diff --git a/project_report.md b/project_report.md
index 5da6ccb..e8a7976 100644
--- a/project_report.md
+++ b/project_report.md
@@ -7,18 +7,20 @@ The system combines:
 - Facial Action Units (AUs)
 - Eye-tracking features (fixations, saccades, blinks, pupil behavior)
 
-It covers:
-- Data ingestion and conversion
-- Sliding-window feature generation
-- Exploratory analysis
+Apart from this, several machine learning model architectures are presented and evaluated.
+
+Content:
+- Dataset generation
+- Exploratory data analysis
 - Model training experiments
-- Real-time inference from SQLite
-- MQTT publishing
-- Optional Linux `systemd` scheduling
+- Real-time inference with SQlite, systemd and MQTT
+- Repository file inventory
+- Additional nformation 
 
-## 2) End-to-End Workflow
 
-### 2.1 Data Ingestion and Conversion
+## 2) Dataset generation
+
+### 2.1 Data Access, Filtering, and Data Conversion
 
 Main scripts:
 - `dataset_creation/create_parquet_files_from_owncloud.py`
@@ -59,7 +61,18 @@ Runtime behavior:
 Operational note:
 - `DB_PATH` and other paths are currently code-configured and must be adapted per deployment.
 
-### 2.4 Model Training
+## 3) EDA
+TO DO
+
+- `EDA/EDA.ipynb` - main EDA notebook
+- `EDA/distribution_plots.ipynb` - distribution visualization
+- `EDA/histogramms.ipynb` - histogram analysis
+- `EDA/researchOnSubjectPerformance.ipynb` - subject-level analysis
+- `EDA/owncloud_file_access.ipynb` - ownCloud exploration/access notebook
+- `EDA/calculate_replacement_values.ipynb` - fallback/median computation notebook
+- `EDA/login.yaml` - local auth/config artifact for EDA workflows
+
+## 4) Model Training
 
 Location:
 - `model_training/` (primarily notebook-driven)
@@ -77,7 +90,7 @@ Supporting utilities:
 - `model_training/tools/mad_outlier_removal.py`
 - `model_training/tools/evaluation_tools.py`
 
-### 2.5 Real-Time Prediction and Messaging
+## 5) Real-Time Prediction and Messaging
 
 Main script:
 - `predict_pipeline/predict_sample.py`
@@ -99,7 +112,7 @@ Expected payload form:
 }
 ```
 
-### 2.6 Scheduled Prediction (Linux)
+### 5.1 Scheduled Prediction (Linux)
 
 Files:
 - `predict_pipeline/predict.service`
@@ -110,7 +123,7 @@ Role:
 - Run inference repeatedly without manual execution
 - Timer/service configuration can be customized per target machine
 
-## 3) Runtime Configuration
+## 5.2 Runtime Configuration
 
 Primary config file:
 - `predict_pipeline/config.yaml`
@@ -127,7 +140,7 @@ Important:
 - The repository currently uses environment-specific absolute paths in some scripts/configs.
 - Paths should be normalized before deployment to a new machine.
 
-## 4) Data and Feature Expectations
+## 5.3) Data and Feature Expectations
 
 Prediction expects SQLite rows containing:
 - `_Id`
@@ -141,8 +154,13 @@ Common feature groups:
 - Blink count/duration statistics
 - Pupil mean and IPA
 
-## 5) Installation and Dependencies
+## 6) Installation and Dependencies
+Due to unsolvable dependency conflicts, several environemnts need to be used in the same time. 
+### 6.1 Environemnt for camera handling
+TO DO
 
+
+### 6.2 Environment for predictions
 Install base requirements:
 ```bash
 pip install -r requirements.txt
@@ -154,16 +172,16 @@ Typical key packages in this project:
 - `paho-mqtt`
 - optional data access stack (`pyocclient`, `h5py`, `tables`)
 
-## 6) Repository File Inventory
+## 7) Repository File Inventory
 
-### 6.1 Root
+### Root
 
 - `.gitignore` - Git ignore rules
 - `readme.md` - minimal quickstart documentation
 - `project_report.md` - full technical documentation (this file)
 - `requirements.txt` - Python dependencies
 
-### 6.2 Dataset Creation
+### Dataset Creation
 
 - `dataset_creation/parquet_file_creation.py` - local source to parquet conversion
 - `dataset_creation/create_parquet_files_from_owncloud.py` - ownCloud download + parquet conversion
@@ -182,7 +200,7 @@ Typical key packages in this project:
 - `dataset_creation/camera_handling/camera_stream.py` - baseline camera streaming script
 - `dataset_creation/camera_handling/db_test.py` - DB test utility
 
-### 6.3 EDA
+### EDA
 
 - `EDA/EDA.ipynb` - main EDA notebook
 - `EDA/distribution_plots.ipynb` - distribution visualization
@@ -192,7 +210,7 @@ Typical key packages in this project:
 - `EDA/calculate_replacement_values.ipynb` - fallback/median computation notebook
 - `EDA/login.yaml` - local auth/config artifact for EDA workflows
 
-### 6.4 Model Training
+### Model Training
 
 #### CNN
 - `model_training/CNN/CNN_simple.ipynb`
@@ -233,7 +251,7 @@ Typical key packages in this project:
 - `model_training/tools/mad_outlier_removal.py`
 - `model_training/tools/evaluation_tools.py`
 
-### 6.5 Prediction Pipeline
+### Prediction Pipeline
 
 - `predict_pipeline/predict_sample.py` - runtime prediction + MQTT publish
 - `predict_pipeline/config.yaml` - runtime database/model/scaler/mqtt config
@@ -242,12 +260,12 @@ Typical key packages in this project:
 - `predict_pipeline/predict.timer` - systemd timer unit
 - `predict_pipeline/predict_service_timer_documentation.md` - Linux service/timer guide
 
-### 6.6 Generic Tools
+### Generic Tools
 
-- `tools/db_helpers.py` - common SQLite utilities used by prediction path
+- `tools/db_helpers.py` - common SQLite utilities used to get newest sample for prediction
 
-## 7) Known Technical Notes
+## 8) Additional Information
 
-- Several paths are hardcoded for a specific runtime environment and should be parameterized for portability.
+- Several paths are hardcoded on purpose to ensure compability with the jetsonboard at the OHM-UX driving simulator.
 - Camera and AU processing are resource-intensive; version pinning and hardware validation are recommended.