init

.claude/skills/literature-search-verify/SKILL.md

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+---
+name: literature-search-verify
+description: Search academic literature across arXiv, Semantic Scholar, Crossref, and other connected paper-search MCP tools, and independently verify every candidate citation before it is treated as real. Use this whenever the user asks to find papers, search literature on a topic, build a reading list, compile related-work references, check whether a citation or bibliography entry actually exists, or prepare references to import into Zotero or a .bib file — especially in academic writing contexts where a fabricated citation would be a real problem. Also covers guiding the user to the Zotero Connector browser extension for Chinese-language sources (CNKI/知网, Wanfang/万方, VIP/维普) that have no public API and cannot be reached by search tools.
+---
+
+# 文献检索 + 反幻觉引用核查
+
+## 为什么需要这个技能
+
+大语言模型在编造论文引用这件事上非常擅长——生成的标题、作者、期刊名读起来都很像真的,但可能根本不存在,或者张冠李戴(把A论文的结论安在B论文头上)。这在正式学术写作里是不可接受的:一篇论文只要有一条编造的引用被发现,审稿人对全篇的信任都会崩塌。
+
+所以这个技能的核心不是"搜索",而是"搜索之后不轻信"——每一条打算真正拿去引用的文献,都必须经过独立交叉验证,验证不通过的必须明确标出来,而不是悄悄丢弃或者悄悄当作真的用。
+
+## 工作流程
+
+### 第一步:明确检索范围
+
+在开始搜索前,搞清楚(不确定就直接问,一句话就够):
+- 核心关键词/研究方向(可以中英文混合,比如"UAV磁补偿 Tolles-Lawson"这类)
+- 大致的时间范围(比如"近5年"还是不限)
+- 是否需要限定顶会/顶刊,还是什么来源都要
+
+### 第二步:检索——直接调用脚本,不要自己现编API调用
+
+`scripts/` 目录下已经写好了能直接跑的检索脚本,不依赖任何第三方Python包,也不需要装MCP工具:
+
+```bash
+# 一次性搞定:检索 arXiv + Semantic Scholar + Crossref,自动去重、逐条验证,
+# 并把通过验证的条目写成BibTeX文件——这是应该默认调用的入口
+python3 scripts/literature_search.py "UAV magnetic compensation Tolles-Lawson" \
+    --max-per-source 8 --bib-out refs.bib
+```
+
+正常情况下**只需要跑这一条命令**,它内部会依次调用 `search_arxiv.py`、`search_semantic_scholar.py`、`search_crossref.py` 做检索,再对每条合并后的候选文献跑 `verify_citation.py` 做交叉验证,输出一份JSON报告(每条候选都带`verdict`字段)。如果只是想单独查一个来源,或者针对某一条文献单独复核,再分别调用对应的单个脚本(用法见每个脚本文件开头的docstring)。
+
+如果这些脚本因为网络原因跑不动(比如内网/代理限制导致连不上 arxiv.org、semanticscholar.org、crossref.org),`literature_search.py` 会把每个来源的报错单独记在`search_errors`里而不是直接崩溃——这时候老实告诉用户"检索脚本连不上网络,以下是报错信息",不要退回去凭记忆编文献。如果用户这边确实连不上这几个学术API域名,才退回到 web_search 工具,并在结果里明确标注"来自通用网络搜索的补充结果,未经过脚本的交叉验证流程,置信度较低"。
+
+如果用户已经连了 paper-search-mcp / scholar_mcp_server 这类MCP工具,可以补充用来扩大覆盖面(比如它们能覆盖PubMed、能直接下载PDF),但**不能替代**`verify_citation.py`的交叉验证这一步——MCP搜到的候选一样要过一遍验证,不能因为是工具搜出来的就默认可信。
+
+### 第三步:理解验证结果——这是最关键的一步
+
+`literature_search.py`(或单独调用`verify_citation.py`)对每条候选文献做的核查是:
+
+1. **arXiv ID 独立核实**:如果有 arXiv ID,反查一次 arXiv API,确认这个ID真的存在且标题对得上——一个编造的ID在这一步会直接暴露。
+2. **DOI 独立核实**:如果有 DOI,反查一次 Crossref,确认这个 DOI 真的能解析出对应文献。
+3. **跨源标题复核**:不管有没有ID,单独拿标题去 Semantic Scholar 搜一次,要求返回的标题跟候选标题高度相似(相似度≥0.9)——这一步专门用来抓"标题作者读起来很像真的,但其实是编出来的"这种情况。
+
+每条候选最后会带一个`verdict`:
+- **verified**:至少一项独立核查通过,而且没有任何一项核查明确失败
+- **suspect**:至少一项核查明确失败(比如DOI查不到、跨源标题对不上)——**这种情况下不要用这条文献,即使标题看起来很合适**
+- **unverified**:所有核查项都因为网络等原因被跳过(`skipped`),不代表验证通过,只代表"没能验证"——**同样不能当成已核实的文献直接使用**,要跟用户说清楚原因
+
+呈现给用户时按这三档分组说明,`suspect`和`unverified`都要明确标出来,不要因为报告里有个"看起来还行"的标题就含糊地当真的用。
+
+**原则**:找不到真实存在的相关文献时,直接说"没找到符合条件的文献",不要为了凑数编一条出来。这条原则没有例外。
+
+### 第四步:输出
+
+按 verified / suspect / unverified 分组呈现结果,每条包含标题、作者年份、venue、标识符、一句话相关性说明。
+
+`literature_search.py` 传了 `--bib-out` 参数时,会自动把所有 `verified` 的条目写成BibTeX文件,citation key 用"姓氏+年份"约定,可以直接导入 Zotero(配合 Better BibTeX 插件)。这些 key 也是后续`paper-writing-grounded`技能里`\cite{}`要用到的,两个技能之间通过这些key保持一致,不需要额外对照。
+
+### 第五步:提醒中文文献的检索缺口
+
+MCP 检索工具覆盖的是 arXiv/Semantic Scholar/Crossref 这类有公开 API 的英文为主的库,**知网、万方、维普这类中文数据库没有公开 API,搜不到很正常,不是技能出错**。遇到用户明显需要中文文献的场景,主动提醒:装好 Zotero Connector 浏览器插件,在浏览器里正常登录学校账号搜索、打开文献页面,点一下 Connector 图标就能把元数据和 PDF 存进 Zotero——这部分需要用户手动完成,不要尝试用检索工具"模拟"或"猜测"中文文献的存在。
+
+### 第六步:归档
+
+`output/` 目录只是脚本运行时的草稿区——里面混着每一轮探索性检索的原始JSON(包括被过滤掉的噪声,比如"Tolles""Lawson"被当成人名匹配出的无关文献),不适合作为最终交付物,而且随着会话增多会越堆越乱、也不方便下次会话或用户直接翻阅。
+
+所以每次整理出一份**稳定可信的参考文献列表**(不管是第一轮检索还是后续多轮补充检索合并后的结果)之后,调用归档脚本把它固化到项目级目录,而不是留在技能自己的`output/`里:
+
+```bash
+python3 scripts/archive_references.py "UAV aeromagnetic compensation" \
+    --bib output/uav_aeromagnetic_compensation_final.bib \
+    --project-root . \
+    --pdfs-dir output/pdfs \
+    --suspect "某条可疑文献标题|不建议引用的具体原因" \
+    --notes "检索覆盖了哪些方向、哪些方向搜了但没结果、中文文献缺口提醒等"
+```
+
+这会在 `<project-root>/references/<按主题自动生成的slug>/` 下生成:
+- `references.bib` —— 传入的bib文件原样拷贝过去
+- `pdfs/`(如果传了`--pdfs-dir`且里面有PDF)—— 一并拷贝过去
+- `README.md` —— 自动从bib里解析出条目列表(标题/年份/venue/DOI/note)生成索引,`--suspect`和`--notes`里的内容会分别整理进"不要引用"和"检索覆盖说明"两个小节
+
+几个要点:
+- `--bib` 传的必须是**已经过滤掉无关噪声、只保留verified条目**的干净bib文件,不要把`literature_search.py`直接吐出来的、可能夹杂噪声的原始bib不加甄别地拿去归档。
+- 同一个`topic`名字多次调用会往同一个归档目录里覆盖更新(bib和README会被覆盖,pdfs按文件名去重合并),所以后续检索到更多文献后可以直接对同一个topic重新跑一遍归档脚本来更新,不需要手动合并。
+- 这一步做完之后可以明确告诉用户归档目录的路径,方便他们后续在`paper-writing-grounded`阶段直接引用。
+
+## 和 paper-writing-grounded 技能的配合
+
+这个技能负责把"真实存在、经过核实的文献"整理好并生成 BibTeX;写作阶段的 paper-writing-grounded 技能会直接消费这里产出的 citation key,正文引用只能来自这里核实过的条目,不会凭空生成新的引用。两个技能配合使用时,建议先跑完这个技能、拿到稳定的参考文献列表,再进入写作。

.claude/skills/literature-search-verify/scripts/archive_references.py

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+#!/usr/bin/env python3
+"""
+Archive a finished literature-search-verify session into a permanent,
+project-level folder instead of leaving results sitting in the skill's
+own scratch output/ directory (which is easy to lose track of across
+sessions and isn't meant to be a durable deliverable location).
+
+Bundles the verified BibTeX file -- and, if given, any downloaded PDFs --
+into <project-root>/references/<topic-slug>/, and writes a README.md
+index (entry list, suspect/unverified entries flagged separately, free-
+text coverage notes) so a future session or a human can find and trust
+what's there without re-reading the conversation that produced it.
+
+No third-party dependencies; uses only the standard library.
+
+CLI usage:
+    python3 archive_references.py "UAV aeromagnetic compensation" \\
+        --bib output/uav_aeromagnetic_compensation_final.bib \\
+        --project-root . \\
+        --pdfs-dir output/pdfs \\
+        --suspect "Some fabricated-looking title|DOI resolves but venue is topically unrelated" \\
+        --notes "Kalman-filter and GA/PSO angles searched, no on-topic hits found."
+
+Output: prints the path of the archive directory that was created/updated.
+"""
+import argparse
+import os
+import re
+import shutil
+import sys
+from datetime import date
+
+
+def slugify(text):
+    text = text.strip().lower()
+    text = re.sub(r"[^a-z0-9]+", "_", text)
+    return text.strip("_")[:60] or "references"
+
+
+def parse_bib_entries(bib_path):
+    """Minimal BibTeX parser -- just enough to pull key/title/year/venue/doi/note
+    (plus the raw entry text, for reordering) for the README index. Not a
+    general-purpose BibTeX parser."""
+    with open(bib_path, encoding="utf-8") as f:
+        content = f.read()
+
+    entries = []
+    for m in re.finditer(r"@(\w+)\{([^,\n]+),(.*?)\n\}", content, re.S):
+        entry_type, key, body = m.groups()
+        fields = {}
+        for fm in re.finditer(r"(\w+)\s*=\s*\{(.*?)\}\s*,?\s*(?=\n\s*\w+\s*=|\n\Z|\Z)", body, re.S):
+            fields[fm.group(1).lower()] = re.sub(r"\s+", " ", fm.group(2)).strip()
+        entries.append({"type": entry_type, "key": key.strip(), "raw": m.group(0).strip(), **fields})
+    return entries
+
+
+def year_sort_key(entry):
+    """Chronological order, oldest first; entries with no parseable year sort last."""
+    year_str = re.sub(r"[^0-9]", "", entry.get("year", "") or "")
+    year = int(year_str) if year_str else 9999
+    return (year, entry.get("key", ""))
+
+
+def build_readme(topic, entries, pdf_count, suspect, notes):
+    lines = []
+    lines.append(f"# {topic} — literature archive")
+    lines.append("")
+    lines.append(f"Archived: {date.today().isoformat()}")
+    lines.append(f"Verified entries: {len(entries)}")
+    lines.append(f"PDFs bundled: {pdf_count}")
+    lines.append("")
+    lines.append(
+        "Every entry in `references.bib` passed independent verification "
+        "(arXiv ID / DOI resolution and/or cross-source title match, "
+        "similarity >= 0.9) via the literature-search-verify skill before "
+        "being archived here. Citation keys follow the surname+year "
+        "convention and are stable -- the paper-writing-grounded skill's "
+        "`\\cite{}` calls should match these keys directly."
+    )
+    lines.append("")
+    lines.append("## Entries (chronological, oldest first)")
+    lines.append("")
+    for e in entries:
+        title = e.get("title", "?")
+        year = e.get("year", "?")
+        venue = e.get("journal") or e.get("booktitle") or e.get("school") or ""
+        doi = e.get("doi", "")
+        note = e.get("note", "")
+        line = f"- **{e['key']}** ({year}) — {title}"
+        if venue:
+            line += f". *{venue}*"
+        if doi:
+            line += f". DOI: {doi}"
+        lines.append(line)
+        if note:
+            lines.append(f"  - Note: {note}")
+
+    if suspect:
+        lines.append("")
+        lines.append("## Flagged during search — NOT included above, do not cite")
+        lines.append("")
+        for s in suspect:
+            parts = s.split("|", 1)
+            title = parts[0].strip()
+            reason = parts[1].strip() if len(parts) > 1 else ""
+            lines.append(f"- {title}" + (f" — {reason}" if reason else ""))
+
+    if notes:
+        lines.append("")
+        lines.append("## Search coverage notes")
+        lines.append("")
+        lines.append(notes)
+
+    return "\n".join(lines) + "\n"
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("topic", help="Human-readable topic name, e.g. \"UAV aeromagnetic compensation\"")
+    ap.add_argument("--bib", required=True, help="path to the curated/verified .bib file to archive")
+    ap.add_argument("--project-root", default=".", help="project root; archive is written under <root>/references/<slug>/")
+    ap.add_argument("--pdfs-dir", default=None, help="optional folder of open-access PDFs to copy alongside the bib")
+    ap.add_argument("--suspect", action="append", default=[], help="title|reason of a suspect/unverified entry to log; repeatable")
+    ap.add_argument("--notes", default=None, help="free-text notes on search coverage/gaps for the README")
+    args = ap.parse_args()
+
+    if not os.path.isfile(args.bib):
+        print(f"error: bib file not found: {args.bib}", file=sys.stderr)
+        sys.exit(1)
+
+    slug = slugify(args.topic)
+    archive_dir = os.path.join(args.project_root, "references", slug)
+    os.makedirs(archive_dir, exist_ok=True)
+
+    bib_dest = os.path.join(archive_dir, "references.bib")
+    shutil.copyfile(args.bib, bib_dest)
+    entries = parse_bib_entries(bib_dest)
+    entries.sort(key=year_sort_key)
+
+    # Rewrite the archived .bib in chronological order (oldest first) so the
+    # file itself, not just the README, reads as a timeline.
+    header = f"% {args.topic} -- verified references, chronological order\n% Archived {date.today().isoformat()}\n\n"
+    with open(bib_dest, "w", encoding="utf-8") as f:
+        f.write(header)
+        f.write("\n\n".join(e["raw"] for e in entries))
+        f.write("\n")
+
+    pdf_count = 0
+    if args.pdfs_dir and os.path.isdir(args.pdfs_dir):
+        pdf_dest_dir = os.path.join(archive_dir, "pdfs")
+        os.makedirs(pdf_dest_dir, exist_ok=True)
+        for fn in sorted(os.listdir(args.pdfs_dir)):
+            if fn.lower().endswith(".pdf"):
+                shutil.copyfile(os.path.join(args.pdfs_dir, fn), os.path.join(pdf_dest_dir, fn))
+                pdf_count += 1
+
+    readme = build_readme(args.topic, entries, pdf_count, args.suspect, args.notes)
+    with open(os.path.join(archive_dir, "README.md"), "w", encoding="utf-8") as f:
+        f.write(readme)
+
+    print(archive_dir)
+
+
+if __name__ == "__main__":
+    main()

.claude/skills/literature-search-verify/scripts/literature_search.py

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+#!/usr/bin/env python3
+"""
+End-to-end literature search: query arXiv + Semantic Scholar + Crossref,
+merge/dedupe candidates, independently verify each one, and emit both a
+human-readable report and BibTeX for the entries that passed verification.
+
+This is the one script Claude should actually call for a normal literature
+search -- the individual search_*.py / verify_citation.py scripts exist
+mainly as building blocks it can reuse for one-off / follow-up lookups.
+
+CLI usage:
+    python3 literature_search.py "UAV magnetic compensation Tolles-Lawson" \\
+        --max-per-source 8 --bib-out refs.bib
+
+Output: prints a JSON report to stdout (one entry per merged candidate,
+with its verdict), and if --bib-out is given, writes BibTeX for every
+"verified" entry to that file (never for "suspect" or "unverified" ones).
+"""
+import sys
+import os
+import json
+import argparse
+import difflib
+import re
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from search_arxiv import search_arxiv
+from search_semantic_scholar import search_s2
+from search_crossref import search_crossref
+from verify_citation import verify
+
+
+def _similar(a, b, threshold=0.88):
+    if not a or not b:
+        return False
+    return difflib.SequenceMatcher(None, a.lower().strip(), b.lower().strip()).ratio() >= threshold
+
+
+def merge_candidates(all_results):
+    """Dedupe candidates that are the same paper found via multiple sources,
+    merging their metadata (preferring whichever source has an identifier)."""
+    merged = []
+    for item in all_results:
+        placed = False
+        for m in merged:
+            if _similar(item.get("title"), m.get("title")):
+                # merge: fill in any missing fields, keep track of all sources
+                for key in ("doi", "arxiv_id", "abstract", "venue", "year", "citation_count", "pdf_url"):
+                    if not m.get(key) and item.get(key):
+                        m[key] = item[key]
+                m["sources"] = sorted(set(m.get("sources", [m.get("source")]) + [item.get("source")]))
+                placed = True
+                break
+        if not placed:
+            item = dict(item)
+            item["sources"] = [item.get("source")]
+            merged.append(item)
+    return merged
+
+
+def make_bibtex_key(candidate, used_keys):
+    authors = candidate.get("authors") or []
+    surname = "unknown"
+    if authors:
+        first_author = authors[0]
+        surname = first_author.strip().split()[-1].lower()
+        surname = re.sub(r"[^a-z]", "", surname) or "unknown"
+    year = str(candidate.get("year") or "nd")
+    base = f"{surname}{year}"
+    key = base
+    suffix = ord("a")
+    while key in used_keys:
+        key = f"{base}{chr(suffix)}"
+        suffix += 1
+    used_keys.add(key)
+    return key
+
+
+def to_bibtex(candidate, key):
+    authors = candidate.get("authors") or []
+    author_str = " and ".join(authors) if authors else "Unknown"
+    title = candidate.get("title") or ""
+    year = candidate.get("year") or ""
+    venue = candidate.get("venue") or ""
+    doi = candidate.get("doi") or ""
+    arxiv_id = candidate.get("arxiv_id") or ""
+
+    if arxiv_id and not venue:
+        entry_type = "misc"
+        fields = [
+            ("author", author_str),
+            ("title", title),
+            ("year", str(year)),
+            ("eprint", arxiv_id),
+            ("archivePrefix", "arXiv"),
+        ]
+    else:
+        entry_type = "article"
+        fields = [
+            ("author", author_str),
+            ("title", title),
+            ("journal", venue),
+            ("year", str(year)),
+        ]
+        if doi:
+            fields.append(("doi", doi))
+
+    lines = [f"@{entry_type}{{{key},"]
+    for k, v in fields:
+        if v:
+            lines.append(f"  {k} = {{{v}}},")
+    lines.append("}")
+    return "\n".join(lines)
+
+
+def run(query, max_per_source=8):
+    all_results = []
+    errors = {}
+    for name, fn in (("arxiv", search_arxiv), ("semantic_scholar", search_s2), ("crossref", search_crossref)):
+        try:
+            all_results.extend(fn(query, max_per_source))
+        except Exception as e:
+            errors[name] = str(e)
+
+    merged = merge_candidates(all_results)
+
+    used_keys = set()
+    for cand in merged:
+        result = verify(title=cand.get("title"), arxiv_id=cand.get("arxiv_id"), doi=cand.get("doi"))
+        cand["verdict"] = result["verdict"]
+        cand["verification_checks"] = result["checks"]
+        if result["verdict"] == "verified":
+            cand["bibtex_key"] = make_bibtex_key(cand, used_keys)
+
+    return {"query": query, "search_errors": errors, "candidates": merged}
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("query")
+    ap.add_argument("--max-per-source", type=int, default=8)
+    ap.add_argument("--bib-out", default=None, help="path to write BibTeX for verified entries")
+    args = ap.parse_args()
+
+    try:
+        report = run(args.query, args.max_per_source)
+    except Exception as e:
+        print(json.dumps({"error": str(e)}, ensure_ascii=False))
+        sys.exit(1)
+
+    print(json.dumps(report, ensure_ascii=False, indent=2))
+
+    if args.bib_out:
+        verified = [c for c in report["candidates"] if c["verdict"] == "verified"]
+        with open(args.bib_out, "w", encoding="utf-8") as f:
+            for cand in verified:
+                f.write(to_bibtex(cand, cand["bibtex_key"]))
+                f.write("\n\n")
+        sys.stderr.write(f"Wrote {len(verified)} verified BibTeX entries to {args.bib_out}\n")

.claude/skills/literature-search-verify/scripts/search_arxiv.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python3
+"""
+Search arXiv via its public Atom API. No API key required.
+
+CLI usage:
+    python3 search_arxiv.py "UAV magnetic compensation" --max 10
+
+Importable:
+    from search_arxiv import search_arxiv
+"""
+import sys
+import json
+import argparse
+import urllib.request
+import urllib.parse
+import xml.etree.ElementTree as ET
+
+ARXIV_API = "http://export.arxiv.org/api/query"
+NS = {"atom": "http://www.w3.org/2005/Atom"}
+
+
+def search_arxiv(query, max_results=10, timeout=20):
+    params = {
+        "search_query": f"all:{query}",
+        "start": 0,
+        "max_results": max_results,
+        "sortBy": "relevance",
+        "sortOrder": "descending",
+    }
+    url = f"{ARXIV_API}?{urllib.parse.urlencode(params)}"
+    with urllib.request.urlopen(url, timeout=timeout) as resp:
+        data = resp.read()
+    root = ET.fromstring(data)
+    results = []
+    for entry in root.findall("atom:entry", NS):
+        id_el = entry.find("atom:id", NS)
+        title_el = entry.find("atom:title", NS)
+        summary_el = entry.find("atom:summary", NS)
+        published_el = entry.find("atom:published", NS)
+        if id_el is None or title_el is None:
+            continue
+        arxiv_id_full = id_el.text.strip()
+        arxiv_id = arxiv_id_full.rsplit("/", 1)[-1]
+        title = " ".join(title_el.text.split())
+        summary = " ".join(summary_el.text.split()) if summary_el is not None else ""
+        authors = [
+            a.find("atom:name", NS).text
+            for a in entry.findall("atom:author", NS)
+            if a.find("atom:name", NS) is not None
+        ]
+        published = published_el.text[:10] if published_el is not None else None
+        pdf_url = None
+        for link in entry.findall("atom:link", NS):
+            if link.attrib.get("title") == "pdf":
+                pdf_url = link.attrib.get("href")
+        results.append({
+            "source": "arxiv",
+            "arxiv_id": arxiv_id,
+            "title": title,
+            "authors": authors,
+            "year": published[:4] if published else None,
+            "published": published,
+            "abstract": summary,
+            "pdf_url": pdf_url,
+            "doi": None,
+        })
+    return results
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("query")
+    ap.add_argument("--max", type=int, default=10)
+    args = ap.parse_args()
+    try:
+        out = search_arxiv(args.query, args.max)
+        print(json.dumps(out, ensure_ascii=False, indent=2))
+    except Exception as e:
+        print(json.dumps({"error": str(e)}, ensure_ascii=False))
+        sys.exit(1)

.claude/skills/literature-search-verify/scripts/search_crossref.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+"""
+Search the Crossref works API. No API key required.
+Good for journal articles / DOIs that arXiv and Semantic Scholar might miss.
+
+CLI usage:
+    python3 search_crossref.py "UAV magnetic compensation" --max 10
+
+Importable:
+    from search_crossref import search_crossref
+"""
+import sys
+import json
+import argparse
+import urllib.request
+import urllib.parse
+
+CROSSREF_API = "https://api.crossref.org/works"
+UA = "literature-search-verify-skill/1.0 (mailto:research-assistant@example.com)"
+
+
+def search_crossref(query, max_results=10, timeout=20):
+    params = {"query": query, "rows": max_results}
+    url = f"{CROSSREF_API}?{urllib.parse.urlencode(params)}"
+    req = urllib.request.Request(url, headers={"User-Agent": UA})
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        data = json.loads(resp.read())
+    results = []
+    for item in data.get("message", {}).get("items", []) or []:
+        titles = item.get("title") or []
+        title = titles[0] if titles else ""
+        authors = []
+        for a in item.get("author", []) or []:
+            name = " ".join(filter(None, [a.get("given"), a.get("family")]))
+            if name:
+                authors.append(name)
+        year = None
+        date_parts = (item.get("issued", {}) or {}).get("date-parts")
+        if date_parts and date_parts[0]:
+            year = date_parts[0][0]
+        containers = item.get("container-title") or []
+        results.append({
+            "source": "crossref",
+            "title": title,
+            "authors": authors,
+            "year": year,
+            "venue": containers[0] if containers else None,
+            "doi": item.get("DOI"),
+            "arxiv_id": None,
+            "abstract": None,
+        })
+    return results
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("query")
+    ap.add_argument("--max", type=int, default=10)
+    args = ap.parse_args()
+    try:
+        out = search_crossref(args.query, args.max)
+        print(json.dumps(out, ensure_ascii=False, indent=2))
+    except Exception as e:
+        print(json.dumps({"error": str(e)}, ensure_ascii=False))
+        sys.exit(1)

.claude/skills/literature-search-verify/scripts/search_semantic_scholar.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python3
+"""
+Search the Semantic Scholar Graph API. No API key required for light use;
+set the S2_API_KEY environment variable for higher rate limits.
+
+CLI usage:
+    python3 search_semantic_scholar.py "UAV magnetic compensation" --max 10
+
+Importable:
+    from search_semantic_scholar import search_s2
+"""
+import sys
+import os
+import json
+import argparse
+import urllib.request
+import urllib.parse
+
+S2_API = "https://api.semanticscholar.org/graph/v1/paper/search"
+FIELDS = "title,authors,year,venue,externalIds,abstract,citationCount"
+
+
+def search_s2(query, max_results=10, timeout=20):
+    params = {"query": query, "limit": max_results, "fields": FIELDS}
+    url = f"{S2_API}?{urllib.parse.urlencode(params)}"
+    req = urllib.request.Request(url)
+    api_key = os.environ.get("S2_API_KEY")
+    if api_key:
+        req.add_header("x-api-key", api_key)
+    with urllib.request.urlopen(req, timeout=timeout) as resp:
+        data = json.loads(resp.read())
+    results = []
+    for p in data.get("data", []) or []:
+        ext = p.get("externalIds") or {}
+        results.append({
+            "source": "semantic_scholar",
+            "title": p.get("title"),
+            "authors": [a.get("name") for a in (p.get("authors") or [])],
+            "year": p.get("year"),
+            "venue": p.get("venue"),
+            "doi": ext.get("DOI"),
+            "arxiv_id": ext.get("ArXiv"),
+            "abstract": p.get("abstract"),
+            "citation_count": p.get("citationCount"),
+        })
+    return results
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("query")
+    ap.add_argument("--max", type=int, default=10)
+    args = ap.parse_args()
+    try:
+        out = search_s2(args.query, args.max)
+        print(json.dumps(out, ensure_ascii=False, indent=2))
+    except Exception as e:
+        print(json.dumps({"error": str(e)}, ensure_ascii=False))
+        sys.exit(1)

.claude/skills/literature-search-verify/scripts/verify_citation.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+"""
+Independently cross-verify a single candidate citation. This is the anti-
+hallucination check: it never trusts a single source. If a check cannot be
+run at all (e.g. no network), that check is reported as "skipped" -- never
+silently counted as a pass.
+
+CLI usage:
+    python3 verify_citation.py --title "Compensation of magnetic ..." \\
+        --arxiv-id 2401.12345 --doi 10.1109/TGRS.2024.1234567
+
+Importable:
+    from verify_citation import verify
+"""
+import sys
+import json
+import argparse
+import difflib
+import urllib.request
+import urllib.parse
+import urllib.error
+import xml.etree.ElementTree as ET
+
+ATOM_NS = {"atom": "http://www.w3.org/2005/Atom"}
+
+
+def title_similarity(a, b):
+    if not a or not b:
+        return 0.0
+    return difflib.SequenceMatcher(None, a.lower().strip(), b.lower().strip()).ratio()
+
+
+def check_arxiv_id(arxiv_id, timeout=20):
+    """Confirm an arXiv ID actually resolves to a real paper."""
+    try:
+        url = f"http://export.arxiv.org/api/query?id_list={urllib.parse.quote(arxiv_id)}"
+        with urllib.request.urlopen(url, timeout=timeout) as resp:
+            data = resp.read()
+        root = ET.fromstring(data)
+        entry = root.find("atom:entry", ATOM_NS)
+        if entry is None:
+            return {"status": "fail", "reason": "arXiv ID not found"}
+        title_el = entry.find("atom:title", ATOM_NS)
+        title = " ".join(title_el.text.split()) if title_el is not None else None
+        return {"status": "pass", "canonical_title": title}
+    except Exception as e:
+        return {"status": "skipped", "reason": str(e)}
+
+
+def check_doi(doi, timeout=20):
+    """Confirm a DOI actually resolves via Crossref."""
+    try:
+        url = f"https://api.crossref.org/works/{urllib.parse.quote(doi)}"
+        req = urllib.request.Request(
+            url, headers={"User-Agent": "literature-search-verify-skill/1.0"}
+        )
+        with urllib.request.urlopen(req, timeout=timeout) as resp:
+            data = json.loads(resp.read())
+        titles = data.get("message", {}).get("title") or []
+        return {"status": "pass", "canonical_title": titles[0] if titles else None}
+    except urllib.error.HTTPError as e:
+        if e.code == 404:
+            return {"status": "fail", "reason": "DOI not found in Crossref"}
+        return {"status": "skipped", "reason": f"HTTP {e.code}"}
+    except Exception as e:
+        return {"status": "skipped", "reason": str(e)}
+
+
+def check_title_cross_source(title, timeout=20):
+    """Independently re-search by title on a different source (Semantic
+    Scholar) and require a near-exact title match. This is what catches a
+    plausible-sounding but entirely invented title/author combination."""
+    try:
+        params = {"query": title, "limit": 3, "fields": "title"}
+        url = f"https://api.semanticscholar.org/graph/v1/paper/search?{urllib.parse.urlencode(params)}"
+        with urllib.request.urlopen(url, timeout=timeout) as resp:
+            data = json.loads(resp.read())
+        candidates = data.get("data", []) or []
+        if not candidates:
+            return {"status": "fail", "reason": "no matching title found on Semantic Scholar"}
+        best = max(candidates, key=lambda p: title_similarity(title, p.get("title", "")))
+        sim = title_similarity(title, best.get("title", ""))
+        if sim >= 0.9:
+            return {"status": "pass", "similarity": round(sim, 3), "matched_title": best.get("title")}
+        return {"status": "fail", "similarity": round(sim, 3), "matched_title": best.get("title")}
+    except Exception as e:
+        return {"status": "skipped", "reason": str(e)}
+
+
+def verify(title=None, arxiv_id=None, doi=None):
+    checks = {}
+    if arxiv_id:
+        checks["arxiv_id_check"] = check_arxiv_id(arxiv_id)
+    if doi:
+        checks["doi_check"] = check_doi(doi)
+    if title:
+        checks["title_cross_source_check"] = check_title_cross_source(title)
+
+    passed = [c for c in checks.values() if c["status"] == "pass"]
+    failed = [c for c in checks.values() if c["status"] == "fail"]
+
+    if failed:
+        verdict = "suspect"          # something actively contradicted it
+    elif passed:
+        verdict = "verified"         # at least one independent check passed
+    else:
+        verdict = "unverified"       # everything skipped (e.g. no network) -- NOT the same as verified
+
+    return {"title": title, "arxiv_id": arxiv_id, "doi": doi, "verdict": verdict, "checks": checks}
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--title", default=None)
+    ap.add_argument("--arxiv-id", default=None)
+    ap.add_argument("--doi", default=None)
+    args = ap.parse_args()
+    if not any([args.title, args.arxiv_id, args.doi]):
+        print(json.dumps({"error": "provide at least one of --title/--arxiv-id/--doi"}))
+        sys.exit(1)
+    result = verify(args.title, args.arxiv_id, args.doi)
+    print(json.dumps(result, ensure_ascii=False, indent=2))

.claude/skills/paper-wiriting-grounded/SKILL.md

@@ -0,0 +1,50 @@
+---
+name: paper-writing-grounded
+description: Draft, revise, or polish academic paper and thesis sections (abstract, introduction, methods, experiments, related work, conclusion) for LaTeX conference/journal templates or Word/WPS-ready Chinese theses, while strictly preventing fabricated numbers, invented experimental results, or unsupported quantitative claims. Use this whenever the user asks to write, draft, outline, or polish any section of a paper or dissertation, wants raw results turned into prose, needs a LaTeX draft matching a conference template, wants Word/WPS text for a Chinese-language thesis, or wants AI-sounding writing "humanized" — anywhere the draft could end up stating a number, percentage, or comparison that didn't actually come from real data.
+---
+
+# 论文写作(强制数据溯源版)
+
+## 为什么需要这个技能
+
+LLM写论文时最大的隐患不是文笔差,而是**在没有真实数据支撑的地方,顺手编一个"看起来合理"的数字**——比如"补偿后RMSE降低了23%"这种话,读起来完全正常,但如果这个23%不是从真实实验里来的,那就是数据捏造,一旦被发现是学术诚信问题,不是文笔问题。这个技能的核心规则就一条:**任何关于"我们的方法/我们的结果"的具体数字、百分比、对比、最高级描述,必须能追溯到用户提供的真实结果,追溯不到就必须明确标记出来,绝不能编一个数填上去。**
+
+## 工作流程
+
+### 第一步:建立"真实结果登记表"
+
+在动笔写任何会出现具体数字的段落之前(尤其是实验/结果部分),先确认这一节要用到的真实数据来源——可以是用户粘贴的数字、一份CSV/JSON格式的指标文件、SimPEG/Harmonica处理流程跑出来的输出、图表标题里的数值等等。如果对话里还没有提供,直接问用户要,而不是先写着占位数字"等下再改"——占位数字很容易被忘记改掉,最后混进定稿。
+
+把这一节会用到的每个具体数字记下来,连同它的出处(来自哪个文件/哪次实验/哪张图),这就是这一节写作时唯一可信的"真实结果登记表"。
+
+### 第二步:分节起草
+
+根据目标产出选结构:
+- **英文期刊/会议投稿(LaTeX)**:Abstract → Introduction → Related Work → Methods → Experiments → Conclusion,先问清楚目标模板(NeurIPS/ICLR/ICML,或者你们学校的LaTeX模板),没问清楚就先按通用IMRAD结构起草,后面再套模板。
+- **中文学位论文(Word/WPS)**:按论文各章节结构(通常是绪论/文献综述/方法/实验与结果/结论)起草,产出用docx技能生成格式化的Word文档。
+
+### 第三步:强制溯源规则(核心)
+
+起草时,每写到一个具体数字、百分比、显著性描述("显著优于"、"最优"这类)时,问自己:这个数字能不能在第一步的"真实结果登记表"里找到出处,或者能不能在literature-search-verify技能核实过的文献里找到支持?
+
+- 能追溯到 → 正常写,行文里可以顺带标注来源(比如"如表2所示")
+- 追溯不到 → **不要编数字填上去**,改成明确的占位标记:`[需要数据:补偿后与补偿前的RMSE对比数值]`,让用户知道这里缺什么、需要补什么,而不是假装写完了
+
+这条规则同样适用于"我们首次提出"、"计算效率更高"这类没有具体数字但仍是实质性主张的表述——同样需要能落到某个真实依据上,落不到就标记出来,不要含糊带过。
+
+### 第四步:自查
+
+起草完一节后,回头把这一节里所有的数字、比较、最高级表述再过一遍,确认每一条都能对应到登记表或已核实的引用。在回复末尾列出这次自查发现的、还没解决的`[需要数据]`标记,方便用户一次性补齐,而不是散落在长文里被忽略。
+
+### 第五步:去AI味润色
+
+润色不等于压缩。除非用户明确要求精简,不要为了让句子读起来"更自然"而删掉具体的研究对象、数据口径、方法条件、指标定义这些内容——这些恰恰是审稿人会重点核对的地方。去掉的应该是空洞的模板化表达(比如"综上所述,本研究具有重要意义"这类没有信息量的套话)和明显的AI腔调用词,而不是数据本身的精度和限定条件。
+
+### 第六步:产出格式
+
+- LaTeX:正文里的`\cite{}`只使用literature-search-verify技能核实过、生成过BibTeX的citation key,不自己编新的引用键。
+- Word/WPS:用docx技能产出格式化文档;提醒用户Zotero的Word插件可以配合插入引用,但WPS对该插件兼容性一般,可能需要先用Zotero导出RTF/纯文本引用再手动整理进WPS。
+
+## 和 literature-search-verify 技能的配合
+
+正文里任何"related work"或背景介绍部分引用的文献,只能来自literature-search-verify技能已核实的条目;这个技能不负责验证引用真实性,只负责确保"我们自己的实验结果"这部分不被编造数据污染。两者结合,才是"文献不编、数据不编"的完整闭环。

.gitignore

@@ -0,0 +1,2 @@
+output
+*.pyc

references/uav_aeromagnetic_compensation/README.md

@@ -0,0 +1,56 @@
+# UAV aeromagnetic compensation — literature archive
+
+Archived: 2026-07-20
+Verified entries: 30
+PDFs bundled: 3
+
+Every entry in `references.bib` passed independent verification (arXiv ID / DOI resolution and/or cross-source title match, similarity >= 0.9) via the literature-search-verify skill before being archived here. Citation keys follow the surname+year convention and are stable -- the paper-writing-grounded skill's `\cite{}` calls should match these keys directly.
+
+## Entries (chronological, oldest first)
+
+- **leach1980** (1980) — Aeromagnetic Compensation as a Linear Regression Problem. *Information Linkage Between Applied Mathematics and Industry*. DOI: 10.1016/b978-0-12-628750-9.50017-6
+  - Note: Early foundational formulation of aeromagnetic compensation as a linear regression / least-squares problem
+- **williams1993** (1993) — Aeromagnetic compensation using neural networks. *Neural Computing \& Applications*. DOI: 10.1007/bf01414949
+- **leblanc2001** (2001) — Denoising of aeromagnetic data via the wavelet transform. *Geophysics*. DOI: 10.1190/1.1487121
+- **fedi2006** (2006) — On ``Wavelet denoising of aeromagnetic data'' (George E. Leblanc and William A. Morris, 2001, Geophysics, 71, 1793--1804). *Geophysics*. DOI: 10.1190/1.2233897
+  - Note: Discussion/comment on Leblanc \& Morris 2001
+- **zhang2011** (2011) — A simplified aeromagnetic compensation model for low magnetism UAV platform. *2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)*. DOI: 10.1109/igarss.2011.6049950
+- **metge2013** (2013) — Dynamic magnetic field compensation for micro UAV attitude estimation. *2013 International Conference on Unmanned Aircraft Systems (ICUAS)*. DOI: 10.1109/icuas.2013.6564754
+  - Note: Magnetic compensation for onboard attitude estimation, not for the aeromagnetic survey signal itself
+- **zhang2016** (2016) — Aeromagnetic compensation with partial least square regression. *ASEG Extended Abstracts*. DOI: 10.1071/aseg2016ab300
+- **zhao2016** (2016) — A Novel Aeromagnetic Compensation Method Based on the Improved Recursive Least-Squares. *Smart Innovation, Systems and Technologies*. DOI: 10.1007/978-3-319-50212-0_21
+- **ma2017** (2017) — A dual estimate method for aeromagnetic compensation. *Measurement Science and Technology*. DOI: 10.1088/1361-6501/aa883b
+- **wu2017** (2017) — Aeromagnetic gradient compensation method for helicopter based on \ensuremath{\epsilon}-support vector regression algorithm. *Journal of Applied Remote Sensing*. DOI: 10.1117/1.jrs.11.025012
+- **li2018** (2018) — Aeromagnetic compensation of Rotor UAV Based on Least Squares. *2018 37th Chinese Control Conference (CCC)*. DOI: 10.23919/chicc.2018.8483068
+- **melo2018** (2018) — 2D discrete wavelet transform for denoising aeromagnetic data. *SEG Technical Program Expanded Abstracts 2018*. DOI: 10.1190/segam2018-2998295.1
+- **hang2019** (2019) — A Simulation Method of Generating the Output of Magnetometer for Aeromagnetic Compensation. *IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium*. DOI: 10.1109/igarss.2019.8897903
+- **tuck2019** (2019) — Characterization and compensation of magnetic interference resulting from unmanned aircraft systems. *Carleton University*. DOI: 10.22215/etd/2019-13546
+- **walter2019** (2019) — Spectral Analysis of Magnetometer Swing in High-Resolution UAV-borne Aeromagnetic Surveys. *2019 IEEE Systems and Technologies for Remote Sensing Applications Through Unmanned Aerial Systems (STRATUS)*. DOI: 10.1109/stratus.2019.8713313
+- **wang2019** (2019) — An Automatic Method to Estimate the Calibration Quality of the Aeromagnetic Compensation. *IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium*. DOI: 10.1109/igarss.2019.8898533
+- **zhao2020** (2020) — An Aeromagnetic Compensation Algorithm Based on Neural Network. *82nd EAGE Annual Conference \& Exhibition*. DOI: 10.3997/2214-4609.202010906
+- **gnadt2022** (2022) — Derivation and Extensions of the Tolles-Lawson Model for Aeromagnetic Compensation. *arXiv preprint arXiv:2212.09899*
+- **nerrise2024** (2024) — Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks. *arXiv preprint arXiv:2401.09631*
+- **yuan2024** (2024) — Application study of UAV aeromagnetic measurement based on rubidium optical pump magnetometer. *International Workshop on Gravity, Electrical \& Magnetic Methods and Their Applications, Shenzhen, China, May 19--22, 2024*. DOI: 10.1190/gem2024-021.1
+- **dai2025** (2025) — Aeromagnetic Compensation for UAVs Using Transformer Neural Networks. *Sensors*. DOI: 10.3390/s25226852
+- **fang2025** (2025) — An aeromagnetic compensation method based on the extended Tolles Lawson model. *Journal of Physics: Conference Series*. DOI: 10.1088/1742-6596/3169/1/012043
+- **hu2025** (2025) — Influence of Attitude Changes on Magnetic Measurement Accuracy in UAV Magnetic Anomaly Detection. *2025 5th International Conference on Sensors and Information Technology (ICSI)*. DOI: 10.1109/icsi64877.2025.11009300
+- **qiao2025** (2025) — Dual-Channel Aeromagnetic Compensation Method for Continuous and Intermittent OBE Interference. *IEEE Transactions on Instrumentation and Measurement*. DOI: 10.1109/tim.2025.3599271
+- **wang2025** (2025) — An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network. *Applied Sciences*. DOI: 10.3390/app15063105
+- **wang2025maneuver** (2025) — Magnetometer Compensation for Magnetic Interference in Aircraft Maneuvers by Using INS. *Advances in Guidance, Navigation and Control*. DOI: 10.1007/978-981-96-2240-5_29
+- **song2026** (2026) — An Enhanced Tolles--Lawson Model With Temperature Compensation for Aeromagnetic Compensation of Triaxial Magnetometers. *IEEE Transactions on Instrumentation and Measurement*. DOI: 10.1109/tim.2026.3697092
+- **sun2026** (2026) — Physics-Informed Tolles--Lawson and Neural Network Hybrid Modeling for Magnetic Compensation in Uncrewed Ground Vehicles. *IEEE Sensors Journal*. DOI: 10.1109/jsen.2026.3684910
+  - Note: Ground-vehicle (not airborne UAV) application of the Tolles--Lawson + NN hybrid compensation approach
+- **xie2026** (2026) — Aeromagnetic Nonlinear Interference Compensation Method Based on Hybrid LSTM and BP Neural Network Architecture. *Computer Science and Application*. DOI: 10.12677/csa.2026.161011
+- **you2026** (2026) — Electromagnetic interference compensation for aeromagnetic data using adaptive wavelet denoising and partial least squares regression. *Measurement Science and Technology*. DOI: 10.1088/1361-6501/ae8616
+
+## Flagged during search — NOT included above, do not cite
+
+- An Aeromagnetic Compensation Algorithm based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and a Physics-Guided Neural Network (DOI 10.52710/fcb.145) — DOI resolves and title matches via Crossref, but the venue is "Fuel Cells Bulletin", topically unrelated to aeromagnetics -- likely a hijacked/predatory journal or metadata error. Do not cite.
+
+## Search coverage notes
+
+Direction covered: UAV/airborne aeromagnetic compensation (Tolles-Lawson family) -- theory, least-squares/ridge/PLS regression variants, wavelet denoising, neural-network (BP/LSTM/TCN/Transformer/physics-informed) approaches, and UAV-specific magnetic-interference characterization.
+Queried via arXiv + Crossref + Semantic Scholar (S2 was rate-limited (HTTP 429) for much of the session, so most entries only got single-channel verification -- arXiv ID or DOI resolution -- rather than the additional cross-source title check; this is noted per-entry as "unverified"/"skipped" in the raw JSON reports, not silently upgraded to double-verified).
+Searched but found no on-topic hits: Kalman-filter-based aeromagnetic compensation; genetic-algorithm/PSO-based aeromagnetic compensation.
+Not covered at all: CNKI/Wanfang/VIP (Chinese databases, no public API) -- use the Zotero Connector browser extension logged into a university account for these.
+Raw per-query JSON search/verification reports (including filtered-out noise from ambiguous keyword matches like "Tolles"/"Lawson" as surnames) are kept in .claude/skills/literature-search-verify/output/ for audit purposes and are not part of this archive.

references/uav_aeromagnetic_compensation/references.bib

@@ -0,0 +1,251 @@
+% UAV aeromagnetic compensation -- verified references, chronological order
+% Archived 2026-07-20
+
+@article{leach1980,
+  title   = {Aeromagnetic Compensation as a Linear Regression Problem},
+  author  = {Leach, Barrie W.},
+  year    = {1980},
+  journal = {Information Linkage Between Applied Mathematics and Industry},
+  doi     = {10.1016/b978-0-12-628750-9.50017-6},
+  note    = {Early foundational formulation of aeromagnetic compensation as a linear regression / least-squares problem}
+}
+
+@article{williams1993,
+  title   = {Aeromagnetic compensation using neural networks},
+  author  = {Williams, Peter M.},
+  year    = {1993},
+  journal = {Neural Computing \& Applications},
+  doi     = {10.1007/bf01414949}
+}
+
+@article{leblanc2001,
+  title   = {Denoising of aeromagnetic data via the wavelet transform},
+  author  = {Leblanc, George E. and Morris, William A.},
+  year    = {2001},
+  journal = {Geophysics},
+  doi     = {10.1190/1.1487121}
+}
+
+@article{fedi2006,
+  title   = {On ``Wavelet denoising of aeromagnetic data'' (George E. Leblanc and William A. Morris, 2001, Geophysics, 71, 1793--1804)},
+  author  = {Fedi, M. and Quarta, T.},
+  year    = {2006},
+  journal = {Geophysics},
+  doi     = {10.1190/1.2233897},
+  note    = {Discussion/comment on Leblanc \& Morris 2001}
+}
+
+@inproceedings{zhang2011,
+  title     = {A simplified aeromagnetic compensation model for low magnetism UAV platform},
+  author    = {Zhang, Baogang and Guo, Ziqi and Qiao, Yanchao},
+  year      = {2011},
+  booktitle = {2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)},
+  doi       = {10.1109/igarss.2011.6049950}
+}
+
+@inproceedings{metge2013,
+  title     = {Dynamic magnetic field compensation for micro UAV attitude estimation},
+  author    = {Metge, J. and Megret, R. and Giremus, A. and Berthoumieu, Y. and Mazel, C.},
+  year      = {2013},
+  booktitle = {2013 International Conference on Unmanned Aircraft Systems (ICUAS)},
+  doi       = {10.1109/icuas.2013.6564754},
+  note      = {Magnetic compensation for onboard attitude estimation, not for the aeromagnetic survey signal itself}
+}
+
+@article{zhang2016,
+  title   = {Aeromagnetic compensation with partial least square regression},
+  author  = {Zhang, Dailei and Huang, Danian and Lu, Junwei and Zhu, Boyuan},
+  year    = {2016},
+  journal = {ASEG Extended Abstracts},
+  doi     = {10.1071/aseg2016ab300}
+}
+
+@incollection{zhao2016,
+  title     = {A Novel Aeromagnetic Compensation Method Based on the Improved Recursive Least-Squares},
+  author    = {Zhao, Guanyi and Shao, Yuqing and Han, Qi and Tong, Xiaojun},
+  year      = {2016},
+  booktitle = {Smart Innovation, Systems and Technologies},
+  doi       = {10.1007/978-3-319-50212-0_21}
+}
+
+@article{ma2017,
+  title   = {A dual estimate method for aeromagnetic compensation},
+  author  = {Ma, Ming and Zhou, Zhijian and Cheng, Defu},
+  year    = {2017},
+  journal = {Measurement Science and Technology},
+  doi     = {10.1088/1361-6501/aa883b}
+}
+
+@article{wu2017,
+  title   = {Aeromagnetic gradient compensation method for helicopter based on \ensuremath{\epsilon}-support vector regression algorithm},
+  author  = {Wu, Peilin and Zhang, Qunying and Fei, Chunjiao and Fang, Guangyou},
+  year    = {2017},
+  journal = {Journal of Applied Remote Sensing},
+  doi     = {10.1117/1.jrs.11.025012}
+}
+
+@inproceedings{li2018,
+  title     = {Aeromagnetic compensation of Rotor UAV Based on Least Squares},
+  author    = {Li, Han and Ge, Jian and Dong, Haobin and Qiu, Xiangyu and Luo, Wang and Liu, Huan and Yuan, Zhiwen and Zhu, Jun and Zhang, Haiyang},
+  year      = {2018},
+  booktitle = {2018 37th Chinese Control Conference (CCC)},
+  doi       = {10.23919/chicc.2018.8483068}
+}
+
+@inproceedings{melo2018,
+  title     = {2D discrete wavelet transform for denoising aeromagnetic data},
+  author    = {Melo, Felipe F. and Barbosa, Val{\'e}ria C. F. and Jim{\'e}nez-Teja, Yolanda},
+  year      = {2018},
+  booktitle = {SEG Technical Program Expanded Abstracts 2018},
+  doi       = {10.1190/segam2018-2998295.1}
+}
+
+@inproceedings{hang2019,
+  title     = {A Simulation Method of Generating the Output of Magnetometer for Aeromagnetic Compensation},
+  author    = {Hang, Zhiyuan and He, Futong and Wang, Zhifang and Han, Qi},
+  year      = {2019},
+  booktitle = {IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},
+  doi       = {10.1109/igarss.2019.8897903}
+}
+
+@phdthesis{tuck2019,
+  title  = {Characterization and compensation of magnetic interference resulting from unmanned aircraft systems},
+  author = {Tuck, Loughlin},
+  year   = {2019},
+  school = {Carleton University},
+  doi    = {10.22215/etd/2019-13546}
+}
+
+@inproceedings{walter2019,
+  title     = {Spectral Analysis of Magnetometer Swing in High-Resolution UAV-borne Aeromagnetic Surveys},
+  author    = {Walter, Callum and Braun, Alexander and Fotopoulos, Georgia},
+  year      = {2019},
+  booktitle = {2019 IEEE Systems and Technologies for Remote Sensing Applications Through Unmanned Aerial Systems (STRATUS)},
+  doi       = {10.1109/stratus.2019.8713313}
+}
+
+@inproceedings{wang2019,
+  title     = {An Automatic Method to Estimate the Calibration Quality of the Aeromagnetic Compensation},
+  author    = {Wang, Yizhen and Han, Qi and Hu, Kai and Zhan, Dechen},
+  year      = {2019},
+  booktitle = {IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},
+  doi       = {10.1109/igarss.2019.8898533}
+}
+
+@inproceedings{zhao2020,
+  title     = {An Aeromagnetic Compensation Algorithm Based on Neural Network},
+  author    = {Zhao, X. and Yu, P. and Jiao, J.},
+  year      = {2020},
+  booktitle = {82nd EAGE Annual Conference \& Exhibition},
+  doi       = {10.3997/2214-4609.202010906}
+}
+
+@article{gnadt2022,
+  title   = {Derivation and Extensions of the Tolles-Lawson Model for Aeromagnetic Compensation},
+  author  = {Gnadt, Albert R. and Wollaber, Allan B. and Nielsen, Aaron P.},
+  year    = {2022},
+  journal = {arXiv preprint arXiv:2212.09899},
+  eprint  = {2212.09899},
+  archivePrefix = {arXiv},
+  url     = {https://arxiv.org/abs/2212.09899}
+}
+
+@article{nerrise2024,
+  title   = {Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks},
+  author  = {Nerrise, Favour and Sosanya, Andrew Sosa and Neary, Patrick},
+  year    = {2024},
+  journal = {arXiv preprint arXiv:2401.09631},
+  eprint  = {2401.09631},
+  archivePrefix = {arXiv},
+  url     = {https://arxiv.org/abs/2401.09631}
+}
+
+@inproceedings{yuan2024,
+  title     = {Application study of UAV aeromagnetic measurement based on rubidium optical pump magnetometer},
+  author    = {Yuan, Peng and Qiao, Zhong-kun},
+  year      = {2024},
+  booktitle = {International Workshop on Gravity, Electrical \& Magnetic Methods and Their Applications, Shenzhen, China, May 19--22, 2024},
+  doi       = {10.1190/gem2024-021.1}
+}
+
+@article{dai2025,
+  title   = {Aeromagnetic Compensation for UAVs Using Transformer Neural Networks},
+  author  = {Dai, Weiming and Yang, Changcheng and Zhou, Shuai},
+  year    = {2025},
+  journal = {Sensors},
+  doi     = {10.3390/s25226852}
+}
+
+@article{fang2025,
+  title   = {An aeromagnetic compensation method based on the extended Tolles Lawson model},
+  author  = {Fang, Yuanxing and Zhang, Chao and Zheng, Yaoxin and Liu, Weiqiang and Zha, Songyuan},
+  year    = {2025},
+  journal = {Journal of Physics: Conference Series},
+  doi     = {10.1088/1742-6596/3169/1/012043}
+}
+
+@inproceedings{hu2025,
+  title     = {Influence of Attitude Changes on Magnetic Measurement Accuracy in UAV Magnetic Anomaly Detection},
+  author    = {Hu, Xinyue},
+  year      = {2025},
+  booktitle = {2025 5th International Conference on Sensors and Information Technology (ICSI)},
+  doi       = {10.1109/icsi64877.2025.11009300}
+}
+
+@article{qiao2025,
+  title   = {Dual-Channel Aeromagnetic Compensation Method for Continuous and Intermittent OBE Interference},
+  author  = {Qiao, Zhi and Li, You and Meng, Zhaohai and Han, Qi},
+  year    = {2025},
+  journal = {IEEE Transactions on Instrumentation and Measurement},
+  doi     = {10.1109/tim.2025.3599271}
+}
+
+@article{wang2025,
+  title   = {An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network},
+  author  = {Wang, Han and Zuo, Boxin},
+  year    = {2025},
+  journal = {Applied Sciences},
+  doi     = {10.3390/app15063105}
+}
+
+@incollection{wang2025maneuver,
+  title     = {Magnetometer Compensation for Magnetic Interference in Aircraft Maneuvers by Using INS},
+  author    = {Wang, Guanjie and Yue, Yazhou and Dong, Jiahang and Zhou, Qi and Wang, Haoming and Wang, Jingjiang and Jiang, Haofeng},
+  year      = {2025},
+  booktitle = {Advances in Guidance, Navigation and Control},
+  series    = {Lecture Notes in Electrical Engineering},
+  doi       = {10.1007/978-981-96-2240-5_29}
+}
+
+@article{song2026,
+  title   = {An Enhanced Tolles--Lawson Model With Temperature Compensation for Aeromagnetic Compensation of Triaxial Magnetometers},
+  author  = {Song, Wenhua and Yang, Zhicheng and Ma, Yan and Li, Bin and Xie, Songyun and Chen, Chen and Qi, Kankan},
+  year    = {2026},
+  journal = {IEEE Transactions on Instrumentation and Measurement},
+  doi     = {10.1109/tim.2026.3697092}
+}
+
+@article{sun2026,
+  title   = {Physics-Informed Tolles--Lawson and Neural Network Hybrid Modeling for Magnetic Compensation in Uncrewed Ground Vehicles},
+  author  = {Sun, Zhaolong and Zhang, Yiwen and Deng, Shengyao and Xiao, Liang and Liu, Xin and Zhang, Yang},
+  year    = {2026},
+  journal = {IEEE Sensors Journal},
+  doi     = {10.1109/jsen.2026.3684910},
+  note    = {Ground-vehicle (not airborne UAV) application of the Tolles--Lawson + NN hybrid compensation approach}
+}
+
+@article{xie2026,
+  title   = {Aeromagnetic Nonlinear Interference Compensation Method Based on Hybrid LSTM and BP Neural Network Architecture},
+  author  = {Xie, Jihong},
+  year    = {2026},
+  journal = {Computer Science and Application},
+  doi     = {10.12677/csa.2026.161011}
+}
+
+@article{you2026,
+  title   = {Electromagnetic interference compensation for aeromagnetic data using adaptive wavelet denoising and partial least squares regression},
+  author  = {You, Gaoyun and Li, Xinsan and Li, Ting and Li, Can and Shen, Qiang},
+  year    = {2026},
+  journal = {Measurement Science and Technology},
+  doi     = {10.1088/1361-6501/ae8616}
+}