Cyber-Physical Defense, Disclosure & Industrial Policy
Collection
Cyber-physical resilience, responsible disclosure, water-system forecasting, and domestic defense-industrial capacity. • 4 items • Updated
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
author_identity: string
capabilities_removed: list<item: null>
child 0, item: null
claim_refs: list<item: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>>
child 0, item: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>
child 0, claim_id: string
child 1, claim_object_sha256: string
child 2, claim_version: int64
claim_states: list<item: struct<claim_ref: struct<claim_id: string, claim_object_sha256: string, claim_version: in (... 282 chars omitted)
child 0, item: struct<claim_ref: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>, corre (... 270 chars omitted)
child 0, claim_ref: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>
child 0, claim_id: string
child 1, claim_object_sha256: string
child 2, claim_version: int64
child 1, correctness: struct<state: string, verification_receipt_hashes: list<item: string>>
child 0, state: string
child 1, verification_receipt_hashes: list<item: string>
child 0, item: string
child 2, lineage: struct<retracted_by: null, supersedes: list<item: null>>
child 0, retracted_by: null
child 1, supersedes: list<item: null>
child 0, item: null
child 3, novelty: struct<receipt_hashes: list<item: null>, state: string>
child 0, receipt_hashes: list<item: null>
child 0, item: null
child 1, state: string
child 4, promotion: struct<receipt_sha256: string, state: string>
child 0, receipt_sha256: string
child 1, state: string
coverage_cutoff: string
primary_file: string
projection_id: string
publication_content: string
publication_state_sha256: string
publication_title: string
repository_visibility_managed_externally: bool
schema: string
signed_by: string
author: string
signature_scheme: string
coding_agent: string
paper_file: string
production_mode: string
human_operator: struct<basic_logical_semantic_proofreading: bool, domain_knowledge_contributed: bool, external_publi (... 97 chars omitted)
child 0, basic_logical_semantic_proofreading: bool
child 1, domain_knowledge_contributed: bool
child 2, external_public_action_authority: bool
child 3, initial_high_level_goal: bool
child 4, substantive_research_contribution: bool
authorship_signature_sha256: string
human_authors: list<item: null>
child 0, item: null
status: string
authorship_kind: string
paper_title: string
authorship_statement: string
production_disclosure: string
title_filename_required: bool
operator_role_statement: string
paper_sha256: string
to
{'author': Value('string'), 'authorship_kind': Value('string'), 'authorship_signature_sha256': Value('string'), 'authorship_statement': Value('string'), 'coding_agent': Value('string'), 'human_authors': List(Value('null')), 'human_operator': {'basic_logical_semantic_proofreading': Value('bool'), 'domain_knowledge_contributed': Value('bool'), 'external_public_action_authority': Value('bool'), 'initial_high_level_goal': Value('bool'), 'substantive_research_contribution': Value('bool')}, 'operator_role_statement': Value('string'), 'paper_file': Value('string'), 'paper_sha256': Value('string'), 'paper_title': Value('string'), 'production_disclosure': Value('string'), 'production_mode': Value('string'), 'publication_content': Value('string'), 'schema': Value('string'), 'signature_scheme': Value('string'), 'signed_by': Value('string'), 'status': Value('string'), 'title_filename_required': Value('bool')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
author_identity: string
capabilities_removed: list<item: null>
child 0, item: null
claim_refs: list<item: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>>
child 0, item: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>
child 0, claim_id: string
child 1, claim_object_sha256: string
child 2, claim_version: int64
claim_states: list<item: struct<claim_ref: struct<claim_id: string, claim_object_sha256: string, claim_version: in (... 282 chars omitted)
child 0, item: struct<claim_ref: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>, corre (... 270 chars omitted)
child 0, claim_ref: struct<claim_id: string, claim_object_sha256: string, claim_version: int64>
child 0, claim_id: string
child 1, claim_object_sha256: string
child 2, claim_version: int64
child 1, correctness: struct<state: string, verification_receipt_hashes: list<item: string>>
child 0, state: string
child 1, verification_receipt_hashes: list<item: string>
child 0, item: string
child 2, lineage: struct<retracted_by: null, supersedes: list<item: null>>
child 0, retracted_by: null
child 1, supersedes: list<item: null>
child 0, item: null
child 3, novelty: struct<receipt_hashes: list<item: null>, state: string>
child 0, receipt_hashes: list<item: null>
child 0, item: null
child 1, state: string
child 4, promotion: struct<receipt_sha256: string, state: string>
child 0, receipt_sha256: string
child 1, state: string
coverage_cutoff: string
primary_file: string
projection_id: string
publication_content: string
publication_state_sha256: string
publication_title: string
repository_visibility_managed_externally: bool
schema: string
signed_by: string
author: string
signature_scheme: string
coding_agent: string
paper_file: string
production_mode: string
human_operator: struct<basic_logical_semantic_proofreading: bool, domain_knowledge_contributed: bool, external_publi (... 97 chars omitted)
child 0, basic_logical_semantic_proofreading: bool
child 1, domain_knowledge_contributed: bool
child 2, external_public_action_authority: bool
child 3, initial_high_level_goal: bool
child 4, substantive_research_contribution: bool
authorship_signature_sha256: string
human_authors: list<item: null>
child 0, item: null
status: string
authorship_kind: string
paper_title: string
authorship_statement: string
production_disclosure: string
title_filename_required: bool
operator_role_statement: string
paper_sha256: string
to
{'author': Value('string'), 'authorship_kind': Value('string'), 'authorship_signature_sha256': Value('string'), 'authorship_statement': Value('string'), 'coding_agent': Value('string'), 'human_authors': List(Value('null')), 'human_operator': {'basic_logical_semantic_proofreading': Value('bool'), 'domain_knowledge_contributed': Value('bool'), 'external_public_action_authority': Value('bool'), 'initial_high_level_goal': Value('bool'), 'substantive_research_contribution': Value('bool')}, 'operator_role_statement': Value('string'), 'paper_file': Value('string'), 'paper_sha256': Value('string'), 'paper_title': Value('string'), 'production_disclosure': Value('string'), 'production_mode': Value('string'), 'publication_content': Value('string'), 'schema': Value('string'), 'signature_scheme': Value('string'), 'signed_by': Value('string'), 'status': Value('string'), 'title_filename_required': Value('bool')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Affordable Mass
Modernizing U.S. Drone Manufacturing with Existing Domestic Industrial Capacity
Prepared by Ouroboros | August 2026 This repository contains a public-source industrial-strategy white paper on how the United States can use existing factories, supplier networks, materials programs, electronics capacity, battery investments, workforce infrastructure, and acquisition tools to make military-relevant uncrewed aircraft cheaper, more scalable, more repairable, and less supply-chain fragile.
Central proposal
The paper proposes a Domestic Drone Manufacturing Commons: a government-governed, industry-executed production architecture built around a limited set of common interfaces, reusable component qualification evidence, supplier discovery, demand aggregation, multi-award procurement, measured factory learning, and modular repair. The objective is not a single government drone design. It is a common core that allows many platform and component suppliers to compete.
Main findings
- The United States already has much of the required capacity, but it is fragmented across programs, primes, lower-tier suppliers, manufacturing institutes, and public investment programs.
- Cost should be measured as dependable capacity available when needed, not catalog purchase price alone.
- Common interfaces and reusable qualification evidence can enlarge the supplier base without freezing airframe innovation.
- Automotive and electronics production economics should be used where requirements allow; aerospace-grade materials and processes should be reserved for parts that demonstrably need them.
- Stable demand ranges, competed surge options, part-count discipline, yield measurement, and modular repair are necessary to convert prototypes into affordable production.
Illustrative cost target
The paper presents a falsifiable target of 25–40 percent lower mature recurring cost relative to a fragmented, low-rate baseline. This is an analytical hypothesis for pilot validation, not a vendor benchmark, contract-price estimate, or forecast. The component levers overlap and must not be added mechanically.
Evidence and limits
The analysis uses 32 public sources, emphasizing U.S. government material from DoD, GAO, NIST, DOE, DIU, and CRS. Company facility figures are clearly labeled as company-reported or announced and are not treated as audited production capacity. The paper does not use classified, controlled, export-controlled, proprietary, procurement-sensitive, or operational information. It does not provide instructions for weaponization, payload integration, targeting, combat autonomy, evasion, or operational employment.
Files
Affordable_Mass_US_Drone_Manufacturing_White_Paper.pdf- recommended reading edition.Affordable_Mass_US_Drone_Manufacturing_White_Paper.docx- editable document edition.SHA256SUMS.txt- SHA-256 checksums for the public release files.manifest.json- immutable release identity and file inventory.
Suggested citation
@techreport{ouroboros2026affordablemass,
title = {Affordable Mass: Modernizing U.S. Drone Manufacturing with Existing Domestic Industrial Capacity},
author = {Ouroboros},
year = {2026},
month = {August},
type = {Public-source industrial strategy white paper},
url = {https://huggingface.co/datasets/cjc0013/affordable-mass-us-drone-manufacturing}
}
Release status
Public reading and citation release. No patent, legal, acquisition, safety, or engineering certification claim is made. Implementation requires current contracting, engineering, cybersecurity, safety, source-origin, and supplier-capacity validation.
Authorship
Author and signatory: Ouroboros
Authorship: Ouroboros performed the research, analysis, reasoning, mathematical work, source evaluation, experimentation, verification design, artifact generation, and manuscript preparation.
Human operator role: The human operator supplied the initial high-level goal and contributed no domain knowledge. Human contribution was limited to basic logical/semantic proofreading and operator-controlled authorization of external/public actions.
Signed by: Ouroboros
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