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We demonstrate through high-fidelity particle-in-cell (PIC) simulations a simple approach for efficiently generating $20+$ GeV electron beams with the necessary charge, energy spread, and emittance for use as an injector in a future linear collider or a next generation XFEL. A high quality injected bunch is generated by self-focusing an unmatched electron driver in a nonlinear plasma wakefield. Over pump depletion distances, the drive beam dynamics and self-loading effects lead to high energy, low-energy spread output beams. For plasma densities of ${10}^{18}\mathrm{c}{\mathrm{m}}^{-3}$ , PIC simulation results indicate that self-injected beams with $0.52\mathrm{n}\mathrm{C}$ charge can be accelerated to 20 GeV with projected core energy spreads of $\lesssim 1\%$ , normalized slice emittances of $110\mathrm{n}\mathrm{m}$ , peak normalized brightness of $\gtrsim {10}^{19}\mathrm{A}/{\mathrm{m}}^2/{\mathrm{rad}}^2$ , and transfer efficiencies of $\gtrsim 44\%$ . 

This study gathered data into a construction robot schema (CRS) with an initial data structure that can be used to collect and exchange various construction robots’ information based on the data requirements of construction planners for robotics operations. To develop the CRS, the study conducted a systematic literature review using the Web of Science database to filter and identify relevant papers which were published from 2018 to 2022. Based on 279 eligible papers, the study identified significant information which involved data requirements of the construction domain on robotics using Nvivo software. To structure the information, the study summarized the information into parameters then categorized, defined, matched data types, and exemplified for these parameters. All the parameters were grouped into four categories, including ontological properties, operational requirements, activity, and safety. As a result, CRS supports data structure including 4 categories and 35 parameters with corresponding definitions, data types, examples, and references. 

ABSTRACT Atmospheric methane (CH₄) concentrations are accelerating global warming as net emissions increase. Observing systems that quantify sources remain too sparse and fragmented to detect trends—especially in remote regions where climate‐driven natural emissions may be rising. We provide a framework for quantifying uncertainty reductions through the implementation of a global ecosystem‐methane observing system designed to: (i) substantially lower uncertainty in sectoral and regional emissions, (ii) separate co‐occurring anthropogenic and natural fluxes, and (iii) trend detection at regional scales to verify mitigation progress and provide early warning of natural feedbacks. Using bottom‐up inventories and process‐model ensembles for 2014–2023, we show that anthropogenic emissions remain uncertain by ∼32% globally, while natural sources—tropical and boreal‐arctic wetlands, fires, and inland waters—carry far larger uncertainties (+ 70%) and trend uncertainties reaching ∼200%. Additional observations must match spatial emission structure to increase observability of emissions: high‐resolution satellite constellations for point sources combined with expanded flux networks and wetland mapping for diffuse sources, and denser ground‐based atmospheric column measurements to restore observability in under‐sampled tropics and high latitudes. Notional analyses indicate that targeted additions of flux towers and ∼20 in situ atmospheric column concentration instruments per key tropical region could reduce continental‐scale uncertainties at modest cost.