NTSB CAROL · Event
Event MIA74DLD97
Aircraft involved
Historical record (pre-1982)
NTSB recorded this accident in the pre-1982 coded-field schema — structured fields rather than free-text narrative. Decoded codes use established NTSB single-letter taxonomies; cause factors remain verbatim pending the Form 6120.4 codebook lookup.
Aircraft
TEENIE TWO · N28363
Damage
Substantial
Craft type
Airplane
Classification
Accident
Light condition
Dawn
Weather
VMC
Phase of flight
Takeoff — initial climb (C1)
Operator type
D
Airport
VENICE AIRPARK
Kind of flying
B0
Weather at impact
Sky
SCATTERED-ABOVE 1000 FEET
Wind
220° / 10 kt
Temp
90° F
Aircraft history
Serial number
000000000101
Total time
35 hrs
Pilot
Certificate
Commercial — instrument
Total hours
800
Age
67
Investigator remarks
DAMAGED PROP BLADE IMPROPERLY REPAIRED.
Cause factors
- 68/C/D1 C PERSONNEL IMPROPER MAINTENANCE (OWNER PERSONNEL) Cause
- 74/C/FA C POWERPLANT BLADES Cause
- 88/C/AH C MISCELLANEOUS ACTS,CONDITIONS PREVIOUS DAMAGE Cause
- 88/C/95 C MISCELLANEOUS ACTS,CONDITIONS MATERIAL FAILURE Cause
- 64/B/25 B PILOT IN COMMAND IMPROPER LEVEL OFF
- 88/L/49 L MISCELLANEOUS ACTS,CONDITIONS SEPARATION IN FLIGHT
- 88/K/94 K MISCELLANEOUS ACTS,CONDITIONS OVERLOAD FAILURE Joint factor
Decoded against the NTSB Form 6120.4 cause-factor codebook (ct_Pre1982 table). Each row shows the raw triplet, modifier (Cause / Factor / etc.), category, and specific code.
Source: NTSB pre-1982 historical archive. Docket
3 1688.
Source file
NTSB_1974_3_1688.txt.
Modern CAROL record on
data.ntsb.gov ↗.
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Related research
What the literature says.
Academic papers and agency reports matching this event's aircraft type. Sourced from NASA NTRS, NTSB Safety Studies, FAA CAMI, AOPA Air Safety Institute, Embry-Riddle Scholarly Commons, arXiv, and the Semantic Scholar academic graph.
- arXiv 2026 · arXiv preprint
A Disaster-Aware Integrated TN-NTN System-Level Simulator for Resilient 6G Wireless Networks
Non-terrestrial networks (NTN) have been standardized by the 3rd generation partnership project (3GPP) as a key component of future 6G systems to enhance coverage and resilience.
- arXiv 2026 · arXiv preprint
Model Predictive Communication for Timely Status Updates in Low-Altitude Networks
Timely information delivery in low-altitude networks is critical for many time-sensitive applications, such as unmanned aerial vehicle (UAV) navigation, inspection, and surveillance.
- arXiv 2026 · arXiv preprint
Communications-Aware NMPC for Multi-Rotor Aerial Relay Networks Under Jamming Interference
Multi-Rotor Aerial Vehicles (MRAVs) are increasingly used in communication-dependent missions where connectivity loss directly compromises task execution.
- arXiv 2026 · arXiv preprint
Super-resolution of turbulent reacting flows on complex meshes using graph neural networks
State-of-the-art deep learning models have been extensively utilized to reconstruct small-scale structures from coarse-grained data in turbulent flows.
- arXiv 2026 · arXiv preprint
Path Planning Optimisation for SParse, AwaRe and Cooperative Networked Aerial Robot Teams (SpArC-NARTs): Optimisation Tool and Ground Sensing Coverage Use Cases
A networked aerial robot team (NART) comprises a group of agents (e.g., unmanned aerial vehicles (UAVs), ground control stations, etc.) interconnected by wireless links.
- arXiv 2026 · arXiv preprint
UAV-Assisted 6G Communication Networks for Railways: Technologies, Applications, and Challenges
Unmanned Aerial Vehicles (UAVs) are crucial for advancing railway communication by offering reliable connectivity, adaptive coverage, and mobile edge services .
Browse the full corpus — academia portal ↗