Integrate arXiv processing script with standardized architecture

## Description
A fully integrated arXiv processing script that maintains its unique academic data processing capabilities while following the project's established architectural patterns and performance standards.

### Technical Overview
The arXiv processing script `arxiv_process.py` processes fetched data from the arXiv API from the `arxiv_fetch.py` and ensures consistency with the project's processing pipeline architecture identifying optimization opportunities.

### Similarities with Other Process Scripts
The arXiv processing script follows the established patterns found in GCS and GitHub processing scripts:

## Shared Architecture:
• Standard argument parsing with `--quarter`, `--enable-save`, `--enable-git` flags
• Common imports and setup using shared.py module
• Consistent error handling with pygments-formatted tracebacks
• Git integration for fetch, merge, add, commit, and push operations
• CSV output with csv.QUOTE_ALL and Unix line terminators
• Pandas-based data processing and aggregation

## Common Processing Pattern:
1. Parse arguments and setup paths
2. Git fetch and merge
3. Load CSV data from fetch phase
4. Process data into multiple aggregated views
5. Save processed data to CSV files
6. Git add, commit, and push changes

### Key Differences and Unique Features

arXiv-Specific Processing:
• **License categorization**: Processes Creative Commons licenses with academic focus (CC0, CC BY, CC BY-
SA, etc.)
• **Academic metadata**: Handles arXiv-specific fields like categories, publication years, and author
counts
• **Multi-dimensional analysis**: Creates 7 different processed outputs:
• License totals
• Free Cultural Works categorization
• Restriction level analysis (0-3 scale)
• Category-based totals (academic disciplines)
• Year-based totals (publication timeline)
• Author count analysis

Data Processing Efficiency:
• Uses itertuples(index=False) for memory-efficient row iteration
• Implements domain-specific license classification logic
• Processes 4 separate input files vs. 3 for GCS

### Areas for Integration Review

1. Code Standardization: Ensure consistent function naming and documentation patterns
2. Performance Optimization: Review data loading and processing efficiency compared to GCS script
3. Error Handling: Verify comprehensive error coverage for academic data edge cases
4. Output Consistency: Align CSV column naming conventions across all processing scripts
5. Git Integration: Ensure commit messages follow project standards

### Expected Outcome
A fully integrated arXiv processing script that maintains its unique academic data processing capabilities while following the project's established architectural patterns and performance standards.

## Implementation

- [x] I would be interested in implementing this feature.

Start by comparing arxiv_process.py and arxiv_fetch.py with the GCS and GitHub processing scripts, using shared.py to identify the established architecture. Review argument handling, CSV outputs, error handling, performance, and Git operations; done means the arXiv script preserves its academic outputs while matching the project's shared patterns and standards.

Scritto dal modello di indicizzazione a partire dal testo della issue.