dataesr/scientific-paragraphs-categorization huggingface dataset:
Information on the sources To ease the use of STeVECat, we provide a table with information on all spectra and associated extragalactic sources in the catalog. Following the conventions adopted by GammaCat and VTSCat, we report the assigned id that we use in the catalog for each source. We also provide sky coordinates in right ascension and declination for each source using the SIMBAD Astronomical Database. 3 When possible, we assign a redshift measurement to each source in STeVECat, either from a literature review or from dedicated spectroscopic redshift measurements  [8]. Each redshift has a tag corresponding to the reliability of the measurement (solid measurement, uncertain measurement, lower limit or unknown redshift). STeVECat lists 58 sources with a solid redshift measurement. In contrast, 39 sources with known redshift are represented in the catalog GammaCat,4 whereas 32 sources with known redshift are included in BW15.
Conclusion Since the first TeV detection of Markarian 421 in 1992, the VHE gamma-ray sky has grown significantly. To enable analysis of the entire extragalactic TeV sky, we created STeVECat, the largest database of extragalactic VHE spectra to date. STeVECat collects published extragalactic very-high energy spectra and associated observational meta-data. The data is formatted in accordance with the standards used in publicly accessible repositories, and the entire catalog can be quickly loaded with GammaPy, the Science Analysis Tool chosen by the Cherenkov Telescope Array Observatory. STeVECat collects 403 spectra from 173 journal publications, making it the most comprehensive collection of VHE extragalactic spectra to date. Each of these spectra reports the observed spectrum, which means it can be used to infer EBL properties.
Introduction Large-scale optimization algorithms, benefiting from fast convergence, capable of utilizing modern computing infrastructures, and dealing with distributed datasets are becoming compulsory for solving inverse problems in modern imaging  [23]. The evergrowing need for fast processing solutions that can operate on high-dimensional problems (i. e implying a huge number of variables) calls for the development of parallel methods harnessing the power of distributed computational architectures. In addition, the expansion of IoT systems and remote highly parallel computing induce new network issues with specific constraints. For instance, instabilities may occur whenever the volume of data dwarfs the memory capacity of a single agent or when the processing power is shared (potentially unevenly) between devices  [46].
YS k C and finally send the latter to the master. The majorant mapping is set as a block diagonal matrix, allowing synchronous parallel updates, as described in  [10]. This approach could be interpreted as a special case of BD3MG with a single worker (potentially composed of several subworkers) sending its update (potentially composed of several sub-updates) to the central process S k " tS k c u cPC. Thanks to the specific structure of the majorant mapping in BP3MG, there is no mismatch in information between central process and workers in this synchronous version, the delay vector i k always equals k. Nonetheless, the block diagonal form of the majorant mapping of BP3MG is at the price of a lower quality of approximation of the cost function, which might result in slower convergence. All methods are implemented in Python using the built-in Multiprocessing library as well as Numpy and Scipy for both data manipulation and scientific computing.
Each volume of interest is selected through an automatic search of connected components within a filtered and binarized version of the observed volume. Then, FIGARO method is ran, yielding parameters (i. e. , mean, covariance, scaling, shift) of a 3D Gaussian shape. This allows to build, through a simple interpolation strategy, a model for a depth-variant PSF with truncated support of size M " M X ˆMY ˆMZ " 21 ˆ21 ˆ21 (see more details in  [48, Sec. 2. 4] ). Since no ground truth is available, the regularization parameters pλ, δ, κ, ηq " p10 2 , 2, 10, 10 ´3q, are selected by retrospective visual inspection. The reconstruction shown in Figure  6  exhibits clear contrasts and sharpness Comparative videos of the original and restored volume are available at https://github. com/mathieuchal/BD3MG.
Continuous Data The Gaussian mixture model (Section 2. 1) is the most common approach for model-based clustering of multivariate continuous data. The model has been used since the origins of model-based clustering (see also Section 1. 1). The mclust software package  (Banfield & Raftery 1993 , Fraley & Raftery 2002 , Scrucca et al. 2016 ) is one of the most commonly used software packages for model-based clustering of continuous data. The model underlying mclust assumes that the data arise from a finite Gaussian.
SOFTWARE While a range of software packages is available to implement model-based clustering, here we describe five widely used packages. ■ The preeminent software package for model-based clustering is mclust  (Scrucca et al. 2016 (Scrucca et al. , 2022)) , which is used to cluster continuous data. The software uses Gaussian mixture models with parsimonious covariance structures and facilitates model-based clustering, classification, density estimation, dimension reduction, and visualization. ■ The poLCA software package  (Linzer & Lewis 2011 ) is a widely used package for clustering categorical data. Based on the latent class analysis (LCA) model, poLCA uses the EM algorithm and Newton-Raphson for model fitting. The package includes latent class regression, which is an extension of LCA to include covariates.
■ The clustMD software package  (McParland & Gormley 2017 ) clusters mixed data. The software can model data consisting of continuous, binary, ordinal and/or nominal variables using a parsimonious mixture of latent Gaussian variable models. ■ The flexmix software package  (Leisch 2004; Grün & Leisch 2007 , 2008)  implements model-based clustering for a range of component distributions. The package can handle a wide range of data types (continuous, categorical, count, etc. ) and allows for clustering with covariates. ■ The mixtools software package  (Benaglia et al. 2009).
Sequence Data Sequence data arise when observations consist of strings of categorical values selected from a finite set. Sequence data arise in many social science applications, in particular, where life-course trajectories are collected, as well as in many other application domains, including computer science and biology. Erosheva et al. (2014)  provided a review of a number of finite mixture models for lifecourse sequence data, including the group-based trajectory and growth mixture models. More recently,  Murphy et al. (2021)  developed a distance-based mixture model for clustering life-course trajectory data; this approach was implemented in the MEDseq software  (Murphy et al. 2022). A number of approaches for model-based clustering of sequences have been developed which are based on Markov chain models  (Melnykov 2016b , Zhang et al.
Rank Data Rank data arise when a set of judges are asked to list a set of objects in order of preference. The resulting data are ordered lists of objects. Rank data arise in a number of contexts, including voting, education, and marketing. A number of model-based clustering methods have been developed for ranking data. Gormley & Murphy (2006 , 2008)  developed finite mixtures of Plackett-Luce  (Plackett 1975 ) and Benter  (Benter 1994 ) models for clustering rank data; model inference was implemented in a maximum likelihood framework. Mollica & Tardella (2014 , 2017 , 2021)  further developed the finite mixture of Plackett-Luce modeling framework by considering an extension of the Plackett-Luce model and Bayesian methods for inference. Furthermore, the PLMIX software  (Mollica & Tardella 2020)  facilitates fitting the mixture of Plackett-Luce models (and extensions) to rank data.
An alternative approach to clustering rank data was proposed by Murphy & Martin (2003) using a finite mixture of distance-based models, known as Mallows models  (Mallows 1957). This modeling approach was extended by  Busse et al. (2007) , who developed computationally efficient algorithms for inference. More recently, Bayesian mixtures of Mallows models have been proposed for clustering rank data by  Vitelli et al. (2017)  and  Liu et al. (2019). The BayesMallows software  (Sørensen et al. 2020)  facilitates fitting mixtures of Mallows models, with a number of rank distances. Related to the Mallows modeling approach, Biernacki & Jacques (2013) proposed a model-based clustering approach for rank data based on insertion and sort algorithms.
Network Data Network data arise when the connections (or relations) between entities or nodes are recorded. Often the connections are binary in nature, but it is also possible to have weighted connections (showing the strength of the connection); these data arise widely in biology and the social sciences. The emphasis when clustering social network data is on clustering the entities into meaningful groups. A number of models have been proposed for model-based clustering of network data. For example,  Snijders & Nowicki (1997)  and  Nowicki & Snijders (2001)  proposed the stochastic blockmodel for clustering network data. The stochastic blockmodel assumes a latent clustering variable and that connections are formed independently, conditional on the latent clustering variable. The stochastic blockmodel has been extended in a number of directions, including the mixed membership stochastic blockmodel  (Airoldi et al.
2008 ) and the overlapping stochastic blockmodel  (Latouche et al. 2011). The latent position cluster model (LPCM), developed by  Handcock et al. (2007)  for clustering network data, is based on a latent Gaussian mixture model. The LPCM was extended by  Krivitsky et al. (2009)  to account for complex structures that arise in network data. The latentnet software  (Krivitsky & Handcock 2008 , 2020)  has been developed to cluster network data using the LPCM. Gormley & Murphy (2010)  include node covariates within the LPCM, with implementation available through the MEclustnet software package  (Gormley & Murphy 2019). Wasserman et al. (2007 ), Salter-Townshend et al. (2012 ), and Bouveyron et al. (2019, chapter 10)  provide reviews of a number of model-based clustering approaches for network data.
Variable Selection Many applications of model-based clustering involve high-dimensional data, which are difficult to model. To extend the scope of model-based clustering to high-dimensional settings, a number of approaches have been developed for simultaneously clustering data and performing variable selection. Raftery & Dean (2006)  proposed a model-based clustering approach that includes variable selection; the approach is based on the same Gaussian mixture model as mclust (Section 2. 1). This approach was further developed and extended by  Maugis et al. (2009a,b)  and  Celeux et al. (2011). The clustvarsel software package  (Scrucca & Raftery 2018 ) implements this variable selection approach. Dean & Raftery (  2010 ) also developed a model-based clustering approach with variable selection for categorical data, using the LCA model (Section 4.
2). This approach was extended by  Fop et al. (2017)  to partition data variables into clustering, redundant, and irrelevant variables; both approaches are implemented in the LCAvarsel software package  (Fop & Murphy 2017). Detailed reviews of variable selection methods and clustering high-dimensional data are given by  Celeux et al. (2014) ,  Fop & Murphy (2018) and McParland & Murphy (2019).
For example, DNA methylation data are collected at a scale of circa 1 million variables being recorded per observation, of which there are typically few. While mixtures of latent variable models have potential given their dimension reduction characteristics (e. g. ,  McLachlan et al. 2002) , they break down at such scale. Similarly, for data sets with large n, the reliance on the likelihood function often means the model-based clustering approach is intractable. While  Antonazzo et al. (2021)  proposed a binned technique for scalable model-based clustering on huge data sets, there is much scope for further novel developments here. Section 3. 3 alluded to the trade-off between the use of complex component densities and the number of clusters. The idea of cluster merging  (Baudry et al. 2010) , as used to arrive at Figure  2 , suggests one way of alleviating this tension.
ACKNOWLEDGMENTS The authors would like to thank the members of the Working Group in Model-Based Clustering for discussions that strongly informed this work. This work was supported by  Science Foundation Ireland  grants ( 12/RC/2289_P2 ,  16/RC/3835 ), a stay at  Collegium de Lyon , and  National Institutes of Health (NIH)  grant  R01 HD-70936  from the  Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).
Haystack Technologies has developed DASH7 XR Mode version 2. 0, which can significantly improve the range performance of LoRa networks. The DASH7 XR Mode uses NASA-derived error correction resulting in an enhancement in range performance by 10-20 times when compared to a comparable LoRaWAN implementation. With this advancement, developers can safely work with better data rates, expect extended battery life, and lower levels of packet error. The recent combination of three encoding technologies which allows LPWAN-types of packets to yield maximum gain has been used to enhance the DASH7 networking stack. The improvements in the Haystack decoder implementation allow data rates as high as 50 kbps an adaptive modulation of encoding rate, and better real-world signal propagation over LoRaWAN by 14 dB at maximum range.
ELTRES can establish stable communication over 100km at a speed of 100km/h while consuming very little battery power. This device uses the Global Navigation Satellite System (GNSS) and encrypts data at the physical layer, making it ideal for outdoor applications. ELTRES has a receiver sensitivity of -144dB, an effective transmission rate of 80bps, and supports only unidirectional communication. However, it has an advantage over competing technologies due to its precise GNSS synchronization which enables the device to broadcast more bursts than other technologies, resulting in better system capacity, higher frequency use, efficiency, and scalability. ELTRES can utilize a total of 95625 slots across 17 channels throughout Europe thus making it more efficient than Sigfox and LoRaWAN. Figure  1  illustrates that ELTRES comprises of two distinct components: a transmitter utilized by the user to transmit data and a receiver that receives the transmitted data.
A. A SOFTWARE SYSTEM FOR HIGH THROUGHPUT AND LOW LATENCY DATA TRANSFERS IN WIDE-AREA NETWORKS. The software system UDT+FEC addresses the various issues related to the network performance in high bandwidthdelay product (BDP) networks, and offers special benefits for transferring large amounts of data over wide-area networks (WAN), as suggested in  [27]. This system uses the 2D-XOR FEC extension of UDT (user data-gram protocol) to achieve comparable throughput and latency with other advanced tools. Moreover, UDT+FEC offers the advantage of lower variance in message arrival times at the receiver. The relative benefits of UDT+FEC were assessed by comparing it with GridFTP coupled with CUBIC, BBR, and parallel streams, using different benchmarks. It was found that UDT+FEC exhibits significantly less volatility in message delay arrival time as measured by message inter-arrival times in one-way transmissions  [28].
The utilization of this methodology facilitates concurrent processing, thereby empowering the encoder and decoder to manage information at a pace that surpasses the capability of singular encoder and decoder circuits. The method under consideration presents a notable benefit in terms of providing parallel processing capabilities to enable rapid encoding and decoding operations. This renders it a viable option for handling binary data streams with high data rates. LoRaWAN is a widely adopted IoT protocol for utilization in the physical layer for long-range wide-area networks. The physical layer of networks has been the subject of a recent article  [35]  proposing a novel approach that employs an iterative identification and decoding (IDD) technique in conjunction with a blind channel estimator.
The table 2 summarizes various research papers that propose solutions for improving the performance of wireless communication systems along-with their pitfalls as research gaps. The LoRaFFEC protocol proposes fragmentation and forward error correction to improve data delivery rate for LoRaWAN deployments with varying packet losses. However, the literature on ECCs for wireless communication systems does not utilize the constraints faced by wireless body area network (WBAN) nodes during ECC design, due to lack of emphasis on the impact of using cell design methodology logic style on other performance parameters. Other papers propose the use of multilevel coding schemes and massive MIMO in cognitive radio networks, explore the energy efficiency of specific ECC implementations in wireless sensor networks, and propose FEC-based systems which can provide high throughput and low latency on wide-area networks.
Paper does not investigate the effect of varying channel conditions or the impact of the signal-to-noise ratio (SNR) on the performance of the different coding schemes  [24]  The paper proposes a new design for moderate-length high-rate irregular LDPC codes that can be encoded efficiently. Paper does not investigate the performance of the proposed codes under different channel conditions, which could affect their error-correcting capabilities  [26]  The paper presents an expression for critical distance and shows that analog the decoders are the most energy-efficient ECC solution for certain WSN environments and applications. Need to determine energy efficiency of specific ECC implementations in WSNs in different environments, frequencies, and decoder types. [28] The paper presents UDT+FEC, a software system that uses 2D-XORforward error correction to provide high throughput, low latency, and low-variance latency on wide-area networks, while also identifies future work to optimize the implementation and its evaluations under different conditions.
In 2018, PLOS Biology announced CellProfiler 3. 0, which has become one of the most used pieces of image analysis software in biology. The rapid adoption of this software speaks to the importance of user experience to disseminate new methods of bioimage informatics. This article is part of the PLOS Biology 20th Anniversary Collection. Microscopy has drastically evolved over the past few decades, pushing back the frontiers in all dimensions and bringing new insights about the organization of biological processes in space and time. Microscopy is no longer an observational tool, but a quantitative tool for biology. This progress has been made as a result of collaborative efforts between the fields of biology, physics, and chemistry, but also computer sciences. Nowadays computational methods are one of the components of most microscopic acquisitions (e.
Furthermore, it was one of the first software platforms to pave the way for deep learning integration, allowing users to apply existing models or train new ones. Software is an essential component of bioimage informatics, enabling researchers to access and utilize newly developed methods and algorithms. However, software is more than just a gateway to accessing these methods; it is also crucial for disseminating these methods to a broader audience. From a user perspective, a new investigation comes with the need to finetune parameters of workflows, and the power and impact of CellProfiler comes from its usability for this purpose. The minimal criteria for usability that software should meet to ensure it has a broad impact have already been discussed  [8] , criteria that are easily met by CellProfiler  [6, 7].
The effort required to develop software usability takes time and resources but is not recognized by the academic world, including funders, as a scientific output. These efforts have to be shared between bioimage informaticians and biologists or bioimage analysts who are the software users. Software users can help by acknowledging the use of implemented methods instead of only citing the main software platform's reference paper. They also need to understand the main principles of the methods implemented in the software in order to apply them correctly. For this purpose, good teaching materials should explain not only how to use the software but also how the methods work and under which assumptions. Advertising and getting feedback on how to use methods is also important; the Image. sc forum is a great venue for that  [10] , as well as repositories of bioimaging software such as BioImage Informatics Index.
PPG is part of the  IBISA MicroPICell facility (Biogenouest ), member of the national infrastructure  France-Bioimaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). PPG is supported by the  ANR CROCOVAL  ( ANR-18-CE45-0015 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
2023; Vacher et al. 2023a) , and the following results therefore represent an optimistic view of the true sky in terms of complexity. In reality, there will be additional structures, both spatially and spectrally, and fully resolving these would in principle require even more data than considered here, for instance in terms of additional frequency bands, wider frequency range, or dedicated 3D constraints on the Galactic magnetic field. The following optimization results should therefore be considered as a lower bound on what is actually needed for a future production analysis. The paper is organized as follows. In Sect. 2 we provide a brief overview of the LiteBIRD concept and current baseline, and we define a set of alternative configurations to be considered. In Sect. 3 we describe the simulations used for the present analysis, and in Sect.
Simulations The LiteBIRD simulations considered in this paper are generated directly in pixel space, and account only for sky signal and white noise. The sky emission components for all 22 frequency channels are generated using the PySM (Python Sky Model; Thorne et al. 2017) package. The LFT and MFT channels are defined according to the baseline instrument configuration in LiteBIRD Collaboration (2022), while the HFT channels are generated in six versions, called M0-M5, as tabulated in Table  1. Here, M0 is the LiteBIRD baseline, and M1-M5 are the frequency extended data sets. The instrumental summary parameters listed in this table are derived from first principles using the same methodology as the baseline configuration in LiteBIRD Collaboration (2022), properly accounting for realistic optics, bandpass, and bolometer effects.
1. To account for possible spatial variations in the foreground SED, the linear combination weights are computed separately in needlet space, allowing for localized optimization both in terms of sky position and angular scale. This method has been applied to a wide range of data sets, including Planck (e. g. , Planck Collaboration IX 2016) and LiteBIRD. For further details, see Appendix B. 2. Finally, we consider the more recently developed constrained moments ILC method (cMILC;  Remazeilles et al. 2021) , which combines the moment-expansion and NILC methods. Specifically, additional constraints are imposed on the ILC weights that explicitly cancels out individual foreground components through their Taylor-expanded SEDs. Each moment corresponds to one additional linear constraint in the ILC solution, and the combination of foreground-specific and the variance constraints is solved through a single Lagrange multiplier system.