意昂集团

The standard cosmological analysis with the Lycx forest relies on a continuum fitting procedure that suppresses information on large scales and distorts the three-dimensional correlation function on all scales. In this work, we present the first cosmological forecasts without continuum fitting distortion in the Lycx forest, focusing on the recovery of large-scale information. Using idealized synthetic data, we compare the constraining power of the full shape of the Lycx forest auto-correlation and its cross-correlation with quasars using the baseline continuum fitting analysis versus the true continuum. We find that knowledge of the true continuum enables a similar to 10% reduction in uncertainties on the Alcock-Paczy & nacute;ski (AP) parameter and the matter density, ohm m. We also explore the impact of large-scale information by extending the analysis up to separations of 240 h-1Mpc along and across the line of sight. The combination of these analysis choices can recover significant large-scale information, yielding up to a similar to 15% improvement in AP constraints. This improvement is analogous to extending the Lycx forest survey area by similar to 40%.

LLM agents for enterprise systems of record cannot be evaluated on customer production data, and no existing substitute provides ground truth. We present the Era by Eon Benchmark for evaluating LLM agents that use enterprise tools. The benchmark is built around a complete fictional company. It includes product simulators, company-specific internal databases, benchmark questions, and computed answer keys. Industry, company size, business model, application portfolio, and a seed define each company. One seeded entity graph supplies shared company data to simulators of Salesforce, Zendesk, Slack, Gong, and other products. A questionconditioned generator creates the schemas and records for internal databases. It takes shared entities, keys, and values from the same graph before generating database-specific facts. Both mechanisms therefore describe one consistent enterprise estate. Every expected answer is computed from the final records, so grading is exact. Design and answer-key checks validate the internal databases. A realism scorecard and adversarial detector validate the entity graph. Across 23 generated companies, the mean realism score rose from 61.8 to 97.0, with zero records flagged as synthetic. In the reported simulator-track comparison, nine models answered the same 33 questions three times each. Accuracy estimates ranged from 42.4

Synthetic relational data is normally produced by a model trained on a real dataset, and its quality is measured as the distance to that dataset. This paper describes a generator that has no real dataset at either end. Given an industry, a company size, a business model, a set of business applications, and a random seed, it produces a complete fictional enterprise: a workforce, a customer base, sales deals, support tickets, recorded calls, chat messages, and documents, all consistent with one another. One entity graph is projected into the native formats of 66 business products, so the same customer appears in the CRM, the support desk, and the call system under one identity. Because no real counterpart exists, realism is built in from cited reference statistics and verified by reference-free measurement: a five-axis scorecard of 28 statistical checks, an adversarial detector that hunts for the marks of synthetic generation, and a set of soundness checks that include a classifier test against an independently shuffled copy of the data. Because these instruments existed before the generator was tuned, progress is measured under a fixed yardstick: over 23 generated companies, mean realism climbed from 60.3 to 99.1, the weakest company from 41.1 to 94.9, and the detector, which initially flagged 55.2

Abstract Distribution system operators (DSOs) are increasingly expected to deploy Local Flexibility Markets (LFMs) to manage congestion and integrate distributed energy resources. This paper proposes a decision-oriented framework that organizes LFM design into 13 interdependent dimensions and makes their cross-effects explicit through a directed S–E–C–B dependency matrix (Strongly coupled, Enables, Correlated, Background). The method does not prescribe a single blueprint; rather, it reveals prerequisite clusters – most notably the interaction of market sequencing, system operator cooperation, and market timing – and identifies enabling backbones from forecasting and baselines toward product, pricing and clearing choices. The resulting map delivers three practical outcomes for DSOs: a diagnostic checklist to assess technical feasibility, market coherence and governance consistency; a risk-allocation lens linking timing and activation rules to forecast uncertainty and provider participation risk; and an implementation map that identifies decision dependencies while highlighting independent degrees of freedom. By translating a dispersed literature into a reproducible structure, the framework reduces design ambiguity, supports stakeholder alignment and provides a transparent basis for piloting and scale-up under heterogeneous regulatory and system contexts.

Direct handset-to-satellite (DHTS) communication is emerging as a core capability of 6G non-terrestrial networks, enabling standard devices to directly access low Earth orbit (LEO) satellites. While LEO provides the physical access layer for DHTS, large-scale device connectivity introduces challenges in mobility management, interference control, spectrum efficiency, and constellation-wide coordination. Relay-only LEO architectures are insufficient to manage massive handset access under dynamic traffic and energy constraints. This article introduces a hierarchical architecture in which direct handset-to-LEO access is supported by multi-orbit space-based data centers (SBDCs) spanning LEO, medium Earth orbit (MEO), and geostationary Earth orbit (GEO). In this framework, LEO satellites handle radio access and real-time inference, while higher orbital layers provide regional aggregation, global orchestration, and compute-aware routing. By embedding distributed in-orbit computing, energy-aware scheduling, and AI-driven hierarchical control, the constellation evolves from a passive relay network into an intelligent multi-layer system capable of supporting large-scale DHTS services. We discuss key enabling technologies, envisioned multi-orbit integrated Earth-space compute architecture, and open research challenges in integrating multi-orbit computing, highlighting pathways toward scalable and resilient 6G DHTS networks.