diff --git a/README.md b/README.md
index 9ae79af..657ba17 100644
--- a/README.md
+++ b/README.md
@@ -21,15 +21,13 @@ First Author: [**Jie Yin 殷杰**](https://github.com/sjtuyinjie?tab=repositorie
 
 
 
-> [🎯!TIP]
-> strongly recommend that the newly proposed SLAM algorithm be tested on our [M2DGR](https://github.com/SJTU-ViSYS/M2DGR) / [M2DGR-plus](https://github.com/SJTU-ViSYS/M2DGR-plus) / [Ground-Challenge](https://github.com/sjtuyinjie/Ground-Challenge) benchmark, because our data has following features:
-
-## 🎯 NOTICE
-### We strongly recommend that the newly proposed SLAM algorithm be tested on our [M2DGR](https://github.com/SJTU-ViSYS/M2DGR) / [M2DGR-plus](https://github.com/SJTU-ViSYS/M2DGR-plus) / [Ground-Challenge](https://github.com/sjtuyinjie/Ground-Challenge) benchmark, because our data has following features:
-1. **Rich sensory information** including vision, lidar, IMU, GNSS,event, thermal-infrared images and so on
-2. **Various scenarios** in real-world environments including lifts, streets, rooms, halls and so on.
-3. Our dataset brings **great challenge** to existing cutting-edge SLAM algorithms including [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM) and [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3). If your proposed algorihm outperforms these SOTA systems on our benchmark, your paper will be much more convincing and valuable.
-4. 🔥 Extensive excellent **open-source** projects have been built or evaluated on M2DGR/M2DGE-plus so far, for examples, [**Ground-Fusion**](https://github.com/SJTU-ViSYS/Ground-Fusion), [LVI-SAM-Easyused](https://github.com/Cc19245/LVI-SAM-Easyused), [Log-LIO](https://github.com/tiev-tongji/LOG-LIO), [Swarm-SLAM](https://github.com/MISTLab/Swarm-SLAM), [VoxelMap++](https://github.com/uestc-icsp/VoxelMapPlus_Public), [GRIL-Cali](https://github.com/SJTU-ViSYS/Ground-Fusion), [LINK3d](https://github.com/YungeCui/LinK3D), [i-Octree](https://github.com/zhujun3753/i-octree), [LIO-EKF](https://github.com/YibinWu/LIO-EKF), [Fast-LIO ROS2](https://github.com/Lee-JaeWon/FAST_LIO_ROS2), [HC-LIO](https://github.com/piluohong/hc_lio), [LIO-RF](https://github.com/YJZLuckyBoy/liorf), [PIN-SLAM](https://github.com/PRBonn/PIN_SLAM), [LOG-LIO2](https://github.com/tiev-tongji/LOG-LIO2), [Section-LIO](https://github.com/mengkai98/Section-LIO)and so on!
+> [!TIP]
+> 🎯 We strongly recommend that the newly proposed SLAM algorithm be tested on our [M2DGR](https://github.com/SJTU-ViSYS/M2DGR) / [M2DGR-plus](https://github.com/SJTU-ViSYS/M2DGR-plus) / [Ground-Challenge](https://github.com/sjtuyinjie/Ground-Challenge) benchmark, because our data has following features:
+> 1. **Rich sensory information** including vision, lidar, IMU, GNSS,event, thermal-infrared images and so on
+> 2. **Various scenarios** in real-world environments including lifts, streets, rooms, halls and so on.
+> 3. Our dataset brings **great challenge** to existing cutting-edge SLAM algorithms including [LIO-SAM](https://github.com/TixiaoShan/LIO-SAM) and [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3). If your proposed algorihm outperforms these SOTA systems on our benchmark, your paper will be much more convincing and valuable.
+> 4. 🔥 Extensive excellent **open-source** projects have been built or evaluated on M2DGR/M2DGE-plus so far, for examples, [**Ground-Fusion**](https://github.com/SJTU-ViSYS/Ground-Fusion), [LVI-SAM-Easyused](https://github.com/Cc19245/LVI-SAM-Easyused), [Log-LIO](https://github.com/tiev-tongji/LOG-LIO), [Swarm-SLAM](https://github.com/MISTLab/Swarm-SLAM), [VoxelMap++](https://github.com/uestc-icsp/VoxelMapPlus_Public), [GRIL-Cali](https://github.com/SJTU-ViSYS/Ground-Fusion), [LINK3d](https://github.com/YungeCui/LinK3D), [i-Octree](https://github.com/zhujun3753/i-octree), [LIO-EKF](https://github.com/YibinWu/LIO-EKF), [Fast-LIO ROS2](https://github.com/Lee-JaeWon/FAST_LIO_ROS2), [HC-LIO](https://github.com/piluohong/hc_lio), [LIO-RF](https://github.com/YJZLuckyBoy/liorf), [PIN-SLAM](https://github.com/PRBonn/PIN_SLAM), [LOG-LIO2](https://github.com/tiev-tongji/LOG-LIO2), [Section-LIO](https://github.com/mengkai98/Section-LIO)and so on!
+
 
 
 ## Table of Contents