless Steps (FS) APOLLO is a + 50,000 hr audio resource established by CRSS-UTDallas capturing all communications between NASA-MCC personnel, backroom staff, and Astronauts across manned Apollo Missions. Such a massive audio resource without metadata/unlabeled corpus provides limited benefit for communities outside Speech-and-Language Technology (SLT). Supplementing this audio with rich metadata developed using robust automated mechanisms to transcribe and highlight naturalistic communications can facilitate open research opportunities for SLT, speech sciences, education, and historical archival communities. In this study, we focus on customizing keyword spotting (KWS) and topic detection systems as an initial step towards conversational understanding. Extensive research in automatic speech recognition (ASR), speech activity, and speaker diarization using manually transcribed 125 h FS Challenge corpus has demonstrated the need for robust domain-specific model development. A major challenge in training KWS systems and topic detection models is the availability of word-level annotations. Forced alignment schemes evaluated using state-of-the-art ASR show significant degradation in segmentation performance. This study explores challenges in extracting accurate keyword segments using existing sentence-level transcriptions and proposes domain-specific KWS-based solutions to detect conversational topics in audio streams. 

Recent developments in deep learning strategies have revolutionized Speech and Language Technologies(SLT). Deep learning models often rely on massive naturalistic datasets to produce the necessary complexity required for generating superior performance. However, most massive SLT datasets are not publicly available, limiting the potential for academic research. Through this work, we showcase the CRSS-UTDallas led efforts to recover, digitize, and openly distribute over 50,000 hrs of speech data recorded during the 12 NASA Apollo manned missions, and outline our continuing efforts to digitize and create meta-data through diarization of the remaining 100,000hrs. We present novel deep learning-based speech processing solutions developed to extract high-level information from this massive dataset. Fearless-Steps APOLLO resource is a 50,000 hrs audio collection from 30-track analog tapes originally used to document Apollo missions 1,7,8,10,11,&13. A customized tape read-head developed to digitize all 30 channels simultaneously has been deployed to expedite digitization of remaining mission tapes. Diarized transcripts for these unlabeled audio communications have also been generated to facilitate open research from speech sciences, historical archives, education, and speech technology communities. Robust technologies developed to generate human-readable transcripts include: (i) speaker diarization, (ii) speaker tracking, and (iii) text output from speech recognition systems. 

INTRODUCTION: CRSS-UTDallas initiated and oversaw the efforts to recover APOLLO mission communications by re-engineering the NASA SoundScriber playback system, and digitizing 30-channel analog audio tapes – with the entire Apollo-11, Apollo-13, and Gemini-8 missions during 2011-17 [1,6]. This vast data resource was made publicly available along with supplemental speech & language technologies meta-data based on CRSS pipeline diarization transcripts and conversational speaker time-stamps for Apollo team at NASA Mission Control Center, [2,4]. In 2021, renewed efforts over the past year have resulted in the digitization of an additional +50,000hrs of audio from Apollo 7,8,9,10,12 missions, and remaining A-13 tapes. Cumulative digitization efforts have enabled the development of the largest publicly available speech data resource with unprompted, real conversations recorded in naturalistic environments. Deployment of this massive corpus has inspired multiple collaborative initiatives such as Web resources ExploreApollo (https://app.exploreapollo.org) LanguageARC (https://languagearc.com/projects/21) [3]. ExploreApollo.org serves as the visualization and play-back tool, and LanguageARC the crowd source subject content tagging resource developed by UG/Grad. Students, intended as an educational resource for k-12 students, and STEM/Apollo enthusiasts. Significant algorithmic advancements have included advanced deep learning models that are now able to improve automatic transcript generation quality, and even extract high level knowledge such as ID labels of topics being spoken across different mission stages. Efficient transcript generation and topic extraction tools for this naturalistic audio have wide applications including content archival and retrieval, speaker indexing, education, group dynamics and team cohesion analysis. Some of these applications have been deployed in our online portals to provide a more immersive experience for students and researchers. Continued worldwide outreach in the form of the Fearless Steps Challenges has proven successful with the most recent Phase-4 of the Challenge series. This challenge has motivated research in low level tasks such as speaker diarization and high level tasks like topic identification. IMPACT: Distribution and visualization of the Apollo audio corpus through the above mentioned online portals and Fearless Steps Challenges have produced significant impact as a STEM education resource for K-12 students as well as a SLT development resource with real-world applications for research organizations globally. The speech technologies developed by CRSS-UTDallas using the Fearless Steps Apollo corpus have improved previous benchmarks on multiple tasks [1, 5]. The continued initiative will extend the current digitization efforts to include over 150,000 hours of audio recorded during all Apollo missions. ILLUSTRATION: We will demonstrate WebExploreApollo and LanguageARC online portals with newly digitized audio playback in addition to improved SLT baseline systems, the results from ASR and Topic Identification systems which will include research performed on the corpus conversational. Performance analysis visualizations will also be illustrated. We will also display results from the past challenges and their state-of-the-art system improvements. 

Abstract Magnetized plasma interactions are ubiquitous in astrophysical and laboratory plasmas. Various physical effects have been shown to be important within colliding plasma flows influenced by opposing magnetic fields, however, experimental verification of the mechanisms within the interaction region has remained elusive. Here we discuss a laser-plasma experiment whereby experimental results verify that Biermann battery generated magnetic fields are advected by Nernst flows and anisotropic pressure effects dominate these flows in a reconnection region. These fields are mapped using time-resolved proton probing in multiple directions. Various experimental, modelling and analytical techniques demonstrate the importance of anisotropic pressure in semi-collisional, high- β plasmas, causing a reduction in the magnitude of the reconnecting fields when compared to resistive processes. Anisotropic pressure dynamics are crucial in collisionless plasmas, but are often neglected in collisional plasmas. We show pressure anisotropy to be essential in maintaining the interaction layer, redistributing magnetic fields even for semi-collisional, high energy density physics (HEDP) regimes. 

Abstract The Deep Underground Neutrino Experiment (DUNE), a 40-kton underground liquid argon time projection chamber experiment, will be sensitive to the electron-neutrino flavor component of the burst of neutrinos expected from the next Galactic core-collapse supernova. Such an observation will bring unique insight into the astrophysics of core collapse as well as into the properties of neutrinos. The general capabilities of DUNE for neutrino detection in the relevant few- to few-tens-of-MeV neutrino energy range will be described. As an example, DUNE’s ability to constrain the $$\nu _e$$ ν e spectral parameters of the neutrino burst will be considered. 

Abstract The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE’s sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach. 

Abstract The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is determined, based on a full simulation, reconstruction, and event selection of the far detector and a full simulation and parameterized analysis of the near detector. Detailed uncertainties due to the flux prediction, neutrino interaction model, and detector effects are included. DUNE will resolve the neutrino mass ordering to a precision of 5 $$\sigma $$ σ , for all $$\delta _{\mathrm{CP}}$$ δ CP values, after 2 years of running with the nominal detector design and beam configuration. It has the potential to observe charge-parity violation in the neutrino sector to a precision of 3 $$\sigma $$ σ (5 $$\sigma $$ σ ) after an exposure of 5 (10) years, for 50% of all $$\delta _{\mathrm{CP}}$$ δ CP values. It will also make precise measurements of other parameters governing long-baseline neutrino oscillation, and after an exposure of 15 years will achieve a similar sensitivity to $$\sin ^{2} 2\theta _{13}$$ sin 2 2 θ 13 to current reactor experiments.