dataesr/scientific-paragraphs-categorization huggingface dataset:
Cloud phase determination Layers identified as cloud then undergo a cloud phase determination step. Here, the layer-integrated linear depolarisation ratio and attenuated backscatters are used in a manner similar to that used in CALIPSO data processing. Hu et al. (2009)  showed that this combination of parameters can effectively discriminate ice from liquid and horizontally oriented ice crystals. The liquid water relationship was calculated using Monte-Carlo lidar radiative transfer simulation results  (Hu et al. , 2007)  for a range of extinctions and particle sizes. For ATLID lidar, Monte Carlo simulations have shown that the relationship between layer-integrated attenuated backscatter and layer depolarisation is expected to be similar to those measured by CALIPSO  (Donovan et al. , 2015).
, 2013; Delanoë and Hogan, 2010 , https://www. icare. univ-lille. fr/ dardar/documentation-dardar-mask/, last access: 20 May 2023), which combines CloudSat and CALIPSO measurements. The AC-TC product will, however, incorporate the improvements in target classification provided by Earth-CARE's new high-spectral-resolution lidar and Doppler radar capabilities, as shown below. The AC-TC processor assigns all pixels in an EarthCARE granule to the classifications defined in Table  5  by merging A-TC and C-TC products according to a synergistic decision matrix (Fig. 6 ). Some preprocessing is needed for the input products so that the atmospheric profile obtained from each instrument has the same altitude sampling. ATLID and synergistic data products use a common joint standard grid (X-JSG;  Eisinger et al.
(2022). The Halifax scene is one of the three test scenes developed based on highresolution cloud-resolving model data from the Environment and Climate Change Canada (ECCC) GEM (Global Environmental Multiscale) model merged with CAMS (Copernicus Atmosphere Monitoring Service) aerosol data. The construction of the model fields is described in detail in  Qu et al. (2022). The radiative transfer and instrument simulation methods applied in order to produce EarthCARE ATLID, CPR, MSI and BBR  (Wehr et al. , 2023 ) simulated L1 data are described in  Donovan et al. (2023a). The ECCC model scenes were produced to facilitate the development and testing of the EarthCARE processors implemented in the Payload Data Ground Segment (PDGS). Figures  7  and 8  show some sample true model fields along with simulated L1 ATLID and CPR data for the Halifax scene.
A strict ltering was applied using R (R core team 2022), which removed 2,218 'duplicate' targets that fully or partially matched another target locus. These 'duplicates' re ect mainly bioinformatics errors (Stacks errantly splitting reads into separate loci) and/or repetitive elements or paralogous regions in the genome. With only unique genomic regions represented in our ltered dataset, BLAT was used again to map our 831 remaining targets to an existing chromosome-scale genome assembly  (Pearse et al. , 2019)  and using the same strict ltering, produced a list of 385 potential targets for the design of microhaplotype markers. Finally, we used the graphical interface of Stacks to assess overall variability, the potential for primer design, and the number of potential population-speci c alleles of our loci in the 32 sequenced individuals and used these criteria to select loci for primer design.
Primer design: We designed primers for 192 variable loci with the software Primer 3  (Untergasser et al. , 2012)  implemented in Geneious v. R11 (Kearse et al. , 2012), using the Santa Lucia (1998) melting temperature (Tm) calculation and salt correction method. The length range of primers was 18-27 bp (target length of 20 bp) and contained between 25 and 50% GC bases (optimal content of 50%), allowing a max Tm difference of 2°C between primers and otherwise using Primer 3 default parameters. We targeted an optimal product size of 130 bp (in the range of 90-145 bp), because short and uniform lengths of target sequences are important factors for PCR success (i. e. , uniform ampli cation among loci). Following initial testing and evaluation, loci with poor ampli cation or low polymorphism were removed from the panel resulting in the nal list of 124 presumably neutral loci (Supp.
Bioinformatics processing and panel nalization Reads were de-multiplexed by the MiSeq Analysis Software (Illumina inc. Shen et al. , 2005). Paired-end reads were combined using the Fast Length Adjustment of SHort reads (FLASH Magoč and Salzberg 2011) and mapped to the Stacks consensus sequences for the target loci using the Burrows-Wheeler Aligner (BWA-MEM, Li and Durbin 2009). Mapped reads were converted from Sequence Alignment/Map (SAM) les to Binary Alignment/Map (BAM) les with SAMtools  (Li et al. , 2009). We identi ed variable sites using FreeBayes (Garrison and Marth 2012); the positions of all SNPs for each locus were recorded in a variant call format (VCF) le. This VCF was then passed to microhaplot (Ng et al. , DOI: 10. 5281/zenodo. 820110), a microhaplotype dedicated software implemented as an R (R core team 2022) package and associated Shiny app (http://shiny.
Phylogeographic Utility In addition to the individuals genotyped for the panel development, samples from 28 additional populations were genotyped along with additional samples for some populations that were already included. This study is based on 124 loci genotyped in a total of 1,831 tissue samples from 58 populations (Supp. Table  1 ). Genetic diversity estimates (i. e. , He, Ho, Na, Fis, pairwise Fst) were calculated for each population and across all populations using the MStoolkit  (Park, 2008)  as well as the R package 'diveRsity' (R core team 2022;  Keenan et al. , 2013) , and Allelic Richness (A R ) was calculated using the software HPrare v1. 1  (Kalinowski 2005). Genetic distances between populations were also analyzed with phylogenetic trees from the software PHYLIP  (Felsenstein, 1993).
A neighbour-joining tree  (Saitou & Nei, 1987) , representing genetic distance between populations, was calculated with PHYLIP using pairwise chord distances (Cavalli-Sforza & Edwards, 1967). The stability of the tree topology was examined using the Seqboot program, with 1,000 bootstrap replicates. Discriminant analysis of principal components (DAPC) was performed with the R package 'adegenet'  (Jombart, Devillard & Balloux, 2010). The number of axes that maximize the results of the DAPC was de ned by a "DAPC Cross-Validation" test  (Jombart & Collins, 2015) , and 150 PCA components were kept for the population panel, for each DAPC. In order to observe core patterns more clearly, each of the multivariate analyses had clearly differentiated populations removed for the next DAPC. Finally, individual-based ancestry evaluations were also implemented using the model-based clustering program STRUCTURE  (Pritchard et al.
Population Genetics Statistics Estimates of genetic diversity were calculated using the nal dataset consisting of 124 loci and 1,831 genotyped individuals from 58 populations. No consistent deviations from HWE observed across populations. Average allelic richness among populations was 1. 79, mean values of both expected and observed heterozygosity were 0. 26, and the average number of alleles per locus was 2. 18. The Sheepheaven Creek samples displayed the lowest genetic diversity estimates for all metrics (Supp. Table  1 ), with expected heterozygosity (0. 06), observed heterozygosity (0. 06), allelic richness (1. 15), and number of alleles per loci (1. 18). On the other hand, steelhead from Feather River Hatchery and Mokelumne River Hatchery showed the highest expected heterozygosity (0.
Acknowledgements This work was supported by the  NOAA Southwest Fisheries Science Center, Interagency Agreement  (IA)  R15PG0006  between the  U. S Department of the Interior, Bureau of Reclamation  and the  National Marine Fisheries Service, ISblue  project,  Interdisciplinary graduate school  for the blue planet ( ANR-17-EURE-0015 ), and by a grant from the  French government  under the program " Investissements d'Avenir ". The authors thank  Eric Anderson  for help with primer design and for reviewing the draft manuscript. The authors declare no competing interests.
The balanced dataset always gives a better accuracy rate  [2]. In my previous paperwork, we have detected malicious applications by executing payload in the emulator by emulating sensor events, acute binary translations, and hardware-assisted virtualizations using system call sequences  [3, 4]. In the existing approach, feature importance is ignored and the calculation of feature weights is irrelevant to the classification model. The state-of-the-art for weighting methods in features makes the decision support system more competitive  [5]. The novel approach of recognizing malware by capturing the memory dump of the suspicious process can be represented as an image with Red-Green-Blue (RGB) combination using obfuscation and encryption techniques  [6]. The research is carried out in three different phases gathering memory data, RGB Image representation, and visual feature extraction.
In this paper, they have proposed a method for extracting features from metamorphic malware based on structural analysis obtained from theoretical and practical knowledge. They have also used dimensional reduction of features for finding a better accuracy rate  [7]. The unique datasets are created by executing payloads in sandboxing environment taking into concern the important parameters taken from benchmarking datasets. In our proposed work, we have taken recent malicious android applications based on various families like adware, backdoor, Trojan, zero-day exploits, file indicators, etc. with a minimum of 1000 samples from each category taken from benchmark repositories and created a dataset. The dataset is implemented with supervised learning logistic regression and unsupervised learning multilayer perceptron with multiple regression and builds the model.
Related Works Using binary classification approaches to examine Android malware is nearing a saturation point in machine learning research. In recent days, the use of clustering algorithms to analyse massive datasets, as well as feature selection to rate malware, may be the best option. When reviewing harmful android apps with Crowdoid, Sasidharan et al. [11]  address how sensitive data is maintained. A Hidden Markov Model (HMM) has been employed by the researchers to observe state transitions and their related data An API sequence, suspicious API filtering, classification, and test and training data were obtained from the disassembly of APK files using IDA pro (Integrated Disassembly). In addition to Cuckoo sandboxing, Ubuntu is employed as the operating system for the execution of payload.
Using elements like API, specific API calls, and categories based on signatures, Garcia et al. [12]  were able to extract macro viral traits. To detect Android malware, Zhang et al. [13]  employed opcode sequences to distribute learning datasets for training and testing on GPUs in the form of a convolutional neural network (CNN) and long short-term memory (LSTM) (LSTM). Receiver Operating Characteristic (ROC) curves were used to discover these rates. According to Yilin et al. , portfolio optimization with return prediction employing multilayer perceptron (DMLP), LSTM and CNN, as well as SVR, RF and ARIMA  [14]  is explained. According to Razgallah et al. [15] , malware detections were studied, and they made recommendations for future research. There have also been other limits mentioned that may lead to a more accurate anti-malware system.
There are sixteen recommendations based on both static and dynamic analysis. The feature extraction and classifier module of Repass Droid is used to monitor user-space activity. Google play, Drebin, Genome, AndroZoo, Contagio, VirusShare, Appchina, and PRAGuard datasets can be used to collect adware, backdoor Trojans, SMS attacks as well as signs of file enumeration. Android application and malware detection can be improved by using approaches such as reverse engineering, according to a survey by Sihag et al. [16]. Huang et al. [17]  illustrate how Android applications may be made covert for the payload using reverse auditing and malicious injection. Code obfuscation and prevention are also on the list of easy-toimplement APK security features. Android hardening analysis, emulation, game theory and device binding have all been used to achieve this goal.
Detecting malicious code in the system has also been a focus. Datadriven hybrid data assimilation is presented by Nellaivadivelu et al. [18]  as a multilayer perceptron-based approach for capturing state variables in Decision Analysis (DA) situations. For the purposes of observation, context, and analysis, this research drew on the solver package and forecast model sources based on satellites optimised for software. It was used in the work of Millar et al. [19]  to examine Android malware detectors. They concentrated on the most critical aspects for malware detection, these methods such as simple transformation, DSA transformation, and composite DSA transformation are compared to antivirus. Impossible features are extracted using bloom filters and a known malware family's signature database.
Dataset and Preliminaries The android malware dataset collected CCCS-CIC-AndMal-2020, DREBIN, and MALGENOME which consists of the benign and malicious applications of different categories  [25]. We have used AF Logical mobile forensics tool to extract features from the APK file and match them with the benchmark dataset for verifying the application. Tab. 1 shows the number of samples for each category along with the year of availability5 we also consider unique data sets collected from dynamic analysis through sandboxing approach. The summary of existing malware detection studies helps in understanding the type of classifiers with limitations as shown in Tab. 2. In our proposed approach we have overcome the weakness of the existing approach by using more permission vulnerable feature permissions and by fixing more target labels in MLP as shown in Tab.
As a countermeasure, the security check is done at the gateway, and the channel is secured. The Trojan as the name specifies, dropper drops payload in respective target place and launches event based on logical bomb used as a secret weapon to target victim. These are classified into various families and identified with the help of port numbers. This is an unusual type of attack where unexpected combinations of threats are exploited with kits to deliver file infectors into vulnerable systems. The users are driven to a malicious site that contains such kinds of kits. The stolen information is saved. (Dynamic Link Library) DLL file and uploaded to command and control servers. The ransomware makes victims have to pay some ransom for them to work again these types of malicious software when installed will stop some or a few events.
Existing Classifiers 4. 1 Logistic Regression In the traditional approach, under classification, the primary classifier is linear or logistic regression based on the need of the user for training the model. The dataset is made by collecting datasets from repositories along with the own result obtained from the hybrid analysis. In the first part, logistic regression is supervised classification where our target variable is a discrete value stating whether the application is malicious or not called binary classification  [27]. This model uses the sigmoid function given below in the equation. g z ð Þ ¼ 1 1 þ e ^À z (1) The logistic regression is categorized based on either low/high or high/low precision-recall where in the first case we reduce the false negative for sensitive data and in the latter reduce the false positives.
Naïve Bayes Classifiers This approach collection of algorithms where every pair classification is done independently, we divide it into the first feature matrix second response vector. The first part contains dependent features and later contains prediction in simple terms output. The equations given below explain the working principle of the classifier. P ðXjYÞ P YjX ð ÞP X ð Þ P Y ð Þ (6) where X and Y are events and checking whether P(Y)? 0 considering the probability of occurrence of X assuming Y is true termed as evidence. The prior and posterior probability have to be monitored. In our paper, we have used a Gaussian classifier. The demerit with this approach model will assume '0' as output in case the dataset has errors or missing values.
(7)  and  (8). This reduces to minimum variance. p 2 Ä size l À 1 ½ þsize l ½ ð Þ (8) W ½i ¼ np:random:randn size l; size l À 1 ð Þ Ã np:sqrt 2Ä size 1 À 1 ð Þ þ size l ð Þ The multiple output regression can have multiple output/target hidden layers which will be compared with features selected from the dataset. In our approach, we took a synthetic dataset taken from the repository Canadian university of cyber security and chose a data set to our requirement. The pseudocode given below explains the implementation of a model for decision-making with a better accuracy rate.
(  7 ) and (  8 ) Step 9: Load dataset into Google Colab platform. Step 10: Call make regression and pass number of samples, number of features, target and fix the random state. Step 11: Call Sequential Function. Step 12: Add Input and Output Layer. Step 13: Assign a random weight to the activator function. Step 14: Enumerate Fold and split train and test data from the dataset. Step 15: Fit and evaluate the model. Step 16: Print accuracy score, confusion matrix, and classification report.
Evaluation Measures and Experimental Setup The training and testing of the dataset are done by splitting data into 80% training and 20% testing data. We can have used label encoders for the target value we compute accuracy, precision, recall, and f-score for evaluating the proposed method, True Positive (TP) predicted the case to be in YES and present in it. False Positives (FP) predicted the case to be YES but NOT. True Negative (TN) predicted not to be in YES but NOT in it. In the last case, False Negative (FN) predicted the case not to be in YES but actually in YES. From the above results of MLP neural network implementation with backpropagation, we test the learning algorithms by comparing results with logistic regression in binary classification. We have 100 epochs for validating and testing the model.
The first process is cross-validation by splitting data taken 5 folds based on features from the dataset. We select random samples and test on split test and train data. The results show the accuracy, precision, and recall for the model and achieve the optimal result of 96. 5% for the neural network classifier compared to logistic regression as shown in Tab. 5 below achieved from Method MLP Multi-Output Algorithm. F-1 waited for the mean of precision and recall, the proportion of true positives among data classified as positive. Based on the comparison of existing MLP Multi-Output Algorithm (continued) The input for confusion matrix results obtained from classification algorithms and output is selected data where we take into the number of instances, proportions of predicted and proportions of actual represents the target variable.
11. The X-axis attributes contain a horizontal axis and the Y-axis attribute determines the position on the vertical axis like the color, size, and shapes of various attacks possible in android applications. The ranking of malware helps in reducing data and selecting attributes for making a better decision support system. The class-labeled datasets and scores attribute taking into consideration correlation with the class as shown in Fig. 12  below. The filter-method technique used for feature selection for extracting the feature selection is mainly used for binary classification based on nearest neighbor instance pairs. The selection is done by nearest hit and nearest miss instance neighbors to scoring as shown in the formula given below. W i ¼W i À x i ÀnearHit i ð Þ 2 þ x i ÀnearMiss i ð Þ 2 (9) Fig.
13  below shows the top feature permission gathered from the dataset and its respective scores using this technique. The novelty of the proposed work is to ensure that the best sensitive permission features can be extracted from larger data sets with a better accuracy rate compared to the existing work. In this approach, we have selected the best features that can be used for ranking android malware using multilayer perceptron with multiple regression and achieved better accuracy compared with logistic regression. The dataset collected from repositories is taken into dataset according to different malware categories that are seen to be more vulnerable in recent days. The unobserved android malware is identified and provides the solution for threats like zero-day exploits which cannot be tracked by antivirus scanners using a signature-based approach.
The technical part covers analyzing the android malware using sandboxing environment preparing reports from dynamic analysis and giving that as input for the deep learning model. The ranking method will enhance the system's ability to identify the threat and can be used in online antivirus scanners. We have tested with recent payloads collected from benchmarking dataset. As the functions of each application are increasingly powerful it has become mandatory for us to protect the user from vulnerable threats as we know that most of the Applications on the Android platform are not encrypted. As the next generation moving towards smart cities where most users will be using the android application for various purposes like banking, finance, fitness and health, social applications the possibility of threats might increase in the case of unencrypted applications as well as weaker applications.
In fine, the mobility data produced by OLYMPUS are combined with air quality values from CHIMERE to produce a dynamic representation of the exposure of city dwellers in urbanized areas  (Elessa Etuman et al. , 2020). The outline of this article is as follows. We will first present the operating principles (Section 2) and the implementation (Section 3) of the OLYMPUS-CHIMERE platform over the Greater Paris area. The outputs of the modeling platform will then (Section 4) be discussed and compared with outputs from a CHIMERE simulation using a state-of-theart bottom-up emission inventory built from reporting data. The validation of the simulation should confirm the ability of the OLYMPUS CHIMERE platform to produce a relevant and comprehensive representation of air quality in a given urban situation, using an activitybased approach for emission calculation.
OLYMPUS has demonstrated its ability to reproduce population specificities by learning from survey data, and to reproduce population mobility practices such as total and average commuting distances per individual, or modal split by distance class. Energy consumption data were also validated by feedback with values estimated by the regional energy agency for the whole territory. Finally, emissions produced by OLYMPUS for each class of emitters have been deeply compared and validated against reference inventories on the Paris region. For more details on the OLYMPUS operating principles and on the validation, please refer to Elessa Etuman and Coll (2018).
CHIMERE Emission data from OLYMPUS are used as input in the Eulerian Chemical Transport Model (CTM) CHIMERE. The CHIMERE model, which constitutes the second block of the platform, has been used since the early 2000s by a wide community of stakeholders, both for regulatory and research purposes regarding air quality and atmospheric chemistry from the regional to the hemispheric scale (eg. Cholakian et al. , 2021; Coll et al. , 2009; Menut et al. , 2020; Thunis et al. , 2017). CHIMERE is regularly evaluated in the frame of its operational missions and has been part of several model intercomparison and model-observation comparison studies  (Solazzo et al. , 2012; Theobald et al. , 2019; Ciarelli et al. , 2019). As a eulerian CTM, CHIMERE follows a deterministic approach integrating the evolution of pollutant concentrations over time and space for an area of interest.