Analysis Scheme Research Articles

Efficient algorithm for thermal nondestructive testing and evaluation by considering the heteroscedastic nature of noise sources in infrared thermography

Abstract Thermal Imaging is a promising Non Destructive Testing & Evaluation (NDT & E) approach to monitor the health
of composite materials. Among various post processing approaches adopted in thermal imaging for NDT & E, statistical
analysis schemes gained importance due to their reliability and data reduction capabilities. This paper provides an insight
to a factor analysis-based statistical approach to detect the hidden defects in the Glass Fiber Reinforced Polymer (GFRP)
sample. The proposed approach models the observed data covariance into combination of temporal signal covariance
and noise covariance matrices. The modeling of the diagonal covariance matrix (with different elements) is motivated by
the presence of heterogeneity in the experimental data obtained from GFRP sample. This novel method is based on the
coordinate descent technique, which estimates the covariance matrix of the noise variances iteratively by minimizing the
negative log likelihood function. The obtained results from the chosen GFRP samples compared with the widely used
statistical Principal Component Thermography (PCT) technique illustrate the improved performance in terms of defect
detection with the proposed technique.

Towards non-virtual inertia control of renewable energy for frequency regulation: Modeling, analysis and new control scheme

Currently, when renewable generation participates in frequency regulation, the traditional control method is to emulate synchronous generators through virtual inertia control. However, virtual inertia has a time delay, so essentially, it is a fast power response. Meanwhile, virtual inertia control is likely to be affected by frequency fluctuation since it responds to the derivative of frequency. Hence, it’s worth exploring non-virtual inertia control for renewable energy when participating in frequency regulation. For this reason, a novel two-segment droop control scheme for renewable energy frequency regulation is proposed in this research. Firstly, the extended system frequency regulation (SFR) model, which contains virtual inertia with time delay, is built and analytically solved by order decrement based on the Routh approximation method. Afterwards, according to the analytical solution, time delay affects the frequency response of renewable energy. It can also be analytically proved that the non-virtual inertia control, e.g., sole droop control, could replace virtual inertia under the same frequency deviation. Still, more energy may be needed for frequency regulation. Furthermore, a novel two-segment droop control is presented, and to analytically prove its ability to replace virtual inertia, the impulse function balancing principle and the integration by parts algorithm were adopted to address the initial conditions of the differential equation. Based on the analytical expression, it can be analytically proved that a lower frequency deviation can be obtained under the same frequency regulation energy. Accordingly, a parameter-setting method for two-segment droop control was proposed. Finally, the effectiveness of the proposed method is verified by using a two-area system frequency response model, and the results reveal that it can be used to replace virtual inertia and has better performance.

Functional and pragmatic parameters of speech acts with the explicit mode of ignorance in scientific dialogue

The article is about identifying functional aspects of 1st-person statements with the explicit mode of ignorance (messages like I do not know… / We don’t know… I don’t know / We don’t know ...) in scientific discussions as a genre of scientific discourse. The relevance of the analysis is predetermined by the necessity to study various parameters of discourse (including both epistemic and pragmatic) as closely connected. The main research method is contextual and pragmalinguistic analysis. The sources of the material are transcripts of modern English and Russian scientific discussions in various fields of knowledge. The article identifies the illocutionary properties of ignorant statements, establishes their pragmatic functions, as well as determines their position in the traditional classification of speech acts. Taking into account the illocutionary orientation of references to ignorance makes it possible to identify representative, interrogative, axiological varieties of ignorance messages as well as their subtypes differentiated on the basis of the degree of their expressiveness, their vector of evaluation, the nature of the representative and interrogative role of speech acts with the mode of ignorance: fully expressive and conditionally expressive, positive and negative, preventive and hesitative, etc. The criterion “pragmatic functions” helps us specify additional functions of references to ignorance in scientific dialogue: to indicate a certain degree of the speaker’s epistemic responsibility for their words, to serve as a means of avoiding the answer, a way to prevent criticism, etc. Moreover, the work identifies factors determining the illocutionary nature of ignorance speech acts: the surface structure of the message, the general context of utterance, the position of the ignorance message in the dialogical unity, sincerity and felicity conditions of speech acts formulated as ignorance messages. References to ignorance are concluded to be quite a significant phenomenon in the structure of scientific dialogue, since they perform a number of important functions in it. The analysis scheme can be used to study statements with other explicit modes (knowledge, opinions, etc.) in various types of discourse.

Epidemic of multiple Treponema pallidum strains in men who have sex with men in Japan: efficient multi-locus sequence typing scheme and indicator biomarkers

BackgroundThe challenges in culturing Treponema pallidum have hindered molecular-biological analysis. This study aims to establish a molecular epidemiological analysis of syphilis among Japanese men who have sex with men (MSM) and to investigate the relationship between bacteremia and associated pathophysiology.MethodsWe used whole blood specimens from syphilis-diagnosed individuals in Tokyo, collected between February 2019 and June 2022. All individuals were MSM, and most were people with HIV (97.2%). We used a multi-locus sequence typing (MLST) scheme for epidemiological analysis. Sequences for MLST (TP0136, TP0548, and TP0705) were obtained.ResultsOut of 71 whole blood samples, 26 samples (36.6%) were positive for TP0136, and we sequenced three loci for MLST in 22 samples (31.0%). The most frequently detected sequence type (ST) was ST3 (n = 9), followed by ST6 (n = 6). Phylogenetic analysis revealed that 12 samples belonged to the SS14-like group (60%), and 8 samples belonged to the Nichols-like group (40%). Treponema pallidum subsp. endemicum (TEN), the cause of bejel was detected in three samples (12%). There was a significant association between TP0136 detection rate and C- reactive protein (CRP) (77.0% at a cut-off:0.5 mg/dL).ConclusionBoth SS14-like and Nichols-like strains were circulating concurrently, and TEN could have been sexually transmitted among MSM with HIV. Elevated CRP may indicate the presence of the pathogen in the blood.

Novel Statistical Analysis Schemes for Frequency-Modulated Thermal Wave Imaging for Inspection of Ship Hull Materials

Novel Statistical Analysis Schemes for Frequency-Modulated Thermal Wave Imaging for Inspection of Ship Hull Materials

B-040 Direct Aspiration of Cryogenic Matrix Tubes on the Siemens Atellica System With Epic-Beaker Integration of External Barcode IDs for Analysis Small Volume Biobanked Specimens

Abstract Background Most core chemistry and immunochemistry clinical analyzers are designed for direct sampling of primary blood tubes with volumes ranging from 3.5-10 mL. This limits analysis of low volume pediatric specimens and biobanked aliquoted samples from clinical trials without manual technologist intervention. This work outlines a microsampling and data transmission scheme for analysis of 500 uL aliquots of biobanked serum collected for the Nutrition for Precision Health study powered by the NIH All of Us Research Program without relabeling secondary aliquot tubes after receipt. Methods Commercial plastic clip bearings sourced from IGUS (LCM-08-02) were used to stabilize the matrix tube in a standard 5 mL Sarstedt aliquot tube. 96-well format Matrix Tubes (ThermoFisher 3719-11) with pre-printed 10-digit side barcodes were programmed in the Atellica software as a custom false bottom tube with specifications height 75 mm, diameter 12 mm, bottom offset 41 mm and were loaded using a dedicated false bottom tray. Manifest files (.csv) containing the sample ID, clinical trials participant ID, collection date/time, age and sex were ingested into Epic via an Incoming Orders HL7 interface. Non-human patient records were generated using the participant ID as an external patient ID and patient name and calculating the Date of Birth based on the age in years given in the manifest file. Test orders were created based on assigned testing for the sample ID in the manifest. The sample ID corresponding the preprinted Matrix Tube barcode ID was inserted into the Epic container identifier field. Results Test orders were automatically transmitted to the Atellica Data Manger (ADM) allowing specimens to be run on the Atellica System (CH and IM modules) and results transmitted to Epic utilizing the established Data Innovations (DI) interface and processes used for patient samples without the need for Epic generated labels. Successful scanning and recognition of Matrix Tube barcodes was 100%. Complete aspiration of a Comprehensive Metabolic Panel, Lipid Panel, Iron, TIBC, hsCRP, insulin, and H, I, L indices occurred for 50% of samples tested without intervention. Approximately 250 uL of the 500 uL aliquot remained after aspiration of all tests (247.7 uL total volume) from the Matrix Tube. No probe crashes were observed despite the narrower 8 mm diameter of the Matrix Tubes versus the standard 12- or 13-mm diameter of primary blood collection tubes. Conclusions This Epic-DI-ADM data workflow and microsampling adaptation for Matrix Tube aspiration allows greater container type flexibility for the receipt and analysis of external samples delivered in non-primary blood tubes for clinical laboratories. Future work involving adjustment of the false bottom tube offset depth may reduce unusable sample volume allowing for a higher percentage of complete aspiration without intervention. This work is significant because it suggests existing clinical analyzers are easily adaptable to microsampling smaller volume tubes without significant effort.

Comparing phase sensitive detection and Fourier analysis of modulation excitation spectroscopy data exemplified by Ambient Pressure X-ray Photoelectron Spectroscopy

Dynamic processes in catalysis are gaining increased attention and could very well be one of the next frontiers in surface science. One way to study such processes is to induce chemical changes on the surface for example by periodically adjusting the (electro)chemical potential in situ and identify the resulting spectral changes. Often this is referred to as Modulation Excitation Spectroscopy (MES). Using Ambient Pressure Photoelectron Spectroscopy data, we here discuss and compare the analysis of MES data using both Phase Sensitive Detection (PSD) and Fourier analysis. We discuss that PSD determines the component magnitude at a user-defined phase value while Fourier analysis provides the maximum oscillation amplitude and respective phase value of oscillating spectral features. We discuss advantages and disadvantages of the different analysis schemes and explore how the full time-evolution can be obtained.

Joint estimation of SOH and RUL for lithium batteries based on variable frequency and model integration

The safe and stable operation of lithium-ion batteries in electric vehicles is crucial. Aiming at the problems of a large workload of online estimation and prediction and inability to weigh the whole life cycle of the battery in the joint estimation of SOH (State of Health) and RUL (Remaining Useful Life) for traditional lithium-ion batteries, this paper proposes an optimization scheme for the joint analysis of SOH-RUL. First, the article extracts suitable health features. It utilizes an integrated Extreme Learning Machine (ELM) to estimate the SOH of the battery and inputs the estimates into a Regression Vector Machine (RVM) model for RUL prediction. To better cope with the phenomenon of "sudden capacity change" in batteries, this paper divides the entire life cycle into the early and late stages. A lower estimation frequency and model integration are employed in the early stage.In contrast, the estimation frequency and model integration are increased later to balance speed and safety in total life cycle estimation. Finally, validation was performed on the NASA and CALCE datasets, comparing the effect of constant estimated frequency and model integration with that of varying estimated frequency and model integration at different aging stages. The RMSE of SOH estimation error for both datasets is within 2% during the early and late stages, and the SOH estimation error for the entire lifecycle is within 2%. The absolute RUL prediction error for the NASA dataset is below five times; for the CALCE dataset, it is below 20 times in most cases.

Data-Driven Method for Nonlinear Modal Interaction Identification in Fighter Aircraft

The purpose of this paper is to report a new data-driven analysis methodology for identifying nonlinear modal interactions and its application to aeroelastic wind-tunnel data from a generic fighter configuration, which reveals global modal interactions in this system caused by multiple resonance capture events initiated by local nonlinearities. It is shown that during supersonic testing at the Transonic Dynamics Tunnel, vibro-impacts at the embedded external fuel tank shakers on the inboard underwing stations of the generic fighter introduce strong nonlinearity that gives rise to nonlinear modal interactions. Specifically, time–frequency analysis of the acceleration data demonstrates that 6∶5 and 7∶6 transient resonance captures occur across the aircraft exclusively during this vibro-impact forcing window. Further analysis suggests that these resonance captures are dependent on Mach number. These results show experimentally that local vibro-impacts can cause global nonlinear effects in an aeroelastic system and that these effects can be identified using the new methodology herein. The data-driven analysis scheme employed shows applicability to full-aircraft flight test data for identifying nonlinear modal interactions without knowledge of the precise nonlinear contributors.

A three-dimensional unsteady flow of Casson nanofluid with suspension of microorganisms with variable thermal conductivity: A modified Fourier theory approach

The study of nanofluids is important due to valuable applications in drug delivery systems, thermal processes, solar management systems, microfluidic devices, extrusion phenomenon, chemical processes etc. The objective of current analysis is to investigates the enhanced thermal performances of magnetized Casson nanofluid with suspension of microorganisms. An unsteady periodically oscillating flow due to bidirectional moving porous surface have been considered. The analysis is subject to assumptions of variable thermal conductivity. The modelled problem is based on implementation of modified Fourier theories. The Buongiorno nanofluid model is used to predicts the significance of Brownian motion and thermophoresis effects. Chemical reaction species are also attributed. For solution methodology, homotopy analysis scheme is implemented. The convergence region is specified. The physical impact of problem is visualized. The significant applications of parameters have been focused. The claimed results offer significance in cooling systems, chemical reactions, air conditioning, solar thermal systems, automotive radiators, heat exchangers in power plants etc.

A Review on Some Physico-Chemical Compositions of Bread Wheat Determined by Using Destructive and Non-Destructive Methods

Some physico-chemical compositions of bread wheat (Triticum aestivum) were determined by using destructive and non-destructive methods. Standardized conventional method by Association of Official Analytical Chemists (AOAC) food analysis scheme was used for the destructive approach while near infrared spectrophotometer was used to measure some proximate compositions of wheat as a non-destructive method[1]. Near infrared techniques for obtaining the proximate compositions in the bread wheat was found to be more economical and faster without destroying the actual proximate compositions of the grains. The proximate compositions of bread wheat determined by using non-destructive measurement were moisture content 11.07 ± 0.03%, crude fiber 3.00 ± 0.00% and carbohydrate 71.80 ± 0.35% and the proximate compositions of bread wheat determined by using the destructive measurement were 9.50 ± 0.10%, 2.67 ± 0.07% and 67.28 ± 0.39%, respectively. Statistical analysis showed that there was no significant difference between the values obtained by the destructive and non-destructive methods for the determination of the physico-chemical compositions of bread wheat samples. Therefore, non-destructive method is economical, fast and save time compared to the crushing of the sample and addition of chemical reagents in the samples.

Development and verification of an MC/MOC two-step scheme for neutronic analysis of FCM-fueled micro gas-cooled reactor

Gas-cooled microreactors are known for compact designs, high thermal-to-electric efficiencies, long refueling cycles, and flexible deployment capabilities, representing a groundbreaking solution to address the energy requisites of special scenarios. Fully ceramic microencapsulated (FCM) fuel is widely used in gas-cooled microreactors, bringing challenges to neutronic analysis methods. In this paper, an Monte Carlo/Method of Characteristics (MC/MOC) two-step scheme is developed and verified based on the reference case. In this scheme, the continuous-energy Monte Carlo calculations are used for reference calculation and multi-group cross-section generation. The multi-group Monte Carlo calculations are used for multi-group cross-section verification and the MOC solver verification. Fuel multi-group cross-sections are generated with the explicit fuel assembly model by continuous-energy Monte Carlo calculations, and structure multi-group cross-sections are generated with the simplified core model by continuous-energy Monte Carlo calculations. The core calculations are conducted with the MOC calculations. For verification, parameters such as power distribution, neutron spectrum, and control devices worth will be compared. Core calculation results show that the relative errors of MOC results are within −329 pcm, ±5%, ±6%, and ± 2 % for Keff, power distribution, neutron spectrum, and control device worth, separately. Moreover, the computation cost of MOC is only 6.6 % of the reference computation cost. The figure of merit results show that the MC-MOC scheme exhibits improved computational efficiency for neutronic analysis of FCM-fueled micro gas-cooled reactor.

Patient safety in the pandemic: Experiences of charge nurses.

The aim of this study was to identify charge nurses' experiences ensuring patient safety during the coronavirus disease 2019 (COVID-19) pandemic. The research was conducted with charge nurses between July 2021 and February 2022. Research data were collected using a semi-structured interview with two main questions, six sub-questions and an audio recording. Interviews were conducted with 12 charge nurses and lasted ≈30 min. The content analysis method was used to transfer the collected data to written documents and define them to analyse the individual interview data. Content analyses were conducted independently by three different researchers. Inclusion and exclusion criteria for content were established to ensure consistency among all researchers. Researchers independently developed a coding scheme for content analysis based on the research objectives and applied it to the content. Intercoder reliability was assessed with Cohen's kappa coefficient to measure the consistency of coding among researchers. Three main themes were identified as a result of the content analysis. The themes identified after independent examination by the three researchers were 'ensuring safety and protection', 'sustaining motivation and resilience' and 'organizational restructuring and challenges'. This study highlights the challenges faced by charge nurses during the COVID-19 pandemic and their critical role in maintaining patient safety. The findings emphasize the importance of organizational adaptability, continuous motivation and comprehensive risk management strategies. Charge nurses played a key role in enhancing safety measures and fostering a culture of resilience among healthcare staff. Moving forward, these insights should guide health policies and practices to better prepare for future public health crises, ensuring the safety of both patients and staff.

Coherent fading suppression of distributed acoustic sensing based on time–frequency domain linkage algorithm

Abstract Due to the inherent characteristic of coherent fading phenomenon, the performance of distributed acoustic sensing system will be severely degraded, producing anomalous information and positioning errors. In order to suppress the influence of this phenomenon, the data array of demodulated results is obtained after processing the collected distributed Rayleigh scattering signals along the sensing fiber, which can be utilized to construct a two-dimension (2D) image with the time–distance domain information. Thus, this allows us to remove the abnormal information that are introduced by fading noise. Based on this principle, a morphology compositional analysis (MCA) scheme, an effective time–frequency domain linkage algorithm, is applied to the constructed 2D images to enhance the sensing accuracy. Compared with traditional methods including the 2D mean value filter (MVF) and 2D wavelet transform (WT), the proposed method makes full use of the independence of image source property and abnormity feature so that the abnormal information caused by the coherent fading can be eliminated without deteriorating the source information along the sensing fiber. Experimental results show that the anomaly rate of the time–distance domain information along the sensing fiber for the proposed method, the MVF method and the WT method can be reduced by 92.43%, 65.05% and 70.18% respectively. In addition, the isolation degree of the MCA, MVF and WT methods are 41, 16.02 and 23.35 times higher than that treated by traditional method, respectively. Also, the stability of the time-domain vibration can be improved and the spatial resolution would not be influenced by the proposed method compared with the traditional method. This scheme will promote the development of distributed acoustic sensor system with high-accuracy and high-quality without any hardware modification.

Universal strategy for rapid design and analysis of gas detection peptide chips with positional preference

The design and analysis of gas detection chips directly affect their detection efficiency and applicability. Detection devices are currently restricted by detection principles, facing drawbacks like intricate structural design, limited applicability, and low detection efficiency. We have designed a complete set of design and analysis scheme for a peptide gas detection chip. First, we selected specific and high-affinity peptide combinations from existing peptide-gas affinity datasets. Then, the peptide chip's arrangement was grouped according to the variations in peptides' affinity towards different gases. Peptides were arranged based on their affinity levels within each group, striking a balance between discrimination and flexibility in the design of the chip. Finally, we evaluated the analysis methods by generating simulated data based on a reference affinity matrix constructed from actual data. Due to the preprocessing role of chip design on affinity data, all methods can effectively accomplish gas classification. In gas concentration prediction tasks, our method reduced mean square error to 0.41, significantly outperforming other methods. This gas detection scheme shortens the development cycle of chip design and analysis methods, fully utilizing the specificity of peptides, enhancing gas analysis effectiveness, and demonstrating the agile development of gas detection chips.

Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis

Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis

Multivariate Analysis and Optimization Scheme of the Relationship between Leaf Nutrients and Fruit Quality in ‘Bingtang’ Sweet Orange Orchards

Citrus trees require a balanced and adequate supply of macronutrient and micronutrient elements for high yield and fruit quality. Foliar nutrient analysis has been widely used in fruit-tree nutrient diagnosis and fertilization calculation. However, there is no information on ways to produce optimal fruit quality in sweet oranges. In the present study, fruit and leaf samples were collected from 120 ‘Bingtang’ sweet orange [Citrus sinensis (L.) Osbeck] orchards during four consecutive years (2019–2022). Parameters of leaf nutrition and fruit quality were analyzed based on these samples. Diagnostic results based on leaf classification standards indicated that the most deficient elements were Ca, Mg, and B, followed by N and Zn. Fruit quality, determined by single fruit weight (SFW), fruit shape index (FSI), total soluble solids (TSS), titratable acidity (TA), vitamin C (Vc), and maturation index (MI = TSS/TA) during fruit maturation, exhibited inconsistent responses to leaf mineral nutrition concentrations. The leaf-nutrient optimum values for high quality of the ‘Bingtang’ sweet orange fruit were ranges of 2.41–4.92% N, 0.10–0.28% P, 1.30–2.11% K, 2.99% Ca, 0.26–0.41% Mg, 340–640 mg/kg S, 89.65–127.46 mg/kg Fe, 13.48–51.93 mg/kg Mn, 2.60–13.84 mg/kg Cu, 15.59–51.48 mg/kg Zn, and 53.95 mg/kg for B. These results suggest the leaf-nutrient optimum values for diagnosis can be used not only to identify the nutrient constraints but also to provide guidance for the establishment of fertilization regimes in citrus cultivation.

Genetic diversity and incidence of cassava bacterial blight (CBB) caused by Xanthomonas phaseoli pv. manihotis in Burkina Faso

AbstractCassava bacterial blight (CBB), caused by Xanthomonas phaseoli pv. manihotis (Xpm), poses a significant threat to cassava production in Burkina Faso. Prior to this study, the geographical distribution, incidence and origin of CBB introductions were poorly understood. In this investigation, we assessed the incidence of CBB in major cassava‐cropping regions and analysed the genetic diversity of 344 Xpm strains collected from 19 fields between 2015 and 2016, using a multilocus variable number of tandem repeat analysis (MLVA) scheme targeting 14 microsatellite markers. Our results reveal the presence of CBB in three out of four cropping regions, with incidences ranging from 0% to 100% at locality and variety scale. Burkina Faso's Xpm strains exhibited high genetic diversity at multiple levels, including country scale (He = 0.6), cropping region (0.43 to 0.57), locality (0.25 to 0.50) and field (0.25 to 0.52). Pairwise comparisons among populations from different cropping areas indicated a strong genetic differentiation. The categorical minimum spanning tree reveals that the Cascades region has the higher diversity indices and consequently could be the origin site of dispersion of Xpm in the other cropping regions of cassava. Human activities play a major role in the dissemination of Xpm through cassava cuttings. The importance of the sanitary state of cuttings must be emphasized in order to avoid CBB and ensure good cassava production.

Large‐signal stability analysis for hybridized vehicular power supply systems of rolling stocks

AbstractHybridized vehicular power supply (HVPS) are acquiring widespread adoption in various applications such as providing power for passenger or heavy‐duty electric vehicles, more‐electric ships, electrified rolling stocks, and more. Stability analysis is a critical issue for HVPS, but it presents significant challenges due to their prominent high‐dimensional and nonlinear characteristics. This paper presents a large‐signal stability analysis scheme for HVPS of rolling stocks, enabling the assessment of system stability under sudden load changes. Based on the state‐space averaging method, a mathematical model for the large‐signal behavior of the system is established. Subsequently, the small‐signal stability parameter range of the system is determined by analyzing the eigenvalues of the Jacobian matrix. To further analyze the stability of the system during large‐signal disturbances, a nonlinear decoupling transformation approach is employed to decompose the original high‐order state equations into multiple lower‐order equations. For the decoupled lower‐order subsystems, their stability is analyzed using phase portrait, and the region of attraction (ROA) is determined using the inverse trajectory method. Based on the stability analysis results, the controller is enhanced to bring the points outside the ROA back within it, thereby mitigating the transient instability phenomenon of the system. The effectiveness of the proposed method was demonstrated using two case studies.

Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble.

We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known. Experiments are performed with the Lorenz 96 dynamics, which display spatiotemporal chaos and for which solid benchmarks for DA performance exist. The accuracy of the states obtained from the learned analysis approaches that of the best possibly tuned ensemble Kalman filter and is far better than that of variational DA alternatives. Critically, this can be achieved while propagating even just a single state in the forecast step. We investigate the reason for achieving ensemble filtering accuracy without an ensemble. We diagnose that the analysis scheme actually identifies key dynamical perturbations, mildly aligned with the unstable subspace, from the forecast state alone, without any ensemble-based covariances representation. This reveals that the analysis scheme has learned some multiplicative ergodic theorem associated to the DA process seen as a non-autonomous random dynamical system.