Update README.md

README.md

@@ -49,31 +49,27 @@ language:
 tags:
 - wireless
 ---
+
+
+
 # 📶 Beam-Level (5G) Time-Series Dataset
 
-This dataset introduces a **novel multivariate time series** specifically curated to support research to enable **accurate prediction of KPIs** across communication networks, as illustrated below:
+This dataset introduces a **novel multivariate time series** specifically curated to support research in enabling **accurate prediction of KPIs** across communication networks, as illustrated below:
 
 <p align="center">
-  <img src="images/network.png" alt="Base station, cells, and beams" />
+  <img src="images/network.png" alt="Base station, cells, and beams" />
 </p>
 
-Precise forecasting of network traffic is critical for:
-- Optimizing **network management**
-- Enhancing **resource allocation efficiency**
-
-This task is of both **practical and theoretical importance** to researchers in networking and machine learning, offering a strong benchmark for state-of-the-art (SOTA) time series models.
+Precise forecasting of network traffic is critical for optimizing **network management** and enhancing **resource allocation efficiency**. This task is of both **practical and theoretical importance** to researchers in networking and machine learning, offering a strong benchmark for state-of-the-art (SOTA) time series models.
 
 ---
 
 ## 📂 Dataset Overview
 
-- **Beams:** 2,880  
-- **Base Stations:** 30  
-- **Cells per Station:** 3  
-- **Beams per Cell:** 32  
-- **Frequency:** Hourly  
-- **Duration:** 5 weeks + 2 target weeks  
-- **Total Hours (per beam):** Up to 840 (train) and 1176 (total)
+The dataset comprises:
+* **2,880 Beams** across 30 Base Stations (3 Cells per Station, 32 Beams per Cell).
+* **Hourly frequency**.
+* **Duration:** 5 weeks + 2 target weeks, totaling up to 840 training hours and 1176 total hours per beam.
 
 ---
 
@@ -81,61 +77,48 @@ This task is of both **practical and theoretical importance** to researchers in
 
 ### 🏋️‍♂️ Training Set (Weeks 0–5)
 
-| File Name                     | Metric                          |
-|------------------------------|----------------------------------|
-| `DLThpVol_train_0w-5w.csv`   | Downlink throughput volume       |
-| `DLThpTime_train_0w-5w.csv`  | Throughput transmission time     |
-| `DLPRB_train_0w-5w.csv`      | PRB (Physical Resource Block) usage |
-| `MR_number_train_0w-5w.csv`  | User count (Measurement Reports) |
+| File Name | Metric |
+|---|---|
+| `DLThpVol_train_0w-5w.csv` | Downlink throughput volume |
+| `DLThpTime_train_0w-5w.csv` | Throughput transmission time |
+| `DLPRB_train_0w-5w.csv` | PRB (Physical Resource Block) usage |
+| `MR_number_train_0w-5w.csv` | User count (Measurement Reports) |
 
 ### 🎯 Forecast Targets
 
 #### 📆 6th Week (Week 5–6)
 
-| File Name                      | Metric                          |
-|--------------------------------|----------------------------------|
-| `DLThpVol_test_5w-6w.csv`      | Downlink throughput volume       |
-| `DLThpTime_test_5w-6w.csv`     | Throughput transmission time     |
-| `DLPRB_test_5w-6w.csv`         | PRB usage                        |
-| `MR_number_test_5w-6w.csv`     | User count                       |
+| File Name | Metric |
+|---|---|
+| `DLThpVol_test_5w-6w.csv` | Downlink throughput volume |
+| `DLThpTime_test_5w-6w.csv` | Throughput transmission time |
+| `DLPRB_test_5w-6w.csv` | PRB usage |
+| `MR_number_test_5w-6w.csv` | User count |
 
 #### 📆 11th Week (Week 10–11)
 
-| File Name                        | Metric                          |
-|----------------------------------|----------------------------------|
-| `DLThpVol_test_10w-11w.csv`      | Downlink throughput volume       |
-| `DLThpTime_test_10w-11w.csv`     | Throughput transmission time     |
-| `DLPRB_test_10w-11w.csv`         | PRB usage                        |
-| `MR_number_test_10w-11w.csv`     | User count                       |
-
+| File Name | Metric |
+|---|---|
+| `DLThpVol_test_10w-11w.csv` | Downlink throughput volume |
+| `DLThpTime_test_10w-11w.csv` | Throughput transmission time |
+| `DLPRB_test_10w-11w.csv` | PRB usage |
+| `MR_number_test_10w-11w.csv` | User count |
 
 ---
 
 ## 🧪 Dataset Splits
 
 <p align="center">
-  <img src="images/dataset_split.png" alt="Dataset train/forecast split" />
+  <img src="images/dataset_split.png" alt="Dataset train/forecast split" />
 </p>
 
-- **Training Set:** First 5 weeks  
-- **Forecast Targets:**  
-  - **Week 6** (immediate future)  
-  - **Week 11** (long-term future)
+The dataset is split into a **Training Set** (first 5 weeks) and **Forecast Targets** for Week 6 (immediate future) and Week 11 (long-term future).
 
 ---
 
 ## 📄 Data Format
 
-Each CSV file follows this structure:
-
-- **`Time` column**:  
-  - Ranges:
-    - `0–839` for training (weeks 1–6)  
-    - `0–167` for week 6  
-    - `168–335` for week 11  
-
-- **Beam columns (`0_0_0`, ..., `29_2_31`)**:  
-  - Each uniquely identifies one of the **2,880 beams** across 30 base stations.
+Each CSV file contains a `Time` column and multiple beam columns (e.g., `0_0_0` to `29_2_31`). The `Time` column ranges from `0–839` for training (weeks 1–6), `0–167` for week 6, and `168–335` for week 11. Each beam column uniquely identifies one of the **2,880 beams** across 30 base stations.
 
 ---
 
@@ -143,13 +126,10 @@ Each CSV file follows this structure:
 
 If you use this dataset in your research, please cite:
 
-> **L. Fechete et al.**,  
-> *Goal-Oriented Time-Series Forecasting: Foundation Framework Design*,  
-> arXiv:2504.17493 (2025)
+> **L. Fechete et al.**, *Goal-Oriented Time-Series Forecasting: Foundation Framework Design*, arXiv:2504.17493 (2025)
 
 ---
 
 ## 🔗 Code Repository
 
-The official codebase for working with this dataset is available here:  
-👉 [https://github.com/netop-team/gotsf](https://github.com/netop-team/gotsf)
+The official codebase for working with this dataset is available here: 👉 [https://github.com/netop-team/gotsf](https://github.com/netop-team/gotsf)