Adjusted small things, norhting serious

src/assets/i18n/en.json

@@ -299,10 +299,10 @@
     "END_NODE": "End Node",
     "WALL": "Wall",
     "CLEAR_NODE": "Clear",
-    "DIJKSTRA": "Dijkstra",
-    "ASTAR": "A*",
+    "DIJKSTRA": "Start Dijkstra",
+    "ASTAR": "Start A*",
     "NORMAL_CASE": "Test Scenario",
-    "EDGE_CASE": "A* Edge Case",
+    "EDGE_CASE": "A* Edge Case Scenario",
     "CLEAR_BOARD": "Clear Board",
     "VISITED": "Visited",
     "PATH": "Path",
@@ -311,7 +311,9 @@
     "EXPLANATION": {
       "TITLE": "Algorithms",
       "DIJKSTRA_EXPLANATION": " is guaranteed to find the shortest path if all edge costs are non-negative. Advantage: optimal and without heuristics. Disadvantage: often visits a large number of nodes (can be slower for large grids).",
-      "ASTAR_EXPLANATION": " extends Dijkstra with a heuristic (e.g. Manhattan distance) and can therefore search in a much more targeted manner. Advantage: often significantly faster with good heuristics; with permissible heuristics, the path remains optimal. Disadvantage: highly dependent on heuristics (poor heuristics ≈ Dijkstra)."
+      "ASTAR_EXPLANATION": " extends Dijkstra with a heuristic (e.g. Manhattan distance) and can therefore search in a much more targeted manner. Advantage: often significantly faster with good heuristics; with permissible heuristics, the path remains optimal. Disadvantage: highly dependent on heuristics (poor heuristics ≈ Dijkstra).",
+      "NOTE": "Note",
+      "DISCLAIMER": "This A* implementation is deliberately kept simple. It only moves in four directions and each step costs 1. The heuristic is minimal and only serves to demonstrate the principle of A* compared to Dijkstra. The goal is not an optimal or production-ready A* algorithm, but a clear visualisation of how heuristics can speed up the search."
     },
     "ALERT": {
       "START_END_NODES": "Please select a start and end node before running the algorithm."