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[ci] fix spellcheck errors
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Binary_JData_Specification.md

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@@ -70,7 +70,7 @@ UBJSON Specification Draft 12 (https://github.com/ubjson/universal-binary-json)
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by adding native support for **N-dimensional packed arrays** - an essential data
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structure for scientific applications - and **columnar table format** for packed
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objects to offer efficient storage of tables and repeating objects, such as
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Python Pandas DataFrames and NumPy arrays-of-objects. These optimized
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Python Pandas DataFrame and NumPy arrays-of-objects. These optimized
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container constructs greatly reduce storage overhead and redundancy,
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enhancing data exchange efficiency.
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A dictionary for mapping string-value rows/columns should be indicated by
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a payload-less optimized string-array.
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**Use case:** Repeated/categorical string values with low cardinality.
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**Use case:** Repeated/categorical string values.
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**Schema syntax:**
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```
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An offset-table based storage is used for storing string vectors of variable lengths.
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It first concatenates all strings into a single linear buffer, and assigns an integer as
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the offset from the begining of the buffer for each string element. The use of
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the offset from the beginning of the buffer for each string element. The use of
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offset-table should be indicated by an payload-less optimized array containing only the
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optimized type `[$<type>]`.
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- Range: [1, 12]
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- **day**: Unsigned 8-bit integer (byte 3)
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- Range: [1, 31]
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- Uses proleptic Gregorian calendar
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- Uses Gregorian calendar
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##### Example
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##### Example
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Datetime for 2024-01-15 10:30:00.123456 UTC:
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DateTime for 2024-01-15 10:30:00.123456 UTC:
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```
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[E][U][6][U][8][0x40][0x15][0xE3][0xED][0xED][0x0C][0x06][0x00]
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```
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#### <a name="ext_complex64"/>complex64 (Type ID: 8)
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Represents a complex number with single-precision (32-bit) floating-point
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components. Compatible with NumPy's `complex64` dtype.
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components. Compatible with NumPy's `complex64` data type.
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##### Payload Format (8 bytes)
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#### <a name="ext_complex128"/>complex128 (Type ID: 9)
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Represents a complex number with double-precision (64-bit) floating-point
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components. Compatible with NumPy's `complex128` dtype and Python's `complex`.
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components. Compatible with NumPy's `complex128` data type and Python's `complex`.
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##### Payload Format (16 bytes)
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