To generate test cases for Problem F, we need to create random datasets adhering to the constraints specified in the problem statement. Here is a test case generator function for the problem:

```python
import random

def tcgen():
    # Generate a random number of datasets
    num_datasets = random.randint(1, 100)  # Let's decide to generate between 1 and 100 datasets
    datasets = []

    for _ in range(num_datasets):
        # Generate a single dataset with random n
        n = random.randint(1, 100000)
        datasets.append(n)
    
    # Append the terminating dataset
    datasets.append(0)

    return datasets

# Example usage:
if __name__ == "__main__":
    test_cases = tcgen()
    for case in test_cases:
        print(case)
```

### Explanation
1. **Number of Datasets:** We randomly select the number of datasets to generate (between 1 and 100 datasets in this example).
2. **Generating Each Dataset:** For each dataset, we generate a random integer \( n \) within the specified constraint \( 1 \leq n \leq 100,000 \).
3. **Termination:** After generating the datasets, we append a terminating dataset where \( n = 0 \).
4. **Output:** The function returns a list of datasets including the terminating dataset. In the example usage, we print each dataset.

This function ensures that the generated test cases are within the constraints and include the necessary terminating condition.