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DOE OSTI2022

Optimal vocabulary selection approaches for privacy-preserving deep NLP model training for information extraction and cancer epidemiology

With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the data and subject identities have also become an important issue and essential research topic. Without intentional safeguards, machine learning models may find patterns and features to improve task performance that are associated with private personal information. The privacy vulnerability of deep learning models for information extraction from medical textural contents needs to be quantified since the models are exposed to private health information and personally identifiable information. The objective of the study is to quantify the privacy vulnerability of the deep learning models for natural language processing and explore a proper way of securing patients’ information to mitigate confidentiality breaches. The target model is the multitask convolutional neural network for information extraction from cancer pathology reports, where the data for training the model are from multiple state population-based cancer registries. This study proposes the following schemes to collect vocabularies from the cancer pathology reports; (a) words appearing in multiple registries, and (b) words that have higher mutual information. We performed membership inference attacks on the models in high-performance computing environments. The comparison outcomes suggest that the proposed vocabulary selection methods resulted in lower privacy vulnerability while maintaining the same level of clinical task performance.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Genomic diversifications of five Gossypium allopolyploid species and their impact on cotton improvement

Abstract Polyploidy is an evolutionary innovation for many animals and all flowering plants, but its impact on selection and domestication remains elusive. Here we analyze genome evolution and diversification for all five allopolyploid cotton species, including economically important Upland and Pima cottons. Although these polyploid genomes are conserved in gene content and synteny, they have diversified by subgenomic transposon exchanges that equilibrate genome size, evolutionary rate heterogeneities and positive selection between homoeologs within and among lineages. These differential evolutionary trajectories are accompanied by gene-family diversification and homoeolog expression divergence among polyploid lineages. Selection and domestication drive parallel gene expression similarities in fibers of two cultivated cottons, involving coexpression networks and N 6 -methyladenosine RNA modifications. Furthermore, polyploidy induces recombination suppression, which correlates with altered epigenetic landscapes and can be overcome by wild introgression. These genomic insights will empower efforts to manipulate genetic recombination and modify epigenetic landscapes and target genes for crop improvement.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Universal Tripartite Entanglement in One-Dimensional Many-Body Systems

Motivated by conjectures in holography relating the entanglement of purification and reflected entropy to the entanglement wedge cross section, we introduce two related non-negative measures of tripartite entanglement g and h. We prove structure theorems which show that states with nonzero g or h have nontrivial tripartite entanglement. We then establish that in one dimension these tripartite entanglement measures are universal quantities that depend only on the emergent low-energy theory. For a gapped system, we argue that either g≠0 and h=0 or g=h=0, depending on whether the ground state has long-range order. For a critical system, we develop a numerical algorithm for computing g and h from a lattice model. We compute g and h for various CFTs and show that h depends only on the central charge whereas g depends on the whole operator content.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1994

Results of ozone measurements in Northern Germany: A case study

At most of the German ozone recording stations which have records over a sufficiently long period, the results of the summer months of 1989 showed the highest values since the beginning of the measurements. One of the reasons for this phenomenon was the high duration of sunshine in that summer; for example, in Potsdam near Berlin in May 1989 the sunshine duration was the highest in May since the beginning of the records in 1893. For that reason we selected this summer for a case study. The basis for the study was mainly the ozone measuring stations of the network of Lower Saxony and the Federal Office of Environment (Umweltbundesamt). The results of these summer measurements point to intense sources of ozone, probably in form of gaseous precursors, in the Middle German industrial areas near Leipzig and Halle and in Northwestern Czechoslovakia, with coal-mining, chemical and petrochemical industries, coking plants and others. The maps of average ozone concentrations, number or days with high ozone maxima, ozone-windroses of the stations, etc., suggest that these areas could be a main source of precursors and of photochemical ozone production in summer smog episodes in Central Europe. Stations on the North Sea coast, at which early ozone measurements were made by our institute in 1973/74 are compared with similarly located stations of the Lower Saxon network in 1989 and the results show a reversal of the ozone-windroses. In 1973/74, the highest ozone concentrations were correlated with wind directions from the sea while in 1989 these concentrations were correlated with directions from the continent. In the recent years, photochemical ozone production on the continent is probably predominant, while in former years the higher ozone content of the maritime subpolar air masses has been explained by stratospheric-tropospheric exchange.

Schmidt, Manfred↗

DOE OSTI2021

Factors controlling pore network development of thermally mature Early Palaeozoic mudstones from the Baltic Basin (N Poland)

Understanding the formation of pore space, especially in low porosity shales (as source rocks and as unconventional resources), is critical to the oil and gas industry, since pores control the space available for hydrocarbon and participate in hydrocarbon transport. We examined 87 Ordovician and Silurian mudstone samples collected from four wells located in the Pomeranian part of the Baltic Basin (northern Poland), one of the primary Polish targets for hydrocarbon exploration. These samples represent the Pelplin, the Paslek, the Jantar, the Prabuty, and the Sasino Formations, which still requires more detailed porosity studies. Our study aimed to identify factors controlling porosity development, by applying bulk techniques (organic petrology and TOC analyses, quantitative mineralogy, and porosimetry) as well as nano- to microscale techniques (thin section petrography, electron microscopy). The studied samples are mainly argillaceous mudstones. The results of porosimetry measurements, combined with image analysis, indicate that the pores of all studied rocks are dominated by micropores (pores < 2 nm in diameter), mesopores (2-50 nm in diameter) and small macropores. The SEM images showed three main pore types: a) voids related to clay mineral aggregates, b) pores inside organic matter particles, and c) pores between other mineral grains. In the Jantar and Sasino mudstones, the organic matter content and its thermal maturity control porosity. The occurrence of solid bitumen in the rocks from these formations reduces samples’ mesoporosity because of the pore-clogging effect. In contrast, in the Paslek and Prabuty Formations, there is low organic matter content and specific surface area and the volume of mesopores increase with clay minerals content. In the Pelplin mudstones, there are no prevailing factors controlling porosity. As a result, we suggest that a combination of SEM image analysis and dual liquid porosity (DLP) measurements is a powerful method to assess porosity available for petroleum flow in mudstones.

03 NATURAL GAS↗

DOE OSTI2020

Defining the Environmental Adaptations of Genus Devosia: Insights into its Expansive Short Peptide Transport System and Positively Selected Genes

Devosia are well known for their dominance in soil habitats contaminated with various toxins and are best characterized for their bioremediation potential. In this study, we compared the genomes of 27 strains of Devosia with aim to understand their metabolic abilities. The analysis revealed their adaptive gene repertoire which was bared from 52% unique pan-gene content. A striking feature of all genomes was the abundance of oligo- and di-peptide permeases (oppABCDF and dppABCDF) with each genome harboring an average of 60.7±19.1 and 36.5±10.6 operon associated genes respectively. Apart from their primary role in nutrition, these permeases may help Devosia to sense environmental signals and in chemotaxis at stressed habitats. Through sequence similarity network analyses, we identified 29 Opp and 19 Dpp sequences that shared very little homology with any other sequence suggesting an expansive short peptidic transport system within Devosia. The substrate determining components of these permeases viz. OppA and DppA further displayed a large diversity that separated into 12 and 9 homologous clusters respectively in addition to large number of isolated nodes. We also dissected the genome scale positive evolution and found genes associated with growth (exopolyphosphatase, HesB_IscA_SufA family protein), detoxification (moeB, nifU-like domain protein, alpha/beta hydrolase), chemotaxis (cheB, luxR) and stress response (phoQ, uspA, luxR, sufE) were positively selected. The study highlights the genomic plasticity of the Devosia spp. for conferring adaptation, bioremediation and the potential to utilize a wide range of substrates. The widespread toxin-antitoxin loci and ‘open’ state of the pangenome provided evidence of plastic genomes and a much larger genetic repertoire of the genus which is yet uncovered.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer

Abstract Leaf-level hyperspectral reflectance has become an effective tool for high-throughput phenotyping of plant leaf traits due to its rapid, low-cost, multi-sensing, and non-destructive nature. However, collecting samples for model calibration can still be expensive, and models show poor transferability among different datasets. This study had three specific objectives: first, to assemble a large library of leaf hyperspectral data (n=2460) from maize and sorghum; second, to evaluate two machine-learning approaches to estimate nine leaf properties (chlorophyll, thickness, water content, nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur); and third, to investigate the usefulness of this spectral library for predicting external datasets (n=445) including soybean and camelina using extra-weighted spiking. Internal cross-validation showed satisfactory performance of the spectral library to estimate all nine traits (mean R2=0.688), with partial least-squares regression outperforming deep neural network models. Models calibrated solely using the spectral library showed degraded performance on external datasets (mean R2=0.159 for camelina, 0.337 for soybean). Models improved significantly when a small portion of external samples (n=20) was added to the library via extra-weighted spiking (mean R2=0.574 for camelina, 0.536 for soybean). The leaf-level spectral library greatly benefits plant physiological and biochemical phenotyping, whilst extra-weight spiking improves model transferability and extends its utility.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Comparative genomic analysis of thermophilic fungi reveals convergent evolutionary adaptations and gene losses

Thermophily is a trait scattered across the fungal tree of life, with its highest prevalence within three fungal families (Chaetomiaceae, Thermoascaceae, and Trichocomaceae), as well as some members of the phylum Mucoromycota. We examined 37 thermophilic and thermotolerant species and 42 mesophilic species for this study and identified thermophily as the ancestral state of all three prominent families of thermophilic fungi. Thermophilic fungal genomes were found to encode various thermostable enzymes, including carbohydrate-active enzymes such as endoxylanases, which are useful for many industrial applications. At the same time, the overall gene counts, especially in gene families responsible for microbial defense such as secondary metabolism, are reduced in thermophiles compared to mesophiles. We also found a reduction in the core genome size of thermophiles in both the Chaetomiaceae family and the Eurotiomycetes class. The Gene Ontology terms lost in thermophilic fungi include primary metabolism, transporters, UV response, and O-methyltransferases. Comparative genomics analysis also revealed higher GC content in the third base of codons (GC3) and a lower effective number of codons in fungal thermophiles than in both thermotolerant and mesophilic fungi. Furthermore, using the Support Vector Machine classifier, we identified several Pfam domains capable of discriminating between genomes of thermophiles and mesophiles with 94% accuracy. Using AlphaFold2 to predict protein structures of endoxylanases (GH10), we built a similarity network based on the structures. We found that the number of disulfide bonds appears important for protein structure, and the network clusters based on protein structures correlate with the optimal activity temperature. Thus, comparative genomics offers new insights into the biology, adaptation, and evolutionary history of thermophilic fungi while providing a parts list for bioengineering applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

The structure of CaO–MgO–Al 2 O 3 –SiO 2 melts and glasses doped with FeO X –NiO

Neutron and x-ray diffraction measurements have been performed on CaO–MgO–Al 2 O 3 –SiO 2 (CMAS) glasses doped with NiO–Fe X O at room temperature, along with x-ray measurements on aerodynamically levitated liquids at ≥2000 K. The disordered structures have been modeled using empirical potential structure refinement to investigate the relation between the aluminosilicate network and the modifying cations. The SiO 4 and AlO 4 tetrahedra are found to have wider Si–O and Al–O bond distance distributions in the glass, and the first Ca–O n coordination shell is highly distorted, redistributing different populations of long and short bonds between the liquid and the glass. The addition of Fe and Ni at low aluminosilicate content increases the number of free oxygens not bonded to AlO 4 or SiO 4 . Mg–O and Fe–O are both found to be predominantly fourfold and fivefold in the liquid and glassy states. Despite these low coordination numbers, their bond angle distributions indicate that they are predominantly in nontetrahedral-type geometries, with ferrous and ferric iron possessing similar coordination environments. The Ca–O and Mg–O average coordination numbers and enthalpies of solution are consistent with their higher reactivity within relatively acidic aluminosilicate melts.

36 MATERIALS SCIENCE↗

NASA NTRS1990

Evidence for asymmetric inertial instability in the FIRE satellite dataset

One of the main goals of the First ISCCP Regional Experiment (FIRE) is obtaining the basic knowledge to better interpret satellite image of clouds on regional and smaller scales. An analysis of a mesoscale circulation phenomenon as observed in hourly FIRE satellite images is presented. Specifically, the phenomenon of interest appeared on satellite images as a group of propagating cloud wavelets located on the edge of a cirrus canopy on the anticylonic side of a strong, upper-level subtropical jet. These wavelets, which were observed between 1300 and 2200 GMT on 25 February 1987, are seen most distinctly in the GOES-West infrared satellite picture at 1800 GMT. The purpose is to document that these wavelets were a manifestation of asymmetric inertial instability. During their lifetime, the wavelets were located over the North American synoptic sounding network, so that the meteorological conditions surrounding their occurrence could be examined. A particular emphasis of the analysis is on the jet streak in which the wavelets were imbedded. The characteristics of the wavelets are examined using hourly satellite imagery. The hypothesis that inertial instability is the dynamical mechanism responsible for generating the observed cloud wavelets was examined. To further substantiate this contention, the observed characteristics of the wavelets are compared to, and found to be consistent with, a theoretical model of inertia instability by Stevens and Ciesielski.

Stevens, Duane E.↗

NASA NTRS2005

Proceedings of the Fifth Integrated Communications, Navigation, and Surveillance (ICNS) Conference and Workshop

Contents includes papers on the following: JPDO: Inter-Agency Cooperation for the Next Generation ATS; R&T Programs; Integrated CNS Systems and Architectures; Datalink Communication Systems; Navigation, System Demonstrations and Operations; Safety and Security Initiatives Impacting CNS; Global Communications Initiatives; Airborne Internet; Avionics for System-Level Enhancements; SWIM (System Wide Information Management); Weather Products and Data Dissemination Technologies; Airsapce Communication Networks; Surveillance Systems; Workshop Breakouts Sessions and ; ICNS Conference Information.

Fujikawa, Gene↗

DOE OSTI2024

Organ-delimited gene regulatory networks provide high accuracy in candidate transcription factor selection across diverse processes

Organ-specific gene expression datasets that include hundreds to thousands of experiments allow the reconstruction of organ-level gene regulatory networks (GRNs). However, creating such datasets is greatly hampered by the requirements of extensive and tedious manual curation. Here, we trained a supervised classification model that can accurately classify the organ-of-origin for a plant transcriptome. This K-Nearest Neighbor-based multiclass classifier was used to create organ-specific gene expression datasets for the leaf, root, shoot, flower, and seed in Arabidopsis thaliana . A GRN inference approach was used to determine the: i. influential transcription factors (TFs) in each organ and, ii. most influential TFs for specific biological processes in that organ. These genome-wide, organ-delimited GRNs (OD-GRNs), recalled many known regulators of organ development and processes operating in those organs. Importantly, many previously unknown TF regulators were uncovered as potential regulators of these processes. As a proof-of-concept, we focused on experimentally validating the predicted TF regulators of lipid biosynthesis in seeds, an important food and biofuel trait. Of the top 20 predicted TFs, eight are known regulators of seed oil content, e.g., WRI1, LEC1, FUS3. Importantly, we validated our prediction of MybS2, TGA4, SPL12, AGL18, and DiV2 as regulators of seed lipid biosynthesis. We elucidated the molecular mechanism of MybS2 and show that it induces purple acid phosphatase family genes and lipid synthesis genes to enhance seed lipid content. This general approach has the potential to be extended to any species with sufficiently large gene expression datasets to find unique regulators of any trait-of-interest.

09 BIOMASS FUELS↗

DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗

DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗

NASA NTRS1992

Zebra: A striped network file system

The design of Zebra, a striped network file system, is presented. Zebra applies ideas from log-structured file system (LFS) and RAID research to network file systems, resulting in a network file system that has scalable performance, uses its servers efficiently even when its applications are using small files, and provides high availability. Zebra stripes file data across multiple servers, so that the file transfer rate is not limited by the performance of a single server. High availability is achieved by maintaining parity information for the file system. If a server fails its contents can be reconstructed using the contents of the remaining servers and the parity information. Zebra differs from existing striped file systems in the way it stripes file data: Zebra does not stripe on a per-file basis; instead it stripes the stream of bytes written by each client. Clients write to the servers in units called stripe fragments, which are analogous to segments in an LFS. Stripe fragments contain file blocks that were written recently, without regard to which file they belong. This method of striping has numerous advantages over per-file striping, including increased server efficiency, efficient parity computation, and elimination of parity update.

Hartman, John H.↗

NASA NTRS2006

Sensor to User - NASA/EOS Data for Coastal Zone Management Applications Developed from Integrated Analyses: Verification, Validation and Benchmark Report

The NASA Applied Sciences Program seeks to transfer NASA data, models, and knowledge into the hands of end-users by forming links with partner agencies and associated decision support tools (DSTs). Through the NASA REASoN (Research, Education and Applications Solutions Network) Cooperative Agreement, the Oceanography Division of the Naval Research Laboratory (NRLSSC) is developing new products through the integration of data from NASA Earth-Sun System assets with coastal ocean forecast models and other available data to enhance coastal management in the Gulf of Mexico. The recipient federal agency for this research effort is the National Oceanic and Atmospheric Administration (NOAA). The contents of this report detail the effort to further the goals of the NASA Applied Sciences Program by demonstrating the use of NASA satellite products combined with data-assimilating ocean models to provide near real-time information to maritime users and coastal managers of the Gulf of Mexico. This effort provides new and improved capabilities for monitoring, assessing, and predicting the coastal environment. Coastal managers can exploit these capabilities through enhanced DSTs at federal, state and local agencies. The project addresses three major issues facing coastal managers: 1) Harmful Algal Blooms (HABs); 2) hypoxia; and 3) freshwater fluxes to the coastal ocean. A suite of ocean products capable of describing Ocean Weather is assembled on a daily basis as the foundation for this semi-operational multiyear effort. This continuous realtime capability brings decision makers a new ability to monitor both normal and anomalous coastal ocean conditions with a steady flow of satellite and ocean model conditions. Furthermore, as the baseline data sets are used more extensively and the customer list increased, customer feedback is obtained and additional customized products are developed and provided to decision makers. Continual customer feedback and response with new improved products are required between the researcher and customer. This document details the methods by which these coastal ocean products are produced including the data flow, distribution, and verification. Product applications and the degree to which these products are used successfully within NOAA and coordinated with the Mississippi Department of Marine Resources (MDMR) is benchmarked.

Hall, Callie↗

DOE OSTI2020

Transparent bridge for monitoring crypto-partitioned wide-area network

This disclosure is directed to monitoring a crypto-partitioned, or cipher-text, wide-area network (WAN). A first computing device may be situated in a plain-text portion of a first enclave behind a first inline network encryptor (INE). A second device may be positioned in a plain-text portion of a second enclave behind a second INE. The two enclaves may be separated by a cipher-text WAN, over which the two enclaved may communicate. The first computing device may receive a data packet from the second computing device. The first computing device may then determine contents of a header of the data packet. The first computing device may, based at least in part on the contents of the header of the data packet, determine a status of the cipher-text WAN.

97 MATHEMATICS AND COMPUTING↗