dataesr/scientific-paragraphs-categorization huggingface dataset:
By fusing the parameter partial derivative matrix (as shown in Fig. 3 ), SPOT supports fusion computation of single-arc parameters, multi-arc parameters, and global parameters in multi-arc orbit determination. It also supports various convergence control threshold settings during the iterations. These include the residual RMS change percentage, parameter correction percentage, and maximum limit of the number of iterations and other control indicators. The software can perform error analysis on solution results, including calculating and outputting parameter correction values, posterior errors, parameter covariance matrix, observation residual plots, and other results. In addition, SPOT can store and retrieve various computation results of precise orbit determination. In the light time calculation, SPOT considers precise light time computation affected by relativistic effects  (Moyer, 2005).
Since the calculation of each term on the right side of Eq. (  1 ) requires and as inputs, the equation requires an iterative computation. The termination condition of the iteration is either when the difference between the current iteration's light time equivalent distance and the previous iteration's light time equivalent distance is less than 0. 01 mm or when the iteration count exceeds 10. Various observation modes can be precisely modeled only after obtaining precise light time. t 1 t 2 t 2 -t 1 = + R S + ∑ i R i # (1) r 12 c r 12 R S R i i R S = ln [ ] # (2) In the dynamic model module, SPOT implements basic dynamic models such as spherical harmonic gravity eld, ellipsoidal harmonic gravity eld, particle gravitational perturbation, relativistic effect perturbation, empirical acceleration, and tidal perturbation, satisfying the dynamic modeling requirements of most detectors for precise orbit determination.
Furthermore, to invert the small body gravity eld and other related dynamic parameters; the software further implements the N-Plate precise radiation pressure model, planetary atmospheric drag, and comet jetting models with high precision. Depending on the different exploration targets, SPOT can call different dynamic models by customization to achieve highprecision integration orbits of the detector. The software's numerical integration module considers the eighth-order Runge-Kutta method and sets reasonable error tolerances, ensuring the reliability of integrating orbit equations of motion and variational equations. Additionally, the module implements interpolation method for discrete integration results, enabling e cient computation of state vector, state transition matrix, and sensitivity matrix for any given time during the light time computation and measurement model construction.
The integrator also supports setting the state vector at any given time as the initial state vector and supports forward, backward, and bidirectional integration processes starting from this point. Moreover, multi-arc integrators are implemented to perform more e cient numerical integration through parallel computation of different arc segments. SPOT also considers precise spatiotemporal reference frameworks and coordinate systems. The software represents the integer and decimal parts of the motion equation time scale -Barycentric Dynamic Time (TDB) in seconds separately and implements numerical calculation methods between the data structures, signi cantly reducing the error in time expression and eliminating its impact on precision orbit determination. In terms of the spatial reference framework, it supports the conversion between the inertial and non-inertial frames of major celestial bodies.
For the Earth and Mars, the software implements high-precision coordinate transformation tool based on the equinox of the earth and the "Path nder" orientation model of Mars  (Konopliv et al. , 2006 (Konopliv et al. , , 2011 (Konopliv et al. , , 2016)). It can also read ephemeris les from different coordinate reference frameworks to support other reference frameworks. In addition, SPOT also implements some commonly used coordinate systems and their conversions, including rectangular coordinate system, spherical coordinate system, and RTN coordinate system. In the measurement model module, SPOT constructs a series of deep space measurement models, including two-way and three-way Doppler, two-way range, image landmarks, and optical angle measurement models. The software also considers correction models such as tide correction, troposphere correction, and ionosphere correction for each observation model.
The results show that the addition of intersatellite links can improve the accuracy of low-order gravity eld coe cients of small bodies by an order of magnitude in contrast to approaches using only ground tracking observation data. At the same time, the reduction of the orbital height of the probe and the lower measurement noise of the inter-satellite links can further reduce the uncertainty of the inversion parameters. The in uence of small body ephemeris errors of 10 km and the certain solar radiation pressure perturbation error on the solution results can be ignored (see Fig. 7 ). The various error analysis functions provided by SPOT software can provide important experimental design guidance and data analysis support for small body research, and of great signi cance for the development of small body research and exploration missions.
Radio Data Processing for Rosetta Probe Mission On August 6, 2014, the European Space Agency's Rosetta probe arrived near comet 67P/Churyumov-Gerasimenko  (Taylor et al. , 2015). The Rosetta mission ended on September 30, 2016, when the probe deliberately crashed onto the comet's surface. SPOT explored and implemented the process of solving the comet's gravity eld, by using radio tracking data and onboard image landmark observation data from the phase when the Rosetta probe orbited comet 67P. SPOT determined two sets of parameters in the precise orbit determination. One set is the arc parameters, including the initial state vector of Rosetta, the solar radiation pressure scale factor, and the velocity increment Δ𝒗 caused by unloading the momentum wheel. These parameters vary for each arc and need to be separately determined.
Conclusion In conclusion, the planetary geodesy group at Wuhan University has developed a system for precise orbit determination of deep space probes and gravity eld inversion for small bodies, called the Small Body Precise Orbit Toolkit (SPOT). This paper introduces the SPOT system from perspective of small body gravity eld solution software implementation, simulation task analysis, and measurement data processing, and veri es the accuracy and reliability of the software. SPOT can provide reference for the precise orbit determination of future small body exploration missions in engineering. In scienti c research, this software can further invert the gravity eld and related dynamic parameters of small bodies, helping to reveal their internal structure and composition, and thus understand their origin, formation, and evolution.
This is also the key focus for breakthroughs related to SPOT now and in the future. The subsequent work will focus on Rosetta data, improve the comet 67P/CG jet model, and comprehensively solve all the tracking data before and after the perihelion of comet 67P/CG to calculate a high-degree gravity eld model for comet 67P/CG, providing more precise gravity eld constraints for studying its non-uniform internal structure. In addition, the team will conduct a system analysis of on-board data autonomous navigation, orbit determination, and dynamic parameter solving for the "Tianwen-2" mission, providing important references for the processing and analysis of real measurement data for "Tianwen-2". In the future, the SPOT software will further expand its functionalities to systematically process radio, onboard image, and ground/space-based optical data for solar system bodies, including precise orbit determination of solar system celestial body probes, inversion of celestial body gravity elds and rotation parameters, and calculation of solar system planetary ephemeris.
On one hand, the SPOT software platform can be used as external independent software to conduct detailed comparisons with the engineering orbit determination software in the telemetry and control system, providing references to ensure the accuracy and reliability of the engineering orbit determination software. Furthermore, after the SPOT software's functions are improved, it will be possible to systematically process ground-based radio tracking data for solar system celestial body probes, on-board data laser ranging, radar, imaging, and other multi-source tracking data, as well as terrestrial and spatial optical image data obtained from observing natural celestial bodies, to comprehensively solve the gravity eld, rotation, tidal dissipation, tidal elastic response parameters, and celestial body ephemeris of solar system celestial bodies.
2009; Dong, et al. 2021; Dowling and Wolff 2023). Furthermore, epistatic interactions between mitochondrial and nuclear genome are also suspected to modulate mtDNA genetic variation given the key biological processes happening in the mitochondria and the tight coordination between the two genome compartments  (Wolff, et al. 2014; Sloan, et al. 2015; Rand, et al. 2018; Dowling and Wolff 2023; Nguyen, et al. 2023). For example, an increasing number of studies in mosquitoes suggests that mitochondrial respiration and the associated production of reactive oxygen species (ROS) play a significant role in mosquito immune response and metabolic processes involved in pathogens and insecticides resistance  (Van Leeuwen, et al. 2008; Ding, et al. 2020). These epistatic interactions are often neglected in ecology and evolution due to the limited number of studies with adequate datasets to test for these effects.
Excluding the CR removed most of the ambiguities and gaps remaining in the alignment (Fig. S3 ). The previous and present bioinformatic pipelines generated very similar mtDNA assemblies for each sample with one exception (samples ID: Aara_SRS408148, Fig. S4 ). Beside that sample which resulted from a label mistake in the DRYAD repository of  Fontaine, et al. (2015) , mitogenome sequence pairs for each sample were nearly identical with a number of nucleotide differences of 0. 4 on average (25% quartile: 0. 0; 75% quartile: 1. 0, max: 3. 0) (Fig. S4 ). We augmented this alignment with newly assembled mitogenome sequences from three An. bwambae samples of lower sequencing quality than the other samples (Table  S1 ). These mitogenomes assembled with the two pipelines also generated similar sequences with a slightly lower sequence identity (>99.
coluzzii, An. gambiae, and the hybrid (taxonomically uncertain) populations, with possible mito-nuclear interactions. To test this hypothesis, we conducted a genome-wide association study (GWAS), testing which SNPs on the nuclear genome were significantly associated with the cryptic mtDNA lineage occurrence (which is considered here as a binary variable). The GWAS was conducted considering covariates including the 6 first PCs (Fig. S11 ) to account for population genetic structure, Wolbachia occurrence (Table  S5  and Fig. S9 ), and sex. As such analysis requires unrelated samples  (Uffelmann, et al. 2021) , we excluded closely related sample pairs in the Ag1000G dataset with kinship coefficient exceeding the level of 2 nd degree relatives (Table  S6 ). The KING-robust method  (Manichaikul, et al.
2010) , which relaxes the assumption of genetic homogeneity within population, identified multiple related pairs of samples within populations equal or exceeding the level of 2 nd degree relatives, with some cases of fullsib or parent-offspring's, and even rare cases of monozygotic twins between pairs of mosquitoes (see Fig. S12 , S13, Table  S7 , and S8). Full-siblings or parent-offspring's relationships in mosquitoes can occur if samples originated from larvae from a single female for example. Monozygotic twin's relationship can either reflect sample duplicates in the dataset or highly inbred samples as would be observed in samples coming from a laboratory colony. Unsurprisingly, the most impacted population was the Kenyan (KEA) one. Its peculiar genetic make-up is similar to a laboratory colony, as was previously spotted in The Ag1000G Consortium (2017).
However, instances of full siblings and monozygotic twins were found in the populations from Cameroon (CMS) and Angola (AOM) (see Fig. S12 , S13, Table  S7 , and S8). Overall, removing 98 samples from the dataset (Table  S9 ) resolved all the issues allowing only up to the 3 rd degree relative association between sample pairs. The cleaned SNPs dataset used in the GWAS included 1044 unrelated samples (Fig. S11b ) and 7,858,575 nuclear biallelic SNPs (Table  S10 ). After removing related samples, and accounting for population structure, sex, and Wolbachia occurrence as covariates, the quantile-to-quantile plot and the genomic inflation factor (Lambda) was close to 1, indicating that the genomic control of the GWAS was adequate (Fig. S14 ). The GWAS identified 14 SNPs significantly associated with the cryptic mtDNA lineage occurrence with p-values lower than the Bonferroni adjusted threshold of 4.
gambiae and An. coluzzii with strong signal of recent positive selection and association with metabolic insecticide resistance involving also the mitochondrial oxidative phosphorylation (OXPHOS) respiratory chain (The Ag1000G Consortium 2017;  Ingham, Tennessen, et al. 2021; Lucas, et al. 2023 ) (see also the Ag1000G Selection Atlas, in prep; https://malariagen. github. io/agam-selection-atlas/0. 1-alpha3/index. html). A total of 32 genes overlap with this focal region (Fig. S16 ). Among them is the well-known cytochrome p450 encoded by CYP9K1, an important metabolic insecticide resistance gene  (Main, et al. 2015; Vontas, et al. 2018; Lucas, et al. 2023). Even if that gene is within the elevated p-value region, it is located 62kb upstream from the first skyscraper signal. The first highest signal overlapped with AGAP000820 (CPR125 -cuticular protein RR-2 family 125), AGAP00822 and AGAP00823 (CD81 antigen).
Sampling and whole genome short read data We retrieved whole genome short read (WG-SR) data (100 bp paired-end Illumina sequencing) from 74 mosquito specimens for six species of the AGC from  Fontaine, et al. (2015) , including An. gambiae s. s,  An. coluzzii, An. arabiensis, An. quadriannulatus, An. melas, and An. merus. As mtDNA genomes of these samples were previously assembled, we compared them with the ones produced using the new pipeline developped in the present study. We extracted reads that did not map to the nuclear reference genome and used them to assemble mitogenome. We included also WG-SR data from three specimens of a seventh species -An. bwambae -that were generated as part of the Anopheles 16 Genomes Project  (Fontaine, et al. 2015; Neafsey, et al. 2015). See the complete sampling details in Figure  S1  and Table  S1.
We also retrieved WG-SR data from The Ag1000G Consortium (2020) phase 2-AR1 release consisting of 1142 wildcaught mosquito specimens including An. gambiae s. s. (n=720), An. coluzzii (n=283) and hybrid (n=139) from 16 geographical sites (Figure  1 , Table  S2 ). Previously generated An. gambiae reference mitochondial genome (GenBank ID: L20934. 1)  (Beard, et al. 1993 ) was used to guide the assembly of the AutoMitoG pipeline. The mitochondrial sequences of An. christyi and An. epiroticus  from Fontaine, et al. (2015)  were also included as outgroups sequences for phylogeographic and phlogenetic analyses.
Mitochondrial genomes assembly and alignment Information about the software versions is provided in Table  S3. From the WG-SR files mapped to nuclear reference genomes (bam files) obtained from  Fontaine, et al. (2015)  and The Ag1000G Consortium (2020), we extracted reads that did not mapped to the nuclear reference genome and converted them to paired-reads fastq files using Samtools  (Li, et al. 2009 ) and Picard Tools (http://broadinstitute. github. io/picard/). We wrote the AutoMitoG [Automatic Mitochondrial Genome assembly] pipeline to streamline the mitochondrial genome assembly process (available at https://github. com/jorgeamaya/automatic_genome_assembly, Fig. S2 ). As a general overview, the pipeline starts by randomly sampling paired reads from each file at a 5% rate. Then, the pipeline proceeds to assemble the mitogenome using a modified version of MITObim  (Hahn, et al.
2013 ) to assemble the mtDNA genomes. Subsampling of the paired-reads is performed to achieve two purposes: (1) minimize the number of ambiguous base calls -these result from conflicting pairing of reads from mitochondrial origin with reads possibly originating from nuclear mitochondrial DNA copies (NUMTs), reads with sequencing errors, and reads that originated from possible contamination. Since the number of reads from mitochondrial origin is orders of magnitude larger in the WG-SR data than the number of reads from other sources, subsampling safely reduces offending reads; (2) to normalize the dataset coverage, which speeds up MITObim calculations, as the proportion of mitochondrial reads can differ between samples and studies. Indeed, MITObim performs best with sequencing depth between 100 and 120x for Illumina reads  (Hahn, et al.
gambiae and An. coluzzii samples from The Ag1000G Consortium (2020) into a reduced multidimensional space using a non-metric multidimensional scaling (nMDS). For that purposed, we calculated a p-distance matrix among sequences using MEGA v. 7  (Kumar, et al. 2016 ) and performed the nMDS using the ecodist Rpackage  (Goslee and Urban 2007). The nMDS results were further processed using scikit-learn v. 0. 22. 1  (Pedregosa, et al. 2011)  to identify major clusters in the data set, using a hierarchical clustering algorithm. MtDNA genetic structure in natural populations of An. gambiae and An. coluzzii We first assessed how the mtDNA variation of 1142 sequences of An. gambiae and An. coluzzii samples from The Ag1000G Consortium (2020) partitioned among different levels of structuration using an analysis of molecular variance (AMOVA)  (Excoffier, et al.
1992 ). We considered 3 hierarchical levels of stratification: between species, among populations within species, and within populations. The AMOVA was conducted with the TN93+gamma model of sequence evolution using the poppr R-package  (Kamvar, et al. 2014 ) and the AMOVA function derived from the APE v5. 6-4 R-package  (Paradis, et al. 2004; Paradis and Schliep 2019). Significance test was conducted using 1000 permutations. The analysis was conducted considering only populations from the Ag1000G that were taxonomically unambiguous (n=938, see Fig. 1 ), thus removing the hybrid taxonomic uncertain populations from The Gambiae (GMS) and Guinea-Bissau (GWA), as well as the taxonomically uncertain population from Kenya (KEA). Then, we quantified the level of genetic differentiation among populations by calculating the pairwise F ST differences using Arlequin v3.
This rarefaction analysis was applied for estimating the standardized number of segregating site (S), the number of haplotypes (H), the nucleotide diversity (π), the Tajima's D, and the Achaz's Y using python scripts and the c-library libDiversity. Results for each statistic were summarized as rarefaction curves. Isolation-by-distance (IBD) was computed following  Rousset (1997). We derived the unbounded level of genetic differentiation F ST /(1-F ST ) between pairs of populations and correlated the genetic distance with the geographic distance, expressed as the great circle distance (in log 10 unit) globally across species, and also for each species separately. The strength and significance of the IBD was tested using a Mantel test implemented in ade4 R-package  (Dray and Dufour 2007)  with 1000 permutations of the geographic distance matrix.
Detection of Wolbachia infection in natural populations of the Ag1000G MtDNA variation can be strongly impacted by cytoplasmic conflict with the endosymbiont Wolbachia  (Galtier, et al. 2009; Dong, et al. 2021) , and this latter has been reported in the AGC  (Baldini, et al. 2014; Shaw, et al. 2016; Gomes, et al. 2017; Gomes and Barillas-Mury 2018; Jeffries, et al. 2018; Ayala, et al. 2019; Chrostek, et al. 2019; Straub, et al. 2020; Jeffries, et al. 2021). Therefore, we used the unmapped WG-SR data to diagnose the infection status of each mosquito specimens of An. gambiae and An. coluzzii from the Ag1000G phase-II (The Ag1000G Consortium 2020). To that end, we screened the unmapped Ag1000G WG-SR data to detect Wolbachia specific sequences using MagicBlatst v. 1. 1. 5 (NCBI)  (Boratyn, et al.
2019)  following the procedure described in  Pascar and Chandler (2018). WG-SR reads that did not map to the nuclear reference genome were compared to selected reference wsp, ftsZ, and groE operon sequences isolated from Wolbachia samples that are representative of supergroups A to D. For our analysis, we used the Wolbachia sequence database of  Pascar and Chandler (2018) , which includes 61 sequences of Wolbachia type A to D. We added four new sequences assembled by the authors to their database. These are Wolbachia sequences of type B also found in An. gambiae specimens  (Pascar & Chandler 2018). Using the same (strict) detection criterion as in  Pascar and Chandler (2018) , a minimum of three reads with at least 98bp length and 95% identity had to match with the same Wolbachia sequences for the specimen to be considered as infected.
We selected randomly 100,000 biallelic SNPs from the free-recombining part of the genome of An. gambiae and An. coluzzii on chromosome 3L (from position 15Mb to 41Mb). To remain consistent with The Ag1000G Consortium (2017, 2020), we followed the same filtering procedure. We performed a LD-pruning using the function locate_unlinked from Python's module scikit-allel version 1. 2. 1  (Miles and Harding 2016)  to ensure independence among SNPs. Specifically, we scanned the genome in windows of 500bp slid by steps 200bp and excluded SNPs with an r 2 ≥ 0. 1. This process was repeated 5 times to ensure most SNPs in LD were removed. A PCA was then performed as described in The Ag1000G Consortium (2017, 2020), using scikit-allel version 1. 2. 1  (Miles and Harding 2016). Results were plotted highlighting specimens according to their locality of origin.
Then, it uses the calculated model to predict the data based on the independent variable and, finally, estimates a percentage of error in the predictions. As this method does not provide any significance test with a p-value, the 95% confidence interval was calculated using 1000 permutations. Finally, we conducted the formal GWAS-like analysis to evaluate the associations between the two main mtDNA phylogenetic lineages and the nuclear SNP genotype variation, considering the following covariates: the population structure using PC-scores, the Wolbachia infection status, and sex. We performed the GWAS using the program SNPTEST v2. 5. 4-beta3 (Marchini et al. 2007 ). The main mtDNA lineage were used as "phenotype values" defined as the phylogenetic mtDNA clusters from the hierarchical clustering of the NMDS analysis.
After normalization, we used the PC scores of the PCA obtained from Scikit-allele as continuous covariates, Wolbachia infection status and sex as binary covariates. Only unrelated samples (n=1053) and SNPs with a MAF ≥ 0. 01 (n=7,858,575, Table  10 ) were considered in this analysis. The threshold to assess the significance of the GWAS was defined following a Bonferroni corrected p-value accounting for the number of independent genomic blocks in the genome (0. 05/1,139,052 = 4. 39e-8). The number of independent genomic blocks in our data set was approximated by the number of independent SNPs as determined with Plink v 1. 90 (Table  S10 ). The results of the GWAS were plotted as Manhattan and QQ-plots in R. We produced a list of genes IDs that contained one or more significant SNPs from the GWAS within the CDS or within 1kb upstream or downstream from the CDS according to the general feature format file https://doi.