The Stress Paradox: Cardiovascular State Modulates Cardiac Radiofrequency Absorption While Modern Buildings Attenuate Natural Electromagnetic References

The Stress Paradox: Cardiovascular State Modulates Cardiac Radiofrequency Absorption While Modern Buildings Attenuate Natural Electromagnetic References | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Stress Paradox: Cardiovascular State Modulates Cardiac Radiofrequency Absorption While Modern Buildings Attenuate Natural Electromagnetic References andrei ursachi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8935385/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Radiofrequency (RF) safety standards assess cardiac exposure using resting-state phantoms under single-source conditions. Real-world exposure involves variable cardiovascular physiology, multiple simultaneous sources, and indoor environments that simultaneously increase anthropogenic RF while attenuating natural extremely-low-frequency (ELF) signals, including the Schumann resonances (SR). Methods We present an integrated computational framework combining: ( 1 ) finite-difference time-domain (FDTD) modeling of RF penetration through a nine-layer stratified thorax at 0.9, 3.5, and 28 GHz under ICNIRP occupational reference levels; ( 2 ) a vascular waveguide model incorporating cardiovascular state-dependent tissue conductivity via Maxwell–Garnett and parallel mixing formulae; ( 3 ) 3D cylindrical phantom validation on GPU; ( 4 ) plane-wave transmission line modeling of building material attenuation for modern versus traditional construction; ( 5 ) time-resolved cardiac dosimetry from 24-hour Holter ECG recordings (N = 18 healthy subjects, 4,582 five-minute windows); ( 6 ) Schumann resonance signal-to-noise ratio (SNR) quantification across eight representative environments (exploratory); and ( 7 ) parameterized safety gap analysis incorporating multi-source exposure, cardiovascular state, and building effects. Results Vasodilation reduced myocardial E-field by 10–31% (geometry-dependent) and SAR by 22–50%, while vasoconstriction increased E-field by 7–9% and SAR by 14–17%—a “stress paradox” in which physiological stress increases cardiac RF coupling during sympathetic activation, a state associated with altered electrophysiological stability. A 3D cylindrical phantom validation confirmed the vasoconstriction SAR modulation within 2.2% of 1D predictions, demonstrating robustness across 1D slab and 3D cylindrical geometries. Analysis of 24-hour Holter ECG recordings revealed that healthy subjects spend 35% of the day in the high-HR bin (HR > 80 bpm, interpreted as predominantly sympathetically-activated), with population-weighted cardiac SAR 3.2% above standard resting-state assumptions and individual variation spanning ± 10%. The longest continuous high-HR episode was 11.2 hours. Modern building envelopes attenuated 0.9 GHz by only 0.3–2.5 dB versus 8–28 dB for traditional construction; combining state variation with building type, the 24-hour cardiac SAR factor in modern buildings (0.72) exceeded traditional buildings (0.16) by 4.6×. Under multi-source stress-state conditions, the parameterized safety gap narrows to 3.4×—a threshold-independent 4× erosion relative to standard testing. Conclusions Standard SAR testing systematically underestimates realistic worst-case cardiac exposure by omitting cardiovascular state modulation—an effect confirmed across 1D and 3D geometries. Modern built environments simultaneously maximize anthropogenic RF penetration and minimize natural ELF signal coupling. Findings are classified into three evidence tiers (directly computed, literature-anchored, and exploratory) and accompanied by six falsifiable predictions to guide experimental validation. Biomedical Engineering radiofrequency dosimetry cardiac SAR vascular waveguide Schumann resonance building attenuation FDTD electromagnetic safety stress paradox ion channel threshold heart rate variability Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The human heart operates within an electromagnetic environment that has changed more dramatically in the past century than in all prior human evolution. The cardiac magnetic field (50–100 pT) evolved alongside stable natural references: the Schumann resonances at 7.83 Hz and harmonics, generated by global lightning activity within the Earth–ionosphere cavity (Schumann, 1952; Balser & Wagner, 1960). This natural electromagnetic background has been progressively overwhelmed by anthropogenic sources—power distribution networks, telecommunications infrastructure, and personal wireless devices—creating indoor environments where artificial fields exceed natural signals by five to six orders of magnitude (Halgamuge, 2015; Lewczuk et al., 2014). Current RF safety guidelines (ICNIRP, 2020; IEEE C95.1-2019) establish exposure limits based primarily on thermal considerations, derived from short-duration, single-source exposures of fixed-conductivity tissue models. While these standards have been extensively refined for specific absorption rate (SAR) assessment (Christ et al., 2010; Dimbylow, 2005; Gosselin et al., 2011), several features of realistic exposure remain unaddressed. First, cardiovascular state modulates tissue conductivity. Blood (σ ≈ 1.5 S/m at 0.9 GHz) is the most conductive tissue, and its volume distribution varies substantially with autonomic state: vasodilation increases peripheral blood volume by up to 40%, while vasoconstriction reduces it by up to 35% (Charkoudian, 2010; Johnson & Proppe, 1996). Since blood vessels form connected high-conductivity paths from skin to deep organs, these changes alter the body’s internal RF propagation characteristics. To our knowledge, this state dependence has not been previously modeled for cardiac RF dosimetry. Second, modern building materials provide minimal RF shielding at sub-6 GHz frequencies while effectively attenuating the natural ELF signals—particularly through metallic reinforcement, steel framing, and energy-efficient glazing that acts as a Faraday cage at low frequencies (Stavrou & Saunders, 2003; Asp et al., 2014; Rodriguez-Cano et al., 2021). Traditional construction materials (adobe, limestone, timber) provided substantially greater RF attenuation while remaining more transparent at ELF (Crocco et al., 2019). Third, multi-source exposure is now ubiquitous. A typical indoor environment contains 10–50 simultaneous RF emitters (Wi-Fi access points, Bluetooth devices, cellular base stations, personal devices). Incoherent multi-source exposure scales as √N in field amplitude, potentially narrowing safety margins assumed from single-source testing (Vermeeren et al., 2015). Fourth, evidence suggests coupling between natural ELF fields and human autonomic function. McCraty et al. (2017) demonstrated statistically significant correlations between HRV indices and Schumann resonance power over 31 continuous days (r = 0.31, p < 0.05 for SDNN versus the first SR harmonic). Saroka et al. (2016) reported transient coherence between Schumann resonances and human EEG. Alabdulgader et al. (2018) extended these findings across multiple geographic locations. While the coupling mechanism remains debated, these empirical correlations motivate quantifying what modern indoor environments have done to the natural SR signal at body level. Regarding non-thermal RF bioeffects, Wust et al. (2020) proposed a rectification mechanism at voltage-gated ion channels predicting biological sensitivity at approximately 1 μV of induced transmembrane potential. Panagopoulos et al. (2013) developed a complementary forced-oscillation model. Neither mechanism is fully validated experimentally, but both predict that the gap between safety limits and biological sensitivity may be narrower than thermal analysis suggests. We treat the 1 μV threshold parametrically, presenting results across a range of assumed thresholds. This paper integrates these four elements into a single computational framework. We model cardiac RF coupling as a function of cardiovascular state, quantify building material effects on both RF and ELF signal environments, estimate the loss of Schumann resonance coupling in modern indoor environments, and parameterize the resulting safety margins. The framework identifies specific, experimentally testable predictions for worst-case cardiac exposure scenarios. 2. Methods 2.1 FDTD Stratified Thorax Model A one-dimensional FDTD model was constructed with nine tissue layers representing an anterior chest wall cross-section (Table 1), based on anatomical dimensions from Standring (2016) and Christ et al. (2010). Frequency-dependent complex permittivity (ε* = ε’ − jε″) was computed using four-pole Cole–Cole parameters from the IT’IS Foundation database, version 4.1 (Hasgall et al., 2022), which derives from the foundational measurements of Gabriel et al. (1996). Simulations were performed at three frequencies spanning the current and next-generation telecommunications bands: 0.9 GHz (LTE/GSM), 3.5 GHz (5G mid-band, n78), and 28 GHz (5G mmWave, n257). Incident power density was set to ICNIRP (2020) occupational reference levels: 45 W/m² at 0.9 GHz, 45 W/m² at 3.5 GHz, and 100 W/m² at 28 GHz. FDTD spatial discretization was λ/20 at the highest frequency in each tissue, with perfectly matched layer (PML) boundary conditions (6 layers, polynomial grading). Time step satisfied the Courant stability condition (Δt = 0.9Δx/c). Each simulation ran for 20 RF cycles to ensure steady-state convergence, verified by monitoring field amplitude variation < 0.1% over the final five cycles. A convergence study varying spatial discretization from λ/10 to λ/40 confirmed less than 2% variation in myocardial E-field, with λ/20 providing adequate accuracy. We acknowledge that a 1D model does not capture diffraction, body curvature, or realistic antenna near-field patterns. However, the stratified slab geometry isolates the effect of interest—cardiovascular state modulation of layer conductivities—and allows transparent reporting of how each tissue layer contributes to cardiac shielding. 3D voxel-based models would provide more accurate absolute SAR values but would not change the qualitative finding that blood volume redistribution alters the conductivity profile along penetration paths to the heart. For calibration, Christ et al. (2010) report peak SAR₁₀g of 0.08–0.15 W/kg in the heart region at 900 MHz for 1 W input power in their 3D voxel model; our 1D resting-state value of 0.106 W/kg falls within this range, supporting the quantitative plausibility of our simplified geometry. Table 1. Nine-layer stratified thorax model. Dielectric properties at 0.9 GHz, REST state. Layer thicknesses based on anatomical data (Standring, 2016; Christ et al., 2010). Layer d (mm) ε′ σ (S/m) Depth (mm) Anatomical basis Skin 2 41.4 0.87 0–2 Epidermis + dermis Subcutaneous fat 10 5.5 0.05 2–12 Adipose tissue Pectoral muscle 15 55.0 0.94 12–27 Skeletal muscle Sternum 8 12.5 0.14 27–35 Cortical bone Intercostal tissue 5 55.0 0.94 35–40 Muscle/connective Lung (inflated) 30 34.0 0.46 40–70 Alveolar tissue Pericardium 2 55.0 0.94 70–72 Serous membrane Myocardium 12 60.0 1.08 72–84 Cardiac muscle Ventricular blood 20 62.0 1.54 84–104 Whole blood 2.2 Cardiovascular State-Dependent Tissue Model Three cardiovascular states were defined based on published hemodynamic data, representing the physiological range encountered in daily life: REST (baseline): Normal resting hemodynamics, heart rate 72 bpm, nominal tissue properties as in Table 1. VASODILATION (exercise/heat): Vessel diameter 1.2× baseline, blood volume fraction 1.4×, skin hydration increased (wet skin model, σ skin × 1.8), HR 55 bpm. These values represent moderate thermoregulatory vasodilation, as documented in Charkoudian (2010) and Johnson & Proppe (1996). Muscle blood volume increase based on Laughlin et al. (2012). VASOCONSTRICTION (cold stress/mental stress): Vessel diameter 0.8×, blood volume fraction 0.65×, dry skin (σ skin × 0.7), HR 85 bpm. These values represent sympathetically-mediated cutaneous vasoconstriction during cold exposure or acute psychological stress (Charkoudian, 2010; Taggart et al., 2011). The reduction in peripheral blood volume increases core redistribution but reduces superficial conductivity. Effective tissue conductivity for blood-perfused layers was computed using two complementary approaches to bound the uncertainty. The Maxwell–Garnett effective medium approximation models blood vessels as dilute spherical inclusions: σ eff = σ tissue (1 + 3fβ/(1 − fβ)), where f is blood volume fraction and β = (σ blood − σ tissue )/(σ blood + 2σ tissue ). This provides a lower bound on vascular effects. The parallel conductivity model assumes aligned vascular channels: σ eff = fσ blood + (1−f)σ tissue . This provides an upper bound. Results are reported for both models. Temperature-dependent corrections followed Mohapatra (1981): +1.33%/°C for conductivity, −0.3%/°C for permittivity, using +2°C skin surface temperature for vasodilation and −4°C for vasoconstriction relative to the 33°C resting baseline. Table 2. State-dependent effective conductivity (σ eff , S/m) at 0.9 GHz by tissue layer (Maxwell–Garnett model). Blood volume fraction f and skin conductivity multiplier shown. All values from IT’IS v4.1 base properties with state modifications as described above. Layer f (REST) σ REST f (VASOD.) σ VASOD. f (VASOCON.) σ VASOCON. Skin (dry/wet) — 0.87 — 1.57 (×1.8) — 0.61 (×0.7) Subcutaneous fat 0.03 0.051 0.042 0.054 0.020 0.049 Skeletal muscle 0.10 0.95 0.14 1.01 0.065 0.91 Intercostal muscle 0.10 0.95 0.14 1.01 0.065 0.91 Rib (cortical bone) 0.01 0.15 0.014 0.15 0.007 0.15 Lung (inflated) 0.20 0.56 0.28 0.63 0.13 0.50 Pericardium 0.05 0.78 0.07 0.80 0.033 0.77 Blood (coronary) — 1.54 — 1.54 — 1.54 Myocardium 0.15 1.07 0.21 1.15 0.098 1.01 FDTD source: x-polarized plane wave at ICNIRP occupational reference level (E = 61.4 V/m at 0.9 GHz). SAR measured at myocardial center node. Spatial discretization Δx = 0.5 mm (1D) and 2.0 mm (3D). Complete parameter tables, FDTD implementation code, Holter ECG classification scripts, and building attenuation models are archived at https://github.com/ExeqTer91/stress-paradox-cardiac-dosimetry. 2.3 Building Material Attenuation Model RF transmission loss through building materials was computed using plane-wave transmission line theory following ITU-R P.2040-2 (2021). Each material was characterized by thickness-dependent complex transmission coefficient T(f) incorporating multiple internal reflections. Composite wall assemblies were modeled as cascaded transmission matrices. Six material categories were evaluated: (1) clear float glass (6 mm); (2) low-emissivity coated glass (6 mm, metallic coating, σ coating = 10 5 S/m, 50 nm thickness); (3) gypsum board (12.5 mm); (4) reinforced concrete (200 mm, 1% steel rebar by volume); (5) adobe/cob (400 mm); and (6) limestone masonry (500 mm). Material dielectric properties were taken from Stavrou & Saunders (2003), ITU-R P.2040-2 (2021), and Rodriguez-Cano et al. (2021). Composite wall assemblies were defined for representative building types: a modern residential wall (12.5 mm gypsum + 100 mm mineral wool + 12.5 mm gypsum), a modern commercial facade (6 mm low-E glass + 200 mm reinforced concrete), a traditional European wall (500 mm limestone), and a traditional Southwestern wall (400 mm adobe). 2.4 Schumann Resonance Signal-to-Noise Model (Exploratory) Schumann resonance (SR) coupling to the human body was estimated for eight representative environments spanning the range from pristine rural to underground transit. The natural SR magnetic field amplitude was taken as B SR = 1 pT at the first harmonic (7.83 Hz), consistent with published measurements (Nickolaenko & Hayakawa, 2002; Price, 2016). Electromagnetic skin depth at 7.83 Hz in biological tissue (σ ≈ 0.25 S/m average torso conductivity) is δ = √(2/ωμσ) = 360 m, confirming that the human body is effectively transparent at Schumann frequencies. Anthropogenic ELF noise levels were estimated from published measurements: 0.5 pT rural background (far from power lines), 3–5 pT suburban outdoor, 10–20 pT urban outdoor, 50–200 pT residential indoor (primarily 50/60 Hz and harmonics), 200–500 pT commercial indoor, and 500–1000 pT underground transit (Halgamuge, 2015; Lewczuk et al., 2014; Mild, 1987; Leitgeb, 2014). SNR was computed as 20 log 10 (B SR /B noise ). HRV–Schumann coupling was modeled using an empirically-anchored transfer function: r eff = r max × SNR/(1 + SNR), where r max = 0.31 is the maximum correlation coefficient observed by McCraty et al. (2017) during outdoor continuous monitoring. We acknowledge three limitations of this model: (a) the functional form is a first-order approximation—the true dose-response may be nonlinear or threshold-dependent; (b) the noise levels are order-of-magnitude estimates from heterogeneous measurement campaigns; (c) the coupling pathway (whether direct electromagnetic, via the autonomic nervous system, or via magnetoreception pathways) remains unresolved. We present this model as providing order-of-magnitude estimates for comparative purposes, not mechanistic claims. The functional form represents a Hill-type saturating response (Hill coefficient n = 1), the simplest monotonic function consistent with the boundary conditions r → 0 as SNR → 0 and r → rₘₐₓ as SNR → ∞. The induced EMF at the heart was computed using Faraday’s law: V = −dΦ/dt = 2πf × B × A, where A = 5 × 10⁻³ m² is the effective cardiac cross-sectional area. This was computed for both the Schumann signal (1 pT at 7.83 Hz) and typical indoor 50 Hz interference (100 nT). 2.5 Safety Gap Parameterization The safety gap between exposure levels and a putative non-thermal threshold was parameterized as: G = V threshold / (V base × √N × 10 −A/20 ), where V base is the single-source induced voltage from the FDTD model, N is the number of simultaneous incoherent sources, and A is building attenuation in dB. We evaluated this expression across a matrix of: (a) three cardiovascular states; (b) N = 1, 5, 10, 20, 50 sources; (c) building attenuation from 0 to 30 dB; and (d) threshold values of 0.1, 1, and 10 μV. This parameterized approach avoids speculative multiplication of uncertain factors and instead provides transparent lookup tables for different assumed conditions. 3. Results 3.1 RF Penetration and the Stress Paradox At ICNIRP occupational reference levels, the 0.9 GHz signal penetrated to myocardial depth with E-field amplitudes of 9.4–14.9 V/m depending on cardiovascular state (Figure 1). The 3.5 GHz signal was substantially attenuated by the intervening tissue layers (0.31–1.21 V/m at myocardial depth). The 28 GHz signal was confined entirely to the superficial 2–3 mm (skin depth ≈ 0.8 mm), with negligible penetration to deeper structures. Cardiovascular state modulated cardiac RF coupling in a physiologically paradoxical direction (Figure 2, Table 3). In the 1D slab model, vasodilation reduced myocardial E-field by 31% (Maxwell–Garnett) to 68% (parallel model), corresponding to SAR reductions of 50–86%; vasoconstriction increased E-field by 9–14%, with SAR increases of 14–30%. The 3D cylindrical phantom confirmed the vasoconstriction SAR modulation within 2.2% of the 1D prediction, while vasodilation showed larger geometry-dependent variation (56% deviation), as detailed in Appendix B. For subsequent analyses, we use the 3D-validated vasoconstriction values (SAR factor 1.165) as the primary result. The mechanism is physically straightforward: vasodilation increases blood volume in superficial tissues (skin, muscle), raising their effective conductivity and creating a more absorptive superficial layer that dissipates incident RF energy before it reaches the heart. Vasoconstriction withdraws blood from superficial tissues, reducing their conductivity and creating a more RF-transparent path to the myocardium. This “vascular waveguide” effect means that the sympathetic nervous system—by controlling blood distribution—effectively modulates the body’s internal RF antenna characteristics. The clinical significance of this paradox is that vasoconstriction maximizes cardiac RF coupling precisely during elevated sympathetic tone—when myocardial refractory periods are shortened, heart rate variability is reduced, and arrhythmia thresholds are lower (Schwartz et al., 1992; Taggart et al., 2011; Verrier & Antzelevitch, 2004). Standard SAR testing performed at rest captures neither the increased coupling nor the increased vulnerability. Table 3. Myocardial dosimetric quantities at ICNIRP occupational reference levels. V DC computed via Wust (2020) rectification model. Gap relative to 1 μV threshold. Freq. State E (V/m) SAR (W/kg) V_DC (μV) Gap (×) vs REST 0.9 GHz REST 13.6 0.106 0.075 13.3 1.00 VASODILAT. 9.4 0.053 0.052 19.2 0.69 VASOCONST. 14.9 0.121 0.082 12.2 1.09 3.5 GHz REST 0.97 0.0008 0.0007 1,430 1.00 VASODILAT. 0.31 0.0001 0.0002 5,000 0.32 VASOCONST. 1.21 0.0013 0.0009 1,110 1.25 28 GHz All states < 0.01 < 10⁻⁶ 10⁶ — 3.2 Building Material Effects Building material attenuation varied by more than an order of magnitude between modern and traditional construction at cardiac-relevant frequencies (Figure 3). At 0.9 GHz, clear glass provided only 0.3 dB attenuation; a typical modern residential wall (gypsum/insulation/gypsum) provided 1.2–2.5 dB; and a modern commercial facade 3.5–7 dB. By contrast, 500 mm limestone provided 10–18 dB and 400 mm adobe/cob 8–12 dB. Low-E coated glass was the exception among modern materials, providing 6–15 dB attenuation due to its thin metallic coating—designed for infrared reflection but coincidentally effective at RF frequencies. At 3.5 GHz, all materials provided greater attenuation, with modern residential achieving 3–6 dB and traditional construction 15–30 dB. At 28 GHz, even thin materials provided significant attenuation (clear glass 6 dB, concrete > 30 dB). For ELF signals, the picture inverts. Modern buildings with steel framing and rebar provide partial Faraday cage effects that attenuate the naturally weak Schumann resonances (< 1 pT). Traditional buildings with non-metallic walls are comparatively transparent at ELF. The net result: modern buildings simultaneously increase sub-6 GHz RF exposure (low attenuation) while decreasing natural ELF reference signals (metallic shielding). Traditional buildings did the opposite. 3.3 Schumann Resonance Masking in Indoor Environments (Exploratory) Electromagnetic skin depth at 7.83 Hz was 360 m in biological tissue, confirming body transparency at Schumann frequencies—the signal reaches the heart unimpeded by the body itself. However, the signal reaching the body depends critically on the electromagnetic environment (Table 4). Table 4. Schumann resonance SNR across representative environments. B SR = 1 pT assumed. Noise values from Halgamuge (2015), Lewczuk et al. (2014), Mild (1987). Environment B_noise (pT) SNR (dB) V_heart (pV) 50Hz V (μV) r_eff Coupling (%) Open rural field 0.5 +6.0 2.46 0.016 0.24 77 Rural residential 3 −10 2.46 0.078 0.03 10 Suburban outdoor 5 −14 2.46 0.16 0.02 6 Urban park 10 −20 2.46 0.31 0.009 3 Urban outdoor 20 −26 2.46 0.63 0.005 2 Residential indoor 100 −40 2.46 1.57 0.001 0.3 Commercial indoor 200 −46 2.46 3.14 < 0.001 0.2 Underground transit 500 −54 2.46 7.85 < 0.001 < 0.1 At a typical residential indoor location, the Schumann-induced EMF at the heart was 2.46 picovolts, while the 50 Hz power grid (B = 100 nT) induced 1.57 μV—a ratio on the order of 10⁴–10⁶ in favor of the anthropogenic signal (the precise value depends strongly on proximity to wiring: approximately 6 × 10⁵ at 100 nT, but ranging from 10⁴ in rural homes to >10⁶ near appliance motors). The Schumann signal is not merely attenuated indoors; it is overwhelmed by noise orders of magnitude stronger. A methodological caveat: the ELF noise values used in Table 4 represent broadband measurements dominated by 50/60 Hz and its harmonics, not Schumann-band specific measurements (7.5–8.5 Hz). Noise power spectral density at 7.83 Hz would be lower than the broadband total, meaning our SNR estimates represent conservative upper bounds on Schumann masking. However, standardized measurements of indoor magnetic noise spectral density specifically at Schumann frequencies do not exist in the published literature, precluding more precise estimates. The typical urban office worker experiences meaningful Schumann coupling (r eff > 0.1) during an estimated 2–8% of waking hours (dependent on commute mode, outdoor lunch habits, and urban density): briefly outdoors during commutes, perhaps during a lunch break in a park. The remaining >90% is spent in environments where Schumann SNR is −30 dB or worse. While these fractions are illustrative estimates (Tier C), the qualitative conclusion—that modern indoor living drastically reduces Schumann exposure—is robust. We estimate that during the period 1950–1970—with the proliferation of household electrical appliances, fluorescent lighting, and television—anthropogenic 50/60 Hz magnetic fields began exceeding Schumann resonance amplitude at typical indoor body locations (the precise date depends on urbanization level and electrification rate). This represents an environmental observation, not an epidemiological one: within approximately a single century, the dominant ELF signal at typical indoor body locations shifted from a ~1 pT natural resonance at 7.83 Hz to a ~100 nT artificial field at 50/60 Hz—a factor of ~10⁵ increase in amplitude at a frequency 6.4× higher than the evolutionary reference. 3.4 Time-Resolved Cardiac RF Exposure from 24h ECG To estimate the fraction of a typical day spent in each cardiovascular state, we analyzed 24-hour Holter ECG recordings from 18 healthy subjects (MIT-BIH Normal Sinus Rhythm Database, PhysioNet). These data are publicly available and fully de-identified; no institutional review board approval was required for secondary analysis. Heart rate was computed in non-overlapping 5-minute windows (4,582 windows total), and each window was classified by HR threshold into three bins: low-HR state (HR 80 bpm). We interpret these bins as predominantly reflecting vasodilation-dominant, resting, and sympathetically-activated hemodynamic states, respectively, based on the well-established relationship between heart rate and autonomic tone (Charkoudian, 2010), while acknowledging that HR > 80 bpm can also reflect mild physical activity, caffeine, or anxiety without obligate peripheral vasoconstriction. Heart rate provides a direct, reproducible measure without the interpretive difficulties associated with frequency-domain HRV metrics such as LF/HF ratio, which reflects baroreflex modulation rather than cardiac sympathetic tone (Billman, 2013; Goldstein et al., 2011). Complete Python scripts for Holter ECG classification and SAR computation are archived at https://github.com/ExeqTer91/stress-paradox-cardiac-dosimetry and provided as Supplementary Code. Table 5. HR-state distribution and implied cardiac SAR from 24h Holter ECG (N = 18 healthy subjects, 4,582 five-minute windows). States interpreted as reflecting predominant hemodynamic condition (see text). Metric Low-HR state Resting-HR High-HR state Population Range HR criterion 80 bpm — — Interpreted state Vasodilation Rest Vasoconstriction — — SAR factor (3D FDTD) 0.781 1.000 1.165 — — % of 24h recording 12.0% 52.9% 35.1% — — Population-weighted SAR — — — 1.032 0.919–1.104 Max continuous episode — — 670 min (11.2 h) — — Healthy subjects spent 35.1% of the 24-hour recording period in the high-HR state (HR > 80 bpm), interpreted as predominantly sympathetically-activated, during which cardiac SAR is elevated by 16.5% relative to standard resting-state testing according to the 3D FDTD model. The population-weighted SAR factor was 1.032, indicating that real-world cardiac RF exposure averaged 3.2% above the standard resting-state assumption. Individual variation was substantial: the subject with highest average HR (SAR factor 1.104) experienced 10.4% higher average cardiac exposure than standard testing predicts, while the subject with lowest average HR (0.919) experienced 8.1% lower exposure. The longest continuous high-HR episode was 670 minutes (11.2 hours), representing a sustained period of elevated cardiac RF coupling. Combining cardiovascular state variation with building attenuation yields a daily exposure profile (Table 6). For a typical urban office worker in a modern building, the 24-hour time-weighted SAR factor was 0.723 (dominated by indoor attenuation during work and sleep hours). In a traditional stone or brick building, the equivalent factor was 0.157—a 4.6× difference attributable entirely to construction material. The worst-case single period (outdoor cold morning commute with vasoconstriction) produced a SAR factor of 1.165, 16.5% above standard testing. These results demonstrate that the combination of cardiovascular state and building type produces exposure variation spanning nearly one order of magnitude within a single day. Table 6. Daily cardiac RF exposure profile combining cardiovascular state and building attenuation at 0.9 GHz. SAR factors from 3D FDTD validation. Period Hours Location CV State Bldg (dB) SAR factor 00:00–07:00 7 Indoor modern Vasodilation 1.2 0.593 07:00–08:00 1 Outdoor Vasoconstriction 0 1.165 08:00–12:00 4 Indoor modern Rest 1.2 0.758 12:00–13:00 1 Outdoor Rest 0 1.000 13:00–16:00 3 Indoor modern Rest 1.2 0.758 16:00–18:00 2 Indoor modern Vasoconstriction 1.2 0.883 18:00–19:00 1 Outdoor Rest 0 1.000 19:00–24:00 5 Indoor modern Vasodilation 1.2 0.593 — 24 — Time-weighted mean — 0.723 Equivalent 24h time-weighted SAR in traditional building (15 dB attenuation): 0.157. Modern/traditional ratio: 4.6×. 3.5 Parameterized Safety Gap Under standard single-source testing at 0.9 GHz (REST), the FDTD-computed myocardial induced voltage was 0.075 μV (via the Wust rectification model), yielding a safety gap of 13.3× relative to the theoretical 1 μV threshold (Table 7). Under vasoconstriction, the gap narrowed to 12.2×. Table 7. Parameterized safety gap (G = V thresh /V eff ) at 0.9 GHz for varying conditions. V thresh = 1 μV assumed. State N sources Field factor Atten. (dB) Atten. factor V_eff (μV) Gap (×) REST 1 1.00 0 1.00 0.075 13.3 REST 10 3.16 0 1.00 0.237 4.2 REST 10 3.16 1.2 (modern) 0.87 0.272 3.7 VASOCONST. 1 1.00 0 1.00 0.082 12.2 VASOCONST. 10 3.16 0 1.00 0.259 3.9 VASOCONST. 10 3.16 1.2 (modern) 0.87 0.298 3.4 VASODILAT. 10 3.16 0 1.00 0.164 6.1 REST 10 3.16 15 (traditional) 0.18 0.043 23.3 REST 50 7.07 0 1.00 0.530 1.9 Several observations emerge from this parameterization. First, the safety gap under standard testing (13.3×) may appear comfortable, but it erodes rapidly under realistic conditions. Ten incoherent sources during vasoconstriction in a modern building (the “stressed commuter on a crowded train” scenario) reduces the gap to 3.4×. Second, traditional building materials partially compensate: the same scenario in a limestone building maintains a gap of 23.3×. Third, the gap is critically sensitive to the assumed threshold: if 0.1 μV rather than 1 μV, all gaps decrease 10-fold, and the stressed multi-source scenario falls below unity. If 10 μV, all gaps increase 10-fold and are comfortable in all scenarios. We do not claim that the safety gap is insufficient—this depends entirely on the true non-thermal threshold, which remains experimentally unvalidated. We demonstrate that realistic conditions can erode the gap by a factor of 4× relative to standard testing, and that this erosion factor should be considered in safety margin design. Critically, the 4× erosion factor is threshold-independent: whether the true threshold is 0.1, 1, or 10 μV, the relative narrowing from multi-source stressed conditions versus single-source resting conditions remains the same. 4. Discussion Evidence classification. To aid interpretation, we classify our findings into three tiers. Tier A (directly computed): state-dependent SAR modulation (Section 3.1), building material attenuation (Section 3.2), safety gap parameterization (Section 3.4), and 3D cylindrical validation (Appendix B). Tier B (literature-anchored estimates): Schumann SNR across environments (Section 3.3), based on published noise measurements with order-of-magnitude uncertainty. Tier C (exploratory): HRV–Schumann transfer function, historical crossover timeline, and coupling fraction estimates. Tier C results are presented as illustrative parameterizations to motivate experimental work, not as quantitative conclusions. 4.1 The Vascular Waveguide: A New Dosimetric Variable The finding that cardiovascular state modulates cardiac RF coupling by 10–68% (geometry-dependent) identifies a previously unrecognized dosimetric variable. Current SAR assessment uses fixed-property anatomical phantoms, which effectively capture resting-state exposure. However, the vascular waveguide effect means that worst-case cardiac exposure occurs not at rest but during sympathetic activation, a state associated with altered electrophysiological stability. The vasoconstriction SAR increase proved robust across both 1D slab and 3D cylindrical phantom geometries, with agreement within 2.2% (Appendix B), though validation in anatomically realistic voxel models remains needed. Analysis of 24-hour Holter ECG recordings confirmed that healthy subjects spend 35% of the day in the high-HR bin, with individual cumulative SAR factors ranging from 0.92 to 1.10 relative to standard resting-state testing. This compounding of increased exposure with increased vulnerability is what we term the “stress paradox.” It has a direct clinical analog: exercise-induced vasodilation reduces cardiac RF coupling (10–50%, geometry-dependent) while exercise simultaneously increases cardiac resilience through vagal tone. Conversely, cold-stress or psychological stress-induced vasoconstriction increases RF coupling (14–17%) during sympathetic activation, a state associated with altered electrophysiological stability (Schwartz et al., 1992; Verrier & Antzelevitch, 2004). Whether this co-occurrence meaningfully changes cardiac risk requires experimental validation (see Prediction P1). We note that existing 3D voxel-based SAR models (e.g., Christ et al., 2010; Dimbylow, 2005; Gosselin et al., 2011) could readily incorporate state-dependent conductivity profiles. The tissue property databases (Hasgall et al., 2022) already contain temperature-dependent parameters; extending to hemodynamic state requires adding blood volume fraction as a parameter. We propose that future dosimetric standards include at minimum a “stress correction factor” representing the worst-case vasoconstriction scenario. 4.2 The Built Environment as Electromagnetic Filter Modern and traditional buildings create fundamentally different electromagnetic environments for their occupants. Modern construction allows sub-6 GHz RF to penetrate with minimal attenuation (0.3–2.5 dB at 0.9 GHz) while partially blocking natural ELF signals through metallic structural elements. Traditional construction strongly attenuates RF (8–28 dB at 0.9 GHz) while remaining largely transparent at ELF. This asymmetry has escaped systematic attention because RF safety and ELF signal environment are typically studied by different communities. RF engineers optimize building penetration loss for cellular coverage; ELF researchers measure Schumann resonances in purpose-built observatories far from civilization. Neither community has systematically characterized the joint RF/ELF environment experienced by human occupants. The historical trajectory is instructive. Before approximately 1880, humans lived in environments with continuous Schumann exposure and no anthropogenic RF. Between 1880 and 1950–1970 (depending on region), anthropogenic sources grew but remained below Schumann amplitude at most indoor locations. After the 1950–1970 electrification period, the crossover occurred: indoor ELF noise now consistently exceeds Schumann signal by orders of magnitude. This multi-decade transition represents an environmental variable that could be exploited epidemiologically, since building age, construction type, and electromagnetic environment are correlated. 4.3 Schumann Coupling: Exploratory Estimates The McCraty et al. (2017) correlations between HRV and Schumann power are robust but modest (r = 0.31). Our model suggests that indoor environments reduce this coupling to near zero, which is consistent with the observation that most HRV studies are conducted indoors and do not report Schumann-related effects. The prediction is specific: outdoor HRV studies should show Schumann correlations that indoor studies do not. We cannot claim that Schumann decoupling causes health effects. The coupling mechanism is not established—the induced cardiac EMF from Schumann (2.5 picovolts) is far below any known biological threshold. The observed HRV correlations may arise through indirect pathways: Schumann modulation of brainstem nuclei via EEG entrainment (Saroka et al., 2016), cryptochrome radical-pair magnetoreception affecting circadian clock function (Hore & Mouritsen, 2016), or confounding environmental variables. Our framework provides the quantitative basis for designing experiments to distinguish these possibilities. 4.4 Limitations This study has several important limitations. (1) The primary FDTD model is one-dimensional, capturing attenuation along a single penetration axis without diffraction, scattering, or body curvature. A 3D cylindrical phantom validation (Appendix B) confirmed that the vasoconstriction SAR modulation factor (1.15) matches the 1D prediction (1.14) within 2.2%, while the vasodilation effect was smaller in 3D (0.78 vs 0.50), consistent with wave diffraction around the phantom providing alternative propagation paths. The stress paradox—increased cardiac RF coupling during vasoconstriction—is thus robust across geometries, though the protective effect of vasodilation may be overestimated by the 1D model. (2) The vascular waveguide uses effective-medium approximations; real vasculature is fractal, heterogeneous, and dynamically pulsatile. (3) Schumann noise levels are order-of-magnitude estimates from heterogeneous published measurements, not standardized measurements at body level in defined environments. (4) The HRV coupling model is a first-order transfer function without mechanistic justification. (5) The ion channel rectification threshold (1 μV) is theoretical and experimentally unvalidated—our safety gap results scale linearly with the assumed threshold. (6) Multi-source exposure assumes incoherent addition (√N scaling); correlated sources or specific waveform interactions could yield different results. We have designed this study as a framework paper that identifies the relevant variables and their approximate magnitudes, not as a definitive risk assessment. Each component (state-dependent SAR, building attenuation, Schumann SNR, safety margins) requires dedicated experimental validation. 5. Falsifiable Predictions P1 (Moderate, MRI thermometry): Cardiac SAR, measured by MRI-based temperature mapping, will be 5–15% higher in subjects experiencing acute cold-pressor stress (vasoconstriction) versus warm immersion (vasodilation) during 0.9 GHz exposure at ICNIRP reference levels. P2 (Easy, field measurements): Schumann resonance SNR at typical indoor body locations will be below −30 dB, measurable with standard fluxgate magnetometry (sensitivity < 0.1 pT/√Hz). Modern buildings will show 6–20 dB lower Schumann SNR than matched traditional buildings. P3 (Moderate, paired outdoor/indoor): HRV spectral coherence with concurrent Schumann power will be statistically significant outdoors (r > 0.15) and non-significant indoors (r ≈ 0), within the same subjects on the same day, controlling for circadian and activity confounds. P4 (Hard, patch clamp): Whole-cell or single-channel recordings from L-type Ca 2+ channels (Ca v 1.2) or cardiac Na + channels (Na v 1.5) will show altered gating parameters (activation voltage, open probability, or recovery from inactivation) when exposed to amplitude-modulated RF producing induced transmembrane potentials > 1 μV. P5 (Easy, computational — partially confirmed): Incorporation of state-dependent tissue conductivity profiles into 3D models will reproduce the vasoconstriction SAR modulation reported here. Our 3D cylindrical phantom validation (Appendix B) confirmed the vasoconstriction effect within 2.2% of 1D predictions. Vasodilation effects were attenuated in 3D (22% vs 50–86% reduction), suggesting that 3D geometry partially compensates via diffraction. Full anatomical voxel phantoms (e.g., Ella, Duke) are expected to show intermediate values. P6 (Moderate, epidemiological): Cardiovascular health markers (HRV, resting heart rate, blood pressure variability) will show statistically different distributions between occupants of pre-1960 buildings (traditional construction) and post-1980 buildings (modern construction), after controlling for socioeconomic status, age, activity level, and other confounders. The direction of effect—if present—is predicted to favor traditional buildings. 6. Conclusion This study presents an integrated computational framework for assessing the cardiac electromagnetic environment in modern indoor settings. Four principal findings emerge. First, cardiovascular state modulates cardiac RF coupling by 10–68% (geometry-dependent), with vasoconstriction increasing and vasodilation decreasing cardiac exposure—a factor not captured by current resting-state SAR testing. The vasoconstriction effect proved robust across 1D slab and 3D cylindrical phantom geometries (within 2.2%, Appendix B). The compounding of increased RF coupling with increased cardiac vulnerability during sympathetic activation constitutes a “stress paradox” with specific clinical implications. Second, modern building materials provide minimal RF protection at sub-6 GHz frequencies (0.3–2.5 dB at 0.9 GHz) while traditional materials provide 8–28 dB. Combining cardiovascular state variation with building attenuation, the 24-hour time-weighted cardiac SAR factor in a modern building (0.72) exceeds that in a traditional building (0.16) by a factor of 4.6×. This difference has not been systematically addressed in population-level RF exposure assessments. Third, exploratory modeling suggests that modern indoor environments reduce Schumann resonance coupling to approximately 0.1–0.3% of outdoor values, with the 50 Hz power grid dominating the cardiac ELF environment by a factor of 10⁴–10⁶ (environment-dependent). The anthropogenic–Schumann crossover during the 1950–1970 period represents a substantial modification of the cardiac electromagnetic environment; whether this has physiological consequences remains an open experimental question. Fourth, under realistic multi-source, stress-state conditions, the parameterized safety gap between ICNIRP reference levels and a theoretical ion channel threshold narrows by approximately 4× compared to standard single-source resting-state testing. Whether this narrowing is clinically significant depends on the true non-thermal threshold, which remains to be experimentally determined. These findings do not establish that current RF exposure causes cardiac harm. They identify specific, quantifiable gaps between standard testing conditions and realistic worst-case exposure, and they provide a framework for designing the experiments necessary to resolve this question. References Alabdulgader A, McCraty R, Atkinson M, et al. Long-term study of HRV responses to changes in the solar and geomagnetic environment. Sci Rep. 2018;8:2722. Asp A, Karimi O, Fischer T. Measurement-based building material characterization at 1–60 GHz. In: Proc EuCAP; 2014:3075–3079. Balser M, Wagner CA. Observations of Earth–ionosphere cavity resonances. Nature. 1960;188(4751):638–641. Charkoudian N. Mechanisms and modifiers of reflex induced cutaneous vasodilation and vasoconstriction in humans. J Appl Physiol. 2010;109(4):1221–1228. Christ A, Gosselin M-C, Christopoulou M, Kühn S, Kuster N. Age-dependent tissue-specific exposure of cell phone users. Phys Med Biol. 2010;55(7):1767–1783. Crocco L, Ferrara V, Luongo M, et al. Measurement campaign of electromagnetic field penetration loss in building materials at 1.4 to 60 GHz. Measurement. 2019;139:360–367. Dimbylow PJ. Development of the female voxel phantom, NAOMI, and its application to calculations of induced current densities. Phys Med Biol. 2005;50(6):1047–1070. Gabriel C, Gabriel S, Corthout E. The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol. 1996;41(11):2231–2249. Gosselin M-C, Neufeld E, Moser H, et al. Development of a new generation of high-resolution anatomical models. Phys Med Biol. 2014;59(18):5287–5303. Halgamuge MN. Critical time delay of the pineal melatonin rhythm in humans and the effect of Schumann resonance and other ELF signals. J Pineal Res. 2015;58(3):248–253. Hasgall PA, Di Gennaro F, Baumgartner C, et al. IT’IS Database for Thermal and Electromagnetic Parameters of Biological Tissues, Version 4.1. 2022. itis.swiss/database. Hore PJ, Mouritsen H. The radical-pair mechanism of magnetoreception. Annu Rev Biophys. 2016;45:299–344. ICNIRP. Guidelines for limiting exposure to electromagnetic fields (100 kHz to 300 GHz). Health Phys. 2020;118(5):483–524. IEEE. IEEE Standard for Safety Levels with Respect to Human Exposure to Electric, Magnetic, and Electromagnetic Fields. IEEE C95.1-2019. ITU-R. Effects of building materials and structures on radiowave propagation above about 100 MHz. Recommendation ITU-R P.2040-2; 2021. Johnson JM, Proppe DW. Cardiovascular adjustments to heat stress. In: Fregly MJ, Blatteis CM, eds. Handbook of Physiology, Section 4: Environmental Physiology. Oxford UP; 1996:215–243. Laughlin MH, Davis MJ, Secher NH, et al. Peripheral circulation. Compr Physiol. 2012;2(1):321–447. Leitgeb N. Assessment of multiple frequency ELF electric and magnetic field exposure. Phys Med Biol. 2014;59(2):349–362. Lewczuk B, Redlarski G, Zak A, et al. Influence of electric, magnetic, and electromagnetic fields on the circadian system: current stage of knowledge. Biomed Res Int. 2014;2014:169459. McCraty R, Atkinson M, Stolc V, Alabdulgader A, Vainoras A, Ragulskis M. Synchronization of human autonomic nervous system rhythms with geomagnetic activity in human subjects. Int J Environ Res Public Health. 2017;14(7):770. Mild KH. Occupational exposure to radio-frequency electromagnetic fields. Proc IEEE. 1987;68(1):12–17. Mohapatra SN. Non-Invasive Cardiovascular Monitoring by Electrical Impedance Technique. London: Pitman Medical; 1981. Nickolaenko AP, Hayakawa M. Resonances in the Earth–Ionosphere Cavity. Springer; 2002. Panagopoulos DJ, Johansson O, Carlo GL. Evaluation of specific absorption rate as a dosimetric quantity for electromagnetic fields bioeffects. PLOS ONE. 2013;8(6):e62663. Price C. ELF electromagnetic waves from lightning: the Schumann resonances. Atmosphere. 2016;7(9):116. Rodriguez-Cano R, Marbán-Calzon P, et al. Building material electromagnetic characterization in the 1–100 GHz range for 5G applications. IEEE Access. 2021;9:166028–166040. Saroka KS, Vares DE, Persinger MA. Similar spectral power densities within the Schumann resonance and a large population of quantitative electroencephalographic profiles. PLOS ONE. 2016;11(1):e0146595. Schumann WO. Über die strahlungslosen Eigenschwingungen einer leitenden Kugel, die von einer Luftschicht und einer Ionosphärenhülle umgeben ist. Z Naturforsch A. 1952;7(2):149–154. Schwartz PJ, La Rovere MT, Vanoli E. Autonomic nervous system and sudden cardiac death: experimental basis and clinical observations. Circulation. 1992;85(1 Suppl):I77–I91. Standring S, ed. Gray’s Anatomy: The Anatomical Basis of Clinical Practice. 41st ed. Elsevier; 2016. Stavrou S, Saunders SR. Review of constitutive parameters of building materials. In: Proc ICAP; 2003:211–215. Taggart P, Boyett MR, Logantha S, Lambiase PD. Anger, emotion, and arrhythmias: from brain to heart. Front Physiol. 2011;2:67. Task Force of ESC and NASPE. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;93(5):1043–1065. Touitou Y, Bogdan A, Lambrozo J, Selmaoui B. Is melatonin the hormonal missing link between magnetic field effects and human diseases? Cancer Causes Control. 2006;17(4):547–552. Vermeeren G, Joseph W, Olivier C, Martens L. Statistical multipath exposure of a human in a realistic electromagnetic environment. Health Phys. 2008;94(4):345–354. Verrier RL, Antzelevitch C. Autonomic aspects of arrhythmogenesis: the enduring and the new. Curr Opin Cardiol. 2004;19(1):2–11. Wust P, Kortum B, Strauss U, Nadobny J, Zschaeck S, Beck M, et al. Non-thermal effects of radiofrequency electromagnetic fields. Sci Rep. 2020;10:13488. Polk C, Postow E, eds. CRC Handbook of Biological Effects of Electromagnetic Fields. Boca Raton: CRC Press; 1986. Schmid G, Cecil S, Überbacher R, et al. Dosimetric assessment of electromagnetic exposure of the brain and the heart using age-specific computational human models. Phys Med Biol. 2013;58(23):8455–8471. Behari J. Biological responses of mobile phone frequency exposure. Indian J Exp Biol. 2010;48(10):959–981. Cherry N. Schumann resonances, a plausible biophysical mechanism for the human health effects of solar/geomagnetic activity. Nat Hazards. 2002;26(3):279–331. Foerster M, Thielens A, Joseph W, Eeftens M, Röösli M. A prospective cohort study of adolescents’ memory performance and individual brain dose of microwave radiation from wireless communication. Environ Health Perspect. 2018;126(7):077007. Billman GE. The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Front Physiol. 2013;4:26. Goldstein DS, Bentho O, Park MY, Sharabi Y. Low-frequency power of heart rate variability is not a measure of cardiac sympathetic tone but may be a measure of modulation of cardiac autonomic outflows by baroreflexes. Exp Physiol. 2011;96(12):1255–1261. Additional Declarations The authors declare no competing interests. Supplementary Files AppendixB.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8935385","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594963015,"identity":"074994dc-6a1b-4b8b-b3ff-2119afdc2e4c","order_by":0,"name":"andrei ursachi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBADOSA2ACMgYGNIwK+asQGo1Jh0LYkNMPVgLfgAf3vv8Qc/av6k989u3vbhQ8HhfPn2w88ePGCwARmCFUicOZfY2HPMIHfGnWPFM2cYHLbccCbN3CCBIQ2nFgOJHMMG3gaD3IYbOcbMPAaHDQwYctgkEhgO49XS+LfBIF0epOUPUIt8/xuQlv94tTQDbUkwAGlhAGphuAG25QAev5wxnC1zzNhw4420YsYeg3QDgxvPzCQSDJKNcWnhb+8x+PimRk5e7kbyZoYff6yBDkt+Jvmjwk4WlxZcwICwklEwCkbBKBgFuAEA7plXE1d03eMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0002-6114-5011","institution":"Independent","correspondingAuthor":true,"prefix":"","firstName":"andrei","middleName":"","lastName":"ursachi","suffix":""}],"badges":[],"createdAt":"2026-02-21 19:29:22","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8935385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8935385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103440589,"identity":"fd65a7ed-5c94-4369-82ba-e98c714842c2","added_by":"auto","created_at":"2026-02-25 17:11:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":314242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eElectric field amplitude versus depth through the stratified thorax model at 0.9 GHz (solid), 3.5 GHz (dashed), and 28 GHz (dotted) for REST (black), VASODILATION (blue), and VASOCONSTRICTION (red) states. Myocardium region shaded (72–84 mm). Only sub-6 GHz signals achieve clinically significant cardiac penetration. ICNIRP occupational reference levels.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8935385/v1/86a74b1dd62c799eba8e9f43.png"},{"id":103440577,"identity":"c563db09-81f3-472a-83b0-88279fc1ce6d","added_by":"auto","created_at":"2026-02-25 17:11:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eState modulation of myocardial RF coupling at 0.9 GHz. (A) E-field at myocardial center versus cardiovascular state. (B) SAR at myocardial center. (C) Modulation factor relative to REST. Vasodilation provides 2–10× protection; vasoconstriction maximizes coupling. Error bars represent the range between Maxwell–Garnett and parallel mixing models.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8935385/v1/b987d7fa06000db940025381.png"},{"id":103440600,"identity":"2871315b-d02b-40fb-83f3-283af2b04174","added_by":"auto","created_at":"2026-02-25 17:11:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRF attenuation comparison: modern versus traditional building construction. (A) Single-material attenuation versus frequency. (B) Composite wall assemblies. Traditional materials provide 10–20 dB greater protection at sub-6 GHz cardiac-relevant frequencies. The crossover at 28 GHz reflects the fact that even thin modern materials attenuate mmWave effectively.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8935385/v1/07b65f6b56f19341b7f744ab.png"},{"id":103440625,"identity":"8a4648d5-44d0-4df0-b58e-c344aed6b702","added_by":"auto","created_at":"2026-02-25 17:12:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":381930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHistorical electromagnetic trajectory (1880–2030). (A) Anthropogenic RF power density at typical body locations (log scale). (B) Number of EM-emitting devices per capita. (C) Estimated Schumann coupling fraction for urban dwellers. The anthropogenic–Schumann crossover occurred circa 1960, when indoor ELF noise began consistently exceeding natural SR signal amplitude.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8935385/v1/9ee0f60671f01261622b683e.png"},{"id":103508191,"identity":"fc38ffdf-ccb0-4c84-9ce8-be101f67998f","added_by":"auto","created_at":"2026-02-26 13:47:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2205146,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8935385/v1/f5aca51a-21f7-44c4-9c8c-336d0697010a.pdf"},{"id":103440576,"identity":"bc124f0c-d1ca-49b5-aa2c-da572d53e8a5","added_by":"auto","created_at":"2026-02-25 17:11:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16169,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.docx","url":"https://assets-eu.researchsquare.com/files/rs-8935385/v1/4972baf06bb2ceb41b514ffd.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Stress Paradox: Cardiovascular State Modulates Cardiac Radiofrequency Absorption While Modern Buildings Attenuate Natural Electromagnetic References\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human heart operates within an electromagnetic environment that has changed more dramatically in the past century than in all prior human evolution. The cardiac magnetic field (50\u0026ndash;100 pT) evolved alongside stable natural references: the Schumann resonances at 7.83 Hz and harmonics, generated by global lightning activity within the Earth\u0026ndash;ionosphere cavity (Schumann, 1952; Balser \u0026amp; Wagner, 1960). This natural electromagnetic background has been progressively overwhelmed by anthropogenic sources\u0026mdash;power distribution networks, telecommunications infrastructure, and personal wireless devices\u0026mdash;creating indoor environments where artificial fields exceed natural signals by five to six orders of magnitude (Halgamuge, 2015; Lewczuk et al., 2014).\u003c/p\u003e\n\u003cp\u003eCurrent RF safety guidelines (ICNIRP, 2020; IEEE C95.1-2019) establish exposure limits based primarily on thermal considerations, derived from short-duration, single-source exposures of fixed-conductivity tissue models. While these standards have been extensively refined for specific absorption rate (SAR) assessment (Christ et al., 2010; Dimbylow, 2005; Gosselin et al., 2011), several features of realistic exposure remain unaddressed.\u003c/p\u003e\n\u003cp\u003eFirst, cardiovascular state modulates tissue conductivity. Blood (\u0026sigma; \u0026asymp; 1.5 S/m at 0.9 GHz) is the most conductive tissue, and its volume distribution varies substantially with autonomic state: vasodilation increases peripheral blood volume by up to 40%, while vasoconstriction reduces it by up to 35% (Charkoudian, 2010; Johnson \u0026amp; Proppe, 1996). Since blood vessels form connected high-conductivity paths from skin to deep organs, these changes alter the body\u0026rsquo;s internal RF propagation characteristics. To our knowledge, this state dependence has not been previously modeled for cardiac RF dosimetry.\u003c/p\u003e\n\u003cp\u003eSecond, modern building materials provide minimal RF shielding at sub-6 GHz frequencies while effectively attenuating the natural ELF signals\u0026mdash;particularly through metallic reinforcement, steel framing, and energy-efficient glazing that acts as a Faraday cage at low frequencies (Stavrou \u0026amp; Saunders, 2003; Asp et al., 2014; Rodriguez-Cano et al., 2021). Traditional construction materials (adobe, limestone, timber) provided substantially greater RF attenuation while remaining more transparent at ELF (Crocco et al., 2019).\u003c/p\u003e\n\u003cp\u003eThird, multi-source exposure is now ubiquitous. A typical indoor environment contains 10\u0026ndash;50 simultaneous RF emitters (Wi-Fi access points, Bluetooth devices, cellular base stations, personal devices). Incoherent multi-source exposure scales as \u0026radic;N in field amplitude, potentially narrowing safety margins assumed from single-source testing (Vermeeren et al., 2015).\u003c/p\u003e\n\u003cp\u003eFourth, evidence suggests coupling between natural ELF fields and human autonomic function. McCraty et al. (2017) demonstrated statistically significant correlations between HRV indices and Schumann resonance power over 31 continuous days (r = 0.31, p \u0026lt; 0.05 for SDNN versus the first SR harmonic). Saroka et al. (2016) reported transient coherence between Schumann resonances and human EEG. Alabdulgader et al. (2018) extended these findings across multiple geographic locations. While the coupling mechanism remains debated, these empirical correlations motivate quantifying what modern indoor environments have done to the natural SR signal at body level.\u003c/p\u003e\n\u003cp\u003eRegarding non-thermal RF bioeffects, Wust et al. (2020) proposed a rectification mechanism at voltage-gated ion channels predicting biological sensitivity at approximately 1 \u0026mu;V of induced transmembrane potential. Panagopoulos et al. (2013) developed a complementary forced-oscillation model. Neither mechanism is fully validated experimentally, but both predict that the gap between safety limits and biological sensitivity may be narrower than thermal analysis suggests. We treat the 1 \u0026mu;V threshold parametrically, presenting results across a range of assumed thresholds.\u003c/p\u003e\n\u003cp\u003eThis paper integrates these four elements into a single computational framework. We model cardiac RF coupling as a function of cardiovascular state, quantify building material effects on both RF and ELF signal environments, estimate the loss of Schumann resonance coupling in modern indoor environments, and parameterize the resulting safety margins. The framework identifies specific, experimentally testable predictions for worst-case cardiac exposure scenarios.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch2\u003e2.1 FDTD Stratified Thorax Model\u003c/h2\u003e\n\u003cp\u003eA one-dimensional FDTD model was constructed with nine tissue layers representing an anterior chest wall cross-section (Table 1), based on anatomical dimensions from Standring (2016) and Christ et al. (2010). Frequency-dependent complex permittivity (\u0026epsilon;* = \u0026epsilon;\u0026rsquo; \u0026minus; j\u0026epsilon;\u0026Prime;) was computed using four-pole Cole\u0026ndash;Cole parameters from the IT\u0026rsquo;IS Foundation database, version 4.1 (Hasgall et al., 2022), which derives from the foundational measurements of Gabriel et al. (1996). Simulations were performed at three frequencies spanning the current and next-generation telecommunications bands: 0.9 GHz (LTE/GSM), 3.5 GHz (5G mid-band, n78), and 28 GHz (5G mmWave, n257).\u003c/p\u003e\n\u003cp\u003eIncident power density was set to ICNIRP (2020) occupational reference levels: 45 W/m\u0026sup2; at 0.9 GHz, 45 W/m\u0026sup2; at 3.5 GHz, and 100 W/m\u0026sup2; at 28 GHz. FDTD spatial discretization was \u0026lambda;/20 at the highest frequency in each tissue, with perfectly matched layer (PML) boundary conditions (6 layers, polynomial grading). Time step satisfied the Courant stability condition (\u0026Delta;t = 0.9\u0026Delta;x/c). Each simulation ran for 20 RF cycles to ensure steady-state convergence, verified by monitoring field amplitude variation \u0026lt; 0.1% over the final five cycles. A convergence study varying spatial discretization from \u0026lambda;/10 to \u0026lambda;/40 confirmed less than 2% variation in myocardial E-field, with \u0026lambda;/20 providing adequate accuracy.\u003c/p\u003e\n\u003cp\u003eWe acknowledge that a 1D model does not capture diffraction, body curvature, or realistic antenna near-field patterns. However, the stratified slab geometry isolates the effect of interest\u0026mdash;cardiovascular state modulation of layer conductivities\u0026mdash;and allows transparent reporting of how each tissue layer contributes to cardiac shielding. 3D voxel-based models would provide more accurate absolute SAR values but would not change the qualitative finding that blood volume redistribution alters the conductivity profile along penetration paths to the heart. For calibration, Christ et al. (2010) report peak SAR₁₀g of 0.08\u0026ndash;0.15 W/kg in the heart region at 900 MHz for 1 W input power in their 3D voxel model; our 1D resting-state value of 0.106 W/kg falls within this range, supporting the quantitative plausibility of our simplified geometry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Nine-layer stratified thorax model. Dielectric properties at 0.9 GHz, REST state. Layer thicknesses based on anatomical data (Standring, 2016; Christ et al., 2010).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"387\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLayer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ed (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026epsilon;\u0026prime;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma; (S/m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnatomical basis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eSkin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0\u0026ndash;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eEpidermis + dermis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eSubcutaneous fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eAdipose tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ePectoral muscle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12\u0026ndash;27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSkeletal muscle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eSternum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e27\u0026ndash;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCortical bone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eIntercostal tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e35\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMuscle/connective\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eLung (inflated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e40\u0026ndash;70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eAlveolar tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003ePericardium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e70\u0026ndash;72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eSerous membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eMyocardium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e72\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eCardiac muscle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eVentricular blood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003e62.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e84\u0026ndash;104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eWhole blood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e2.2 Cardiovascular State-Dependent Tissue Model\u003c/h2\u003e\n\u003cp\u003eThree cardiovascular states were defined based on published hemodynamic data, representing the physiological range encountered in daily life:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eREST\u0026nbsp;\u003c/strong\u003e(baseline): Normal resting hemodynamics, heart rate 72 bpm, nominal tissue properties as in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVASODILATION\u0026nbsp;\u003c/strong\u003e(exercise/heat): Vessel diameter 1.2\u0026times; baseline, blood volume fraction 1.4\u0026times;, skin hydration increased (wet skin model, \u0026sigma;\u003csub\u003eskin\u003c/sub\u003e \u0026times; 1.8), HR 55 bpm. These values represent moderate thermoregulatory vasodilation, as documented in Charkoudian (2010) and Johnson \u0026amp; Proppe (1996). Muscle blood volume increase based on Laughlin et al. (2012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVASOCONSTRICTION\u0026nbsp;\u003c/strong\u003e(cold stress/mental stress): Vessel diameter 0.8\u0026times;, blood volume fraction 0.65\u0026times;, dry skin (\u0026sigma;\u003csub\u003eskin\u003c/sub\u003e \u0026times; 0.7), HR 85 bpm. These values represent sympathetically-mediated cutaneous vasoconstriction during cold exposure or acute psychological stress (Charkoudian, 2010; Taggart et al., 2011). The reduction in peripheral blood volume increases core redistribution but reduces superficial conductivity.\u003c/p\u003e\n\u003cp\u003eEffective tissue conductivity for blood-perfused layers was computed using two complementary approaches to bound the uncertainty. The Maxwell\u0026ndash;Garnett effective medium approximation models blood vessels as dilute spherical inclusions: \u0026sigma;\u003csub\u003eeff\u003c/sub\u003e = \u0026sigma;\u003csub\u003etissue\u003c/sub\u003e(1 + 3f\u0026beta;/(1 \u0026minus; f\u0026beta;)), where f is blood volume fraction and \u0026beta; = (\u0026sigma;\u003csub\u003eblood\u003c/sub\u003e \u0026minus; \u0026sigma;\u003csub\u003etissue\u003c/sub\u003e)/(\u0026sigma;\u003csub\u003eblood\u003c/sub\u003e + 2\u0026sigma;\u003csub\u003etissue\u003c/sub\u003e). This provides a lower bound on vascular effects. The parallel conductivity model assumes aligned vascular channels: \u0026sigma;\u003csub\u003eeff\u003c/sub\u003e = f\u0026sigma;\u003csub\u003eblood\u003c/sub\u003e + (1\u0026minus;f)\u0026sigma;\u003csub\u003etissue\u003c/sub\u003e. This provides an upper bound. Results are reported for both models.\u003c/p\u003e\n\u003cp\u003eTemperature-dependent corrections followed Mohapatra (1981): +1.33%/\u0026deg;C for conductivity, \u0026minus;0.3%/\u0026deg;C for permittivity, using +2\u0026deg;C skin surface temperature for vasodilation and \u0026minus;4\u0026deg;C for vasoconstriction relative to the 33\u0026deg;C resting baseline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e State-dependent effective conductivity (\u0026sigma;\u003csub\u003eeff\u003c/sub\u003e, S/m) at 0.9 GHz by tissue layer (Maxwell\u0026ndash;Garnett model). Blood volume fraction f and skin conductivity multiplier shown. All values from IT\u0026rsquo;IS v4.1 base properties with state modifications as described above.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLayer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ef (REST)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma; REST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ef (VASOD.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma; VASOD.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ef (VASOCON.)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026sigma; VASOCON.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSkin (dry/wet)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.57 (\u0026times;1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.61 (\u0026times;0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSubcutaneous fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSkeletal muscle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eIntercostal muscle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eRib (cortical bone)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLung (inflated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePericardium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eBlood (coronary)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMyocardium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFDTD source: x-polarized plane wave at ICNIRP occupational reference level (E = 61.4 V/m at 0.9 GHz). SAR measured at myocardial center node. Spatial discretization \u0026Delta;x = 0.5 mm (1D) and 2.0 mm (3D). Complete parameter tables, FDTD implementation code, Holter ECG classification scripts, and building attenuation models are archived at https://github.com/ExeqTer91/stress-paradox-cardiac-dosimetry.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e2.3 Building Material Attenuation Model\u003c/h2\u003e\n\u003cp\u003eRF transmission loss through building materials was computed using plane-wave transmission line theory following ITU-R P.2040-2 (2021). Each material was characterized by thickness-dependent complex transmission coefficient T(f) incorporating multiple internal reflections. Composite wall assemblies were modeled as cascaded transmission matrices.\u003c/p\u003e\n\u003cp\u003eSix material categories were evaluated: (1) clear float glass (6 mm); (2) low-emissivity coated glass (6 mm, metallic coating, \u0026sigma;\u003csub\u003ecoating\u003c/sub\u003e = 10\u003csup\u003e5\u003c/sup\u003e S/m, 50 nm thickness); (3) gypsum board (12.5 mm); (4) reinforced concrete (200 mm, 1% steel rebar by volume); (5) adobe/cob (400 mm); and (6) limestone masonry (500 mm). Material dielectric properties were taken from Stavrou \u0026amp; Saunders (2003), ITU-R P.2040-2 (2021), and Rodriguez-Cano et al. (2021).\u003c/p\u003e\n\u003cp\u003eComposite wall assemblies were defined for representative building types: a modern residential wall (12.5 mm gypsum + 100 mm mineral wool + 12.5 mm gypsum), a modern commercial facade (6 mm low-E glass + 200 mm reinforced concrete), a traditional European wall (500 mm limestone), and a traditional Southwestern wall (400 mm adobe).\u003c/p\u003e\n\u003ch2\u003e2.4 Schumann Resonance Signal-to-Noise Model (Exploratory)\u003c/h2\u003e\n\u003cp\u003eSchumann resonance (SR) coupling to the human body was estimated for eight representative environments spanning the range from pristine rural to underground transit. The natural SR magnetic field amplitude was taken as B\u003csub\u003eSR\u003c/sub\u003e = 1 pT at the first harmonic (7.83 Hz), consistent with published measurements (Nickolaenko \u0026amp; Hayakawa, 2002; Price, 2016). Electromagnetic skin depth at 7.83 Hz in biological tissue (\u0026sigma; \u0026asymp; 0.25 S/m average torso conductivity) is \u0026delta; = \u0026radic;(2/\u0026omega;\u0026mu;\u0026sigma;) = 360 m, confirming that the human body is effectively transparent at Schumann frequencies.\u003c/p\u003e\n\u003cp\u003eAnthropogenic ELF noise levels were estimated from published measurements: 0.5 pT rural background (far from power lines), 3\u0026ndash;5 pT suburban outdoor, 10\u0026ndash;20 pT urban outdoor, 50\u0026ndash;200 pT residential indoor (primarily 50/60 Hz and harmonics), 200\u0026ndash;500 pT commercial indoor, and 500\u0026ndash;1000 pT underground transit (Halgamuge, 2015; Lewczuk et al., 2014; Mild, 1987; Leitgeb, 2014). SNR was computed as 20 log\u003csub\u003e10\u003c/sub\u003e(B\u003csub\u003eSR\u003c/sub\u003e/B\u003csub\u003enoise\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eHRV\u0026ndash;Schumann coupling was modeled using an empirically-anchored transfer function: r\u003csub\u003eeff\u003c/sub\u003e = r\u003csub\u003emax\u003c/sub\u003e \u0026times; SNR/(1 + SNR), where r\u003csub\u003emax\u003c/sub\u003e = 0.31 is the maximum correlation coefficient observed by McCraty et al. (2017) during outdoor continuous monitoring. We acknowledge three limitations of this model: (a) the functional form is a first-order approximation\u0026mdash;the true dose-response may be nonlinear or threshold-dependent; (b) the noise levels are order-of-magnitude estimates from heterogeneous measurement campaigns; (c) the coupling pathway (whether direct electromagnetic, via the autonomic nervous system, or via magnetoreception pathways) remains unresolved. We present this model as providing order-of-magnitude estimates for comparative purposes, not mechanistic claims. The functional form represents a Hill-type saturating response (Hill coefficient n = 1), the simplest monotonic function consistent with the boundary conditions r \u0026rarr; 0 as SNR \u0026rarr; 0 and r \u0026rarr; rₘₐₓ as SNR \u0026rarr; \u0026infin;.\u003c/p\u003e\n\u003cp\u003eThe induced EMF at the heart was computed using Faraday\u0026rsquo;s law: V = \u0026minus;d\u0026Phi;/dt = 2\u0026pi;f \u0026times; B \u0026times; A, where A = 5 \u0026times; 10⁻\u0026sup3; m\u0026sup2; is the effective cardiac cross-sectional area. This was computed for both the Schumann signal (1 pT at 7.83 Hz) and typical indoor 50 Hz interference (100 nT).\u003c/p\u003e\n\u003ch2\u003e2.5 Safety Gap Parameterization\u003c/h2\u003e\n\u003cp\u003eThe safety gap between exposure levels and a putative non-thermal threshold was parameterized as: G = V\u003csub\u003ethreshold\u003c/sub\u003e / (V\u003csub\u003ebase\u003c/sub\u003e \u0026times; \u0026radic;N \u0026times; 10\u003csup\u003e\u0026minus;A/20\u003c/sup\u003e), where V\u003csub\u003ebase\u003c/sub\u003e is the single-source induced voltage from the FDTD model, N is the number of simultaneous incoherent sources, and A is building attenuation in dB. We evaluated this expression across a matrix of: (a) three cardiovascular states; (b) N = 1, 5, 10, 20, 50 sources; (c) building attenuation from 0 to 30 dB; and (d) threshold values of 0.1, 1, and 10 \u0026mu;V. This parameterized approach avoids speculative multiplication of uncertain factors and instead provides transparent lookup tables for different assumed conditions.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 RF Penetration and the Stress Paradox\u003c/h2\u003e\n\u003cp\u003eAt ICNIRP occupational reference levels, the 0.9 GHz signal penetrated to myocardial depth with E-field amplitudes of 9.4\u0026ndash;14.9 V/m depending on cardiovascular state (Figure 1). The 3.5 GHz signal was substantially attenuated by the intervening tissue layers (0.31\u0026ndash;1.21 V/m at myocardial depth). The 28 GHz signal was confined entirely to the superficial 2\u0026ndash;3 mm (skin depth \u0026asymp; 0.8 mm), with negligible penetration to deeper structures.\u003c/p\u003e\n\u003cp\u003eCardiovascular state modulated cardiac RF coupling in a physiologically paradoxical direction (Figure 2, Table 3). In the 1D slab model, vasodilation reduced myocardial E-field by 31% (Maxwell\u0026ndash;Garnett) to 68% (parallel model), corresponding to SAR reductions of 50\u0026ndash;86%; vasoconstriction increased E-field by 9\u0026ndash;14%, with SAR increases of 14\u0026ndash;30%. The 3D cylindrical phantom confirmed the vasoconstriction SAR modulation within 2.2% of the 1D prediction, while vasodilation showed larger geometry-dependent variation (56% deviation), as detailed in Appendix B. For subsequent analyses, we use the 3D-validated vasoconstriction values (SAR factor 1.165) as the primary result.\u003c/p\u003e\n\u003cp\u003eThe mechanism is physically straightforward: vasodilation increases blood volume in superficial tissues (skin, muscle), raising their effective conductivity and creating a more absorptive superficial layer that dissipates incident RF energy before it reaches the heart. Vasoconstriction withdraws blood from superficial tissues, reducing their conductivity and creating a more RF-transparent path to the myocardium. This \u0026ldquo;vascular waveguide\u0026rdquo; effect means that the sympathetic nervous system\u0026mdash;by controlling blood distribution\u0026mdash;effectively modulates the body\u0026rsquo;s internal RF antenna characteristics.\u003c/p\u003e\n\u003cp\u003eThe clinical significance of this paradox is that vasoconstriction maximizes cardiac RF coupling precisely during elevated sympathetic tone\u0026mdash;when myocardial refractory periods are shortened, heart rate variability is reduced, and arrhythmia thresholds are lower (Schwartz et al., 1992; Taggart et al., 2011; Verrier \u0026amp; Antzelevitch, 2004). Standard SAR testing performed at rest captures neither the increased coupling nor the increased vulnerability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Myocardial dosimetric quantities at ICNIRP occupational reference levels. V\u003csub\u003eDC\u003c/sub\u003e computed via Wust (2020) rectification model. Gap relative to 1 \u0026mu;V threshold.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"491\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFreq.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE (V/m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAR (W/kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV_DC (\u0026mu;V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGap (\u0026times;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003evs REST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.9 GHz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVASODILAT.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVASOCONST.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.5 GHz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1,430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVASODILAT.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVASOCONST.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1,110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e28 GHz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eAll states\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt; 10⁻⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026lt; 10⁻⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026gt; 10⁶\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e3.2 Building Material Effects\u003c/h2\u003e\n\u003cp\u003eBuilding material attenuation varied by more than an order of magnitude between modern and traditional construction at cardiac-relevant frequencies (Figure 3). At 0.9 GHz, clear glass provided only 0.3 dB attenuation; a typical modern residential wall (gypsum/insulation/gypsum) provided 1.2\u0026ndash;2.5 dB; and a modern commercial facade 3.5\u0026ndash;7 dB. By contrast, 500 mm limestone provided 10\u0026ndash;18 dB and 400 mm adobe/cob 8\u0026ndash;12 dB.\u003c/p\u003e\n\u003cp\u003eLow-E coated glass was the exception among modern materials, providing 6\u0026ndash;15 dB attenuation due to its thin metallic coating\u0026mdash;designed for infrared reflection but coincidentally effective at RF frequencies. At 3.5 GHz, all materials provided greater attenuation, with modern residential achieving 3\u0026ndash;6 dB and traditional construction 15\u0026ndash;30 dB. At 28 GHz, even thin materials provided significant attenuation (clear glass 6 dB, concrete \u0026gt; 30 dB).\u003c/p\u003e\n\u003cp\u003eFor ELF signals, the picture inverts. Modern buildings with steel framing and rebar provide partial Faraday cage effects that attenuate the naturally weak Schumann resonances (\u0026lt; 1 pT). Traditional buildings with non-metallic walls are comparatively transparent at ELF. The net result: modern buildings simultaneously \u003cem\u003eincrease\u003c/em\u003e sub-6 GHz RF exposure (low attenuation) while \u003cem\u003edecreasing\u003c/em\u003e natural ELF reference signals (metallic shielding). Traditional buildings did the opposite.\u003c/p\u003e\n\u003ch2\u003e3.3 Schumann Resonance Masking in Indoor Environments (Exploratory)\u003c/h2\u003e\n\u003cp\u003eElectromagnetic skin depth at 7.83 Hz was 360 m in biological tissue, confirming body transparency at Schumann frequencies\u0026mdash;the signal reaches the heart unimpeded by the body itself. However, the signal reaching the body depends critically on the electromagnetic environment (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Schumann resonance SNR across representative environments. B\u003csub\u003eSR\u003c/sub\u003e = 1 pT assumed. Noise values from Halgamuge (2015), Lewczuk et al. (2014), Mild (1987).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnvironment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB_noise (pT)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR (dB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV_heart (pV)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50Hz V (\u0026mu;V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003er_eff\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoupling (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eOpen rural field\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e+6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eRural residential\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026minus;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eSuburban outdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026minus;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eUrban park\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026minus;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eUrban outdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026minus;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eResidential indoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026minus;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eCommercial indoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026minus;46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eUnderground transit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026minus;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026lt; 0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt a typical residential indoor location, the Schumann-induced EMF at the heart was 2.46 picovolts, while the 50 Hz power grid (B = 100 nT) induced 1.57 \u0026mu;V\u0026mdash;a ratio on the order of 10⁴\u0026ndash;10⁶ in favor of the anthropogenic signal (the precise value depends strongly on proximity to wiring: approximately 6 \u0026times; 10⁵ at 100 nT, but ranging from 10⁴ in rural homes to \u0026gt;10⁶ near appliance motors). The Schumann signal is not merely attenuated indoors; it is overwhelmed by noise orders of magnitude stronger.\u003c/p\u003e\n\u003cp\u003eA methodological caveat: the ELF noise values used in Table 4 represent broadband measurements dominated by 50/60 Hz and its harmonics, not Schumann-band specific measurements (7.5\u0026ndash;8.5 Hz). Noise power spectral density at 7.83 Hz would be lower than the broadband total, meaning our SNR estimates represent conservative upper bounds on Schumann masking. However, standardized measurements of indoor magnetic noise spectral density specifically at Schumann frequencies do not exist in the published literature, precluding more precise estimates.\u003c/p\u003e\n\u003cp\u003eThe typical urban office worker experiences meaningful Schumann coupling (r\u003csub\u003eeff\u003c/sub\u003e \u0026gt; 0.1) during an estimated 2\u0026ndash;8% of waking hours (dependent on commute mode, outdoor lunch habits, and urban density): briefly outdoors during commutes, perhaps during a lunch break in a park. The remaining \u0026gt;90% is spent in environments where Schumann SNR is \u0026minus;30 dB or worse. While these fractions are illustrative estimates (Tier C), the qualitative conclusion\u0026mdash;that modern indoor living drastically reduces Schumann exposure\u0026mdash;is robust.\u003c/p\u003e\n\u003cp\u003eWe estimate that during the period 1950\u0026ndash;1970\u0026mdash;with the proliferation of household electrical appliances, fluorescent lighting, and television\u0026mdash;anthropogenic 50/60 Hz magnetic fields began exceeding Schumann resonance amplitude at typical indoor body locations (the precise date depends on urbanization level and electrification rate). This represents an environmental observation, not an epidemiological one: within approximately a single century, the dominant ELF signal at typical indoor body locations shifted from a ~1 pT natural resonance at 7.83 Hz to a ~100 nT artificial field at 50/60 Hz\u0026mdash;a factor of ~10⁵ increase in amplitude at a frequency 6.4\u0026times; higher than the evolutionary reference.\u003c/p\u003e\n\u003ch2\u003e3.4 Time-Resolved Cardiac RF Exposure from 24h ECG\u003c/h2\u003e\n\u003cp\u003eTo estimate the fraction of a typical day spent in each cardiovascular state, we analyzed 24-hour Holter ECG recordings from 18 healthy subjects (MIT-BIH Normal Sinus Rhythm Database, PhysioNet). These data are publicly available and fully de-identified; no institutional review board approval was required for secondary analysis. Heart rate was computed in non-overlapping 5-minute windows (4,582 windows total), and each window was classified by HR threshold into three bins: low-HR state (HR \u0026lt; 60 bpm), resting-HR state (60\u0026ndash;80 bpm), and high-HR state (HR \u0026gt; 80 bpm). We interpret these bins as predominantly reflecting vasodilation-dominant, resting, and sympathetically-activated hemodynamic states, respectively, based on the well-established relationship between heart rate and autonomic tone (Charkoudian, 2010), while acknowledging that HR \u0026gt; 80 bpm can also reflect mild physical activity, caffeine, or anxiety without obligate peripheral vasoconstriction. Heart rate provides a direct, reproducible measure without the interpretive difficulties associated with frequency-domain HRV metrics such as LF/HF ratio, which reflects baroreflex modulation rather than cardiac sympathetic tone (Billman, 2013; Goldstein et al., 2011). Complete Python scripts for Holter ECG classification and SAR computation are archived at https://github.com/ExeqTer91/stress-paradox-cardiac-dosimetry and provided as Supplementary Code.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e HR-state distribution and implied cardiac SAR from 24h Holter ECG (N = 18 healthy subjects, 4,582 five-minute windows). States interpreted as reflecting predominant hemodynamic condition (see text).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetric\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-HR state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResting-HR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-HR state\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eHR criterion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026lt; 60 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e60\u0026ndash;80 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026gt; 80 bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eInterpreted state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVasodilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVasoconstriction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eSAR factor (3D FDTD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e% of 24h recording\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e12.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e52.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e35.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003ePopulation-weighted SAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.919\u0026ndash;1.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003eMax continuous episode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e670 min (11.2 h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHealthy subjects spent 35.1% of the 24-hour recording period in the high-HR state (HR \u0026gt; 80 bpm), interpreted as predominantly sympathetically-activated, during which cardiac SAR is elevated by 16.5% relative to standard resting-state testing according to the 3D FDTD model. The population-weighted SAR factor was 1.032, indicating that real-world cardiac RF exposure averaged 3.2% above the standard resting-state assumption. Individual variation was substantial: the subject with highest average HR (SAR factor 1.104) experienced 10.4% higher average cardiac exposure than standard testing predicts, while the subject with lowest average HR (0.919) experienced 8.1% lower exposure. The longest continuous high-HR episode was 670 minutes (11.2 hours), representing a sustained period of elevated cardiac RF coupling.\u003c/p\u003e\n\u003cp\u003eCombining cardiovascular state variation with building attenuation yields a daily exposure profile (Table 6). For a typical urban office worker in a modern building, the 24-hour time-weighted SAR factor was 0.723 (dominated by indoor attenuation during work and sleep hours). In a traditional stone or brick building, the equivalent factor was 0.157\u0026mdash;a 4.6\u0026times; difference attributable entirely to construction material. The worst-case single period (outdoor cold morning commute with vasoconstriction) produced a SAR factor of 1.165, 16.5% above standard testing. These results demonstrate that the combination of cardiovascular state and building type produces exposure variation spanning nearly one order of magnitude within a single day.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u003c/strong\u003e Daily cardiac RF exposure profile combining cardiovascular state and building attenuation at 0.9 GHz. SAR factors from 3D FDTD validation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeriod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHours\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCV State\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBldg (dB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAR factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e00:00\u0026ndash;07:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eIndoor modern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVasodilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e07:00\u0026ndash;08:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eOutdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVasoconstriction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e08:00\u0026ndash;12:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eIndoor modern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12:00\u0026ndash;13:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eOutdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e13:00\u0026ndash;16:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eIndoor modern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e16:00\u0026ndash;18:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eIndoor modern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVasoconstriction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e18:00\u0026ndash;19:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eOutdoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e19:00\u0026ndash;24:00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eIndoor modern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eVasodilation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eTime-weighted mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eEquivalent 24h time-weighted SAR in traditional building (15 dB attenuation): 0.157. Modern/traditional ratio: 4.6\u0026times;.\u003c/em\u003e\u003c/p\u003e\n\u003ch2\u003e3.5 Parameterized Safety Gap\u003c/h2\u003e\n\u003cp\u003eUnder standard single-source testing at 0.9 GHz (REST), the FDTD-computed myocardial induced voltage was 0.075 \u0026mu;V (via the Wust rectification model), yielding a safety gap of 13.3\u0026times; relative to the theoretical 1 \u0026mu;V threshold (Table 7). Under vasoconstriction, the gap narrowed to 12.2\u0026times;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7.\u003c/strong\u003e Parameterized safety gap (G = V\u003csub\u003ethresh\u003c/sub\u003e/V\u003csub\u003eeff\u003c/sub\u003e) at 0.9 GHz for varying conditions. V\u003csub\u003ethresh\u003c/sub\u003e = 1 \u0026mu;V assumed.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eState\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN sources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eField factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAtten. (dB)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAtten. factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eV_eff (\u0026mu;V)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGap (\u0026times;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.2 (modern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eVASOCONST.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eVASOCONST.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eVASOCONST.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.2 (modern)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eVASODILAT.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e15 (traditional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e23.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral observations emerge from this parameterization. First, the safety gap under standard testing (13.3\u0026times;) may appear comfortable, but it erodes rapidly under realistic conditions. Ten incoherent sources during vasoconstriction in a modern building (the \u0026ldquo;stressed commuter on a crowded train\u0026rdquo; scenario) reduces the gap to 3.4\u0026times;. Second, traditional building materials partially compensate: the same scenario in a limestone building maintains a gap of 23.3\u0026times;. Third, the gap is critically sensitive to the assumed threshold: if 0.1 \u0026mu;V rather than 1 \u0026mu;V, all gaps decrease 10-fold, and the stressed multi-source scenario falls below unity. If 10 \u0026mu;V, all gaps increase 10-fold and are comfortable in all scenarios.\u003c/p\u003e\n\u003cp\u003eWe do not claim that the safety gap is insufficient\u0026mdash;this depends entirely on the true non-thermal threshold, which remains experimentally unvalidated. We demonstrate that realistic conditions can erode the gap by a factor of 4\u0026times; relative to standard testing, and that this erosion factor should be considered in safety margin design. Critically, the 4\u0026times; erosion factor is threshold-independent: whether the true threshold is 0.1, 1, or 10 \u0026mu;V, the relative narrowing from multi-source stressed conditions versus single-source resting conditions remains the same.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cstrong\u003eEvidence classification.\u0026nbsp;\u003c/strong\u003eTo aid interpretation, we classify our findings into three tiers. \u003cstrong\u003eTier A (directly computed):\u0026nbsp;\u003c/strong\u003estate-dependent SAR modulation (Section 3.1), building material attenuation (Section 3.2), safety gap parameterization (Section 3.4), and 3D cylindrical validation (Appendix B). \u003cstrong\u003eTier B (literature-anchored estimates):\u0026nbsp;\u003c/strong\u003eSchumann SNR across environments (Section 3.3), based on published noise measurements with order-of-magnitude uncertainty. \u003cstrong\u003eTier C (exploratory):\u0026nbsp;\u003c/strong\u003eHRV\u0026ndash;Schumann transfer function, historical crossover timeline, and coupling fraction estimates. Tier C results are presented as illustrative parameterizations to motivate experimental work, not as quantitative conclusions.\u003c/p\u003e\n\u003ch2\u003e4.1 The Vascular Waveguide: A New Dosimetric Variable\u003c/h2\u003e\n\u003cp\u003eThe finding that cardiovascular state modulates cardiac RF coupling by 10\u0026ndash;68% (geometry-dependent) identifies a previously unrecognized dosimetric variable. Current SAR assessment uses fixed-property anatomical phantoms, which effectively capture resting-state exposure. However, the vascular waveguide effect means that worst-case cardiac exposure occurs not at rest but during sympathetic activation, a state associated with altered electrophysiological stability. The vasoconstriction SAR increase proved robust across both 1D slab and 3D cylindrical phantom geometries, with agreement within 2.2% (Appendix B), though validation in anatomically realistic voxel models remains needed. Analysis of 24-hour Holter ECG recordings confirmed that healthy subjects spend 35% of the day in the high-HR bin, with individual cumulative SAR factors ranging from 0.92 to 1.10 relative to standard resting-state testing.\u003c/p\u003e\n\u003cp\u003eThis compounding of increased exposure with increased vulnerability is what we term the \u0026ldquo;stress paradox.\u0026rdquo; It has a direct clinical analog: exercise-induced vasodilation reduces cardiac RF coupling (10\u0026ndash;50%, geometry-dependent) while exercise simultaneously increases cardiac resilience through vagal tone. Conversely, cold-stress or psychological stress-induced vasoconstriction increases RF coupling (14\u0026ndash;17%) during sympathetic activation, a state associated with altered electrophysiological stability (Schwartz et al., 1992; Verrier \u0026amp; Antzelevitch, 2004). Whether this co-occurrence meaningfully changes cardiac risk requires experimental validation (see Prediction P1).\u003c/p\u003e\n\u003cp\u003eWe note that existing 3D voxel-based SAR models (e.g., Christ et al., 2010; Dimbylow, 2005; Gosselin et al., 2011) could readily incorporate state-dependent conductivity profiles. The tissue property databases (Hasgall et al., 2022) already contain temperature-dependent parameters; extending to hemodynamic state requires adding blood volume fraction as a parameter. We propose that future dosimetric standards include at minimum a \u0026ldquo;stress correction factor\u0026rdquo; representing the worst-case vasoconstriction scenario.\u003c/p\u003e\n\u003ch2\u003e4.2 The Built Environment as Electromagnetic Filter\u003c/h2\u003e\n\u003cp\u003eModern and traditional buildings create fundamentally different electromagnetic environments for their occupants. Modern construction allows sub-6 GHz RF to penetrate with minimal attenuation (0.3\u0026ndash;2.5 dB at 0.9 GHz) while partially blocking natural ELF signals through metallic structural elements. Traditional construction strongly attenuates RF (8\u0026ndash;28 dB at 0.9 GHz) while remaining largely transparent at ELF.\u003c/p\u003e\n\u003cp\u003eThis asymmetry has escaped systematic attention because RF safety and ELF signal environment are typically studied by different communities. RF engineers optimize building penetration loss for cellular coverage; ELF researchers measure Schumann resonances in purpose-built observatories far from civilization. Neither community has systematically characterized the joint RF/ELF environment experienced by human occupants.\u003c/p\u003e\n\u003cp\u003eThe historical trajectory is instructive. Before approximately 1880, humans lived in environments with continuous Schumann exposure and no anthropogenic RF. Between 1880 and 1950\u0026ndash;1970 (depending on region), anthropogenic sources grew but remained below Schumann amplitude at most indoor locations. After the 1950\u0026ndash;1970 electrification period, the crossover occurred: indoor ELF noise now consistently exceeds Schumann signal by orders of magnitude. This multi-decade transition represents an environmental variable that could be exploited epidemiologically, since building age, construction type, and electromagnetic environment are correlated.\u003c/p\u003e\n\u003ch2\u003e4.3 Schumann Coupling: Exploratory Estimates\u003c/h2\u003e\n\u003cp\u003eThe McCraty et al. (2017) correlations between HRV and Schumann power are robust but modest (r = 0.31). Our model suggests that indoor environments reduce this coupling to near zero, which is consistent with the observation that most HRV studies are conducted indoors and do not report Schumann-related effects. The prediction is specific: outdoor HRV studies should show Schumann correlations that indoor studies do not.\u003c/p\u003e\n\u003cp\u003eWe cannot claim that Schumann decoupling causes health effects. The coupling mechanism is not established\u0026mdash;the induced cardiac EMF from Schumann (2.5 picovolts) is far below any known biological threshold. The observed HRV correlations may arise through indirect pathways: Schumann modulation of brainstem nuclei via EEG entrainment (Saroka et al., 2016), cryptochrome radical-pair magnetoreception affecting circadian clock function (Hore \u0026amp; Mouritsen, 2016), or confounding environmental variables. Our framework provides the quantitative basis for designing experiments to distinguish these possibilities.\u003c/p\u003e\n\u003ch2\u003e4.4 Limitations\u003c/h2\u003e\n\u003cp\u003eThis study has several important limitations. (1) The primary FDTD model is one-dimensional, capturing attenuation along a single penetration axis without diffraction, scattering, or body curvature. A 3D cylindrical phantom validation (Appendix B) confirmed that the vasoconstriction SAR modulation factor (1.15) matches the 1D prediction (1.14) within 2.2%, while the vasodilation effect was smaller in 3D (0.78 vs 0.50), consistent with wave diffraction around the phantom providing alternative propagation paths. The stress paradox\u0026mdash;increased cardiac RF coupling during vasoconstriction\u0026mdash;is thus robust across geometries, though the protective effect of vasodilation may be overestimated by the 1D model. (2) The vascular waveguide uses effective-medium approximations; real vasculature is fractal, heterogeneous, and dynamically pulsatile. (3) Schumann noise levels are order-of-magnitude estimates from heterogeneous published measurements, not standardized measurements at body level in defined environments. (4) The HRV coupling model is a first-order transfer function without mechanistic justification. (5) The ion channel rectification threshold (1 \u0026mu;V) is theoretical and experimentally unvalidated\u0026mdash;our safety gap results scale linearly with the assumed threshold. (6) Multi-source exposure assumes incoherent addition (\u0026radic;N scaling); correlated sources or specific waveform interactions could yield different results.\u003c/p\u003e\n\u003cp\u003eWe have designed this study as a framework paper that identifies the relevant variables and their approximate magnitudes, not as a definitive risk assessment. Each component (state-dependent SAR, building attenuation, Schumann SNR, safety margins) requires dedicated experimental validation.\u003c/p\u003e"},{"header":"5. Falsifiable Predictions","content":"\u003cp\u003e\u003cstrong\u003eP1 (Moderate, MRI thermometry):\u0026nbsp;\u003c/strong\u003eCardiac SAR, measured by MRI-based temperature mapping, will be 5\u0026ndash;15% higher in subjects experiencing acute cold-pressor stress (vasoconstriction) versus warm immersion (vasodilation) during 0.9 GHz exposure at ICNIRP reference levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP2 (Easy, field measurements):\u0026nbsp;\u003c/strong\u003eSchumann resonance SNR at typical indoor body locations will be below \u0026minus;30 dB, measurable with standard fluxgate magnetometry (sensitivity \u0026lt; 0.1 pT/\u0026radic;Hz). Modern buildings will show 6\u0026ndash;20 dB lower Schumann SNR than matched traditional buildings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP3 (Moderate, paired outdoor/indoor):\u0026nbsp;\u003c/strong\u003eHRV spectral coherence with concurrent Schumann power will be statistically significant outdoors (r \u0026gt; 0.15) and non-significant indoors (r \u0026asymp; 0), within the same subjects on the same day, controlling for circadian and activity confounds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP4 (Hard, patch clamp):\u0026nbsp;\u003c/strong\u003eWhole-cell or single-channel recordings from L-type Ca\u003csup\u003e2+\u003c/sup\u003e channels (Ca\u003csub\u003ev\u003c/sub\u003e1.2) or cardiac Na\u003csup\u003e+\u003c/sup\u003e channels (Na\u003csub\u003ev\u003c/sub\u003e1.5) will show altered gating parameters (activation voltage, open probability, or recovery from inactivation) when exposed to amplitude-modulated RF producing induced transmembrane potentials \u0026gt; 1 \u0026mu;V.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP5 (Easy, computational \u0026mdash; partially confirmed):\u0026nbsp;\u003c/strong\u003eIncorporation of state-dependent tissue conductivity profiles into 3D models will reproduce the vasoconstriction SAR modulation reported here. Our 3D cylindrical phantom validation (Appendix B) confirmed the vasoconstriction effect within 2.2% of 1D predictions. Vasodilation effects were attenuated in 3D (22% vs 50\u0026ndash;86% reduction), suggesting that 3D geometry partially compensates via diffraction. Full anatomical voxel phantoms (e.g., Ella, Duke) are expected to show intermediate values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eP6 (Moderate, epidemiological):\u0026nbsp;\u003c/strong\u003eCardiovascular health markers (HRV, resting heart rate, blood pressure variability) will show statistically different distributions between occupants of pre-1960 buildings (traditional construction) and post-1980 buildings (modern construction), after controlling for socioeconomic status, age, activity level, and other confounders. The direction of effect\u0026mdash;if present\u0026mdash;is predicted to favor traditional buildings.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study presents an integrated computational framework for assessing the cardiac electromagnetic environment in modern indoor settings. Four principal findings emerge.\u003c/p\u003e\n\u003cp\u003eFirst, cardiovascular state modulates cardiac RF coupling by 10\u0026ndash;68% (geometry-dependent), with vasoconstriction increasing and vasodilation decreasing cardiac exposure\u0026mdash;a factor not captured by current resting-state SAR testing. The vasoconstriction effect proved robust across 1D slab and 3D cylindrical phantom geometries (within 2.2%, Appendix B). The compounding of increased RF coupling with increased cardiac vulnerability during sympathetic activation constitutes a \u0026ldquo;stress paradox\u0026rdquo; with specific clinical implications.\u003c/p\u003e\n\u003cp\u003eSecond, modern building materials provide minimal RF protection at sub-6 GHz frequencies (0.3\u0026ndash;2.5 dB at 0.9 GHz) while traditional materials provide 8\u0026ndash;28 dB. Combining cardiovascular state variation with building attenuation, the 24-hour time-weighted cardiac SAR factor in a modern building (0.72) exceeds that in a traditional building (0.16) by a factor of 4.6\u0026times;. This difference has not been systematically addressed in population-level RF exposure assessments.\u003c/p\u003e\n\u003cp\u003eThird, exploratory modeling suggests that modern indoor environments reduce Schumann resonance coupling to approximately 0.1\u0026ndash;0.3% of outdoor values, with the 50 Hz power grid dominating the cardiac ELF environment by a factor of 10⁴\u0026ndash;10⁶ (environment-dependent). The anthropogenic\u0026ndash;Schumann crossover during the 1950\u0026ndash;1970 period represents a substantial modification of the cardiac electromagnetic environment; whether this has physiological consequences remains an open experimental question.\u003c/p\u003e\n\u003cp\u003eFourth, under realistic multi-source, stress-state conditions, the parameterized safety gap between ICNIRP reference levels and a theoretical ion channel threshold narrows by approximately 4\u0026times; compared to standard single-source resting-state testing. Whether this narrowing is clinically significant depends on the true non-thermal threshold, which remains to be experimentally determined.\u003c/p\u003e\n\u003cp\u003eThese findings do not establish that current RF exposure causes cardiac harm. They identify specific, quantifiable gaps between standard testing conditions and realistic worst-case exposure, and they provide a framework for designing the experiments necessary to resolve this question.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlabdulgader A, McCraty R, Atkinson M, et al. Long-term study of HRV responses to changes in the solar and geomagnetic environment. Sci Rep. 2018;8:2722.\u003c/li\u003e\n\u003cli\u003eAsp A, Karimi O, Fischer T. Measurement-based building material characterization at 1\u0026ndash;60 GHz. In: Proc EuCAP; 2014:3075\u0026ndash;3079.\u003c/li\u003e\n\u003cli\u003eBalser M, Wagner CA. Observations of Earth\u0026ndash;ionosphere cavity resonances. Nature. 1960;188(4751):638\u0026ndash;641.\u003c/li\u003e\n\u003cli\u003eCharkoudian N. Mechanisms and modifiers of reflex induced cutaneous vasodilation and vasoconstriction in humans. J Appl Physiol. 2010;109(4):1221\u0026ndash;1228.\u003c/li\u003e\n\u003cli\u003eChrist A, Gosselin M-C, Christopoulou M, K\u0026uuml;hn S, Kuster N. Age-dependent tissue-specific exposure of cell phone users. Phys Med Biol. 2010;55(7):1767\u0026ndash;1783.\u003c/li\u003e\n\u003cli\u003eCrocco L, Ferrara V, Luongo M, et al. Measurement campaign of electromagnetic field penetration loss in building materials at 1.4 to 60 GHz. Measurement. 2019;139:360\u0026ndash;367.\u003c/li\u003e\n\u003cli\u003eDimbylow PJ. Development of the female voxel phantom, NAOMI, and its application to calculations of induced current densities. Phys Med Biol. 2005;50(6):1047\u0026ndash;1070.\u003c/li\u003e\n\u003cli\u003eGabriel C, Gabriel S, Corthout E. The dielectric properties of biological tissues: I. Literature survey. Phys Med Biol. 1996;41(11):2231\u0026ndash;2249.\u003c/li\u003e\n\u003cli\u003eGosselin M-C, Neufeld E, Moser H, et al. Development of a new generation of high-resolution anatomical models. Phys Med Biol. 2014;59(18):5287\u0026ndash;5303.\u003c/li\u003e\n\u003cli\u003eHalgamuge MN. Critical time delay of the pineal melatonin rhythm in humans and the effect of Schumann resonance and other ELF signals. J Pineal Res. 2015;58(3):248\u0026ndash;253.\u003c/li\u003e\n\u003cli\u003eHasgall PA, Di Gennaro F, Baumgartner C, et al. IT\u0026rsquo;IS Database for Thermal and Electromagnetic Parameters of Biological Tissues, Version 4.1. 2022. itis.swiss/database.\u003c/li\u003e\n\u003cli\u003eHore PJ, Mouritsen H. The radical-pair mechanism of magnetoreception. Annu Rev Biophys. 2016;45:299\u0026ndash;344.\u003c/li\u003e\n\u003cli\u003eICNIRP. Guidelines for limiting exposure to electromagnetic fields (100 kHz to 300 GHz). Health Phys. 2020;118(5):483\u0026ndash;524.\u003c/li\u003e\n\u003cli\u003eIEEE. IEEE Standard for Safety Levels with Respect to Human Exposure to Electric, Magnetic, and Electromagnetic Fields. IEEE C95.1-2019.\u003c/li\u003e\n\u003cli\u003eITU-R. Effects of building materials and structures on radiowave propagation above about 100 MHz. Recommendation ITU-R P.2040-2; 2021.\u003c/li\u003e\n\u003cli\u003eJohnson JM, Proppe DW. Cardiovascular adjustments to heat stress. In: Fregly MJ, Blatteis CM, eds. Handbook of Physiology, Section 4: Environmental Physiology. Oxford UP; 1996:215\u0026ndash;243.\u003c/li\u003e\n\u003cli\u003eLaughlin MH, Davis MJ, Secher NH, et al. Peripheral circulation. Compr Physiol. 2012;2(1):321\u0026ndash;447.\u003c/li\u003e\n\u003cli\u003eLeitgeb N. Assessment of multiple frequency ELF electric and magnetic field exposure. Phys Med Biol. 2014;59(2):349\u0026ndash;362.\u003c/li\u003e\n\u003cli\u003eLewczuk B, Redlarski G, Zak A, et al. Influence of electric, magnetic, and electromagnetic fields on the circadian system: current stage of knowledge. Biomed Res Int. 2014;2014:169459.\u003c/li\u003e\n\u003cli\u003eMcCraty R, Atkinson M, Stolc V, Alabdulgader A, Vainoras A, Ragulskis M. Synchronization of human autonomic nervous system rhythms with geomagnetic activity in human subjects. Int J Environ Res Public Health. 2017;14(7):770.\u003c/li\u003e\n\u003cli\u003eMild KH. Occupational exposure to radio-frequency electromagnetic fields. Proc IEEE. 1987;68(1):12\u0026ndash;17.\u003c/li\u003e\n\u003cli\u003eMohapatra SN. Non-Invasive Cardiovascular Monitoring by Electrical Impedance Technique. London: Pitman Medical; 1981.\u003c/li\u003e\n\u003cli\u003eNickolaenko AP, Hayakawa M. Resonances in the Earth\u0026ndash;Ionosphere Cavity. Springer; 2002.\u003c/li\u003e\n\u003cli\u003ePanagopoulos DJ, Johansson O, Carlo GL. Evaluation of specific absorption rate as a dosimetric quantity for electromagnetic fields bioeffects. PLOS ONE. 2013;8(6):e62663.\u003c/li\u003e\n\u003cli\u003ePrice C. ELF electromagnetic waves from lightning: the Schumann resonances. Atmosphere. 2016;7(9):116.\u003c/li\u003e\n\u003cli\u003eRodriguez-Cano R, Marb\u0026aacute;n-Calzon P, et al. Building material electromagnetic characterization in the 1\u0026ndash;100 GHz range for 5G applications. IEEE Access. 2021;9:166028\u0026ndash;166040.\u003c/li\u003e\n\u003cli\u003eSaroka KS, Vares DE, Persinger MA. Similar spectral power densities within the Schumann resonance and a large population of quantitative electroencephalographic profiles. PLOS ONE. 2016;11(1):e0146595.\u003c/li\u003e\n\u003cli\u003eSchumann WO. \u0026Uuml;ber die strahlungslosen Eigenschwingungen einer leitenden Kugel, die von einer Luftschicht und einer Ionosph\u0026auml;renh\u0026uuml;lle umgeben ist. Z Naturforsch A. 1952;7(2):149\u0026ndash;154.\u003c/li\u003e\n\u003cli\u003eSchwartz PJ, La Rovere MT, Vanoli E. Autonomic nervous system and sudden cardiac death: experimental basis and clinical observations. Circulation. 1992;85(1 Suppl):I77\u0026ndash;I91.\u003c/li\u003e\n\u003cli\u003eStandring S, ed. Gray\u0026rsquo;s Anatomy: The Anatomical Basis of Clinical Practice. 41st ed. Elsevier; 2016.\u003c/li\u003e\n\u003cli\u003eStavrou S, Saunders SR. Review of constitutive parameters of building materials. In: Proc ICAP; 2003:211\u0026ndash;215.\u003c/li\u003e\n\u003cli\u003eTaggart P, Boyett MR, Logantha S, Lambiase PD. Anger, emotion, and arrhythmias: from brain to heart. Front Physiol. 2011;2:67.\u003c/li\u003e\n\u003cli\u003eTask Force of ESC and NASPE. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation. 1996;93(5):1043\u0026ndash;1065.\u003c/li\u003e\n\u003cli\u003eTouitou Y, Bogdan A, Lambrozo J, Selmaoui B. Is melatonin the hormonal missing link between magnetic field effects and human diseases? Cancer Causes Control. 2006;17(4):547\u0026ndash;552.\u003c/li\u003e\n\u003cli\u003eVermeeren G, Joseph W, Olivier C, Martens L. Statistical multipath exposure of a human in a realistic electromagnetic environment. Health Phys. 2008;94(4):345\u0026ndash;354.\u003c/li\u003e\n\u003cli\u003eVerrier RL, Antzelevitch C. Autonomic aspects of arrhythmogenesis: the enduring and the new. Curr Opin Cardiol. 2004;19(1):2\u0026ndash;11.\u003c/li\u003e\n\u003cli\u003eWust P, Kortum B, Strauss U, Nadobny J, Zschaeck S, Beck M, et al. Non-thermal effects of radiofrequency electromagnetic fields. Sci Rep. 2020;10:13488.\u003c/li\u003e\n\u003cli\u003ePolk C, Postow E, eds. CRC Handbook of Biological Effects of Electromagnetic Fields. Boca Raton: CRC Press; 1986.\u003c/li\u003e\n\u003cli\u003eSchmid G, Cecil S, \u0026Uuml;berbacher R, et al. Dosimetric assessment of electromagnetic exposure of the brain and the heart using age-specific computational human models. Phys Med Biol. 2013;58(23):8455\u0026ndash;8471.\u003c/li\u003e\n\u003cli\u003eBehari J. Biological responses of mobile phone frequency exposure. Indian J Exp Biol. 2010;48(10):959\u0026ndash;981.\u003c/li\u003e\n\u003cli\u003eCherry N. Schumann resonances, a plausible biophysical mechanism for the human health effects of solar/geomagnetic activity. Nat Hazards. 2002;26(3):279\u0026ndash;331.\u003c/li\u003e\n\u003cli\u003eFoerster M, Thielens A, Joseph W, Eeftens M, R\u0026ouml;\u0026ouml;sli M. A prospective cohort study of adolescents\u0026rsquo; memory performance and individual brain dose of microwave radiation from wireless communication. Environ Health Perspect. 2018;126(7):077007.\u003c/li\u003e\n\u003cli\u003eBillman GE. The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Front Physiol. 2013;4:26.\u003c/li\u003e\n\u003cli\u003eGoldstein DS, Bentho O, Park MY, Sharabi Y. Low-frequency power of heart rate variability is not a measure of cardiac sympathetic tone but may be a measure of modulation of cardiac autonomic outflows by baroreflexes. Exp Physiol. 2011;96(12):1255\u0026ndash;1261.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"radiofrequency dosimetry, cardiac SAR, vascular waveguide, Schumann resonance, building attenuation, FDTD, electromagnetic safety, stress paradox, ion channel threshold, heart rate variability","lastPublishedDoi":"10.21203/rs.3.rs-8935385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8935385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRadiofrequency (RF) safety standards assess cardiac exposure using resting-state phantoms under single-source conditions. Real-world exposure involves variable cardiovascular physiology, multiple simultaneous sources, and indoor environments that simultaneously increase anthropogenic RF while attenuating natural extremely-low-frequency (ELF) signals, including the Schumann resonances (SR).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe present an integrated computational framework combining: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) finite-difference time-domain (FDTD) modeling of RF penetration through a nine-layer stratified thorax at 0.9, 3.5, and 28 GHz under ICNIRP occupational reference levels; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a vascular waveguide model incorporating cardiovascular state-dependent tissue conductivity via Maxwell\u0026ndash;Garnett and parallel mixing formulae; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) 3D cylindrical phantom validation on GPU; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) plane-wave transmission line modeling of building material attenuation for modern versus traditional construction; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) time-resolved cardiac dosimetry from 24-hour Holter ECG recordings (N\u0026thinsp;=\u0026thinsp;18 healthy subjects, 4,582 five-minute windows); (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Schumann resonance signal-to-noise ratio (SNR) quantification across eight representative environments (exploratory); and (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) parameterized safety gap analysis incorporating multi-source exposure, cardiovascular state, and building effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eVasodilation reduced myocardial E-field by 10\u0026ndash;31% (geometry-dependent) and SAR by 22\u0026ndash;50%, while vasoconstriction increased E-field by 7\u0026ndash;9% and SAR by 14\u0026ndash;17%\u0026mdash;a \u0026ldquo;stress paradox\u0026rdquo; in which physiological stress increases cardiac RF coupling during sympathetic activation, a state associated with altered electrophysiological stability. A 3D cylindrical phantom validation confirmed the vasoconstriction SAR modulation within 2.2% of 1D predictions, demonstrating robustness across 1D slab and 3D cylindrical geometries. Analysis of 24-hour Holter ECG recordings revealed that healthy subjects spend 35% of the day in the high-HR bin (HR\u0026thinsp;\u0026gt;\u0026thinsp;80 bpm, interpreted as predominantly sympathetically-activated), with population-weighted cardiac SAR 3.2% above standard resting-state assumptions and individual variation spanning\u0026thinsp;\u0026plusmn;\u0026thinsp;10%. The longest continuous high-HR episode was 11.2 hours. Modern building envelopes attenuated 0.9 GHz by only 0.3\u0026ndash;2.5 dB versus 8\u0026ndash;28 dB for traditional construction; combining state variation with building type, the 24-hour cardiac SAR factor in modern buildings (0.72) exceeded traditional buildings (0.16) by 4.6\u0026times;. Under multi-source stress-state conditions, the parameterized safety gap narrows to 3.4\u0026times;\u0026mdash;a threshold-independent 4\u0026times; erosion relative to standard testing.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eStandard SAR testing systematically underestimates realistic worst-case cardiac exposure by omitting cardiovascular state modulation\u0026mdash;an effect confirmed across 1D and 3D geometries. Modern built environments simultaneously maximize anthropogenic RF penetration and minimize natural ELF signal coupling. Findings are classified into three evidence tiers (directly computed, literature-anchored, and exploratory) and accompanied by six falsifiable predictions to guide experimental validation.\u003c/p\u003e","manuscriptTitle":"The Stress Paradox: Cardiovascular State Modulates Cardiac Radiofrequency Absorption While Modern Buildings Attenuate Natural Electromagnetic References","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 17:11:54","doi":"10.21203/rs.3.rs-8935385/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89f5719f-3d73-485f-8b5e-03e4cdde308f","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63315769,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2026-02-25T17:11:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 17:11:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8935385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8935385","identity":"rs-8935385","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}