diff --git a/arcos4py/tools/_detect_events.py b/arcos4py/tools/_detect_events.py
index 049968a..14f25de 100644
--- a/arcos4py/tools/_detect_events.py
+++ b/arcos4py/tools/_detect_events.py
@@ -230,8 +230,19 @@ def transportation_linking(
     cost_threshold: float = 0,
     **kwargs: Dict[str, Any],
 ) -> Tuple[np.ndarray, int]:
-    """
-    Optimized pixel-wise transportation linking of clusters across frames.
+    """Optimized pixel-wise transportation linking of clusters across frames.
+
+    Arguments:
+        cluster_labels (np.ndarray): The cluster labels for the current frame.
+        cluster_coordinates (np.ndarray): The cluster coordinates for the current frame.
+        memory_cluster_labels (np.ndarray): The cluster labels for previous frames.
+        memory_coordinates (np.ndarray): The cluster coordinates for previous frames.
+        memory_kdtree (KDTree): KDTree for the previous frame's clusters.
+        epsPrev (float): Frame-to-frame distance, used to connect clusters across frames.
+        max_cluster_label (int): The maximum label for clusters.
+        reg (float): Entropy regularization parameter for unbalanced OT algorithm.
+        reg_m (float): Marginal relaxation parameter for unbalanced OT.
+        cost_threshold (float): Cost threshold for assigning clusters.
 
     Uses unbalanced OT to assign each current pixel to a previous pixel within epsPrev.
     """
@@ -249,14 +260,14 @@ def transportation_linking(
 
     # Build cost matrix between each current pixel and each candidate memory pixel
     curr_coords = cluster_coordinates
-    mem_coords  = memory_coordinates[valid_mem_idx]
+    mem_coords = memory_coordinates[valid_mem_idx]
     cost_matrix = _compute_filtered_distances(curr_coords, mem_coords)
 
     # Uniform distributions
     n_curr = curr_coords.shape[0]
-    n_mem  = mem_coords.shape[0]
+    n_mem = mem_coords.shape[0]
     a = np.ones(n_curr) / n_curr
-    b = np.ones(n_mem)  / n_mem
+    b = np.ones(n_mem) / n_mem
 
     # Solve unbalanced OT
     ot_plan = ot.unbalanced.sinkhorn_unbalanced(a, b, cost_matrix, reg, reg_m)