@misc{101LongShortTerm,
  title = {10.1. {{Long Short-Term Memory}} ({{LSTM}}) --- {{Dive}} into {{Deep Learning}} 1.0.3 Documentation},
  urldate = {2025-12-16},
  howpublished = {https://d2l.ai/chapter\_recurrent-modern/lstm.html},
  file = {/home/lars/Zotero/storage/9V8UN4KK/lstm.html}
}

@misc{102GatedRecurrent,
  title = {10.2. {{Gated Recurrent Units}} ({{GRU}}) --- {{Dive}} into {{Deep Learning}} 1.0.3 Documentation},
  urldate = {2025-12-16},
  howpublished = {https://d2l.ai/chapter\_recurrent-modern/gru.html},
  file = {/home/lars/Zotero/storage/J2IRCC3J/gru.html}
}

@misc{AdsblolGlobe_history_20242024,
  title = {Adsblol/Globe\_history\_2024: 2024 {{Historical}} Data for All Aircrafts Traces Known to Adsb.Lol. {{Openly}} Licensed.},
  urldate = {2025-12-15},
  howpublished = {https://github.com/adsblol/globe\_history\_2024},
  file = {/home/lars/Zotero/storage/WQPXFKQP/globe_history_2024.html}
}

@misc{AdsblolGlobe_history_20252025,
  title = {Adsblol/Globe\_history\_2025},
  year = 2025,
  month = dec,
  urldate = {2025-12-15},
  abstract = {✈️🗄 2025 Historical data for all aircrafts traces known to adsb.lol. Openly licensed.},
  copyright = {ODbL-1.0},
  howpublished = {ADSB.lol}
}

@misc{calzoneIntuitiveExplanationLSTM2025,
  title = {An {{Intuitive Explanation}} of {{LSTM}}},
  author = {Calzone, Ottavio},
  year = 2025,
  month = jul,
  journal = {Medium},
  urldate = {2025-12-15},
  abstract = {Recurrent Neural Networks},
  langid = {english},
  file = {/home/lars/Zotero/storage/S8WW57MP/an-intuitive-explanation-of-lstm-a035eb6ab42c.html}
}

@misc{calzoneIntuitiveExplanationLSTM2025a,
  title = {An {{Intuitive Explanation}} of {{LSTM}}},
  author = {Calzone, Ottavio},
  year = 2025,
  month = jul,
  journal = {Medium},
  urldate = {2025-12-16},
  abstract = {Recurrent Neural Networks},
  langid = {english},
  file = {/home/lars/Zotero/storage/WKKE4DRU/an-intuitive-explanation-of-lstm-a035eb6ab42c.html}
}

@article{harrisArrayProgrammingNumPy2020,
  title = {Array Programming with {{NumPy}}},
  author = {Harris, Charles R. and Millman, K. Jarrod and van der Walt, St{\'e}fan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H. and Brett, Matthew and Haldane, Allan and del R{\'i}o, Jaime Fern{\'a}ndez and Wiebe, Mark and Peterson, Pearu and {G{\'e}rard-Marchant}, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
  year = 2020,
  month = sep,
  journal = {Nature},
  volume = {585},
  number = {7825},
  pages = {357--362},
  publisher = {{Springer Science and Business Media LLC}},
  doi = {10.1038/s41586-020-2649-2}
}

@article{hunterMatplotlib2DGraphics2007,
  title = {Matplotlib: {{A 2D}} Graphics Environment},
  author = {Hunter, J. D.},
  year = 2007,
  journal = {Computing in Science \& Engineering},
  volume = {9},
  number = {3},
  pages = {90--95},
  publisher = {IEEE COMPUTER SOC},
  doi = {10.1109/MCSE.2007.55},
  abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.}
}

@misc{nntikz,
  title = {{{NNTikZ}}: {{TikZ}} Diagrams for Deep Learning and Neural Networks},
  author = {Love, Fraser},
  year = 2024,
  publisher = {GitHub}
}

@misc{paszkePyTorchImperativeStyle2019,
  title = {{{PyTorch}}: {{An Imperative Style}}, {{High-Performance Deep Learning Library}}},
  shorttitle = {{{PyTorch}}},
  author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and K{\"o}pf, Andreas and Yang, Edward and DeVito, Zach and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  year = 2019,
  month = dec,
  number = {arXiv:1912.01703},
  eprint = {1912.01703},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1912.01703},
  urldate = {2025-12-18},
  abstract = {Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning,Computer Science - Mathematical Software,Statistics - Machine Learning},
  file = {/home/lars/Zotero/storage/SAGV7FKX/Paszke et al. - 2019 - PyTorch An Imperative Style, High-Performance Deep Learning Library.pdf;/home/lars/Zotero/storage/P3AZNUD7/1912.html}
}

@article{riahimaneshAnalysisVulnerabilitiesAttacks2017,
  title = {Analysis of Vulnerabilities, Attacks, Countermeasures and Overall Risk of the {{Automatic Dependent Surveillance-Broadcast}} ({{ADS-B}}) System},
  author = {Riahi Manesh, Mohsen and Kaabouch, Naima},
  year = 2017,
  month = dec,
  journal = {International Journal of Critical Infrastructure Protection},
  volume = {19},
  pages = {16--31},
  issn = {1874-5482},
  doi = {10.1016/j.ijcip.2017.10.002},
  urldate = {2025-12-15},
  abstract = {The U.S. Federal Aviation Administration has mandated the use of the Automatic Dependent Surveillance-Broadcast (ADS-B) system by January 2020 as a key component of the NextGen Project, which is intended to upgrade the air traffic control infrastructure and operations. The ADS-B system seeks to replace legacy approaches such as primary and secondary radars by employing global satellite navigation systems to generate precise air pictures for air traffic management. The security of ADS-B is a major concern because the system broadcasts detailed information about aircraft, their positions, velocities and other data over unencrypted data links, making it easy to launch eavesdropping, jamming and message modification attacks on aircraft in flight. This paper discusses ADS-B vulnerabilities and attacks that leverage the ADS-B protocol stack. The paper also presents the security requirements, state-of-the-art attack detection techniques and countermeasures, along with an overall risk analysis of the ADS-B system.},
  keywords = {Air traffic control,Attacks,Automatic Dependent Surveillance-Broadcast (ADS-B),Countermeasures,Risk analysis,Vulnerabilities},
  file = {/home/lars/Zotero/storage/XZHRCQRW/Riahi Manesh and Kaabouch - 2017 - Analysis of vulnerabilities, attacks, countermeasures and overall risk of the Automatic Dependent Su.pdf;/home/lars/Zotero/storage/KKDTXYJ2/S1874548217300446.html}
}

@article{shi4DFlightTrajectory2021,
  title = {4-{{D Flight Trajectory Prediction With Constrained LSTM Network}}},
  author = {Shi, Zhiyuan and Xu, Min and Pan, Quan},
  year = 2021,
  month = nov,
  journal = {IEEE Transactions on Intelligent Transportation Systems},
  volume = {22},
  number = {11},
  pages = {7242--7255},
  issn = {1558-0016},
  doi = {10.1109/TITS.2020.3004807},
  urldate = {2025-12-15},
  abstract = {The increasing aviation activities pose a challenge to ensure a safe and orderly flight. Trajectory prediction is one of the most important forecasting tasks in Air Traffic Management. Accurate prediction is reasonable for safe and orderly flight tasks in civil aviation monitoring. Points of interests play an important role in most land traffic prediction algorithms due to their abilities in positioning and marking. Compared with land traffic, the sparse way-points and shared airways make it difficult for flight trajectory prediction. A constrained Long Short-Term Memory network for flight trajectory prediction is proposed in this paper. According to the dynamic characteristics of the aircraft, we propose three kinds of constraints to climbing, cruising, and descending/approaching phases, in particular, they are Top of climb, Way-points, and Runway direction, correspondingly. Our model is able to keep long-term dependencies with dynamic physical constraints. Density-Based Spatial Clustering of Applications with Noise and Linear Least Squares are used in data segmentation and preprocessing. Sliding windows help maintain the continuity of trajectory. Four-dimensional spatial-temporal trajectory set consisting of spatial position and timestamps is used to prove the efficiency of our approach. Multiple ADS-B ground stations contribute to our experimental dataset. The widely used Long Short-Term Memory network, Markov Model, weighted Markov Model, Support Vector Machine, and Kalman Filter are used for comparison. Quantitative analysis demonstrates that our model outperforms the above-mentioned state-of-the-art models, and lays a good foundation for decision-making in different scenarios.},
  keywords = {Agriculture,constrained LSTM network,Decision making,Forestry,Internet,linear least squares,Product design,Psychology,Quality assessment,Trajectory prediction,trajectory segmentation}
}

@inproceedings{shiLSTMbasedFlightTrajectory2018,
  title = {{{LSTM-based Flight Trajectory Prediction}}},
  booktitle = {2018 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
  author = {Shi, Zhiyuan and Xu, Min and Pan, Quan and Yan, Bing and Zhang, Haimin},
  year = 2018,
  month = jul,
  pages = {1--8},
  issn = {2161-4407},
  doi = {10.1109/IJCNN.2018.8489734},
  urldate = {2025-12-15},
  abstract = {Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making.},
  keywords = {Aircraft,Aircraft navigation,Atmospheric modeling,Delays,Forecasting,Predictive models,Trajectory},
  file = {/home/lars/Zotero/storage/5SHHA862/Shi et al. - 2018 - LSTM-based Flight Trajectory Prediction.pdf}
}

@misc{teamPandasdevPandasPandas2025,
  title = {Pandas-Dev/Pandas: {{Pandas}}},
  shorttitle = {Pandas-Dev/Pandas},
  author = {pandas development {team}, The},
  year = 2025,
  month = sep,
  doi = {10.5281/zenodo.17229934},
  urldate = {2025-10-16},
  abstract = {Pandas is a powerful data structures for data analysis, time series, and statistics.},
  howpublished = {Zenodo},
  keywords = {data science,python},
  file = {/home/lars/Zotero/storage/QV289HHN/17229934.html}
}

@misc{zhangPhasedFlightTrajectory2022,
  title = {Phased {{Flight Trajectory Prediction}} with {{Deep Learning}}},
  author = {Zhang, Kai and Chen, Bowen},
  year = 2022,
  month = mar,
  number = {arXiv:2203.09033},
  eprint = {2203.09033},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2203.09033},
  urldate = {2025-12-15},
  abstract = {The unprecedented increase of commercial airlines and private jets over the next ten years presents a challenge for air traffic control. Precise flight trajectory prediction is of great significance in air transportation management, which contributes to the decision-making for safe and orderly flights. Existing research and application mainly focus on the sequence generation based on historical trajectories, while the aircraft-aircraft interactions in crowded airspace especially the airspaces near busy airports have been largely ignored. On the other hand, there are distinct characteristics of aerodynamics for different flight phases, and the trajectory may be affected by various uncertainties such as weather and advisories from air traffic controllers. However, there is no literature fully considers all these issues. Therefore, we proposed a phased flight trajectory prediction framework. Multi-source and multi-modal datasets have been analyzed and mined using variants of recurrent neural network (RNN) mixture. To be specific, we first introduce spatio temporal graphs into the low-altitude airway prediction problem, and the motion constraints of an aircraft are embedded to the inference process for reliable forecasting results. In the en-route phase, the dual attention mechanism is employed to adaptively extract much more important features from overall datasets to learn the hidden patterns in dynamical environments. The experimental results demonstrate our proposed framework can outperform state-of-the-art methods for flight trajectory prediction for large passenger/transport airplanes.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning},
  file = {/home/lars/Zotero/storage/HNZ8GXSA/Zhang and Chen - 2022 - Phased Flight Trajectory Prediction with Deep Learning.pdf;/home/lars/Zotero/storage/2XCUNEFX/2203.html}
}
