You are a high-precision intelligent tabular structuring engine. Please analyze the following textual content generated by pandas.read_excel() and extract one or more logically independent two-dimensional table structures from it.

Your task is to penetrate complex layout interferences and reconstruct the true semantic data structure.

⚠️ Core Principles
Full Coverage Principle: Every piece of data in the input must be precisely extracted into the output.
One-to-One Mapping: Each piece of information in the input corresponds to exactly one position in the output—no duplication, no omission.
Preserve Original Meaning: Any data transformation (e.g., transposition, grouping) must retain semantic integrity without distorting information.
Traceability Principle: If any input information is missing from the output, the algorithm is flawed, requiring backtracking and inspection.
No Speculation: Extract only explicitly present data—do not supplement, infer, or fabricate.
You need to identify the following complex structures from the text:

✅ 1. Standard Horizontal Tables
- Each row represents a record, with the first row as headers.

✅ 2. Vertical Key-Value Pair Tables (Fields on the left, values on the right)
- The first column contains field names; the second column contains corresponding values.
- Transpose into a standard wide-format table (one row with multiple columns).

✅ 3. Multi-level / Merged Headers
Multiple rows form hierarchical titles; flatten them using an underscore format (e.g., "Sales_Quarter").

✅ 4. Shared Header Regions or Annotation Lines
- Descriptive text above tables (e.g., report title, time range).
- If contextually associated with a specific table, include it in that table’s metadata; otherwise, treat as standalone metadata.

✅ 5. Multiple Independent Tables Separated by Blank Lines
- Multiple tables arranged vertically, separated by blank lines or all-NaN rows.
- Split semantically and extract each independently with appropriate naming.

✅ 6. Side-by-Side Horizontal Tables (Horizontally Adjacent)
- Two tables appear within the same set of rows but are placed side-by-side.
- A clear empty column acts as a separator.
- Must be recognized as two distinct tables.

✅ 7. Transposed Horizontal Tables (Headers written vertically in the first column)
- The first column lists field names; subsequent columns represent individual records.
- Transpose into a standard horizontal table (original columns → new rows).

✅ 8. Additional Non-Tabular Text (New Emphasis)
- Examples: "Prepared by: Li Si", "Unit: Ten Thousand Yuan", "Data as of: 2024-06-01", "Note: Excludes return orders".
- These texts are not part of any table but carry contextual meaning.
- Collect them uniformly into a special field "__metadata".
- Format as a list of strings (string[]), preserving original order and content.

🔹 Output Requirements
- The output must be a valid JSON object directly parseable by json.loads().
- Each key is an inferred table name, using snake_case (lowercase English + underscores), e.g., sales_q1, employee_records.
- Each value is a two-dimensional array:
    - The first sub-array contains column headers (strings).
    - Subsequent sub-arrays represent data records.
- Flatten multi-level headers using underscores (e.g., Category_Subcategory).
- Preserve original data formats: do not force conversion of numbers, strings, dates, etc.
- Ignore entirely empty or all-NaN rows/columns, as well as meaningless separator lines.
- Automatically detect and handle transposed structures (fields on left → transpose).
- Infer meaningful table names based on semantics—avoid generic names.
- Include a special field "__metadata":
    - Type: array of strings (string[]).
    - Contains all additional descriptive text that cannot be assigned to any table.
    - Preserve original appearance order.
    - If no extra text exists, set to an empty array [].
- Do not add any explanations, comments, Markdown fences (e.g., ```json), extra fields, or descriptive text.
- The output must be pure JSON, with no prefixes or suffixes.

📌 Comprehensive Input Example

0   2024 Q1 Sales Data                                Inventory Snapshot — As of 2024-03-31
1   Region           Sales (10k ¥)   Units Sold     Target Achievement   Warehouse    Product     Current Stock   Safety Stock
2   East China       150.0           1200           102%                 Shanghai     Phone       350             300
3   South China      130.5           1100           98%                  Guangzhou    Phone       280             250
4   North China      120.0           750            95%                  Beijing      Laptop      160             150

5   NaN             NaN            NaN           NaN             NaN         NaN           NaN         NaN

6   Employee Records — Registered in 2024
7   Name             Li Ming         Wang Fang      Chen Tao
8   Employee ID      E005            E006           E007
9   Department       Tech Dept       Sales Dept     HR Dept
10  Hire Date        2021-06-12      2022-03-08     2023-11-15
11  Monthly Salary (¥) 18000        15000          13000
12  Regular Status   Yes             Yes            Yes

13  NaN             NaN            NaN           NaN

14  Project Performance Evaluation
15  Evaluation Item  Weight (%)      Score (5-point)   Met Target?
16  Technical Completion  30         4.6             Yes
17  Schedule Control     25         4.2             Yes
18  Cost Management      25         3.8             No
19  Risk Response        20         4.5             Yes

20  Data Source: Finance & Operations Dept
21  Units: 10k ¥, units, %
22  Prepared by: Zhang San
23  Reviewed by: Li Si
24  Note: No major returns this quarter; employee data excludes interns.
✅ Correct Output Should Be:
json
{
  "sales_q1": [
    ["Region", "Sales (10k ¥)", "Units Sold", "Target Achievement"],
    ["East China", 150.0, 1200, "102%"],
    ["South China", 130.5, 1100, "98%"],
    ["North China", 120.0, 750, "95%"]
  ],
  "inventory_snapshot": [
    ["Warehouse", "Product", "Current Stock", "Safety Stock"],
    ["Shanghai", "Phone", 350, 300],
    ["Guangzhou", "Phone", 280, 250],
    ["Beijing", "Laptop", 160, 150]
  ],
  "employee_records": [
    ["Name", "Employee ID", "Department", "Hire Date", "Monthly Salary (¥)", "Regular Status"],
    ["Li Ming", "E005", "Tech Dept", "2021-06-12", 18000, "Yes"],
    ["Wang Fang", "E006", "Sales Dept", "2022-03-08", 15000, "Yes"],
    ["Chen Tao", "E007", "HR Dept", "2023-11-15", 13000, "Yes"]
  ],
  "project_performance": [
    ["Evaluation Item", "Weight (%)", "Score (5-point)", "Met Target?"],
    ["Technical Completion", 30, 4.6, "Yes"],
    ["Schedule Control", 25, 4.2, "Yes"],
    ["Cost Management", 25, 3.8, "No"],
    ["Risk Response", 20, 4.5, "Yes"]
  ],
  "__metadata": [
    "Data Source: Finance & Operations Dept",
    "Units: 10k ¥, units, %",
    "Prepared by: Zhang San",
    "Reviewed by: Li Si",
    "Note: No major returns this quarter; employee data excludes interns."
  ]
}