dataesr/scientific-paragraphs-categorization huggingface dataset:
Leaf Metabolite Profiles at Two Stages of Plant Development Gas Chromatography coupled to Mass Spectrometry (GC/MS) analyses of the leaf metabolome were carried out on the four selected independent transgenic events for each of the four different transgenic maize lines T02291, T02289, T0312 and T022308 that had been grown in the glasshouse (Figure  S1 ). In the leaf samples taken at the vegetative stage of plant development (V) and during the grain filling period fifteen days after silking (15DAS), 128 water-soluble leaf metabolites were detected (Supplemental dataset 1). Following normalization of the amounts of metabolites (Supplemental data set 2), a hierarchical clustering analysis (HCA) showed that in the different transgenic lines and in the WT, metabolites displayed a clear difference in the level of accumulation between the V stage and 15DAS.
Therefore, only the leaf metabolite content at the V stage was used for further comparison of the WT and transgenic lines and for the corresponding statistical analyses. Metabolic and molecular traits were measured in the leaves of plants harvested at the vegetative (V) stage and 15 days after silking (15DAS). The four colored dots for each transgenic line correspond to the four independent transformation events selected for the metabolomic study. The values of the different metabolites were projected on to two biplots of principal components arranged in After t-test statistical analysis (p ≤ 0. 05) followed by a FDR test and correction, only 51 metabolites were found to be significantly different between the WT and the four different transgenic lines at the V stage (Supplemental data set 3).
Correlations between Leaf Metabolites, Shoot Biomass and Yield-Related Traits In order to identify possible functional relationships between leaf metabolite accumulation at the V stage and shoot biomass and kernel yield-related traits, their Pearson correlation coefficients were calculated using the entire metabolomic dataset obtained with the four transgenic lines and the WT. Pearson correlations were then calculated to establish possible relationships between the leaf metabolite content, shoot dry weight and yield-related traits (GY, KN and TKW). They are shown in Supplemental dataset 4 (Pearson correlations) together with their p-value (≤0. 05). Hierarchical clustering of the Pearson correlations allowed the generation of six modules of co-regulated metabolite accumulation (Figure  S3 ).
In the subsequent analyses, each of these six modules were represented by a different colour (red, blue, orange, turquoise, yellow and pink). For example, one of the main characteristics of the yellow module that exhibited a correlation with the agronomic traits was the presence of carbohydrates and a few organic acids. The blue module contained mostly amino acids and a number of C-containing molecules. The orange module was mainly composed of organic acids including those belonging to the TCA cycle. (Supplemental data set 5). The correlations between metabolites and between shoot biomass and KY, for which the most interesting correlations were obtained, are visualized in the network diagram presented in Figure  4A ,B respectively. Their values and the corresponding statistical analyses are presented in Supplemental datasets 4 and 5.
The blue module contained mostly amino acids and a number of C-containing molecules. The orange module was mainly composed of organic acids including those belonging to the TCA cycle. (Supplemental data set 5). The correlations between metabolites and between shoot biomass and KY, for which the most interesting correlations were obtained, are visualized in the network diagram presented in Figure  4A ,B respectively. Their values and the corresponding statistical analyses are presented in Supplemental datasets 4 and 5. The most interesting results were the identification of positive of correlations between shoot biomass and a number of carbohydrates, such as arabinose (0. 59), psicose (0. 65) tagatose (0. 65) and xylose (0. 56), as well as secondary metabolites such as quinic acid (0. 63) and shikimic acid (0.
Oven temperature ramp was 60 • C for 1 min then 10 • C/min to 325 • C for 10 min. Helium constant flow was 1. 1 mL/min. Temperatures were the following: injector: 250 • C, transfer line: 290 • C, source: 230 • C and quadripole 150 • C. The quadrupole mass spectrometer was switched on after a 5. 90 min solvent delay time scanning from 50 to 600 u. Absolute retention times were locked to the internal standard d27-myristic acid using the RTL system provided in Agilent's Masshunter software. Retention time locking reduces run-to-run retention time variation. Samples were randomized. A fatty acid methyl ester mix (C8, C9, C10, C12, C14, C16, C18, C20, C22, C24, C26, C28, C30) was injected in the middle of the queue for external RI calibration. Raw Agilent data files were analyzed with AMDIS (https://chemdata.
nist. gov/dokuwiki/doku. php?id=chemdata:amdis). The Agilent Fiehn GC/MS Metabolomics RTL Library (version June 2008) was employed for metabolite identifications. Peak areas determined with the Masshunter Quantitative Analysis (Agilent) in splitless and split 30 modes. Resulting areas were compiled in one single Excel File for comparison. Peak areas were normalized to Ribitol (internal standard) and Dry Weight. Metabolite contents are expressed in arbitrary units (semi-quantitative determination). The metabolite levels were analysed using the online server MetaboAnalyst 4. 0 based on the R package MetaboAnalystR  [55]. Data processing was conducted through quantile normalization followed by a generalized log transformation and mean centering. After data processing values for Sucrose were discarded because of the predominance of the metabolite and because there were no differences between the leaf samples of the WT and of the transgenic plants.
MetaboAnalyst 4. 0 was used to perform a Hierarchical Clustering Analysis (HCA) using the Euclidean distance and the Ward clustering algorithm as parameters for the leaf metabolites detected at the V stage and 15DAS, PCA and t-test between the WT (A188) and the four independent transgenic events selected in each of the four different transgenic lines T02291, T02289, T0312 and T022308. Pearson correlations were made using cor. test function in R. Correlation heatmap was made using the R package pheatmap (https://github. com/raivokolde/pheatmap). Network visualizations of metabolite correlations in relation to yield-related traits and shoot biomass accumulation were calculated and visualized using Cytoscape v. 3. 7. 1  [56].
Statistics For enzyme activity measurements and yield-related traits measurements, the results are presented as mean values for four individual plants with standard errors (SE = SD/ √ (n -1)). Statistical analyses for metabolomic data were performed using the MetaboAnalyst 4. 0  [56]. Statistical analyses for shoot biomass, yield-related traits, and enzyme activities measurements were performed using Prism 5 (Graphpad, CA, USA). Significant differences (p ≤ 0. 05) were considered between the transgenic and WT plants.
Major transportation surveillance protocols have not been specified with cyber security in mind and therefore provide no encryption nor identification. These issues expose air and sea transport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fake surveillance messages to dupe controllers and surveillance systems. There has been growing interest in conducting research on machine learning-based anomaly detection systems that address these new threats. However, significant amounts of data are needed to achieve meaningful results with this type of model. Raw, genuine data can be obtained from existing databases but need to be preprocessed before being fed to a model. Acquiring anomalous data is another challenge: such data is much too scarce for both the Automatic Dependent Surveillance-Broadcast (ADS-B) and the Automatic Identification System (AIS).
Crafting anomalous data by hand, which has been the sole method applied to date, is hardly suitable for broad detection model testing. This paper proposes an approach built upon existing libraries and ideas that offers ML researchers the necessary tools to facilitate the access and processing of genuine data as well as to automatically generate synthetic anomalous surveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of the approach by discussing work in progress that includes the reproduction of related work, creation of relevant datasets and design of advanced anomaly detection models for both domains of application.
There have been cases of fishing boats voluntarily turning off their transponders and/or changing their identity before fishing in prohibited areas while remaining unnoticed. Similarly in ADS-B, Strohmeier et al. [4]  collected recent incidents in the ATC domain. There is therefore a strong need for detection systems that are capable of consistently dismissing false information and preserving confidence in surveillance technologies such as ADS-B and AIS. There has been a growing interest in conducting research on anomaly detection systems that address these new threats  [5]. Some of these systems are machine learning (ML)-based and thus require significant amounts of data to achieve meaningful results. It is indeed critical for ML researchers to have access to reliable and genuine data sources to train their models.
For ADS-B data, the OpenSky Network is one of the most used references; in terms of accessibility and data history, surveillance data can be easily obtained from almost anywhere in the world. This kind of open-source database does not exist for AIS, but the US Coast Guard has made a reasonable amount of data available (https://marinecadastre. gov/ais/). Access to genuine data is key when building anomaly detection models. Another challenge is to properly evaluate the models' detection capabilities, and this requires enough meaningful anomalous data to be obtained. To date, researchers have crafted their test data sets by modifying recorded surveillance communication flows by hand. Inevitably, the resulting anomalies are quite straightforward (e. g. , a sudden altitude drop, or a progressive latitude drift) and hardly measure the efficiency of the detection models against future, more elaborate attacks.
Indeed, while the few reported incidents of tampering with surveillance data have been simple attacks, it is undeniably urgent to foresee and prepare for more elaborate FDIA scenarios that involve replayed surveillance data and realistic trajectory modifications. This paper presents an approach that automatically generates synthetic but realistic anomalous surveillance data to improve the efficiency and effectiveness of ML-based detection systems. Moreover, it builds upon existing libraries and ideas to allow the feeding of ADS-B data from OpenSky to a ML model. The goal is to offer ML researchers the necessary tools to facilitate the access and processing of data and allow them to design the types of attack scenario they require themselves, while leaving the actual generation of synthetic anomalous data to an existing framework.
Related Work We can find an overview of anomaly detection in  [6]  specifically using deep learning. Anomaly detection is often viewed as an unsupervised task and therefore many applications can be found in the literature. For example, anomaly detection applications can be found in smart grids and water distribution systems  [7]  or surveillance videos  [8]. Fully-supervised approaches for anomaly detection usually ignore data without any labeling  [9]. They perform well on datasets with an equal distribution between training and testing sets compared to unsupervised or self-supervised approaches, but fall behind once the testing data introduce new types of outliers  [10]. Overall, the advances in neural networks and deep learning for anomaly detection show that these techniques have great advantages over traditional machine learning, mainly due to their increased performance with large amounts of data.
1. A lack of a defined pattern in which an anomaly occurs; 2. Noise within the input data; 3. As the length of the time series data increases, the computational complexity also increases. These three issues need to be taken into consideration for both the data processing and the choice of model architectures. They apply to both ADS-B and AIS protocols, and some answers to these challenges can already be found in published papers. Concerning the ATC domain, a large number of works on anomaly detection using solely ADS-B data revolve around the use of deep learning techniques and specifically auto-encoders. To tackle the temporal nature of the data, a large amount of efforts are being devoted to the use of the recurrent neural network (RNN) and specifically long short-term memory (LSTM) units (please refer to the survey presented in  [11]  for a more comprehensive overview of advances in anomaly detection in the ATC domain).
Data Gathering In order to create our large-scale dataset of ADS-B raw data, we used the history database from the online flight tracking network OpenSky  [17]. These data are collected by cooperative ground stations, and the network is mainly maintained by enthusiastic private individuals. As a result, the quality of the data often depends on the performance of the acquisition material used by the seeders. State vector data are the data from the OpenSky betwork with verifications made and with erroneous messages already filtered out. These data did not really fit our requirement of data that arise directly from sensors with no filters to train models with no bias introduced by uncontrolled data cleaning; nonetheless, some cleaning steps were necessary and are detailed in Section 3. 2. Traffic  [18]  is an open-source Python-based tool allowing users to query the OpenSky network database.
Automatic Identification System (AIS) Data Architecture The Automatic Identification System (AIS), as a tracking system, supports maritime traffic surveillance and control. Having AIS activated is mandatory for all vessels with 300 GT (gross tonnage) and above, for all ships which are engaged in international journeys and for all passenger ships. For all other vessels, cheaper and less powerful transmitters are suggested but not required. An AIS transmitter broadcasts vessel information, such as its location, speed or heading. Not all AIS messages contain the same information, and static information, such as the vessel type, the vessel name or its size, is transmitted every 6 min. The nearby ships and coastal stations can receive the messages in a range of about 20 to 40 km. This range limitation is due to the Earth's curvature and the height at which the antenna is installed on ships.
Data Collection Creating a large-scale dataset of raw data, such as in ADS-B, is a challenge in AIS. The data are mostly collected by the coastal base stations and satellites operated by companies and coastal guards. As they are considered critical information, states are often reluctant to open this data freely. When the AIS data are available, they have been preprocessed and cleaned. However, to create a model that can be generalized to every part of the world, it is necessary to use raw data. We can cite two organizations that work to make AIS data available: the first is "Global Fishing Watch", which tries to contact governments directly to access their data, but the raw data they use in their research is not public; the second one is "AIS hub", which aims to become a raw AIS data sharing center where any parties can get memberships to share the data they collect on their own AIS receiving station.
Although the project is interesting, the coastal coverage is still limited, and data from satellites are necessary to cover the non-coastal areas. In our project, we obtain raw data from Statsat, a company that owns satellites. These data are collected by four satellites, but they do not cover the whole Earth simultaneously. Moreover, the AIS protocol's limited capacity causes a great deal of message loss due to collisions in dense areas such as the EU, China, or the US coast. To avoid this problem, we integrate data from ground stations from Norwegian coastal guards into this dataset. The final dataset consists of over 300 million AIS messages.
Preprocessing The data provided by Statsat use the Comment Block (CB) extension of the IEC 61162 standard to exchange AIS data together with additional information such as the complete timestamp, configuration and status messages. The data were decoded and stored inside a NoSQL database. The database was then completed with the coastal guard data. Furthermore, information such as the distance to the satellite and distance to the nearest port was added. Finally, static information was added such as the vessel type, power engine, length and tonnage obtained from EU Fleet Registration. The datasets used to train the models were extracted from the database based on their timestamps, positions and message types. Incoherent, duplicated and incomplete messages were removed. We then used different algorithms to extract relevant sequences of messages-for example, the last 100 messages of a vessel before losing connection-or random sequences passed to the anomaly generator.
This component collects legitimate surveillance messages (in Beast or SBS (site base station) formats for ADS-B, and in the NMEA 0183 format for AIS). obtained either from the Internet or a Mode S receiver. Data take the form of a recording-i. e. , a sequence of surveillance messages-ordered by reception time. 2 FDIA scenario design. The domain expert defines FDIA scenarios to be applied on a recording obtained via the data acquisition component. FDIA scenarios have various parameters, such as a time window, a list of targeted aircraft, triggering conditions, etc. Once designed, FDIA scenarios are translated into a set of alteration directives, which is the output of the component (an alteration directive is a small modification of the initial recording, usually doable by hand). The scenario design is specified via a domain-specific language (DSL), which is further specified depending on the targeted domain (ATC or VTS).
FDI-T for the Training and Testing of Anomaly Detection Models Although FDI-T was initially designed for surveillance systems testing, it also enables the generation of datasets for training and testing ML-based anomaly detection systems. We describe below the four features that were added to the existing testing framework in order to turn it into an efficient synthetic data generation tool that is capable of generating hundreds of thousands of data samples with very little effort. Labelling. A positive side-effect of generating synthetic anomalies is the ability to obtain well-balanced datasets, opening the possibility of supervised learning models. Storing alteration information (which messages were tempered with, what properties were altered) is achieved by directly appending an alteration bitmask at the end of each surveillance message.
Massification capabilities. While test engineers usually aim for a sharp test suite that satisfies all test requirements with as few test cases as possible, data scientists need a significant amount of data in order to properly train and test models. FDI-T in its current state allows users to create one altered recording at a time, and creating a dataset is realistically unfeasible in these conditions. Thus, we added massification capabilities to the DSL: users may first define variables containing lists of values, and thereafter reference the variables inside a scenario instead of a value. The framework creates as many single valued scenarios as there are values in the variable. In the case of a scenario with several references to variables, a single valued scenario is created for each combination of variables values.
ADS-B Datasets and Use-Cases In Section 3, we presented the full architecture to obtain the data necessary to train and evaluate ML-based anomaly detection models. Using this architecture, the training dataset, along with the testing dataset including the original anomalies given by Habler et al. in  [5] , has been recreated automatically, avoiding the cumbersome task of modifying all the messages by hand For the sake of completeness, we added a trajectory alteration which modifies the latitude and the longitude of a flight, an anomaly that Habler et al. could not have obtained by hand. This eventually highlighted the shortcomings of the authors' validation methodology since their model could not detect the altered trajectory  [16]. From this recreated dataset coupled with the opportunity to gather larger amounts of data, the possibility of training a variational auto-encoder  [22]  (VAE) emerged.
While an instance of such a scenario could be handmade, it would take a tremendous amount of time to constitute a dataset. Lastly, we identified two additional use-cases, focusing more on the ability of FDI-T to modify messages coming from a particular sensor: sensor disconnection and sensor drift/offset. Instead of targeting a specific aircraft, a sensor disconnection anomaly specifically targets a sensor or sensors to create a service discontinuity that could impact the OpenSky network or any other data gatherer. Sensor drift/offset consists of the creation of a drift or an offset on all messages sent by a particular sensor. This tampers with the data quality sent by a particular sensor by simulating reception or decoding errors. As a result, in the long run, it could modify the trust factor that the OpenSky network has in a seeder.
As the main goal of this paper is to determine the reproducibility and availability of the tools, it is also worth mentioning that most of the work conducted for the ADS-B raw data gathering, cleaning and processing into training data is routinely published in Github (https://github. com/Wirden/scifly). There is also a Kaggle (https://www. kaggle. com/) dataset underway to ease the sharing with the ML community. The data acquisition chain is already linked to the Traffic library and the OpenSky network for quick experimentation. As for FDI-T, it is available for experimentation and beta-testing upon request to the authors.
AIS Datasets and Associated Learning Objectives The creation of datasets in the maritime domain is also underway, although it is still in the early phases. We are working on supervised learning techniques using FDI-T to create false data combined with a self-supervised method that uses the data history to generate new datasets. The frequency of AIS messages, the content of the messages, and the historical size of a trajectory are irregular. We decided not to sample or regularize the messages and directly used the raw messages as input. In our opinion, the missing messages also contain information that should be given to the model. Concretely, six datasets are currently in the process of creation. They are described below with their related detection objectives.
• With self-supervised techniques: AIS intentional shutdown detection: The shutdown of AIS allows a fisher to hide illegal fishing quite easily. The dataset is composed of a model that predicts if a new message should be received soon, or if the connection will be lost for diverse reasons. Rendez-vous prediction: A dataset containing instances of two vessels that eventually approach and stay close for a certain period of time-a so-called rendez-vous situation. The model predicts, given a path of two vessels, if they will meet in the future. Vessel type identification:A very broad dataset, as the model is trained to determine the type of a vessel given its path. • With FDI-T: Trajectory modification: A dataset that contains random trajectory modifications, speed modifications and/or vessel type modifications.