Rugged, Mobile Data Acquisition System for Coal Mine Roof Safety Research

This federal Request for Quotations (RFQ 75D301-26-Q-79258), issued by the Centers for Disease Control and Prevention (CDC) on behalf of the NIOSH Pittsburgh Mining Research Division (PMRD), seeks a firm-fixed-price purchase order for one complete, field-ready rugged mobile data acquisition system. The system will collect and analyze synchronized, high-frequency sensor data from roof bolting machines operating in underground coal mines to evaluate machine response and mine-roof conditions. The acquisition is unrestricted under NAICS code 334515. Quoters must provide all necessary hardware, software, licenses, and accessories, ensuring compatibility with the government's existing Siemens Simcenter Testlab software. The contractor must deliver the system within six months of award to the NIOSH facility in Pittsburgh, Pennsylvania, and provide a standard commercial warranty along with technical support. Key submission requirements include a completed Technical Compliance Matrix, supporting manufacturer documentation, and, if applicable, an Artificial Intelligence (AI) Compliance and Risk Management Plan. Proposals using AI will be evaluated on a Pass/Fail basis, and failure to provide an acceptable plan will render the quote ineligible. The government intends to make a single award to the responsible quoter submitting the lowest-priced, technically acceptable (LPTA) quotation. Quotations are due by September 22, 2026, at 9:00 AM Eastern Time, with questions due by September 18, 2026.

This Statement of Requirements (SOR) details a federal solicitation (RFQ 75D301-26-Q-79258) issued by the National Institute for Occupational Safety and Health (NIOSH), Pittsburgh Mining Research Division (PMRD). NIOSH requires a rugged, mobile data acquisition system to support coal mine roof safety research by collecting and analyzing synchronized, high-frequency sensor data from underground roof bolting machines. The contractor must deliver a complete, field-ready commercial system within six months of the contract award. Key technical specifications include a minimum of 12 simultaneous data acquisition channels (expandable to 24), a 16-bit minimum resolution, and a 200 kHz minimum sample rate per channel. The system must feature universal signal conditioning for various sensors, wireless control and monitoring capabilities, onboard storage, integrated video recording, and CAN bus compatibility. Due to the harsh underground mining environment, the hardware must be compact (under 10" x 12" x 12"), lightweight (under 20 lbs), fanless, IP67-rated for dust and water resistance, and capable of withstanding extreme temperatures, shock, and vibration. Additionally, the system must be fully compatible and interoperable with NIOSH’s existing Siemens Simcenter Testlab software. The selected contractor must be the manufacturer or an authorized distributor capable of providing technical support, with final inspection and acceptance conducted by NIOSH upon delivery to their Pittsburgh, Pennsylvania facility.

This document is Attachment 2 (Technical Compliance Matrix) for Request for Quotation (RFQ) 75D301-26-Q-79258, issued by the federal government. Its purpose is to evaluate and verify that a quoter's proposed rugged, mobile data acquisition system meets the minimum technical requirements for coal mine roof safety research. The matrix requires vendors to confirm compliance (Yes/No), detail their proposed configuration and part numbers, explain how they meet each specification, and cite supporting manufacturer documentation. Key technical requirements include: - A minimum of 12 simultaneous data acquisition channels (expandable to 24), 16-bit resolution, and a sample rate of 200 kHz or 80 kHz bandwidth per channel. - Standalone operation capability and universal signal conditioning supporting IEPE accelerometers, strain gauges, and other transducers. - Ruggedized physical specifications, including DC power, fanless cooling, a compact footprint (under 10x12x12 inches), weight under 20 pounds, MIL-STD-810 level shock and vibration resistance (5 g RMS / 50 g peak), an IP67 ingress rating, and operating temperatures from 0°F to 120°F. - Wireless connectivity for remote control and monitoring, synchronized video integration, CAN bus capability, and guaranteed interoperability with the Government's existing Siemens Simcenter Testlab software environment. Quoters must fully complete and sign this matrix to prevent their proposal from being deemed technically unacceptable.

This document is an "Artificial Intelligence (AI) Use Compliance and Risk Management Plan" template, designed for federal contractors who plan to utilize AI during contract performance for the CDC or HHS, where AI development is not the primary deliverable. Its main purpose is to identify, assess, and manage the compliance, security, and operational risks associated with integrating AI tools into government contracts. The template requires contractors to provide detailed information across nine key areas: - **Background & Ownership:** Identifies the specific AI tool, its classification (e.g., Generative AI, Machine Learning), the supported contract tasks, and the contractor/government stakeholders involved. - **Technical & Hosting Details:** Captures vendor details, model types, API requirements, and hosting environments (such as FedRAMP-authorized clouds). - **Data Governance:** Details what government information will be processed, stored, or generated, and establishes restrictions against using government data to train or fine-tune models. - **Risk & Impact Assessment:** Evaluates whether the AI system is "high-impact"—affecting individual rights, safety, or critical agency decisions—and outlines human-in-the-loop verification processes. - **Security & Monitoring:** Defines safeguards against data leakage and prompt injection, and outlines incident response protocols. - **Contractor Affirmation:** Mandates formal contractor agreement to only use approved tools, protect government data, and update the compliance plan as AI usage evolves.