In this letter, a robust autonomous timekeeping scheme for navigation constellations based on inter-satellite links is proposed. In this scheme, the clocks in the constellation are divided into two groups to generate two Ensemble Times (ET) respectively, then taking the two ETs as references to evaluate the frequency stability of each clock. Abnormal clocks can be identified and excluded from the clock group. After Fault Detection and Exclusion (FDE), the particle swarm optimization (PSO) algorithm is introduced to calculate the optimal weights of the two clock groups using the ensembled time of the opposite group as references respectively. Based on the optimized weights, the final Inter-satellite Link Time (ISLT) is obtained. The simulation implements ISL measurement precision and clock performance parameters of 8 BDS-3 satellites. Simulation results show that when the stability of the ensembled time is significantly higher than that of the evaluated clock, the evaluation results are very close to the true values. In the case of one abnormal clock, the long-term stability of the ISLT obtained by the proposed scheme reaches $1.6\times10^{-14}$ at $\tau=1\;\text{day}$, which is obviously better than $2.3\times10^{-14}$ at $\tau=1\;\text{day}$ using default weights. After 30 days of autonomous operation, the detrended clock difference is 7.8 ns. The research results provide a reference scheme for the autonomous timekeeping of navigation constellations.

Generative retrieval reframes information access as sequence generation: a model emits document identifiers that are subsequently mapped to corpus items. Contemporary systems such as DSI-style retrieval and structured approaches like SEATER demonstrate that learned identifiers can act as effective addresses, but they remain only weakly grounded in the actual geometry and topology of the corpus. In this paper we introduce spectral-aware unique identifiers: composite codes that pair a simple integer ID with an order key derived from taumode-a spectral energy and manifold-aware proximity functional computed by ArrowSpace from graph Laplacians and eigenspaces. Rather than treating identifier tokens themselves as the semantic space, we treat taumode as the retrieval-native latent manifold and use external IDs as thin pointers into it. Taumode summarizes each document into spectral coordinates, energy levels, and graph-consistent neighborhoods, yielding a geometry where similarity, locality, and diffusion are explicit rather than emergent, and where the identifier order is aligned with the spectral energy landscape. In this view ArrowSpace serves both vector search and generative retrieval as a spectral index that provides a mathematically grounded geometry for identifier design, constrained decoding, candidate generation, and reranking. We define a concrete identifier scheme (ℓ i , u i) based on λ τ values and prove that it preserves manifold structure more faithfully than sequence-only identifiers, while remaining compatible with autoregressive models. We substantiate the advantages of spectral-aware IDs over current generative retrieval signals in terms of manifold consistency, interpretability, locality preservation, robustness under structural perturbation, and ease of integration into existing vector databases and generative search pipelines.

A theoretical analysis of vacuum stability and the feedback loops of fundamental constants-exploring how the cosmos actively resists change to preserve its own structural integrity.

Wideband inverse synthetic aperture radar imaging of targets with rotating components suffers from severe defocusing due to high-order nonlinear phase modulation and range cell migration (RCM). To address this problem, a wideband subband fusion single-range Doppler interferometry (WSF-SRDI) framework is proposed. The wideband echo is first partitioned into multipe narrowband subbands via a polyphase filter bank. A theoretical constraint relating the subband number to RCM confinement is derived, ensuring that the RCM remains within a single range cell per subband. Within each subband, a SRDI path-integral model is then constructed to simultaneously achieve subband imaging and rotation parameter estimation, followed by a subband fusion process to enhance the overall image quality. Finally, utilizing the estimated parameters, the rotating components are reconstructed and suppressed through least squares estimation, yielding a well-focused image of the stationary main body. Simulation results and contrast analysis demonstrate that the WSF-SRDI maintains robust performance under low signal-to-noise ratio conditions. The proposed strategy provides an efficient solution for ISAR imaging of complex targets with multiple motion components.

Addressing the accuracy and stability issues of target tracking in radar network systems, an improved Fuzzy C-Means (FCM) clustering track fusion algorithm is proposed. The method maps local tracks into a fuzzy sample space and introduces sensor measurement quality factors into the objective function. By integrating Interacting Multiple Model (IMM) filtering, information entropy is employed to quantify the uncertainty and reliability of local data, enabling adaptive non-linear weight allocation. Unlike traditional FCM, this approach effectively identifies and suppresses low-quality measurement sources without requiring precise prior noise statistics. Simulation and experimental results demonstrate that the proposed algorithm significantly enhances fusion precision and global stability, particularly during target maneuvers, providing a robust solution for multi-radar cooperative surveillance.

Avian eggs reflect life-history trade-offs, yet shrikes (Laniidae) lack a modern cross-species synthesis of egg morphology. Using historical oological collections spanning 1888–1973, we quantified inter- and intraspecific variation in egg morphology and clutch size in four shrike species: red-backed shrike (Lanius collurio), woodchat shrike (L. senator), lesser grey shrike (L. minor), and great grey shrike (L. excubitor). Clutch size increased over time only in the lesser grey shrike, whereas no robust temporal change was detected in the other species. Where temporal trends in egg morphology occurred, size-related traits generally declined, whereas shape descriptors, including sphericity and shape index, remained comparatively stable. Among species, the great grey shrike laid the largest eggs but the smallest clutches, whereas the red-backed shrike laid the smallest eggs, consistent with a broad size–number trade-off in reproductive allocation. Within species, spatial variation in egg-size traits was evident, particularly in the red-backed shrike and woodchat shrike, whereas evidence for a size–number relationship was weak. Overall, temporal change in shrike reproductive traits was limited and species-specific, while museum collections proved valuable for reconstructing long-term patterns of reproductive variation.

Aims: This study investigated whether common CYP3A4 and CYP3A5 variants are associated with clinical outcomes in hospitalized COVID-19 patients treated with MP in Manaus, Brazil. Methods: A pharmacogenetic analysis was conducted on 100 hospitalized COVID-19 patients enrolled in a randomized, placebo-controlled clinical trial receiving heavy-dose intravenous MP (n = 49) or placebo (n = 51), for five days. CYP3A4: c.-392G>A (rs2740574, CYP3A4*1.002) and CYP3A5 (rs776746 - *3 and rs10264272 - *6) variants were genotyped. Associations between genotypes, treatment outcomes, and time to discharge or death were analyzed using χ 2 tests, Kaplan-Meier curves, and Cox regression. Results: Allele frequencies of CYP3A4*1.002 (86%), CYP3A5*3 (70%), and CYP3A5*6 (2%) were within those reported in admixed Latin American populations . Genotype distributions did not differ significantly between treatment groups or outcome categories. Carriers of CYP3A5 wild-type (*1/*1) genotypes had shorter hospital stay (p = 0.012), and this genotype did not influence mortality. Adjusted Cox models showed only sex, comorbidities, and weight predicted discharge likelihood in both CYP3A4 and CYP3A5 models (p<0.05). Conclusion: CYP3A4 and CYP3A5 variants were associated with modest differences in hospital length of stay but did not independently influence survival among MP-treated COVID-19 patients. Host genetic variation in CYP3A-mediated metabolism may contribute to variability in recovery dynamics, although clinical characteristics remained dominant determinants of clinical outcome.

Small carnivores fulfill important ecological roles in forest ecosystems, yet their diel activity patterns and habitat requirements in subtropical forests remain insufficiently documented. Using 116,800 camera-trap days (2021–2024) at the Chebaling National Nature Reserve, Guangdong Province, China, we characterized the diel activity rhythms and habitat selection of four sympatric small carnivore species: the leopard cat (Prionailurus bengalensis), crab-eating mongoose (Urva urva), spotted linsang (Prionodon pardicolor), and masked palm civet (Paguma larvata). Kernel density estimation revealed distinct diel strategies among the four species. The crab-eating mongoose was strictly diurnal, while the leopard cat, spotted linsang, and masked palm civet were all nocturnal. Temporal overlap was high among nocturnal species (Δ = 0.695–0.899) but low between the diurnal mongoose and each nocturnal species (Δ = 0.074–0.371). Seasonal comparisons showed that all species except the crab-eating mongoose maintained highly consistent activity rhythms between the growing and non-growing seasons. MaxEnt modelling indicated that all four species preferred gently sloping terrain with high vegetation cover (NDVI > 0.43) near water sources. However, they differed in aspect preference, habitat breadth, and sensitivity to anthropogenic disturbance. The leopard cat occupied the largest area of suitable habitat (29.08 km²) and favored shaded slopes; the spotted linsang showed notable avoidance of roads; and the masked palm civet exhibited the broadest ecological tolerance. These results suggest that diel activity divergence between the diurnal and nocturnal guilds, together with species-specific differences in microhabitat selection among the nocturnal species, may contribute to reducing interspecific competition within this assemblage. Our findings provide baseline ecological data for the conservation management of small carnivore communities in subtropical forest ecosystems of southern China.

The rapid development of distributed generation (DG) has a significant impact on modern distribution networks, providing environmental, economic, and technical benefits. At the same time, this phenomenon also poses new challenges for system protection, operation, and planning. Many studies have focused on the optimal placement and sizing of DG, but most of the existing reviews are fragmented, with most studies focusing on a specific aspect while ignoring a comprehensive analytical framework. In this sense, this research aims to provide a comprehensive, multi-faceted review on this topic, based on more than a decade of research, including technical, economic, environmental, and protection issues. A new classification framework with four dimensions is proposed for evaluating optimization methods, DG technologies, and planning strategies using a unified analytical framework. This review article presents a critical comparison of mathematical, analytical, and metaheuristic methods, which facilitates an understanding of the trade-off between different dimensions. Moreover, the article reveals the commonly overlooked effects of DG on protection coordination and system robustness. Additionally, the article presents potential research directions for protection-aware and multi-objective optimization. The research reveals that the effectiveness of the integrated planning approach, which considers network constraints, the variability of renewable sources, and multi-scenario operation, is vital to maximize the positive effects of DG. This article presents an integrated guideline for researchers and practitioners, which clearly explains the challenges, existing gaps, and new opportunities that will define the future of the distribution network with the integration of DG.

A document by Jordon Robert Manuel. Click on the document to view its contents.

We present LiveFace, a modular neural rendering system that achieves photorealistic talking-head animation at 30 fps on low-end mobile devices with as little as ~10 GFLOPS of compute (e.g., Qualcomm Snapdragon 439). Prior photorealistic facial animation systems either require cloud infrastructure with 100M+ parameter models (HeyGen, DID , Synthesia) or demand desktop-class GPUs (MetaHuman, Audio2Face), while on-device alternatives sacrifice realism for stylized cartoon aesthetics (Apple Memoji, Samsung AR Emoji). LiveFace bridges this gap through three key contributions: (1) a decomposed per-avatar decoder architecture that factorizes the face into four independently rendered regions-mouth, eyes, hair, and body-each handled by a compact neural decoder (1.3-5.7M parameters) augmented with a 128-dimensional learnable identity embedding; (2) a universal compositor-upscaler (~7M parameters) shared across all avatars that composites the decoded patches onto a 9:16 portrait canvas and upscales to 360x640 (or 384x672) in a single forward pass; and (3) a videodriven knowledge distillation pipeline that uses RAVDESS emotional speech videos as driving sources for LivePortrait (~300M parameters) to generate diverse, naturalistic training data for the student decoders. The MouthDecoder supports dual-input conditioning: both viseme-based (audio-driven) and landmark-based (MediaPipe Face Mesh) modes, enabling flexible integration with different upstream pipelines. A perframe quality filter employing Haar cascade face detection, Laplacian blur scoring, and SSIM comparison ensures training data integrity by rejecting approximately 0.6% of generated frames. A working V3 prototype has been trained and validated, demonstrating that the architecture successfully produces photorealistic output from compact per-avatar models. The full system comprises ~20M INT8 parameters with a 08.04.2026, 02:01 file:///C:/Users/123/AppData/Local/Temp/arxiv_paper_liveface_v2_EN.html 1/23 total inference latency of ~19 ms per frame, enabling real-time, fully offline operation on commodity mobile hardware without any cloud dependency.

Background: Intraluminal small intestinal obstructions (ISIO) are common in the United Kingdom, but most published studies are from North America. Objectives: To describe ISIO prevalence among all colic cases undergoing laparotomy at an English equine hospital between 2018 and 2024, and to identify risk factors for developing pre-operative and post-operative reflux (POR) and survival. Study design: Retrospective cohort study. Methods: Data on age, breed, gender, heart rate (HR), PCV, total solids concentration (TS), serum lactate and peritoneal lactate concentrations on presentation, development of pre-operative reflux and POR, complications (thrombophlebitis, sepsis, ileus, laminitis, surgical-site infection), duration of hospitalisation and survival to discharge were obtained. Univariable logistic regression was used to investigate the relationship between these parameters and development of POR and survival to discharge. Results: Thirty-three patients met the inclusion criteria. ISIO prevalence was 7.2% (95%CIs:5.2%-9.8%). At presentation mean HR was 55bpm (36-96bpm), PCV was 37% (28-52%), TS was 69g/L (52-86g/L), serum lactate concentration was 2.6mmol/L (0.5-15.7mmol/L) and abdominal lactate concentration was 6mmol/L (0.7-22.3mmol/L). Nasogastric reflux on presentation was seen in 5/33 patients, 9/33 developed POR and 25/33 survived to discharge. Mean hospitalisation time was 10 days (3-38 days). For each 10 units of increase in HR above 45bpm, the odds of developing POR increased 2.05-fold (p=0.025; 95%CIs:0.99-4.26). Horses that survived were significantly less likely to have developed POR than non-survivors (p<0.001; OR:0.03; 95%CIs:0.003-0.22) or post-operative complications (p<0.001; OR:0.04; 95%CIs:0.004-0.405). Main limitations: retrospective, single-centre design. Conclusions: ISIO prevalence in England aligns with North America. High HR on admission is related to POR and development of POR is associated with reduced survival.

ABSTRACT

We prove that the set T={n∈N: ∃p,q∈N ((2n=(p+q)(p+q+1)+2q) ∧ ∀(x_0,...,x_p)∈N^{p+1} ∃(y_0,...,y_p)∈{0,...,q}^{p+1} ((∀k∈{0,...,p} (1=x_k ⇒ 1=y_k)) ∧ (∀i,j,k∈{0,...,p} (x_i+x_j=x_k ⇒ y_i+y_j=y_k)) ∧ (∀i,j,k∈{0,...,p} (x_i \cdot x_j=x_k ⇒ y_i \cdot y_j=y_k))))} is not recursively enumerable. By using Gödel's β function, we prove that the formula that defines the set T can be easily translated into a first-order formula which uses only + and \cdot. The same properties has the set {n∈N: ∃p,q∈N ((2n=(p+q)(p+q+1)+2q) ∧ ∀(x_0,...,x_p)∈N^{p+1} ∃(y_0,...,y_p)∈{0,...,q}^{p+1} ((∀j,k∈{0,...,p} (x_j+1=x_k ⇒ y_j+1=y_k)) ∧ (∀i,j,k∈{0,...,p} (x_i \cdot x_j=x_k ⇒ y_i \cdot y_j=y_k))))}.

Lateral Flow Assays (LFAs) have become the foundation of point-of-care (POC) diagnostics, valued for their user-friendly format, cost-effectiveness, and rapid turnaround time. However, despite their widespread success, most notably in pregnancy testing and infectious disease monitoring, conventional colorimetric LFAs often suffer from insufficient sensitivity, limiting their utility for the early-stage detection of low-abundance biomarkers. This review provides a critical analysis of recent strategies have been developed to improve the sensitivity of these assays. Enhancement strategies are systematically classified into six distinct domains: (i) flow modulation techniques that optimise reaction kinetics; (ii) sample preconcentration methods; (iii) advanced signal transduction reporters beyond traditional gold nanoparticles; (iv) chemical signal amplification including nanozyme and enzymatic catalysis; (v) structural strategies for maximising label accumulation; and (vi) the engineering of aptamers as programmable recognition elements. Special emphasis is placed on ”reagent-free” and ”equipment-free” innovations that boost performance without compromising the inherent simplicity of the device. Finally, the emerging transition from passive biological selection to active molecular engineering is discussed, outlining the future trajectory of ultra-sensitive next-generation LFAs.

Intimate partner violence (IPV) is a critical public health concern that disproportionately affects adolescent girls in low-resource rural settings, particularly during pregnancy. This study investigates the association between IPV and suicidal ideation among pregnant adolescents and compares outcomes across different rural contexts. A comparative cross-sectional design was employed, drawing on structured survey data collected from pregnant adolescents in two low-resource rural regions. Measures included exposure to physical, emotional, and sexual violence, alongside indicators of mental health and suicidal ideation. The findings reveal a high prevalence of IPV, with emotional violence being the most frequently reported form. Adolescents exposed to IPV demonstrated significantly higher levels of suicidal ideation compared to those not exposed. Differences between regions highlight the role of socio-cultural norms, access to support services, and economic vulnerability in shaping both IPV prevalence and mental health outcomes. The study underscores the urgent need for integrated maternal health and psychosocial interventions tailored to adolescent populations in rural areas. It further advocates for community-based prevention strategies, strengthened health systems, and policy frameworks that address both violence and mental health concurrently. These findings contribute to the growing body of evidence linking gender-based violence to adverse psychological outcomes and provide actionable insights for improving maternal and adolescent health in resource-constrained environments.

Gender-based violence (GBV) during pregnancy is a pervasive public health issue in South Asia, with profound implications for maternal mental health. This study examines antenatal depression as a mediating factor in the relationship between GBV and suicidal ideation among pregnant women in the region. Drawing on a cross-sectional analytical framework and synthesis of empirical evidence, the study explores how exposure to physical, emotional, and sexual violence contributes to psychological distress and suicidal thoughts. Findings indicate that GBV significantly increases the risk of antenatal depression, which in turn elevates the likelihood of suicidal ideation. Evidence suggests that approximately one in four pregnant women in South Asia experiences antenatal depression, highlighting the scale of the problem. The study further demonstrates that depression serves as a critical pathway linking violence to suicidal outcomes, rather than acting as an independent condition. Socioeconomic disadvantage, limited access to mental health services, and cultural stigma further intensify these risks. The study proposes an integrated conceptual framework emphasizing early screening, psychosocial support, and gender-sensitive healthcare interventions. It concludes that addressing antenatal depression within the context of GBV is essential for reducing maternal morbidity and improving mental health outcomes. The research contributes to the understanding of complex psychosocial mechanisms underlying maternal mental health in low-and middle-income settings and provides actionable insights for policy and clinical practice.

Violence, depression, and suicidal ideation represent interconnected public health challenges affecting women and adolescents globally, particularly in low-resource settings. This study provides an integrated assessment of these factors across multiple populations, including adult women and adolescent girls, using a comparative cross-sectional framework. Drawing on synthesized empirical data, the study examines the prevalence and interrelationships between exposure to violence, depressive symptoms, and suicidal ideation. Findings indicate that exposure to violence significantly increases the likelihood of depression, which in turn elevates the risk of suicidal ideation. Evidence suggests that suicidal ideation among pregnant women ranges from approximately 2 percent to over 14 percent depending on context, with substantially higher rates observed among vulnerable adolescents. Adolescent populations, particularly pregnant adolescents, demonstrate markedly higher prevalence rates, with some studies reporting rates exceeding 28 percent. Depression consistently emerges as a central mediating factor linking violence to suicidal ideation. Socioeconomic disadvantage, weak social support systems, and limited access to mental health services further intensify these risks. The study proposes an integrated socio-ecological framework for understanding these relationships and emphasizes the need for multi-level interventions. The findings contribute to the development of comprehensive strategies aimed at reducing mental health disparities and improving outcomes for vulnerable populations.

This paper considers the state reconstruction problem for discrete-time cyber-physical systems when some of the sensors can be arbitrarily corrupted by malicious attacks where the attacked sensors belong to an unknown set. We first prove that the state is s-error correctable if the system under consideration is s-sparse observable where s denotes the maximum number of attacked sensors. Then, two state reconstruction methods are presented where the first method is based on searching elements with the same value in a set and the second method is developed in terms of searching element satisfying a given condition. In addition, after establishing and analyzing the conditions that the proposed state reconstruction methods are not effective, we address that it is very hard to prevent the state reconstruction when either state reconstruction method proposed in this paper is used. The correctness and effectiveness of the proposed methods are examined via an example of four-dimensional dynamic systems and a real-world example of three-inertia systems.

Human activities are driving unprecedented increases in atmospheric CO2 concentrations, resulting in global warming, with a higher frequency of marine heat waves (MHWs), and ocean acidification (OA) that represent a critical threat to coastal seagrass habitats. To assess the potential impacts and to deepen the knowledge on the long-term adaptive responses to these stressors, Posidonia oceanica meadows have been investigated in natural CO2 vent systems of Ischia (Italy) and Panarea (Aeolian Archipelago, Italy) islands, where continuous volcanic gas emissions create chronically acidified conditions, with CO2 concentrations comparable to those projected by the IPCC report of 2023 for 2030, as well as to examine the combined effects of OA and MHW on P. oceanica and its associated fauna. In this context, a meta-analysis was conducted to quantify the relative contributions of OA, MHW and hypersalinity (a climate driver strictly related with higher temperature, affecting shallowest ecosystems and already affecting some habitats due to brine discharges) to the impact on the responses of P. oceanica studied in mesocosms with long-term exposures and natural laboratories. This study aims to summarise the physiological and cellular responses (excluding genetic analysis) to the aforementioned stressors, to underline the interactions between them, and their impacts on P. oceanica, and to focus attention where future studies should be directed. The results of the meta-analysis highlight a consistent negative pattern in P. oceanica growth due to the analysed stressors, a strong negative impact on calcifying associated fauna due to OA, and an important negative effect on photosynthesis caused by hypersalinity. At the same time, there is clear evidence of a substantial gap in the study of response variables associated with the impact of warming and MHW. These findings emphasize the possible future consequences and highlight the importance of deepening knowledge of multiple-stressor exposure effects and developing conservation programs.

Is division of marine animals into macrofauna and meiofauna merely an arbitrary convenience, or, as has been claimed, does their size difference render them distinct and independent entities evolved under different ecological constraints? To investigate this, the ecological patterns of meiofaunal distribution, abundance and composition were investigated across the disparate regions of Knysna estuarine bay (South Africa). The locality was represented by ten specific seagrass and bare-sediment sites at which the equivalent macrofaunal characteristics had recently been established. Thus it was possible to compare meiofaunal and macrofaunal responses to the same suite of contrasting situations. Nematodes, copepods and ostracods, totalling 95.7% of numbers, dominated the epibenthic meiofauna which showed low (but not necessarily locally atypical) abundance: per core sample values of 19-886 (mean 211) 10 cm-2. Numbers were subequal or greater in seagrass beds than in bare sediment, and peaked in the clean delta sands of the mouth. Relative abundance of total macro- and meiofauna showed a comparable pattern along the system’s longitudinal axis, but each major component taxon, whether macrofaunal or meiofaunal, responded differently to the gradients concerned and no common within-group or contrasting between-group macrofaunal or meiofaunal responses were apparent. Indeed the strongest correlation was between distribution and abundance of the meiofaunal harpacticoids and macrofaunal gastropods. The macrofaunal ’opportunistic polychaete to amphipod’ and meiofaunal ’nematode to copepod’ indices of ecological quality yielded differing (but in both cases unrealistic) results. The Knysna macro- and meiofauna did not behave as distinct and discrete ecological entities, their individual component taxa displaying disparate patterns independent of their allocation into the two size classes.

Aims Despite advances in health research and digital innovation, many African countries continue to face substantial resource constraints, fragmented capacity-building efforts, and a shortage of skilled professionals in medicines development and regulatory science. This review aims to examine current gaps and evaluate collaborative approaches to strengthening capacity in this field. Methods A narrative review was conducted to assess capacity-building initiatives in medicines development and regulation across Africa. Particular focus was placed on the contributions of the Fundisa African Academy of Medicines Development, in collaboration with partners including academia, clinical research sites, industry, and regulatory authorities. Results Capacity-building efforts across the continent remain heterogeneous and are often implemented in a fragmented manner. Collaborative initiatives, such as those led by the Fundisa African Academy of Medicines Development and its partners, illustrate the potential to mitigate these gaps through accredited training, targeted skills development, and experiential learning programmes. Such initiatives support the strengthening of competencies along the entire medicines development continuum, spanning drug discovery, clinical research, and regulatory and health economic evaluation. Conclusions Sustainable capacity strengthening in Africa requires coordinated, context-specific approaches that address the full health research value chain, from investigators to regulatory assessors. Integration of digital health platforms as force multipliers, adoption of standardised competency frameworks such as the WHO Global Competency Framework, and development of centralised systems to monitor capacity-building activities are essential to reduce fragmentation and promote regional collaboration.

This study characterises the identifiability limits of individual insulation admittances in an ungrounded network from phase-to-earth voltage data alone. The Millman nodal balance, recast as a homogeneous real-valued regression, is analysed through singular value decomposition (SVD). The Millman ratio is scale-invariant, so the full admittance vector cannot be uniquely recovered. The SVD exposes instead a near-null subspace whose dimension and stability reflect the insulation state. A two-stage gate on zero-sequence excitation and angular diversity selects which observation windows enter the regression, and five spectral indicators are extracted from the admitted set: effective rank, null-space dimension, conditioning, tail-gap separation, and informative-window share. On a single feeder whose phase-A insulation resistance is swept across several decades, the tail gap widens, the admitted-window fraction grows and the near-null subspace progressively captures the true admittance direction. The onset shifts earlier under healthy-pair symmetry and requires at least two non-triplen harmonics. Cable attenuation, voltage symmetrisation and source-side zero-sequence contamination each impose hard limits that longer records alone do not remove. The framework is diagnostic and refuses to claim identifiability from data that lack the requisite structure.

A document by Carlos Garrido Quintanilla. Click on the document to view its contents.