Connected and software-defined vehicles generate massive volumes of high-frequency telemetry, diagnostics, and geospatial data that must be ingested, processed, and governed at scale. Traditional data lakes are increasingly inadequate for these workloads due to weak consistency guarantees, slow and brittle metadata operations, and limited support for update-heavy time-series ingestion. Modern Lakehouse table formats—Delta Parquet, Apache Iceberg, and Apache Hudi—address many of these gaps by adding ACID transactions, schema evolution, and scalable metadata layers on top of open file formats. However, existing evaluations remain largely generic and do not consider automotive-specific requirements such as real-time ingestion from fleets, change-data-capture (CDC) patterns, route- and region-based analytics, ML dataset reproducibility, and regulated data retention and deletion. This paper presents a domain-specific comparative analysis of Delta Parquet, Apache Iceberg, and Apache Hudi using a realistic synthetic telemetry dataset representing 100 vehicles and more than 200,000 records over 30 days. We benchmark ingestion throughput and latency, analytical query performance—including temporal and geospatial workloads—storage efficiency, compaction behavior, CDC handling, and time-travel semantics. Our results show that Apache Hudi is best suited for real-time, CDC-intensive ingestion; Apache Iceberg delivers the lowest-latency analytical queries and most efficient storage footprint; and Delta Parquet offers the strongest support for ML reproducibility and governance-oriented workflows. Building on these findings, we propose a hybrid Lakehouse architecture that assigns each table format to the pipeline stage where it is most effective and introduce a domain-specific evaluation framework to guide automotive data engineering decisions.
This paper is a case study of the design, cost, and return on investment of a 6 GW Electric Vehicle battery manufacturing facility in Egypt. The paper analyzes the cost of construction, capital equipment, manufacturing materials, building facility equipment, labor, insurance, maintenance, and depreciation. The paper also estimates the management cost and returns on investment. The physical design of the Manufacturing facility is explained in detail in [1].
Financial document digitization remains a critical bottleneck in enterprise workflows, where invoices, receipts, and purchase orders must be accurately parsed into structured data for downstream accounting and analytics systems. Existing OCR-to-LLM pipelines typically process entire document pages as flat text streams, discarding the spatial layout information that is essential for disambiguating semantically overloaded fields in financial documents. In this paper, we propose a Spatial-Aware Document Understanding (SADU) framework that introduces a region-segmented OCR strategy coupled with a novel layout-encoded prompting mechanism for Large Language Models. Our pipeline employs PyMuPDF for PDF rendering, OpenCV-based contour analysis for semantic region segmentation into five document zones (Header, Metadata, Line Items, Totals, and Footer), Tesseract OCR applied independently per region, and a fine-tuned Gemma-2B model that receives spatially tagged OCR output for structured key-value extraction. We evaluate our approach on three publicly available benchmark datasets: ICDAR 2019 SROIE, CORD, and a filtered subset of RVL-CDIP. Experimental results demonstrate that the proposed SADU framework achieves an F1-score of 94.2% on SROIE key information extraction, representing a 4.8% improvement over flat-text OCR-LLM baselines. Comprehensive ablation studies confirm that both region-segmented OCR and layout-encoded prompting contribute independently and synergistically to the observed performance gains. The framework processes documents at an average latency of 1.87 seconds per page on consumer-grade GPU hardware, demonstrating practical viability for real-time enterprise deployment.
Vulnerability management teams often have larger remediation backlogs than available testing, engineering, and change-window capacity. The CISA Known Exploited Vulnerabilities (KEV) catalog reduces uncertainty by identifying vulnerabilities with observed exploitation, but KEV membership alone does not determine which affected assets should be patched first inside a large enterprise. This paper presents a KEV-aware prioritization method that uses KEV as a high-confidence exploitation gate while combining daily Exploit Prediction Scoring System (EPSS) probabilities, Common Vulnerability Scoring System (CVSS) impact characteristics, and local asset context. The method produces remediation tiers, service-level agreement targets, and ticket-level rationale instead of a single opaque score. To make the method testable rather than only conceptual, the paper includes a concrete scoring instantiation, a worked example using CVE-2023-27350, and a public-data retrospective evaluation over three historical KEV cases: PaperCut, MOVEit Transfer, and Ivanti Connect Secure. The result is an explainable and reproducible framework that uses public feeds while preserving the operational judgment needed for enterprise patch triage.
Real-time livestock welfare monitoring during transportation remains a critical challenge in modern agriculture, where non-invasive and scalable solutions are required. This paper presents a computer vision and AI-based framework for automated sheep behavior detection designed as the foundational module of an IoT-enabled monitoring system. Using DeepLabCut for markerless pose estimation, key body points are extracted from video streams and transformed into movement-based statistical features. These features are processed by a lightweight neural network classifier capable of distinguishing grazing, sitting, and running behaviors with 97% training accuracy and 98% testing accuracy. The proposed architecture eliminates the need for wearable sensors, reducing stress on animals while enabling remote, video-based monitoring. The system is designed for integration with cloud connectivity and real-time caretaker interfaces, supporting scalable deployment in transport vehicles. By combining edge-based video analysis with AI-driven behavioral classification, this work demonstrates the feasibility of an intelligent, non-invasive welfare monitoring platform and represents a step toward automated livestock management systems.
