{"id":6722,"date":"2026-05-02T02:58:13","date_gmt":"2026-05-02T02:58:13","guid":{"rendered":"https:\/\/www.herewinpower.com\/?p=6722"},"modified":"2026-05-02T02:58:13","modified_gmt":"2026-05-02T02:58:13","slug":"heavy-lift-drone-battery-procurement-predictable-roi","status":"publish","type":"post","link":"https:\/\/www.herewinpower.com\/ko\/blog\/heavy-lift-drone-battery-procurement-predictable-roi\/","title":{"rendered":"Heavy-Lift Drone Batteries 2026: How to Choose Packs for Predictable ROI"},"content":{"rendered":"<figure class=\"wp-block-image aligncenter size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1441\" height=\"1080\" src=\"https:\/\/www.herewinpower.com\/wp-content\/uploads\/2026\/04\/6s_22ah_E8BDAFE58C85-y9k999b8.jpg\" alt=\"Heavy-lift drone battery procurement in an industrial fleet maintenance workflow\" class=\"wp-image-6721\" srcset=\"https:\/\/www.herewinpower.com\/wp-content\/uploads\/2026\/04\/6s_22ah_E8BDAFE58C85-y9k999b8.jpg 1441w, https:\/\/www.herewinpower.com\/wp-content\/uploads\/2026\/04\/6s_22ah_E8BDAFE58C85-y9k999b8-768x576.jpg 768w, https:\/\/www.herewinpower.com\/wp-content\/uploads\/2026\/04\/6s_22ah_E8BDAFE58C85-y9k999b8-16x12.jpg 16w\" sizes=\"(max-width: 1441px) 100vw, 1441px\" \/><\/figure>\n\n\n\n<p>Heavy-lift fleets rarely lose money because a battery is \u201cbad.\u201d They lose predictability because battery behavior is not consistent enough to model as an asset. This is not a ranked list of batteries. It\u2019s a procurement framework for comparing solutions by variance and predictability.<\/p>\n\n\n\n<p>The practical question isn\u2019t whether a pack can hit a headline C-rate or a lab-rated cycle count. It\u2019s whether you can bound the variance: pack-to-pack, batch-to-batch, and across the duty cycles you actually fly.<\/p>\n\n\n\n<p>That framing is aligned with drone fleet ROI predictability: if energy output is not predictable, dispatch and cost-per-mission become hard to model with confidence. This article uses semi-solid technology as an evaluation candidate\u2014not as a guaranteed outcome\u2014to show how fleets can shift procurement from component specs to ROI predictability.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">How Battery Performance Variance Impacts Fleet ROI in Heavy-Lift Drone Battery Procurement<\/h2>\n\n\n\n<p>Variance shows up as operating friction. The mechanism is usually not complicated, but it compounds across dispatch planning, spares, and maintenance timing.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">How Inconsistent Battery Life Disrupts Fleet Scheduling<\/h3>\n\n\n\n<p>In heavy-lift missions, flight-time planning is a tight envelope: payload mass, hover time, climb profile, wind margin, and temperature constraints all compress the usable operating window.<\/p>\n\n\n\n<p>When battery degradation is inconsistent across packs, the usable window becomes harder to predict. In practice, fleets often observe a pattern like this:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>Pack A and Pack B share the same nominal spec.<\/p><\/li><li><p>Their effective flight time diverges after repeated high-load cycles.<\/p><\/li><li><p>Dispatch begins to rely on conservative assumptions rather than telemetry-backed bounds.<\/p><\/li>\n<\/ul>\n\n\n\n<p>The operational impact tends to present as a chain:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>Inconsistent fade<\/strong> (capacity retention and internal resistance drift differ across packs)<\/p><\/li><li><p><strong>Uncertain mission energy budget<\/strong> (flight time becomes less predictable at a given payload)<\/p><\/li><li><p><strong>Forced schedule redundancy<\/strong> (more \u201cbuffer time\u201d and more spare packs staged)<\/p><\/li><li><p><strong>Lower unit task efficiency<\/strong> (output per hour declines even when utilization looks high)<\/p><\/li>\n<\/ul>\n\n\n\n<p>Procurement can\u2019t fully solve scheduling, but it can define what \u201cacceptable variance\u201d looks like and require evidence that a supplier can control it.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Hidden Cost of Unpredictable Capacity Loss<\/h3>\n\n\n\n<p>A common failure mode in battery purchasing is focusing on CAPEX line items while ignoring the cash behavior of spares.<\/p>\n\n\n\n<p>When performance fluctuates, fleets often add \u201csafety stock\u201d to protect dispatch reliability. That buffer inventory is not free\u2014even before a pack is used:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>It <strong>ties up cash<\/strong> in inventory that exists primarily to absorb uncertainty.<\/p><\/li><li><p>It <strong>increases carrying cost<\/strong> (storage, handling, compliance packaging, periodic checks).<\/p><\/li><li><p>It can quietly <strong>increase operational cost volatility<\/strong>: you\u2019re paying for uncertainty, not performance.<\/p><\/li>\n<\/ul>\n\n\n\n<p>If the fleet\u2019s ROI model requires staging extra packs to keep schedules stable, the battery is already functioning as a risk variable.<\/p>\n\n\n\n<p>A practical procurement question to document early is: What spare ratio is required to keep dispatch stable under our mission mix? If that ratio moves materially by supplier or chemistry, it belongs in TCO.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">C-Rate vs Lifecycle ROI: Rethinking Battery Selection Priorities<\/h3>\n\n\n\n<p>High C-rate performance is visible in the short term, which makes it easy to overweight during evaluation. Lifecycle stability is slower to reveal, which makes it easy to underweight.<\/p>\n\n\n\n<p>For 2026 fleet operations, the weighting often shifts toward a different procurement priority: <strong>stable output and predictable degradation<\/strong>.<\/p>\n\n\n\n<p>This shift doesn\u2019t imply that C-rate is irrelevant. It implies that C-rate should be treated as one axis in a multi-axis selection model:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>Peak power adequacy<\/strong> (does it meet the mission envelope?)<\/p><\/li><li><p><strong>Thermal stability under continuous high load<\/strong> (does the pack stay within controlled temperature gates?)<\/p><\/li><li><p><strong>Degradation predictability<\/strong> (is the fade curve smooth enough to schedule replacements?)<\/p><\/li><li><p><strong>Pack-to-pack consistency<\/strong> (does the fleet scale without widening variance?)<\/p><\/li>\n<\/ul>\n\n\n\n<p>If you want ROI predictability, the selection priority is less \u201chighest short-term performance\u201d and more \u201ctightest operational bounds.\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Evaluating Semi-Solid Technology as a Solution for Operational Stability<\/h2>\n\n\n\n<p>The simplest way to make this decision \u201cmodelable\u201d is to turn variance into a testable acceptance band. Define one representative duty cycle (payload, hover fraction, ambient temperature, charge cadence), then set pass\/fail bounds for the variables that drive dispatch volatility (e.g., thermal rise profile repeatability, capacity retention slope, and internal-resistance drift) across a batch\u2014not a single golden sample.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Minimal Executable Test Framework and Definitions<\/h3>\n\n\n\n<p>To keep \u201cpredictability\u201d actionable, procurement needs a small set of shared definitions and a test skeleton that can be run on any candidate pack family.<\/p>\n\n\n\n<p><strong>Key definitions (use the same duty cycle for all):<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>Variance<\/strong>: the spread (distribution) of a KPI across packs in the same batch under the same duty cycle\u2014not the average value.<\/p><\/li><li><p><strong>Acceptance band<\/strong>: the pass\/fail limits for that spread (for example, a max allowed dispersion in thermal rise or internal resistance drift across the batch).<\/p><\/li><li><p><strong>Energy budget predictability<\/strong>: the variability of usable flight time (or usable energy) at a defined payload and environment.<\/p><\/li><li><p><strong>Dispatch reliability<\/strong>: the share of scheduled missions completed without battery-driven replans; define battery-driven events up front (e.g., thermal alarms, BMS power derates, or SOH-based removals).<\/p><\/li>\n<\/ul>\n\n\n\n<p><strong>A minimal test skeleton (example):<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><p><strong>Lock one representative duty cycle<\/strong> (document it): payload band, hover fraction, ambient temperature band, turnaround cadence, and charge rate limit.<\/p><\/li><li><p><strong>Choose a batch sample size that exposes dispersion<\/strong>: test a minimum of <em>N = 10\u201320 packs per lot<\/em> (more if the fleet will scale quickly). One \u201cgolden sample\u201d is not representative.<\/p><\/li><li><p><strong>Measure the three variance drivers that create dispatch volatility<\/strong>:<\/p><ul><li><p><strong>Thermal repeatability<\/strong>: pack temperature rise profile vs time under the duty cycle (compare the curve shape and peak temperature across packs).<\/p><\/li><li><p><strong>Degradation slope<\/strong>: capacity retention and internal resistance vs cycles under the same SOC window.<\/p><\/li><li><p><strong>Control behavior consistency<\/strong>: BMS-triggered derates\/alarms frequency under the same thresholds.<\/p><\/li><\/ul><\/li><li><p><strong>Turn results into acceptance bands<\/strong> (procurement-ready outputs):<\/p><ul><li><p>Define pass\/fail as <em>bounds on dispersion<\/em> (e.g., 90th\u201310th percentile spread) rather than a single \u201cmeets spec\u201d number.<\/p><\/li><li><p>Require the supplier to provide <strong>lot identifiers + pack serial traceability<\/strong> so field telemetry can be mapped back to manufacturing lots.<\/p><\/li><\/ul><\/li>\n<\/ol>\n\n\n\n<p>This framework doesn\u2019t prove a chemistry is \u201cbetter.\u201d It makes ROI predictability testable, so you can compare candidates by how tightly they stay within operational bounds.<\/p>\n\n\n\n<p>In practice, this is the lens we use at Herewin: we treat \u201csemi-solid\u201d as a design class to evaluate against one core procurement goal\u2014tighter operational stability.<\/p>\n\n\n\n<p>In our <a target=\"_self\" rel=\"follow\" class=\"link\" href=\"https:\/\/www.herewinpower.com\/drone-battery\/semi-solid-battery-vs-traditional-lithium-battery-comparison\/\"><strong>semi-solid vs. traditional lithium battery comparison<\/strong><\/a>, we outline how a gel-like electrolyte approach can affect energy density and lifecycle. The practical question is whether those characteristics reduce variance\u2014especially in thermal behavior, degradation, and pack-to-pack consistency.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Stable Thermal Behavior Under Continuous High Load<\/h3>\n\n\n\n<p>Thermal behavior is one of the fastest paths from \u201cspec compliance\u201d to \u201cfield variance.\u201d In heavy-lift duty cycles, sustained current draw tends to make temperature management a first-order constraint.<\/p>\n\n\n\n<p>When a pack\u2019s thermal behavior is stable, fleets often observe fewer operational interruptions that are triggered by temperature gates:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>fewer forced cooldown intervals<\/p><\/li><li><p>fewer derates that reduce usable thrust margin<\/p><\/li><li><p>fewer mission aborts attributed to thermal alarms<\/p><\/li>\n<\/ul>\n\n\n\n<p>In evaluation, the question is not \u201cdoes it run cool?\u201d It is:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>How repeatable is the thermal rise profile across packs?<\/strong><\/p><\/li><li><p><strong>How does thermal behavior evolve as the pack ages?<\/strong><\/p><\/li><li><p><strong>What is the fleet\u2019s operating envelope where thermal alarms begin to cluster?<\/strong><\/p><\/li>\n<\/ul>\n\n\n\n<p>Semi-solid designs are commonly discussed as having improved safety\/thermal characteristics relative to conventional liquid-electrolyte packs in supplier materials. In a procurement model, those claims translate into a test requirement: validate thermal repeatability under your representative duty cycle, not a generic bench load.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Predictable Performance Degradation Over Lifecycle<\/h3>\n\n\n\n<p>Procurement teams can generally tolerate degradation. They struggle with <em>unexpected<\/em> degradation.<\/p>\n\n\n\n<p>If the degradation curve is smoother and more predictable, fleets can plan replacements and prevent \u201csurprise\u201d pack removals that disrupt schedules. The operational shift is subtle but material:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>from <strong>reactive replacement<\/strong> (failures trigger swaps)<\/p><\/li><li><p>to <strong>planned maintenance<\/strong> (telemetry and thresholds drive scheduled rotation)<\/p><\/li>\n<\/ul>\n\n\n\n<p>The procurement artifact that supports this shift is not a headline cycle-life number. It is a <strong>fleet-facing degradation model<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>defined SOC window assumptions<\/p><\/li><li><p>duty-cycle definition (payload, hover fraction, ambient temperature)<\/p><\/li><li><p>acceptance criteria for variance (e.g., bounds on capacity and internal resistance drift)<\/p><\/li>\n<\/ul>\n\n\n\n<p>Across our drone portfolio, we offer semi-solid options in multiple categories\u2014especially where higher power output, safety margins, and lifecycle stability are priorities (see <a target=\"_self\" rel=\"follow\" class=\"link\" href=\"https:\/\/www.herewinpower.com\/solution\/drones\/\"><strong>Herewin drone battery solutions<\/strong><\/a>). For procurement teams, that breadth matters because it makes it easier to request consistency evidence at the category level\u2014pack families, not one-off prototypes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Consistency Across Battery Packs for Fleet Scalability<\/h3>\n\n\n\n<p>Fleet scalability often breaks on \u201csmall\u201d dispersion: two packs that behave differently are not interchangeable in scheduling.<\/p>\n\n\n\n<p>Higher consistency across packs supports:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>more reliable dispatch planning (packs are closer to fungible)<\/p><\/li><li><p>simpler thresholding for maintenance (one set of triggers fits more of the fleet)<\/p><\/li><li><p>cleaner inventory policy (fewer edge-case spares staged \u201cjust in case\u201d)<\/p><\/li>\n<\/ul>\n\n\n\n<p>In procurement, consistency is less a marketing claim and more a QA and traceability question:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>What batch-level data is available?<\/p><\/li><li><p>What pack-level identifiers link telemetry back to manufacturing lots?<\/p><\/li><li><p>What incoming inspection is feasible without shutting down ops?<\/p><\/li>\n<\/ul>\n\n\n\n<p>This is also where compliance and shipping discipline can affect operational predictability. If lead times and documentation are inconsistent, a fleet may carry more buffer inventory regardless of chemistry. At a minimum, fleets typically align safety\/transport evidence with recognized frameworks such as <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"link\" href=\"https:\/\/unece.org\/transport\/dangerous-goods\/manual-tests-and-criteria\"><strong>UN Manual of Tests and Criteria, Part III, subsection 38.3<\/strong><\/a> for transport testing and battery safety standards like <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"link\" href=\"https:\/\/webstore.iec.ch\/en\/publication\/60106\"><strong>IEC 62133<\/strong><\/a> \uadf8\ub9ac\uace0 <a target=\"_blank\" rel=\"noopener noreferrer nofollow\" class=\"link\" href=\"https:\/\/www.shopulstandards.com\/ProductDetail.aspx?productId=UL2054_6_S_20191007\"><strong>UL 2054<\/strong><\/a>, depending on the market and application. For procurement teams building a compliance baseline, Herewin\u2019s <a target=\"_self\" rel=\"follow\" class=\"link\" href=\"https:\/\/www.herewinpower.com\/blog\/industrial-drone-battery-compliance-guide\/\">industrial drone battery compliance guide<\/a> provides a reference point for how compliance artifacts are framed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Building a Predictable Asset Model for Drone Fleet Operations<\/h2>\n\n\n\n<p>A predictable asset model does not require perfect batteries. It requires metrics that allow uncertainty to be bounded and managed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Dispatch Reliability as a Core KPI for Battery Evaluation<\/h3>\n\n\n\n<p>If fleet ROI is the target, procurement typically ends up answering two operational questions:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><p><strong>Can the fleet dispatch consistently?<\/strong><\/p><\/li><li><p><strong>What is the output per hour when dispatch is stable?<\/strong><\/p><\/li>\n<\/ol>\n\n\n\n<p>Dispatch reliability becomes a battery KPI when battery variance is one of the main constraints on schedule adherence.<\/p>\n\n\n\n<p>You can treat <strong>dispatch reliability<\/strong> as an explicit KPI: it describes whether the fleet can meet scheduled missions without battery-driven replans.<\/p>\n\n\n\n<p>A minimal KPI definition set that procurement can enforce (and ops can measure) typically includes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>Dispatch reliability<\/strong>: fraction of scheduled missions completed without battery-driven reschedules (define what counts as battery-driven).<\/p><\/li><li><p><strong>Throughput per hour<\/strong>: missions or payload-tonne-km per operating hour, with downtime classified.<\/p><\/li><li><p><strong>Energy budget predictability<\/strong>: variance in usable flight time at a defined payload and environment.<\/p><\/li>\n<\/ul>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"><p>In heavy-lift fleets, realized ROI is often bounded by dispatch stability. Battery selection that reduces variance tends to show up as higher realized utilization, not just better spec sheets.<\/p><\/blockquote>\n\n\n\n<p>For deeper battery evaluation frameworks oriented toward industrial use, Herewin\u2019s <a target=\"_self\" rel=\"follow\" class=\"link\" href=\"https:\/\/www.herewinpower.com\/blog\/industrial-drone-battery-buyers-guide-2026\/\">industrial drone battery buyer\u2019s guide (2026)<\/a> is a relevant internal reference.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Reducing Buffer Inventory Through Lifecycle Predictability<\/h3>\n\n\n\n<p>Buffer inventory is a rational response to uncertainty. If the fleet cannot predict which packs will underperform, it increases spares.<\/p>\n\n\n\n<p>If lifecycle behavior is predictable\u2014meaning packs age within a tight band under defined duty cycles\u2014procurement can model a smaller buffer inventory policy.<\/p>\n\n\n\n<p>Two practical changes often follow:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>Lower spare ratio<\/strong> because pack interchangeability improves.<\/p><\/li><li><p><strong>Less cash tied in inventory<\/strong> because procurement can schedule replenishment and retirement with more confidence.<\/p><\/li>\n<\/ul>\n\n\n\n<p>This is also where battery data quality becomes a procurement requirement. If SOC\/SOH estimates are not consistent, variance management becomes harder and the fleet may revert to conservative buffers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Scenario-Based TCO Modeling for Procurement Decisions<\/h3>\n\n\n\n<p>A lightweight template that works well in practice:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>Scenario definition<\/strong>: payload band + hover fraction + ambient temperature band + turnaround cadence<\/p><\/li><li><p><strong>Battery operating window<\/strong>: SOC window + charge rate limit + thermal gate assumptions<\/p><\/li><li><p><strong>Cost inputs<\/strong>: pack price + spare ratio + expected retirement threshold + logistics\/compliance overhead<\/p><\/li><li><p><strong>Outputs to compare<\/strong>: replacement interval band + cost per mission band + dispatch reliability target held\/not held<\/p><\/li>\n<\/ul>\n\n\n\n<p>The decision is rarely \u201clowest cost.\u201d It is often \u201clowest cost that stays within predictable bounds.\u201d<\/p>\n\n\n\n<p>Scenario-based TCO modeling is a way to make that explicit. A practical model structure for heavy-lift fleets is:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p><strong>Define scenarios<\/strong> (not averages):<\/p><ul><li><p>Mission type (hover-heavy vs cruise-heavy)<\/p><\/li><li><p>Payload bands<\/p><\/li><li><p>Ambient temperature bands<\/p><\/li><li><p>Turnaround intensity (charge-and-fly cadence)<\/p><\/li><\/ul><\/li><li><p><strong>Define cost components<\/strong>:<\/p><ul><li><p>acquisition cost (packs + spares)<\/p><\/li><li><p>charging infrastructure and downtime costs (if modeled)<\/p><\/li><li><p>replacement and disposal handling<\/p><\/li><li><p>compliance\/shipping and documentation overhead<\/p><\/li><\/ul><\/li><li><p><strong>Define predictability outputs<\/strong>:<\/p><ul><li><p>expected replacement interval band (not a single date)<\/p><\/li><li><p>spare ratio needed to hold dispatch reliability above a chosen threshold<\/p><\/li><li><p>variance range for cost per mission<\/p><\/li><\/ul><\/li>\n<\/ul>\n\n\n\n<p>If your model can\u2019t produce a bounded cost range per scenario, it is not a procurement model\u2014it is a price comparison.<\/p>\n\n\n\n<p>For readers who want a broader evaluation baseline, Herewin\u2019s <a target=\"_self\" rel=\"follow\" class=\"link\" href=\"https:\/\/www.herewinpower.com\/blog\/industrial-drone-battery-ultimate-guide\/\">industrial drone battery evaluation guide<\/a> can be used as an internal reference for what dimensions are commonly considered.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">From Battery Components to Predictable Fleet Assets<\/h2>\n\n\n\n<p>In heavy-lift operations, batteries aren\u2019t merely consumables. They behave like operating assets when their variance can be bounded, measured, and scheduled.<\/p>\n\n\n\n<p>That reframes procurement priorities:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><p>from <strong>parameters<\/strong> to <strong>outcomes<\/strong><\/p><\/li><li><p>from <strong>unit price<\/strong> to <strong>cost predictability<\/strong><\/p><\/li>\n<\/ul>\n\n\n\n<p>Semi-solid technology is often described as \u201cmore advanced,\u201d but the procurement case is narrower and more testable: evaluate whether it reduces operational uncertainty\u2014thermal excursions, degradation surprises, and pack-to-pack dispersion\u2014under the duty cycles that drive dispatch volatility.<\/p>\n\n\n\n<p>A practical takeaway is to evaluate fleet performance consistency (not just mean performance), and apply a scenario-based TCO assessment that treats predictability as a first-class output.<\/p>","protected":false},"excerpt":{"rendered":"<p>A neutral framework to evaluate semi-solid batteries by variance, dispatch reliability, buffer inventory, and scenario-based TCO.<\/p>","protected":false},"author":3,"featured_media":6721,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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