dataesr/scientific-paragraphs-categorization huggingface dataset:
The physical layer signaling conveys the transport format indication (TFI), particularly information associated with the spreading factor and data burst length used in the modulation process. Each channel is scrambled with an orthogonal variable spreading factor (OVSF) code, and a final scrambling is performed using Gold codes. Different scrambling codes can be used in the system, but, in general, a single scrambling code for each satellite beam is used. The preamble is composed of a sequence of 96 symbols, spread by a pseudorandom noise (PN) code of the period and spreading factor equal to the spreading factor used for the current packet. With the F-SIM physical layer being very close to the terrestrial 3GPP W-CDMA return link standard, there are also benefits from its suitability to be embedded in low-cost handheld user terminals.
Different scrambling codes can be used in the system, but, in general, a single scrambling code for each satellite beam is used. The preamble is composed of a sequence of 96 symbols, spread by a pseudorandom noise (PN) code of the period and spreading factor equal to the spreading factor used for the current packet. With the F-SIM physical layer being very close to the terrestrial 3GPP W-CDMA return link standard, there are also benefits from its suitability to be embedded in low-cost handheld user terminals. One of the W-CDMA uplink features is to have a moderate peakto-average envelope fluctuation (see Figure  3  in  [16] ), as it is based on a power unbalanced dual-BPSK modulation, resulting in an asymmetric 8PSK constellation (see Figure  2  in  [16] ). This, combined with the use of low-code rate FEC, makes F-SIM suitable for driving the user terminal solid-state power amplifier (SSPA) in a moderately compressed mode (i.
spreading factor of SF = 256, the burst duration varies from 32 to 240 ms (300 byte payload), and the minimum C/N for reception is -27. 3 dB (at PER = 10 -4 ). Similar to S-MIM, F-SIM uses dual-BPSK modulation with the FEC coding rate 1/3, as shown in Figure  2. The Turbo code from 3GPP Release 99 specifications has been adopted  [13]. The uplink burst composition is depicted in Figure  3  and further described hereafter. The PDCH carries the random access channel (RACH) data burst followed by a CRC. The PCCH carries physical layer signaling and reference symbols to allow coherent demodulation of the PDCH channel. The physical layer signaling conveys the transport format indication (TFI), particularly information associated with the spreading factor and data burst length used in the modulation process.
Each channel is scrambled with an orthogonal variable spreading factor (OVSF) code, and a final scrambling is performed using Gold codes. Different scrambling codes can be used in the system, but, in general, a single scrambling code for each satellite beam is used. The preamble is composed of a sequence of 96 symbols, spread by a pseudorandom noise (PN) code of the period and spreading factor equal to the spreading factor used for the current packet. With the F-SIM physical layer being very close to the terrestrial 3GPP W-CDMA return link standard, there are also benefits from its suitability to be embedded in low-cost handheld user terminals. One of the W-CDMA uplink features is to have a moderate peakto-average envelope fluctuation (see Figure  3  in  [16] ), as it is based on a power unbalanced dual-BPSK modulation, resulting in an asymmetric 8PSK constellation (see Figure  2  in  [16]  ).
F-SIM Link Layer The F-SIM link layer specifies the state machine of the modem. When there are no data to transmit, the terminal transmit chain is completely off, thus saving on power consumption. The terminal logs into the network with a single transmission (logon request) which is valid for hours. The hub continuously transmits signaling information, which is shared by all terminals, in order to support a large number of terminals. F-SIM is designed to transport native Internet Protocol (IP) packets. The link layer on the terminal side encapsulates each IP packet into one or more fragments according to its length. Each fragment is sent separately, with a minimal encapsulation. On the hub side, fragments are reassembled after demodulation, and the resulting IP packet is routed according to normal IP routing policies.
g. , Eutelsat Ka-Sat), each GW serves a subset of the HTS beams. The size of the beam cluster served by each gateway is related to the feeder link bandwidth. In this case, a number of FL/RL HyperCube GWs have to be geographically distributed. Such remote FL/RL GWs have been connected to the central hub through an IP-based backbone thanks to the modular HyperCube architecture. The RL side of the GW is based on a software-defined radio (SDR) architecture (see Figure  13 ): it adopts an off-the-shelf SDR device to perform down-conversion to the baseband, and to convert, through analogue-to-digital, the aggregated signal. Then, the I-Q digital samples are delivered through a 10GBE connection to the demodulation chain composed of a number of demodulation nodes connected in a cascade configuration.
Each node is built using a rack-mountable server equipped with a central processing unit (CPU) and graphical processor unit (GPU) that share the computational processing. All the digital signal processing (DSP) algorithms required to demodulate F-SIM bursts are executed in the software by running the C++ object code hosted on each demodulation node. DSP algorithms are based on the Intel Math Kernel Library and the NVIDIA Compute Unified Device Architecture (CUDA), respectively, for CPUs and GPUs. This SDR approach has changed the way gateway demodulator processing is developed, once only based on the traditional field-programmable gate array (FPGA) approach. In particular, this approach allows increasing the performance by adopting the newest CPUs/GPUs released on the market by major manufacturers, with marginal costs for the porting of the code.
After about ten years of continuous operations, the viability of the SDR approach for the ground segment of the random access E-SSA system has reached full maturity. The HyperCube platform elements shown in Figure  11  have been integrated in a single 19" rack hosted in the satellite GW (see Figure  12 ). In the case of a multi-beam high-throughput satellite (HTS) (e. g. , Eutelsat Ka-Sat), each GW serves a subset of the HTS beams. The size of the beam cluster served by each gateway is related to the feeder link bandwidth. In this case, a number of FL/RL HyperCube GWs have to be geographically distributed. Such remote FL/RL GWs have been connected to the central hub through an IP-based backbone thanks to the modular HyperCube architecture. The RL side of the GW is based on a software-defined radio (SDR) architecture (see Figure  13 ): it adopts an off-the-shelf SDR device to perform down-conversion to the baseband, and to convert, through analogue-to-digital, the aggregated signal.
Then, the I-Q digital samples are delivered through a 10GBE connection to the demodulation chain composed of a number of demodulation nodes connected in a cascade configuration. Each node is built using a rack-mountable server equipped with a central processing unit (CPU) and graphical processor unit (GPU) that share the computational processing. All the digital signal processing (DSP) algorithms required to demodulate F-SIM bursts are executed in the software by running the C++ object code hosted on each demodulation node. DSP algorithms are based on the Intel Math Kernel Library and the NVIDIA Compute Unified Device Architecture (CUDA), respectively, for CPUs and GPUs. This SDR approach has changed the way gateway demodulator processing is developed, once only based on the traditional field-programmable gate array (FPGA) approach.
In particular, this approach allows increasing the performance by adopting the newest CPUs/GPUs released on the market by major manufacturers, with marginal costs for the porting of the code. After about ten years of continuous operations, the viability of the SDR approach for the ground segment of the random access E-SSA system has reached full maturity. One of the main advantages of the adopted software approach is the simplicity of the porting of the software on different hardware platforms and the ability to easily profit from the advances made by new multipurpose GPUs continuously improved to serve larger markets (e. g. , artificial intelligence (AI), 3D gaming, blockchain processing, video encoding and other computationally intensive applications). This design approach ensures that the latest state-of-the-art COTS equipment can be quickly adopted for the commercial demodulator.
The GEMMA Project The ESA ARTES AT GEMMA project (MBI S. r. L. and AIRBUS Italia S. p. A. (ADSR)). S-MIM and F-SIM rely on DVB-SH and DVB-S2 standards, respectively, on the forward link (FL). Fulfilling the need for an adaptation of the FL air specifications to also operate mobile IoT terminals in different scenarios is the goal of this project. In particular, the main requirements are as follows: (a) The support of both GEO and LEO scenarios, keeping the user terminal inexpensive and easy to operate; (b) The support of both fixed and mobile terminal applications; (c) The support of different types of applications (point-to-point, multicast and broadcast) and data rates also enabling those services relying on data transfer to terminals (e. g. , firmware upgrade); (d) The possibility to implement different Tx/Rx activity modes that could help in reducing the power consumption of the terminal; the possibility to implement loop functionalities (e.
The synergy between these two projects also led to the definition of an air interface to be adopted in GEO Ku and Ka band scenarios, with terminals equipped with low-gain flat (e. g. , patch array) antennas. Such air interface, named IURA (IoT Universal Radio Access), is based on E-SSA on the return link, which is able to work at very low carrier-to-noise power ratios (i. e. , C/N below -20 dB) thanks to the processing gain provided by the large spreading values (up to 256), and an evolved GEMMA waveform on the forward link, in order to deal with values of C/N below -15 dB. These extended C/N ranges enable the possibility to equip the terminals with small low-gain antennas, e. g. , an 8 × 8 patch array, and still be able to operate in typical GEO Ku and Ka band scenarios. This, combined with the offered data rates ranging from some kilobits per second to some tens of kilobits per second with larger flat antennas and the possibility to manage a very high number of devices which sporadically transmit few data bursts, makes it suitable to develop a simple and low-cost terminal for IoT and medium-data rate scenarios.
The IoT-SATBACK and 5G-SENSOR@SEA Projects An example of studies of upper layer enhancements and optimizations is represented by the ESA project IOT-SATBACK  [27]  (MBI S. r. L. Pisa, Italy and Software Radio Systems Ltd. , Cork, UK), whose primary objective is to design, develop, test and demonstrate a testbed capable of providing satellite backhauling services for future NB-IoT. The targeted improvement is to enable new satellite communication services for backhauling M2M and Internet of Things communications, and this is achieved by means of a component named the IP Optimizer which implements optimizations of F-SIM layer 2 and upper layers. The optimization includes techniques for the reduction in the overheads and the IP payload compression which contributes to increasing the backhauling spectral efficiency.
The codebase for E-SSA signal demodulation is developed based on an inherited code from the available E-SSA demodulator, running on similar NVIDIA cards. Optimization efforts are needed to maximize the performance of the demodulator: • Adapting and optimizing the telecommunication performances by maximizing the achievable throughput while, at the same time, minimizing the DC consumption; • Rewriting part of the software to better cope with the flying environment where radiation occasionally causes software errors (i. e. , bitflips). In relation to the latter point, any satellite on-board embedded electronic system is expected to carry out its mission despite faults that may occur due to, e. g. , high-energy particles and cosmic radiation. Fault tolerance to such events can be created by design at two levels  [30].
Firstly, this can be achieved at the hardware level by means of parallel processing units (PPUs) that run the same micro-code and are provided with the same input. Whenever their output differs, this means that a failure has occurred. Recovery is conducted in the hardware by means of a voting system (with three or more parallel units, the correct result is assumed to be the one given by the majority) or roll-back (the last batch of instructions is executed again, until the same result is achieved from all). Secondly, at the software level, an error detection and correction (EDAC) approach should be implemented. This can be conducted by storing the checksum of all routines and static data structures and periodically checking that there have been no bitflips. The design, development and validation of such a complex embedded system implementing such protections is quite high, and rarely justified.
In this case, re-use of COTS hardware or software is problematic because these protection measures are not implemented in typical commercial products (with very few exceptions, e. g. , micro-controllers for high-power electric systems). Furthermore, it is not possible to benefit from mature, stable and proven software stacks coming from the open-source community  [31]. In particular, in  [32] , the authors highlight the advantages of using open-source software, especially the Linux Operating System, on COTS hardware, as a means to achieve short devel- Optimization efforts are needed to maximize the performance of the demodulator:.
• Adapting and optimizing the telecommunication performances by maximizing the achievable throughput while, at the same time, minimizing the DC consumption; • Rewriting part of the software to better cope with the flying environment where radiation occasionally causes software errors (i. e. , bitflips). In relation to the latter point, any satellite on-board embedded electronic system is expected to carry out its mission despite faults that may occur due to, e. g. , high-energy particles and cosmic radiation. Fault tolerance to such events can be created by design at two levels  [30]. Firstly, this can be achieved at the hardware level by means of parallel processing units (PPUs) that run the same micro-code and are provided with the same input. Whenever their output differs, this means that a failure has occurred.
g. , micro-controllers for high-power electric systems). Furthermore, it is not possible to benefit from mature, stable and proven software stacks coming from the open-source community  [31]. In particular, in  [32] , the authors highlight the advantages of using open-source software, especially the Linux Operating System, on COTS hardware, as a means to achieve short development cycles and, hence, foster innovation in the space segment. A possible way to add redundancy at the sub-system level without re-designing the whole demodulator is the following. Firstly, the E-SSA software is enriched by selfmonitoring functions to detect memory corruptions. Such functions may combine checksum verification with a keep-alive mechanism that is triggered periodically by the OS. Secondly, multiple redundant boards, equipped with the same software and fed by the same input, can be installed as the actual on-board payload.
b17; Torres. nc18). Recently, the SEX-DETector statistical framework, relying on the modeling of the transmission of alleles that account for the observed genotypes, was introduced (Muyle. gbe16). The method has been shown to yield high power and high sensitivity with as few as five offspring of each sex, and has successfully been used on a number of species (Muyle. np18; Veltsos. g19; Martin. gbe19; Prentout. gr20). It can be used for several purposes, using a single dataset: testing whether a species has sex chromosomes and of which kind (XY or ZW), identifying sex-linked genes, reconstructing the haplotypes of the gametolog copies, and estimating expression of each of the copies if RNAseq data are used. However, its requirement to produce a controlled cross for sequencing limits its use to species than can be easily bred or cultivated in controlled conditions, and hinders its application to species with long generation times.
As there are typically many more homozygous than heterozygous genotypes, many more errors occur on monomorphic sites than on polymorphic sites. SDpop estimates parameters using polymorphic sites only, and in this subset of all sites, the fraction of sites with errors is higher than in the whole genome, which includes monomorphic sites as well. Thus, the dataset of polymorphic sites on which SDpop runs is enriched in sites with errors; we refer to the error rate among polymorphic sites as the "effective error rate", e e. As an example, for a series of simulations with 2, 5, 10, 20, 50, and 100 individuals per sex, which were all conducted with an error rate of 0. 0001 and a level of polymorphism (π) of 0. 002, e e is 0. 015, 0. 011, 0. 0088, 0. 0068, 0. 0045, and 0. 0030, respectively. The four models of SDpop (without sex chromosomes, with XY chromosomes, with ZW chromosomes, and with both XY and ZW chromosomes) were run on each simulation.
Human data Exome-targeted sequence data for Finnish individuals (Auton. n15) were downloaded from the 1000 genomes project website. To facilitate file handling in the analysis pipeline, only individuals for which all reads were present in two fastq files (forward and reverse) were retained, i. e. a total of 66 individuals (44 females and 22 males). These reads were mapped on the human genome reference GRCh37 after removal of the Y sequence was removed (the reference used includes the primary assembly, i. e. chromosomal plus unlocalized and unplaced contigs, and the rCRS mitochondrial sequence, Human herpesvirus 4 type 1 and the concatenated decoy sequences) using BWA (Li. b09) with standard parameters. The individual alignments to the reference were merged, and the genotypes were called using bcftools (mpileup & call), and the targeted exons (file provided by the 1000 genome project) were extracted using bedtools.
Second, in DETSEX, X and Y-linked sequences are expected to map to different positions, which can be a safe assumption in well-studied species with an old sex chromosomes system (such as humans), but not for more recently evolved sex chromosomes. Thus, SDpop fills a gap in the current panel of methodological approaches for the identification and study of sex chromosomes. It requires input data that have now become classical: short reads sequencing of genomes or transcriptomes of ten to twenty individuals, collected in any population. It uses standard vcf files as input, thus allowing integration in existing genotyping pipelines. Its probabilistic framework and its implementation in a widely used and efficient programming language (C/C++) furthermore allow future developments (including, but not limited to, inference of individual's sex, corrections for population structure).
Acknowledgements We thank  Laurent Duret  for help with the treatment of the 1000 genomes data,  François Gindraud  for help with programming, and  Sylvain Mousset  and  Thibault Latrille  for discussions on population genetics issues. This work was performed using the computing facilities of the  CC LBBE/PRABI , and was supported by funding from the  Agence Nationale de la Recherche  (grant number  ANR-14-CE19-0021-01 ).
Satellite Images Satellite images used for land use dynamics in the Epulu-Ituri-Aru landscape are annual CARPE composites of 0. 025 degree resolution. These composites come from Landsat TM, ETM + and OLI images (respectively, Thematic Mapper, Enhanced Thematic Mapper plus and Operational Land Imager). These composites are made up of four spectral bands: NIR (0. 845-0. 885 µm), RED (0. 63-0. 68 µm), SWIR1 (1. 56-1. 66 µm) and SWIR2 (2. 1-2. 3 µm). These composites have undergone atmospheric, radiometric and geometric corrections  [24]. The Central Africa Regional Program for the Environment (CARPE) composites were chosen because they have no cloud cover and allow the analysis of multi-date changes. They cover all the countries of the Congo Basin and can be downloaded free of charge from the CARPE website (https://carpe.
Field Data Supervised classification generally requires a certain number of training samples and verification samples  [26]. Typically, traditional search uses manual visual interpretation to get points. Thus, the sampling consisted of the selection of the objects according to the spectral profiles defined using the GPS field surveys (surveys from 20 December 2016 to 15 January 2017). Then, the training areas that were chosen, on the images after 2016, for each class correspond to areas considered unchanged (built-up areas and inselberg for example) or having signatures close to the profile of 2016. In total, 950 measurement points have been taken (Table  1 ). This set was split into two groups of data: 665 used for the classification of land use in 2016 (i. e. , 70% geographic coordinates) and 285 points used for the validation of the 2016 classification.
Land Use and Land Cover (LULC) Classification The Random Forest classification (RFC) was applied to the images of 2003, 2010, 2014 and 2016 via the R software  [33]  with the R package "RandomForest"  [34]  to obtain LULC information. RFC  [35]  is a supervised technique of nonparametric statistical methods  [36]. RFC has been used in several studies in the past  [24, 32, 36, [37] [38] [39] [40] [41] [42] [43]. In the RFC, when a sample is entered into the model, each decision tree performs a separate evaluation to determine which category the sample should belong to, and the category that is most often selected is ultimately considered the category sample. The RF method can effectively reduce the uncertainty of a particular algorithm and improve the precision of the discriminant classification.
Land Use and Land Cover (LULC) Classification The Random Forest classification (RFC) was applied to the images of 2003, 2010, 2014 and 2016 via the R software  [33]  with the R package "RandomForest"  [34]  to obtain LULC information. RFC  [35]  is a supervised technique of nonparametric statistical methods  [36]. RFC has been used in several studies in the past  [24, 32, [36] [37] [38] [39] [40] [41] [42] [43]. In the RFC, when a sample is entered into the model, each decision tree performs a separate evaluation to determine which category the sample should belong to, and the category that is most often selected is ultimately considered the category sample. The RF method can effectively reduce the uncertainty of a particular algorithm and improve the precision of the discriminant classification.
Acknowledgments: We warmly thank  Victor Kadiata  for having the  Satellite Observatory of the Forests of Central Africa (OSFAC)  for ensuring the download of satellite images. We sincerely thank  Alastair McNeilage  and  Toussaint Molenge (CARPE)  for recommending us to SOFCA. Big thanks to  Salomon Dyoma Ezadri ,  Alimengo Paul ,  Atimo Michel ,  Kimate Henry ,  Landasi Tsavurume ,  M'vuge Bijoux ,  Mamimolo Jean-Marie ,  Mumbere Jackson ,  Ngenda Elvis ,  Mundele Oscar ,  Yambwa Gloire  for assistance in the field. We also thank the former  Chief Curator/Site Manager of the Okapis Wildlife Reserve and Directors of Parks and Reserves in DR Congo Paulin TSHIKAYA  and the Curator/ Head of Research and Biomonitoring Program  at the  Okapis Wildlife Reserve Balimbaki Aimé  for access to the study site.