dataesr/scientific-paragraphs-categorization huggingface dataset:
(2018) ) to reduce the required bandwidth for data transmission between nodes. Thanks to a parallel implementation, it sometimes outperforms the other approaches. But again, LZ4fast performs better in many situations. The delta coding approach can also be combined as done in  Patauner et al. (2011)  where Huffman coding and delta coding are used together. If the traditional delta coding is not really efficient for floating point coded samples, and more generally inferior to the traditional lossless compression schemes , a more recent approach, named here SL6  (Thomine et al. (2016, US Patent 20,190,044,532) ), that consists in encoding the difference between the signal and an approximated value makes possible to slightly reduce the number of bits used to transmit the signal. A whole compression and decompression framework is available for SL6 as well in hardware and software under commercial license.
To summarize, the Huffman-based entropy-encoders and arithmetic-encoders based algorithms will be compared to the recent class of algorithms known as SL6  (Thomine et al. (2016, US Patent 20,190,044,532) ). The comparison will be done on the SWOT dataset which contains integer and floating point number. The shuffle and bitshuffle preconditioners will be used for the LZ family algorithms, as it has been shown in the past that they help them to compress integer and floating-point samples.
The version of these algorithms are the latest available version. For each of them, the most common variants were tested. The variants are usually identified using one or several parameters. The SL6 algorithm is also benched, again using different parameters, originally designed for the compression of messages between nodes of HPC clusters, its lightweight properties and low computing and memory requirements are properties that make it interesting for the SWOT mission. The parameters are the size of the block and the interpolation mode as explained in  Thomine et al. (2016, US Patent 20,190,044,532). Moreover, we added the shuffle and bitshuffle preconditioners. This lead to a total of 672 different versions. The results are compared to the in-memory data copy memcpy which provides no compression but maximum throughput -.
GB/s measured The workstation used to execute the testbench is an Intel(R) Xeon(R) CPU E5-2620 v3 running at 2. 40 GHz processor, 64 GB of memory. Since this study aims at selecting the algorithms that could be embedded on an onboard computer for a mission, the multicore capacities of the architecture were disabled. Other compression algorithms are excluded from this study. For example the case of Zarr as it is designed to be directly used in Python code, and thus cannot not provide a throughput comparable to the others. Blosc  (Howison (2013) ) is a compression schemes that relies on other compression codecs. It implements fast data accesses to exploit the processor cache and Single Instruction Multiple Data (SIMD) instruction (SSE, Altivec etc. ) and implements shuffle and bitshuffle filters.
This is the same approach for the decompression with D throughput = size orig /time decomp. Here again, the corresponding C v can be calculated from the standard deviation σ. As one of the targeted application is the on-board compression and data transmission, it is important to put emphasis on the algorithms with consistent performances, whatever the type of data. In other words, an algorithm providing extremely has consistent results among the whole dataset. Intuitively, it means the highest throughput and the highest compression C r are preferable but with the lowest standard deviation for both of them. Thus, we propose the metric H-score for a given set of data (fields) calculated on the normalized results (maximum set to 1) using the Equation  1 , with, σ(C r ) the standard deviation of the compression rate, T the throughput and σ(T ) its the standard deviation.
A graphic intuitive illustration of the variables used in the H-score is given in Figure  5. Moreover, we defined α and β coefficients, which are set to one. They can be used to weight either the compression C r (α) or the throughput (β). Finally, the ρ coefficient, set to 1000, is simply used to ease the reading of the results. A low or negative H-score means the algorithms results are heterogeneous within the selected data, on the opposite side, a higher H-score means the algorithms results are homogeneous among the dataset while providing good performances. H -score = ρ 1000 ×(C r -α 1 •σ(rate)) • (T -β 1 •σ(T )) (1) The third metric that was considered in this paper, and proposed by Thomine, is called the tt tx for transmission-throughputthreshold and is expressed in Bytes-per-second. It is used to determine if it is worth compressing the data when a certain transfer C r is available for data transmission.
Data management The number of algorithms is quite important and for each algorithm, many variation are considered. For example, the impact of the algorithms parameters (more than 17 variants for ZStd), but also, the use of preconditioning filters such as shuffle and bitshuffle. Since the volume of data produced by our extended testbench is quite important we decided to store all the results in a relational database we have specifically designed for the analysis of the results. This way, all the data can be extracted and analyzed using a combination of SQL queries. In the same vein, the derivated metrics such as compression C r , compression and decompression throughput, (tt tx and tt wr can be calculated on-the-fly directly in the queries. Moreover, to make it easier, we have designed a graphical user interface (Figure  7 ) to browse through the results, it allows an instant displaying, filtering, ranking and analysis of the different metrics used.
H-score The H-score defined in the previous section emphasize the algorithms that provide consistent compression C r and consistent throughput. This is extremely important when the dataset can be heterogeneous and when the targeted application is realtime compression on a single-core. Since SL6 provides quite high throughput with a compression C r comparable to the other algorithms -and for both a reasonable σ -it scores higher than the others. It is followed by LZ4Fast associated to the shuffle filter (parameter 17), which scores 2 % lower than the SL6 algorithm. The results are visible in Table  6  and in Figure  12.
Memory Usage Memory usage of all tested algorithms is lower than 2 MB as well as for compression and decompression. Because of the necessity of building a dictionary, the Huffman-based ones often reach 2 MB. Their memory usage is not deterministic and depends on the data. The lowest is SL6 with less than 128 kB (for the 64-bit floating point) version. Most of the memory usage Table  7. Average results for the fields from the SWOT file that are the most difficult to compress, the compression Cr and the standard deviation, the compression and decompression throughput and the H-score are displayed. is used for the input and output buffers. Another key point is that the SL6 memory usage is constant and does not depend on the data but only of the size of the words (8, 16, 24, 32, 64 bits), while the choice of the parameters has a limited impact on the memory usage of less than 5 %.
The couple compression rate and throughput is a good way to rank the algorithms. However, since the analysis of the results Another interesting point is that the compression throughput of SL6 is constant for a given datatype, which is quite understandable as it operates in stream processing. Beyond the choice of a compression algorithm for space-to-ground communication, where constraints require approaches that can be easily embedded and have consistent compression and throughput performances, the ground applications related to the SWOT mission is also considered. Compression for ground applications is extremely important too as the SWOT mission may generate around 5 TB of data per day. Indeed, ultimately, the data will be used in numerical models and computed on HPCs, thus, it is important to be able to compress and decompress the messages on-the-fly without any loss of performance.
Compression of HPC messages is a good way to reduce computing time, provided that the compression/decompression is fast enough, that is the reason why we have introduced the transmission-throughput-threshold. In the same vein, to enable smooth storing an manipulating of these data requires, fast compression algorithms are required, ideally that can perform on-the-fly, with no or almost no perceptible delay for the operators. The SL6 approach is the only one which has constant compression time for a given datawidth (64, 32, 16 or 8-bit words). It is also able to compress a signal with fixed-size buffers, which participates to its deterministic characteristics in terms of compression time and hardware resources that are required for the SL6 algorithm execution. Another key point is the determinism of the compression rate, indeed, the storage and database systems will be sized for an expected volume of data.
Conclusion Since space missions are generating more and more data, for example 7 PB is expected for the swot mission, efficient data compression approaches needs to be explored. Huffman-based algorithms are traditionally used for scientific application, especially for netCFD data. It has been shown in the past they are quite efficient especially when associated to to filters such as shuffle and bitshuffle. This paper compared Huffman based algorithms and the SL6 approach. The Huffman-based algorithms require memory (shuffle needs to analyze the entire file) and their performances are heterogenous, strongly depending on the data. When associated to shuffle filter, stream processing is not possible. Alternatively, the SL6 algorithm provides homogeneous results, low memory usage, constant compression time for a given datatype, especially in terms of compression C r.
Acknowledgements. The authors would like to thank the  Centre National des Etudes Spatiales (CNES)  for funding this research and Subnet for providing the sources of the SL6 algorithm. They also would like to thanks  Nicola MARTIN  for the proofreading work. 26 https://doi. org/10. 5194/egusphere-2022-1094 Preprint. Discussion started: 20 December 2022 c Author(s) 2022. CC BY 4. 0 License.
Indeed, for example, the LZ4Fast shuffle has a low H-score because of the important variability in the compression C r : some of the fields are almost not compressed while the LZ4 bitshuffle now appears in the ranking at the third position. Figures15 and 16show that the compression C r of the SL6 algorithm is comparable to the other Huffman-based approaches associated to shuffle and bitshuffle filter. However, it shows superior performances in terms of compression speed. The high score of SL6 is mainly due to the consistent compression C r among the dataset associated to one of the best throughput (3 rd ). Some of the fields are (almost) not compressed by the LZ4 shuffle and LZ4Fast shuffle.
Table4for the values) for the fields comprised in the SWOT file. It is clearly visible that all the algorithms perform well on this dataset, the lowest standard deviations are observerd for SL6 and Zstd associated to the shuffle with. The Pithy algorithm provides good results on some fields too, unfortunately it is not able to compress some fields of the SWOT file. L2 instrument phase correction F64LE L2 delta y F64LE pixel cloud/continuous classification F32LE pixel cloud/power left F32LE L2 ifgram imag F64LE L2 instrument attitude correction F64LE pixel cloud/azimuth index I64LE pixel cloud/regions I32LE L2 phase screen F64LE L2 dark water flag I8LE pixel cloud/range index I64LE L2 ice flag I8LE pixel cloud/ifgram F32LE pixel cloud/x factor right F32LE L2 wet tropo range correction F64LE L2 power left F64LE pixel cloud/dlatitude dphase F64LE L2 ionosphere range correction F64LE L2 dheight dphase F64LE pixel cloud/phase noise std F32LE L2 illumination time F64LE L2 look unit x F64LE L2 look unit z F64LE pixel cloud/incidence angle F64LE pixel cloud/dheight dphase F64LE L2 longitude F64LE L2 reference layover height error F64LE L2 power right F64LE L2 xover roll correction F64LE L2 coherent power F64LE pixel cloud/latitude F64LE pixel cloud/num rare looks F32LE L2 solid earth tide height correction F64LE L2 instrument baseline correction F64LE pixel cloud/illumination time F64LE L2 latitude F64LE pixel cloud/num med looks F32LE L2 pixel area F64LE L2 height F64LE L2 dheight drange F64LE L2 num rare looks F64LE L2 reference layover flag I8LE L2 dlook dphase y F64LE L2 continuous classification F64LE L2 dry tropo range correction F64LE L2 range index I64LE L2 dphase F64LE pixel cloud/dlongitude dphase F64LE pixel cloud/record F32BE L2 delta x F64LE pixel cloud/cross track F64LE L2 dlook dphase z F64LE L2 classification F64LE pixel cloud/classification F64LE pixel cloud/longitude F64LE pixel cloud/height F64LE pixel cloud/coherent power F64LE pixel cloud/sigma0 F32LE L2 sensor s I64LE LZ4 0 LZ4Fast 17 ZSTD 22 lz4fast bshuf 17 zstd bshuf 1 lz4fast shuf 17 zstd shuf 1 LZ shuf 9 LZ bshuf 9 SL6 https://doi.
Generation and genotyping of Foxp3-EGFP animals Several CRISPR sgRNA sequences cleaving immediately 3′ of the stop codon of Foxp3 were designed using the CRISPOR software. The in vitro transcribed sgRNA were purified and one selected for highest cleavage in the rat C6 cell line, as previously described in detail  [55, 56]. Fertilized one-cell stage embryos were collected for subsequent microinjection using a previously published procedure  [57]. Briefly, a mixture of Cas9 protein (50 ng/μl), sgRNA (GCA GGG GTT GGA GCA CTT GC) (10 ng/μl), and donor DNA (2 ng/μl) encoding the 2A selfcleaving peptide and EGFP sequences flanked by homology arms (1 kb each) 5′ and 3′ from the DNA cleavage point (Fig. 1A ) was microinjected both into the male pronucleus and into the cytoplasm of fertilized one-cell stage embryos.
Cytofluorimetry and antibodies Single-cell suspensions from the spleen, thymus, bone marrow, and lymph nodes were prepared as described previously  [59]. Cell suspensions were analyzed using antibodies (antibodies used are listed in supplementary Table  8 ). The incubation period was 30 min at 4 °C, and the analysis was performed with a FACSVerse system (BD Biosciences, Franklin Lakes, NJ) and FlowJo software (Tree Star, Ashland, OR).
A library of 350-800 bp is sequenced on a S1 flowcell on a NovaSeq 6000 (Illumina). Digital gene expression (DGE) profiles are generated by counting for each sample the number of unique UMIs associated with each RefSeq genes. Counts matrix was normalized by a linear model with the R package DESeq2  [61]. Differential gene expression was determined Wald test and corrected with the false discovery rate (FDR) multiple testing method. Genes were considered differentially expressed if the FDR < = 0. 05 and the absolute value of log 2 (Fold Change) greater than 0. 5. Heatmaps were generated with the R package gplots (https:// cran. r-proje ct. org/ web/ packa ges/ gplots/ index. html); Volcano plots were drawn by plotting -log10 (FDR) in function of log2(Fold-Change). Genes were highlighted when absolute value of log2(Fold-Change) was superior to 1 and FDR < = to 0.
05. Principal component analyses (PCA) and confident ellipses were generated with the function coord. ellipse from the R package FactoMineR  [62]  with default parameters. Both pathways from the Kyoto Encyclopedia of genes and genomes (KEGG)  [63]  as well as gene ontologies (GO)  [64]  were tested for enrichment among differentially expressed genes with the R package cluster Profiler  [65]. The significant GO terms and their enrichment scores were filtered with a corrected P-value < = 0. 05 (Benjamini-Hochberg method).
In vitro generated EGFP + induced Tregs EGFP -CD4 + CD25 -and EGFP -CD8 + CD45RC low/-T cells were FACS-sorted from Foxp3-EGFP rats, labeled with CPD 670 and cultured for 3 days in the presence of anti-CD3 and anti-CD28 MAbs with different doses of hTGFbeta (0, 5, 20 μg/ml) and with or without 100 IU of hIL-2 at 37 °C in 5% CO 2. Proliferation of CPD-labeled T cells measured by dilution of the CPD signal and EGFP expression were analyzed using a FACSVerse system (BD Biosciences, Franklin Lakes, NJ) and FlowJo software (Tree Star, Ashland, OR). EGFP + CD4 + and CD8 + cells as well as EGFP -CD4 + and CD8 + cells were sorted at the end of the culture period and used in suppressive assays as described in a section above. Sorted EGFP + cells were also permeabilized, fixed, and analyzed for expression of FOXP3 using anti-FOXP3 antibodies.
Statistical analysis Results are presented as means ± SEM. Statistical analysis between samples was performed by a Mann-Whitney test using GraphPad Prism 4 software (GraphPad Software, San Diego, CA, USA). Differences associated with probability values of a P < 0. 05, b P < 0. 005, c P < 0. 0002, and d P < 0. 0001 were considered statistically significant. anti-FOXP3 antibodies. Compared to untreated cells, EGFP signals were reduced in permeabilized cells, as previously described  [59]. Co-labeling using anti-FOXP3 antibodies and EGFP in permeabilized cells (2 right panels) showed co-staining in CD4 + and CD8 + T cells from Foxp3-EGFP animals. C. EGFP + cells were sorted from spleen, permeabilized and fixed followed by analysis using anti-FOXP3 antibodies. All sorted EGFP + cells expressed FOXP3.
Acknowledgements This work was realized in the context of different programs: Biogenouest by  Région Pays de la Loire ,  IBiSA program, TEFOR (Investissements d'Avenir French Government program ,  ANRII-INSB-0014 ), LabCom  SOURIRAT  project ( ANR-14-LAB5-0008 ),  Labex   IGO  project ( Investissements d'Avenir French Government program ,  ANR-11-LABX-0016-01 ),  IHU-Cesti project (Investissements d'Avenir French Government program ,  ANR-10-IBHU-005 ,  Nantes Métropole  and  Région Pays de la Loire ), and  Fondation Progreffe. We acknowledge  Bernard Malissen  and  Cédric Louvet  for providing the Foxp3-GFP mice.
Funding This work was supported by different programs: Biogenouest by  Région Pays de la Loire ,  IBiSA program, TEFOR (Investissements d' Avenir French Government program ,  ANRII-INSB-0014 ), LabCom  SOURIRAT  project ( ANR-14-LAB5-0008 ),  Labex   IGO  project ( Investissements d' Avenir French Government program ,  ANR-11-LABX-0016-01 ),  IHU-Cesti project (Investissements d' Avenir French Government program ,  ANR-10-IBHU-005 ,  Nantes Métropole  and  Région Pays de la Loire ), and  Fondation Progreffe.
This paper discusses the resilience of the UAV quadrotor to wind disturbances. An unknown input-state observer is presented that uses the Lipschitz method to estimate the internal states and disturbances of the quadrotor and compensate for them by varying the velocities of the four rotors. The observer intends to use existing sensor measurements to estimate the unknown states of the quadrotor and reconstruct the three-dimensional wind disturbances. The estimated states and external disturbances are sent to the PD controller, which compensates for the disturbances to achieve the desired position and attitude, as well as robustness and accuracy. The Lipschitz observer was designed using the LMI approach, and the results were validated using Matlab/Simulink and using the Parrot Mambo mini quadrotor.
II. QUADROTOR MATHEMATICAL MODELING WITH DISTURBANCES The position (x, y, z) of an aircraft's center of mass is measured by GPS sensors, which provide data in the inertial Earth frame F 0 , while the orientation (phi, theta, psi) is measured by Inertial Measurement Units (IMU) in a body frame F B. The state vector of a quadrotor vehicle consists of (x, y, z, ϕ, θ, ψ) and (u, v, w, p, q, r) variables representing the vehicle's position and velocity respectively. As shown in Fig. 1 , the four rotors apply a force orthogonal to the rotation plane of their blades which are aligned with the positive z-axis of the body frame F B and proportional to the rotation speed square, i. e. F i = K F ω 2 i , where i is the i-th rotor. Each force F i generates a torque along the z-axis which is represented by the opposite arm of the aircraft chassis, being lK F ω 2 i , where l is the arm length.
( 24 ) Matrix P and the observer's gain matrix L are chosen so that the estimation error converges to zero, i. e. e → 0 as t → ∞ when (A -LC) ⊤ P + P(A -LC) + γ 2 I + PP < 0 and P > 0 Furthermore,  (25)  ensures the asymptotic stability of e if V > 0 and V < 0. With R = P L, we obtain A ⊤ P + PA -C ⊤ R -RC + γI + PP < 0 (26) Using Schur complement lemma to convert to LMI so that it can be solved by Matlab, we take R = -I , S = P and FIGURE 3: Estimated versus actual wind gusts for the time varying wind gust model. Q = A ⊤ P + PA -C ⊤ R -RC + γ 2 I.
The Lipschitz UIO uses these to estimate the three remaining states as well as the three unknown wind gust components. A simple yet effective PD control algorithm has been developed in Matlab/Simulink with the proposed observer. The Parrot Mambo quadrotor has been used to test the results in simulation and experiments. The wind gusts vectors (W x , W y , W z ) ⊤ affecting the aircraft's position is compensated by the PD controller by varying the four rotor speeds. The PD controller is based on the approximated linear dynamic model of the quadrotor aircraft in hovering condition when ϕ, θ ≈ 0. Let such nominal conditions be described by  (x d , y d , z d , ψ d  ) and the nominal force for hovering, f = g. Hence, the tracking error variables be δx = xx d , δy = yy d , δz = zz d , and δψ = ψ -ψ d.
V. RESULTS AND DISCUSSION The Parrot Rolling Spider minidrone was used together with a base station containing a laptop computer with Matlab/Simulink installed. The Parrot Rolling Spider is a programmable indoor minidrone. Its sensors include a 3-axis gyroscope for attitude sensing, a 3-axis accelerometer, a camera for visual navigation to detect x-y position, and ultrasonic sensors with a pressure sensor that estimates flight altitude. The parameters of the parrot drone are presented in Table  2  The proposed scheme has first been validated in Matlab/Simulink with the parameters of the nonlinear quadrotor model, where its parameters are in Table ??. The purpose of this step is to show the main behavior of the proposed method under ideal conditions with the presence of external disturbances, which in this case are wind gusts.
B. HARDWARE RESULTS The final step is to validate the proposed scheme in experiments on a real parrot rolling spider minidrone. The parrot rolling spider is used together with a base station that has a laptop with Matlab Simulink installed. The parrot rolling spider is a programmable indoor minidrone where the flight control system can be edited to the proposed scheme. The drone has a 3-axis gyroscope for attitude measurement, a 3axis accelerometer, a camera for visual navigation to sense the x-y position, and ultrasonic sensors with a pressure sensor that work together to estimate its flying altitude. The hardware support package for the Parrot Minidrone is available for Matlab. This enables testing and verification to be done in Matlab Simulink on the minidrone. The parameters for the parrot mambo are the same as in Table  2.
A Bluetooth personal area network (PAN) is used to connect the minidrone to the workstation laptop for programming. After completing the controller design, the Flight Controller Subsystem in Simulink is converted into C code and uploaded onto the minidrone through the Bluetooth PAN. Upon uploading the code, a command is given from Simulink to initiate the experiment. The connection between the minidrone and the Simulink environment is in real-time. After a flight experiment, the experimental flight data, including the positions, velocities, and rotor speeds, are transferred from the minidrone to the Matlab workspace via the Bluetooth PAN. Post-flight analysis is then done with the data recorded into the Matlab workspace. The quadrotor receives the desired positions (x d , y d , z d , ψ d ) and the IMU with position sensors measures partial states which are x, y, z, ϕ, θ, ψ, u, v, w.