dataesr/scientific-paragraphs-categorization huggingface dataset:
g. ,  Beaumont, 2010). Although much effort has gone into dimensionality reduction and feature selection for ABC (reviewed in  Blum et al. , 2013; Estoup et al. , 2012) , reducing dimensionality might lead to loss of information if the remaining summaries fail to capture enough information from the data. In this context, supervised machine learning (SML) methods provide attractive solutions for statistical inference. SML methods allow predicting new data points through the use of a training set of labeled simulated data examples, for which true response values are known. This data structure is reminiscent of the ABC reference table. The ability of SML methods to use simulation as a stand-in for observed data is crucial for population genetics applications, where adequately sized data sets with high-confidence labels are currently hard to obtain.
g. , for scenario choice) or regression (e. g. , for estimation of continuous parameters). Pudlo et al. (2016)  recently developed RF algorithms to perform scenario choice from simulated data sets summarized through a large set of statistics, as typically considered in ABC, hence leading to the so-called ABC-RF approach. As compared to classical ABC methods, the ABC-RF approach enables efficient discrimination among scenarios and estimation of the posterior probability of the best scenario, with a lower computational burden. More specifically, ABC-RF and other ABC methods provide consistent results for analyses based on a large number of simulated data sets, but ABC-RF outperforms other ABC methods for analyses of multiple complex scenarios based on a smaller (hence more manageable) number of simulated data sets  (Fraimout et al.
e. , summary statistics as in ABC), (iii) the training of the algorithm, applying it on new (observed) data point(s), and (iv) assessing its performance in term of prediction through the computation of error and accuracy measurements. Any effort to create self-contained, efficient, and user-friendly software packages capable of performing this entire workflow would streamline SML methods and make them more accessible to researchers, including nonspecialist users. To that end, we have implemented in a new computer package a set of RF algorithms to infer population' histories from genetic polymorphisms, building upon an extended version of the population genetics simulator implemented in DIYABC 2. 1. 0 (Cornuet et al. , 2014). The data correspond to various types of genetic markers: microsatellites, DNA sequences and SNPs, including individual-sequencing and poolsequencing SNP data  (Gautier et al.
, 2013; Schlötterer et al. , 2014). A large set of summary statistics has also been implemented to improve the extraction of genetic information from SNP data sets. The resulting package, named DIYABC Random Forest v1. 0, integrates two functionalities in a user-friendly interface: the simulation under custom evolutionary scenarios of polymorphism data (summarized into a large set of descriptive statistics) and RF treatments including various statistical tools to evaluate the power and accuracy of RF-based inferences. Here we describe the main statistical features of DIYABC Random Forest v1. 0 and illustrate its potentialities and functionalities for both scenario choice and parameter estimation through the analyses of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data.
e. , whose response values are known). The training set includes values of a feature vector which is a multidimensional representation of any data point made up of measurements (or features) taken from it. That is, one assumes to have a set of training data of length m of the form {(x 1 , y 1 ),…,(x m , y m )}, where x Є R M. A variety of learning algorithms exist which can generate functions that can perform either classification or regression (reviewed in e. g. ,  Schrider & Kern, 2018). In our inferential framework, SML methods learn from simulations which come from one or several generative model(s) (i. e. , scenario[s]). A relevant way to obtain benefits from generative scenario simulations is the Bayesian paradigm and therefore the ABC type approach  (Beaumont et al. , 2002). Here, the training set is equivalent to the ABC reference table, which includes a given number of data sets that have been simulated for different scenarios using parameter values drawn from prior distributions, each data set being summarized with a set of descriptive statistics.
| Simulation of the training set Before performing RF analyses, one needs to generate a training set. The data sets composing the training set can be simulated under different scenarios and sample configurations, using parameter values drawn from prior distributions. Each resulting data set is summarized using a set of descriptive statistics. We formalized scenarios and prior distributions, and computed summary statistics using the "training set simulation" module of the main pipeline of DIYABC Random Forest v1. 0, which essentially corresponds to an extended version of the population genetics simulator implemented in DIYABC v2. 1. 0 (Cornuet et al. , 2014). As in the latter program, DIYABC Random Forest v1. 0 allows consideration of complex population histories including any combination of population divergence events, symmetrical or asymmetrical admixture events (but not any continuous gene flow between populations) and changes in past population size, with population samples potentially collected at different times.
DIYABC Random Forest v1. 0 accepts various types of molecular data (microsatellites, DNA sequences, and SNPs) evolving under various mutation models and located on various chromosome types (autosomal, X or Y chromosomes, and mitochondrial DNA) for diploid or haploid individuals. To simulate polymorphic data sets at a given SNP locus, we follow the algorithm proposed by  Hudson (1993)  -cf. -s 1 option in the program ms associated to  Hudson (2002). In DIYABC Random Forest v1. 0, it is possible to impose a MAF (minimum allele frequency) criterion on both the observed and simulated data sets. For details, see the user manual of DIYABC Random Forest v1. 0 (https://diyabc. github. io/doc/). In addition to individual-sequencing SNP data (hereafter IndSeq data), DIYABC Random Forest v1. 0 allows the simulation and analyses of pool-sequencing SNP data (hereafter PoolSeq data), which consist of whole-genome sequences of pools of tens to hundreds of individual DNAs  (Gautier et al.
In the case of raw PoolSeq data sets, the total coverage within each pool of each SNP is sampled from a Poisson distribution with a mean corresponding to an arbitrary coverage value (e. g. , 100X) fixed by the DIYABC Random Forest v1. 0 user. It is worth noting that, in contrast to any other types of markers treated in DIYABC Random Forest v1. 0 (including IndSeq SNPs), PoolSeq SNP data are considered as located on autosomal chromosomes only. A criterion somewhat similar to the MAF was implemented for PoolSeq data: the minimum read count (MRC) which is the minimum number of sequence reads for each alleles of a SNP when pooling the reads overall population samples. For details, see the user manual of DIYABC Random Forest v1. 0 (https://diyabc. github. io/doc/).
(2014). For additional details, see the user manual of DIYABC Random Forest v1. 0 (https://diyabc. github. io/doc/). An illustration of the feature vector composed of all above summary statistics is given in Table  S1  for the analysis of two example SNP pseudo-observed data sets. For scenario choice, the feature vector can be expanded by values of the d axes of a linear discriminant analysis (LDA) processed on the above summary statistics (with d equal to the number of scenarios minus 1;  Pudlo et al. , 2016). In the same spirit, for parameter estimation, the feature vector can be completed by values of a subset of the s axes of a partial least squares regression analysis (PLS) also processed on the above summary statistics (with s equal to the number of summary statistics). The number of PLS axes added to the feature vector is determined as the number of PLS axes providing a given fraction of the maximum amount of variance explained by all PLS axes (i.
| Main technical features of the package DIYABC Random Forest v1. 0 The package DIYABC Random Forest v1. 0 is composed of three parts: the data set simulator, the Random Forest inference engine and the graphical user interface. The whole is packaged as a standalone and user-friendly application available at https://diyabc. github. io. The main technical features of the package (implementation, interface, outputs, memory space and computing time) are described in Appendix S1.
| Pseudo-observed data sets Our prediction targets correspond to four pseudo-observed data sets that were generated using the "Synthetic data file generation" module of DIYABC Random Forest v1. 0 under the (admixed) scenario 3 or the (nonadmixed) scenario 6 using the following parameter values: N 1 = 7,000, N 2 = 2,000, N 3 = 4,000, N 4 = 3,000, t 1 = 200,.
| Scenario choice We processed scenario choice analyses grouping scenarios based on the presence or absence of an admixed origin of population 4, and then considered all six scenarios separately. The training sets which included a total of 12,000 simulated data sets (i. e. , 2,000 per scenario) were generated using the "Training set simulation" module of DIYABC Random Forest v1. 0, drawing parameter values into the prior distributions described above and summarizing SNP data using 130 statistics (see Table  S1 ) plus one LDA axis or five LDA axes (i. e. , the number of scenarios minus 1; see  Pudlo et al. , 2016)  computed when comparing the two groups of scenarios or individual scenarios, respectively. We then used the "Random Forest analyses" module of DIYABC Random Forest v1. 0 to process RF treatments on the training sets.
ACK N OWLED G EM ENTS This work was supported by funds from the  French Agence National pour la Recherche  (projects SWING  ANR-16-CE02-0015-01 ,  GANDHI ANR-20-CE02-0018  and ABSint  ANR-18-CE40-0034 ), and the  LabEx NUMEV (NUMEV ,  ANR10-LABX-20 ). We thank  Pierre Pudlo  for useful discussions, and  Jean-Marie Cornuet  and  Alex Dehne Garcia  for computer code expertise at the onset of the  ABC Random Forest project.
Funding information INRAE scientific division  SPE, Grant/ Award Number :  AAP-SPE 2016 ;  LabEx NUMEV , Grant/Award Number: NUMEV and  ANR10-LABX-20 ;  French Agence National pour la Recherche (ANR) , Grant/ Award Number:  SWING ANR-16-CE02-0015-01 ,  GANDHI ANR-20-CE02-0018  and ABSint  ANR-18-CE40-0034.
DATA AVA I L A B I L I T Y S TAT E M E N T For the pseudo-observed and real PoolSeq and IndSeq data sets used as examples in this paper, we provide the corresponding pseudo-observed data sets (read numbers or genotype data and summary statistics: i. e. , <file_name. snp>and statobsRF. txt), the headerRF files (headerRF. txt) and the training set files (reftableRF. bin) at https://github. com/diyab c/MER_publi cation_mater ials/tree/ main/MER_2021_DATAS ET_EXAMPLES.
Unmanned aerial systems (UAVs) are dramatically evolving and promoting several civil applications. However, they are still prone to many security issues that threaten public safety. Security becomes even more challenging when they are connected to the Internet as their data stream is exposed to attacks. Unmanned traffic management (UTM) represents one of the most important topics for small unmanned aerial systems for beyond-line-of-sight operations in controlled lowaltitude airspace. However, without securing the flight path exchanges between drones and ground stations or control centers, serious security threats may lead to disastrous situations. For example, a predefined flight path could be easily altered to make the drone perform illegal operations. Motivated by these facts, this paper discusses the security issues for UTM's components and addresses the security requirements for such systems.
Moreover, we propose UTM-Chain, a lightweight blockchainbased security solution using hyperledger fabric for UTM of low-altitude UAVs which fits the computational and storage resources limitations of UAVs. Moreover, UTM-Chain provides secure and unalterable traffic data between the UAVs and their ground control stations. The performance of the proposed system related to transaction latency and resource utilization is analyzed by using cAdvisor. Finally, the analysis of security aspects demonstrates that the proposed UTM-Chain scheme is feasible and extensible for the secure sharing of UAV data.
Problem Statement and Contribution Although the adoption of IoD in the context of UTM provides flexibility to manage the traffic of autonomous UAVs through the Internet, this leads to many challenges such as increasing the risk of vulnerabilities that may lead to cyberattacks from different adversaries including Global Navigation Satellite System (GNSS) spoofing attacks and data manipulation. Correspondingly, serious consequences such as the increased risk of collision, incorrect advisories, and undesired or unknown flight paths can occur. On the other hand, the UAV needs to communicate with the ground control station (GCS) to achieve an efficient path that satisfies predefined missions. However, when the communication is not secured, a third party can inject incorrect messages to falsify trajectory.
These attacks affect the confidentiality, integrity, and availability of the UTM services. Hence, it is critical to understand and address these security concerns. Ensuring that drone flight data is secure and unalterable will be essential as the industry matures, and further instructions and regulations are put in place by the government  [26] [27] [28]. Moreover, security solutions are needed to protect the UAV flight path without affecting their performance. In general, the flight path can be guaranteed by applying cryptographic mechanisms  [29]. However, low-cost UAVs are resource-constrained in terms of computation, energy, memory, storage, and processing capacity preventing them from implementing advanced and complex cryptosecurity solutions on-board  [30]. Recently, blockchain (BC) technology has evolved in cryptocurrency applications such as Bitcoin  [31]  and it can provide viable solutions to improve security and privacy levels thanks to its decentralized nature and traceability aspect  [23, 32].
Security Issues in Unmanned Traffic Management (UTM) UTM is a drone traffic management system, automated and located in the cloud with the mission of organizing the flight of drones registered with the FAA. The future UTM system will rely on an increase in interconnected systems based on novel technologies (IoD). However, security and vulnerability to attacks are some of the major challenges to the adoption of the UAV traffic management systems, especially when autonomous drones operate to provide near-real-time data to a GCS for timely decisions. In what follows, we address UTM security issues. We identify and classify security threats and challenges facing Unmanned traffic management systems, including those resulting from UTM components vulnerabilities, namely, UAV, GCS, cyber systems (cloud and the Internet), and data communication links between all the components as depicted in Table  2.
Cyber Attacks • Hijacking: is the major researched area in UAV cybersecurity. The third-party can send unauthorized commands to control drones when drones are properly protected or their communication is not secured. Correspondingly, it can cause serious consequences such as failed missions. • GPS spoofing attacks: GPS is used for pathfinding in transportation and enables a drone's navigation. A drone that uses GPS is vulnerable to spoofing attacks due to the lack of encryption. In a GPS spoofing attack, fake GPS signals stronger than the original unencrypted GPS signals can be transmitted into the drone to mislead the UAV's path. GPS spoofed signals are providing false altitude and longitude data to the drone, thus letting it change its trajectory. This attack could result in a loss of accurate positional information, which may even threaten the drone and the safety of nearby occupants in the UTM.
In attacks under denial of service categories, such as jamming, the adversary aims to disrupt the communication link between the UAV and other entities in its network through interference or collision before the reception. Particularly, the jammer within a specific radius at the frequency of the drones generates interference within radio channels. As a result, a receiver such as a ground station or a drone cannot properly receive signals transmitted by an unauthorized sender that can cause unavailable services. • Traffic analysis attack: Traffic analysis attack is a passive attack, which is performed by a third party to examine the UTM traffic to get useful information from the UTM components and network. The traffic contains sensitive data exchanged between UTM nodes like mission plan, location, and telemetry data.
• Man in the middle: The M-I-T-M attack can be successfully established on telemetry and Command-and-Control (C2) data links. The attacker can intercept the exchanged messages between UAV and GCS. Particularly, the exchanged packets can be captured by attackers who then can extract the important information, relaying the packet with modified data. This makes both the drone and the ground control believe that they are communicating with each other successfully without any interception.
Attacks on Cyber Systems (Cloud and Internet) Cybersystems such as the Internet and the cloud are used in the UTM system to coordinate the access and the flight of multiple UAVs. Cloud computing resources are used to offload heavy computations and support the processing and storage of streams of data originated from UAVs  [46]. In general, a regular relational database such as SQL or distributed file systems can be used for storing data and information. When the database is compromised, the security of the UTM is significantly affected.
Proposed System Overview Blockchain can be described as a shared and distributed ledger of transactions  [53]. Blockchain works based on the consensus mechanism that is a fault-tolerant mechanism to reach an agreement on the value of some shared data between participants. When new data is added into the blockchain, a record of added data is added to every participant's ledger and the data cannot be edited or erased. Blockchain allows UAVs, GCSs, and users to communicate with each other securely. The proposed UTM-Chain is a blockchain-based solution for the Internet of Drones that permits to secure UAV flight plan and store UAV's flight data records. The features of UTM-Chain are summarized as follows: • Decentralization: UAVs, GCS, and users can communicate and exchange data directly through a secure and trusted blockchain without any third-party entity involvement.
The Proposed UTM-Chain Architecture Description In this section, we present the architecture of UTM-Chain for blockchain-enabled IoD. The architecture is shown in Figure  1  and consists of five main components including multiple UAV (drones), ground control stations, users, cloud server, and the blockchain network. Blockchain Network: The main role of the blockchain network is to maintain a distributed immutable database of actions performed by users, data collected from drones, and commands from GCS. The recorded transactions are shared between nodes in the network. In our architecture, the users, UAVs, and GCSs act as nodes, storing the whole blockchain and participating in the consensus protocol to verify blocks. • Cloud server: is responsible for offloading computation from the drone to overcome the computing resources' limitations of the UAVs and optimize the mission execution.
Correspondingly, the flying time of the drones can be extended. We propose to use OrbitDB with the Inter-Planetary File System (IPFS) as an off-chain database. The off-chain database is used in order to store UAV data (mission information, flight status details, sensor data) that is too large to be stored in the blockchain efficiently. In fact, OrbitDB is a distributed peer-to-peer database performing with IPFS. The latter generates the hash of the data saved into OrbitDB and stores it as an immutable transaction in the blockchain.
Once the mission request is received, the cloud server will select an available UAV that is already registered to execute the mission. The set of waypoints that the drone will visit are stored in the blockchain. Throughout the in-flight phase and during the mission, the drones constantly update their GCS about their GPS location, flight plan data such as route and destination address, mission details, and telemetry data including sensor data and images/videos. Then, the GCS forward this data to the cloud server, which, in turn, encrypts the collected data and store it in an off-chain database. The latter is a distributed database using the filesharing protocol IPFS. When storing the data, IPFS generates the hash of each stored block. Finally, in the post-flight phase, to be sure that the flight mission is terminated as planned, the cloud server compares the waypoints defined by the user in the preflight phase with the GPS location stored in the off-chain database in the in-flight phase.
If the waypoint matches with the GPS location, the cloud informs the user about the completion of the UAV's flight mission. Otherwise, in case of an unexpected flight termination, the user sends a request to the cloud to get historical flight data records in order to analyze data records to find the root cause of the problem. The latter sends a reading transaction proposal to the blockchain. In the blockchain network, endorsing peers run the chain code in order to validate the user access right. After validating the access, requested data is retrieved from the off-chain database. In fact, the response is sent to the cloud server and the reading transaction is committed into the blockchain. Finally, the cloud server communicates the requested UAV's flight data to the user.
Development Environment We use Hyperledger Fabric  [55] , Hyperledger Composer  [56] , Docker Composer, Docker Engine, and BNC  [57]  as a command-line tool to implement the UTM-Chain architecture. The proposed system's configuration is depicted in Table  3. The blockchain framework is running on an Ubuntu Linux 14. 04 TLS on Intel Core i-7-8500 @2. 9 GHz processor with 5. 4-gigabyte memory with the Hyperledger Composer, composer-restserver, and node. In addition, docker engine is used to configure the docker image and container. Docker composer is used to configure the composer-rest-server. Moreover, we use Hyperledger Fabric, an open-source platform, to implement blockchain applications and composer-playground to create and built the business network definition. To expose the web services, composer-rest-server is used to create the REST API for the entities in the blockchain platform.
A simulated drone executed with the Ardupilot Software-In-The-Loop (SITL) simulator  [58]  was used in our experiments. The simulator uses the same autopilot used in the real drone to run a Copter. In the experiments, the QGroundControl  [59]  ground station, which is an open source ground control station (GCS) software application, was used. We implement the search and rescue case study using the Ardupilot Software-In-The-Loop (SITL)  [58]  in which UAV flight path is secured and access to UAVs data is controlled through a smart contract. The cloud server is responsible for encrypting UAV data over Secure Socket Layer (SSL) and sending encrypted data to a distributed Off-chain database which is OrbitDB with IPFS. Indeed, we use IPFS to store UAV data while the hashes generated for stored data are kept on the blockchain network.