dataesr/scientific-paragraphs-categorization huggingface dataset:
g. pipe arrangement, materials, etc. ) and design considerations such as accounting for any additional load and the effect of differential movement when not all piles are used for heat exchange. The guideline refers to the use of the program PILESIM for the calculation of the thermal performance of an energy pile installation (see Section 2. 2. 1). Detailed information for other EGS are not provided.  In the United Kingdom, the Ground Source Heat Pump Association has published "best practice" guidance  [118]  covering design, installation and materials standards for projects incorporating thermally-activated piles. In terms of design, the guidance discusses the roles and responsibilities of the various parties involved in the project, and key aspects of both the thermal and geotechnical analysis of the thermally-activated pile foundation system.
Consequently, for less common EGS, such as tunnels and basements, there is a need for more information regarding the internal environmental conditions that would dictate the heat flow potential. One barrier to the development and implementation of further analytical and numerical methods is the scarcity of datasets for validation of analysis methods. Partnerships between industry and academia to develop such datasets on operational schemes will be key to future developments and hopefully encourage uptake on EGS systems in the future. A number of short term thermal response test type datasets are becoming available and longer term datasets are starting to be captured  [71]. However, much remains to be done to move to fully validated analysis tools. In addition, it is essential that tools developed in research, are made available in a practice ready format.
Packet Structure Generally, a packet consists of two different parts, namely, the header field and the payload field. The header part is a set of bytes that are usually used to determine packet characteristics, for instance, Start Delimiter, Frame Type 'Binary/ASCII', Node ID and Frame Sequence. The frame payload part is used to store sensor data. Figure  3  shows the ASCII frame structure of the Libelium Waspmote that is used in this work. After applying the encryption function, the Waspmote Frame encrypted with the Advanced Encryption Standard (AES) key is specified as input. This encrypted information becomes the payload of the new encapsulated frame as in Figure  4. Although, the maximum default frame size in the Waspmote is 150 bytes per frame, the frame size depends on three characteristics:  Encryption  Type of XBee module used  Transmission mode.
Packet Structure Generally, a packet consists of two different parts, namely, the header field and the payload field. The header part is a set of bytes that are usually used to determine packet characteristics, for instance, Start Delimiter, Frame Type 'Binary/ASCII', Node ID and Frame Sequence. The frame payload part is used to store sensor data. Figure  3  shows the ASCII frame structure of the Libelium Waspmote that is used in this work. After applying the encryption function, the Waspmote Frame encrypted with the Advanced Encryption Standard (AES) key is specified as input. This encrypted information becomes the payload of the new encapsulated frame as in Figure  4. Although, the maximum default frame size in the Waspmote is 150 bytes per frame, the frame size depends on three characteristics: • Encryption • Type of XBee module used • Transmission mode.
Memory Requirement One of the major challenges in the establishment of a high security system in WSN is the limitation of memory capacity and storage. The constructed scheme occupies only a small memory size and this makes it suitable due to the limited storage capacity in sensor nodes. The binary sketch size of the uploaded program and the bootloader program stored in the Flash memory is 57,230 bytes of a maximum of 122,888 bytes available, and 4987 bytes of chip memory SRAM of a maximum of 8192 bytes available. Figure  7  shows the key administration operation in our scheme. If the leader node revokes any node member from the network, the key update phase will be performed by two of the revoked node neighbors in the line topology.
Memory Requirement One of the major challenges in the establishment of a high security system in WSN is the limitation of memory capacity and storage. The constructed scheme occupies only a small memory size and this makes it suitable due to the limited storage capacity in sensor nodes. The binary sketch size of the uploaded program and the bootloader program stored in the Flash memory is 57,230 bytes of a maximum of 122,888 bytes available, and 4987 bytes of chip memory SRAM of a maximum of 8192 bytes available. Figure  7  shows the key administration operation in our scheme. If the leader node revokes any node member from the network, the key update phase will be performed by two of the revoked node neighbors in the line topology. size of the uploaded program and the bootloader program stored in the Flash memory is 57,230 bytes of a maximum of 122,888 bytes available, and 4987 bytes of chip memory SRAM of a maximum of 8192 bytes available.
Practical Implementation of Proposed Framework In this section, we present the implementation and test measurements of the scheme. The implementation involves four Waspmote nodes, a Waspmote Gateway, four XBee 802. 15. 4 Pro modules with antennae (Figure  8   Below is an outline of the three scenarios used to provide measurements to obtain the optimum configuration. The three scenarios were: (1) Four nodes and the gateway at a distance of 80, 120 or 160 m between end points in line topology. (2) Three nodes and the gateway at a distance of 80, 120 or 160 m between end points in the line topology. (3) One repeater node between sender and the gateway at a distance of 80, 120 or 160 m between end points in the line topology. Figure  9  shows the physical setup of the scheme, where each Waspmote was placed on a fixed pole at a height of 80 cm from the ground.
Received Single Strength Indicator Measurement In order to ascertain the received single strength indicator (RSSI) a test involving the transmission of a fixed amount of data was performed. In this experiment, the received single strength indicator (RSSI) was measured at the gateway. Figure  10  depicts the average value of RSSI which was measured after receiving 300 encrypted packets of 79 bytes in size at a baud rate of 115,200 bps.
Received Single Strength Indicator Measurement In order to ascertain the received single strength indicator (RSSI) a test involving the transmission of a fixed amount of data was performed. In this experiment, the received single strength indicator (RSSI) was measured at the gateway. Figure  10  depicts the average value of RSSI which was measured after receiving 300 encrypted packets of 79 bytes in size at a baud rate of 115,200 bps. These values were measured in the three aforementioned scenarios of distances and repeaters. The signal strength in the 80 m scenario was the strongest in the all case of one, two and three repeaters at exactly -48, -41 and -36 dB respectively. However, in the case of one repeater the 160 m scenario had the minimum signal strength value at -64 dB. While, in the case of all possible scenarios (as mentioned in Section 3) the maximum achievable signal strength is -36 dB, which is the 80 m distance with three repeaters scenario.
Data Set DEMETER was a low-altitude spacecraft which operated between 2004 and 2010. It had an approximately circular orbit with an altitude of about 700 km. The orbit was nearly Sun synchronous, i. e. , the measurements were always performed close to either 10:30 LT or 22:30 LT. The instruments on board performed both wave and particle measurements. These measurements were continuous at geomagnetic latitudes lower than 65 ∘. In the VLF range that we are interested in, frequency-time spectrograms of power spectral density of one electric and one magnetic field component were calculated on board. These spectrograms cover the frequency range up to 20 kHz. The frequency resolution is about 19. 53 Hz, and the time resolution is about 2 s. As the magnetic field data suffer from a significant amount of interferences in the frequency range 1-8 kHz, only electric field data will be used in the present study  [Berthelier et al.
The thick red curves show the positions of the spacecraft at the times when QP emissions were observed simultaneously by DEMETER and by the Kannuslehto station. The thin orange curves show the positions of the satellite at the times when QP emissions were observed only on the ground. Finally, the thick blue curves show the satellite positions at the times when QP emissions were observed by DEMETER, but not on the ground. The Universal Time of the beginning of each satellite half-orbit is shown. Note that during the daytime half-orbits, the satellite moves from north-east to south-west, while during the nighttime half-orbits, the satellite moves from south-east to north-west. The parts of the satellite orbits where the emissions were particularly well pronounced both in DEMETER and ground-based data are marked by capital letters.
These will be used for a detailed conjugate analysis. It can be seen that at the times when both DEMETER and Kannuslehto observe the emissions, DEMETER can be as far as 102 ∘ in longitude. Curiously, there are times when the satellite is very close to the Kannuslehto station, but the QP emissions are observed only by DEMETER. A frequency-time spectrogram of 90 min of observations at Kannuslehto station is shown in Figure  2. The wave power spectral densities expressed in electric field units are color coded according to the scale on the right-hand side. The data were measured on 26 February 2008 between 10:40 UT and 12:10 UT. QP emissions can be identified at frequencies between about 1000 and 2500 Hz. While in the lower frequency part the QP emissions are hiss like and incoherent, individual QP elements are well pronounced at frequencies above about 1500 Hz.
There appear to be some fine-scale differences in the inner structure of individual QP elements, but the limited frequency-time resolution of the DEMETER data does not allow us to investigate these in great detail. It is curious that the intensity of the QP emissions is so much different between DEMETER and Kannuslehto during the interval A, when DEMETER is rather close. This is principally the same phenomenon as already seen in the overall map in Figure  1 , and it will be discussed in more detail in section 4. Moreover, it can be seen that while the intensity of individual QP elements remains about the same in Figure  3d , the intensity of QP elements observed by DEMETER in Figure  3b  varies considerably. This is related to the latitudinal movement of the satellite, and it will be discussed in more detail later.
It is interesting to compare the exact timing of individual QP elements between DEMETER and Kannuslehto and to determine whether there is a detectable time delay between the two instruments or not. This is done in Figure  4. We selected the frequency range between 1900 and 2000 Hz, and we plotted the power spectral density in this frequency range as a function of time. Note that principally the same result is obtained independently on the chosen frequency band, as long as it is the frequency band which contains the QP elements. Figures  4a  and 4b  correspond to the time intervals A and B, respectively; i. e. , they are frequency cuts from Figures  3a  and 3c  and 3b and 3d. The black curves and the scales on the left-hand side correspond to the data measured by the DEMETER spacecraft. The data measured at the Kannuslehto station are depicted using the red curves and the scales on the right-hand side.
The red curve in Figure  4a  is quite noisy, as the wave intensity detected by Kannuslehto during this time interval was rather low. Nevertheless, the four peaks in the wave intensity corresponding to the four distinct QP elements can be identified. The times when these intensity peaks occur in DEMETER and Kannuslehto data are roughly the same, but a small systematic time delay might be hidden in the noise. On the other hand, there is a clear time delay observable in Figure  4b. The red curve is systematically shifted toward earlier times as compared to the black curve; i. e. , the individual QP elements are detected first by the Kannuslehto station, and then, with a distinct time delay, by the DEMETER spacecraft. The black and the red crosses in Figure  4b  show the beginning/ending times of individual QP elements as observed by DEMETER and Kannuslehto, respectively.
These were determined as the intensity minima in between the QP elements, and they will be used in the further analysis. In order to determine numerically the time delays corresponding to the dependencies from Figure  4 , a correlation analysis has been employed. The obtained results are shown in Figure  5. It shows the time delays between the detection of individual QP elements by DEMETER and the Kannuslehto station as a function of frequency. The data points that are plotted by the same symbols of the same color were obtained for the same time interval, but at different frequency ranges. The used time intervals are marked by capital letters, corresponding to the parts of the satellite orbits shown in Figure  1. It can be seen that during the time intervals A and E, the detected time delays were generally very low, below the time resolution of our analysis (approximately 2 s for the DEMETER data and 1 s for the ground-based observations).
Acknowledgments DEMETER was a CNES mission. We thank the engineers from  CNES and scientific laboratories (CBK, IRAP ,  LPC2E , LPP, and  SSD of ESTEC)  who largely contributed to the success of this mission. DEMETER data are accessible from https://sipad-cdpp. cnes. fr. IMAGE magnetometer data can be downloaded from http://www. ava. fmi. fi/image. We thank the institutes who maintain the  IMAGE Magnetometer Array. The work of F. N. and B. B. was supported by  GACR  grant  15-01775Y  and GAUK grant  300216. The work of O. S. was supported by  MSMT  grant  LH 15304 ,  GACR  grant  14-31899S , and by the  Praemium Academiae  award from the  CAS.
Simulation codes Actiwiz is a software developed at CERN to build a radiological hazard assessment for an arbitrary material exposed to the radiological environment of the accelerator complex  [13, 14]. The application was developed to give quick answers to general questions about radiological hazards without the need for the user to implement complex input files with a Monte Carlo code such as FLUKA  [15, 16]. The developers have run thousands of FLUKA simulations  [15, 16]  of nuclide inventories on different materials for 42 typical hadronic spectra and for various positions inside the accelerators' tunnels. The results of these simulations are stored as a database in Actiwiz  [13, 14]  and the user can run calculations on predefined simulated scenarios. The radiological environments available for calculations represent all the accelerators in CERN's complex and include the Linac4 (160 MeV), the PS Booster (1.
The bootstrap, which is a resampling technique with repetition, is used to estimate distributions of the concentration of Ni-63 around an average value. The Ni-63 activities obtained were compared with census data. The present study results suggest that the bootstrap is a robust tool to estimate average activity of DTM nuclides from samples and that this estimation is more precise when the number of repetition and samples increases. It is also found that the estimation of the activity of Ni-63 is more precise when values below the detection limit are excluded. The results of the simulations performed are in very good agreement with results from census data. When the number of resampling is above 500, the relative error of Ni-63 concentration from bootstrap with respect to census data is below 25%.
Affordable CD4 T cell counting has gradually become possible by using simple, compact, robust and low-cost new generation cytometers operating as single-platform volumetric instruments without the use of expensive microbeads  [9, [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]. The recently developed Muse ™ Auto CD4/CD4% system (EMD Millipore Corporation, Merck Life Sciences, KGaA, Darmstadt, Germany) consists of a compact, portable and easy-to-use cell analyzer, software and optimized reagents. Finally, the aim of the present study was to evaluate the usefulness of the simplified Muse ™ Auto CD4/CD4% system for CD4 T cell numeration in absolute count and in percentage, compared against reference flow cytometry method. Because it is critical that country programs consider whether CD4 analyzers can give accurate and reproducible results, as well as being appropriate for the setting, we focused our field evaluation on bias and misclassification probabilities of different CD4 T cell thresholds that are important for ART initiation as a priority according to WHO guidelines.
CD4 T cell measurements CD4 T cell counting was performed in parallel on 2 different systems: (1) the BD FACSCount; and (2) the Muse ™ Auto CD4/CD4% system. Both the BD FACS-Count and the Muse ™ Auto CD4/CD4% systems are based on the flow cytometry principle, using BD FACS-Count and Muse ™ softwares enable automated identification of the lymphocyte populations of interest, and calculation of the CD4 T cells in absolute counts and percentages. The BD FACSCount consists of a single-platform benchtop, a modified flow cytometer, using a standard reagent kit  [9]. The instrument is equipped with a green laser for excitation of 2 fluorescent parameters: phycoerythrin (PE) and tandem fluorochrome composed of PE and indodicarbocyanine (Cy ™ 5) (PE-Cy ™ 5). Recently, new reagents and software have been updated to allow the additional measurements of CD4% essential for monitoring paediatric patients in addition to absolute CD4 T cell  [21] [22] [23].
In this study, the new FACSCount CD4 reagent kit was used, consisting of a single tube containing a mixture of 3 monoclonal antibodies, CD4/CD14/CD15 (conjugated with PE/PE-Cy ™ 5/PE-Cy ™ 5, respectively), a nuclear DNA fluorescent dye and a known number of fluorescent microbeads. Monoclonal antibody to CD14 recognizes a human monocyte/macrophage antigen, whereas monoclonal antibody to CD15 recognizes a human myelomonocytic antigen. The Muse ™ Auto CD4/CD4% system consists of a compact, portable and easy-to-use cell analyzer, software and optimized reagents. The Muse ™ analyser uses novel, miniaturized optics and microcapillary laser technology. In brief, a sample of fluorescent labeled cells is aspirated into a uniquely proportioned microcapillary flow cell. A green laser excites the cells and each cell emits a signal that is individually detected by photomultipliers and a photodiode.
The Muse ™ Auto CD4/CD4% kit is intended to be performed on a Muse ™ Cell Analyzer with Muse ™ Auto CD4% Software, which includes 3 modules: Muse ™ Auto CD4/CD4%, System Check and Complete System Clean. The Muse ™ Auto CD4/CD4% kit is intended to identify and quantify both absolute CD4 T cell counts and CD4% values in whole blood samples. The kit consists of the Muse ™ Auto CD4/CD4% antibody cocktail, a proprietary mixture of antihuman lymphocyte antibodies and a monoclonal anti-human CD4 antibody. The kit also contains Muse ™ 1× Lysing Solution to lyse erythrocytes. The anti-human lymphocyte antibodies detect all human lymphocytes. The CD4 antibody identifies human CD4-positive helper/inducer T cells (HLA Class II reactive) and recognizes a 60-kDa surface antigen. Monocytes also express CD4 but at lower density and have no co-expression of the other lymphocyte markers detected by reagents in this kit.
The CD4% values are the absolute counts of the CD4 T helper cells expressed as a percentage of total lymphocytes in EDTA whole blood samples from adult and pediatric donors. All relevant data and results are immediately available. To ensure quality control of the Muse ™ Auto CD4/ CD4% system and the reference flow cytometric BD FAC-SCount with regard to the performances of the analyzers, the same lots of reagents were used throughout the study. To assess the accuracy of the reference BD FACSCount system, internal quality control reagents consisting of 3 known concentrations of fluorochrome-integrated beads [low (50 beads/µl), medium (250 beads/µl) and high (1000 beads/µl)] were analyzed prior to measuring each batch of stained whole blood samples. Systematic contractual maintenance of the BD FASCCount analyzer was carried out each year by Becton-Dickinson, Douala, Cameroon.
Statistical analyses The following definitions for adults, adolescents, children and infants were used according to the 2015-revised WHO recommendations  [6] : an adult is a person older than 19 years, an adolescent is a person 10-19 years old inclusive, a child is a person younger than 10 years and an infant is a child younger than 1 year of age. The Method Validator software, version 1. 1. 9. 0. (Philippe Marquis, France) and the SAS-PC software (version 8. 2, SAS Institute, Cary, NC, USA) were used for statistical analyses. Firstly, correlations between the absolute and percentage CD4 T cell counts obtained by the reference BD FACSCount system and the Muse ™ Auto CD4/CD4% system were established by the unweighted linear regression as well as the Passing-Bablok nonparametric method which is fairly less sensitive to outliers  [25].
In an attempt to make the processing of RAD-seq data easier and allow rapid and automated exploration of parameters/data for phylogenetic inference, we introduce the perl pipeline RADIS. Users of RADIS can let their raw Illumina data be processed up to phylogenetic tree inference, or stop (and restart) the process at some point. Different values for key parameters can be explored in a single analysis (e. g. loci building, sample/loci selection), making possible a thorough exploration of data. RADIS relies on Stacks for demultiplexing of data, removing PCR duplicates and building individual and catalog loci. Scripts have been specifically written for trimming of reads and loci/sample selection. Finally, RAxML is used for phylogenetic inferences, though other software may be utilized.
Description Processing of raw data has been split up into two steps: data cleaning and data analysis (Supplementary Fig. S1 ). Example datasets are provided with the package (processing time <1 min on 8-cores of a 16-core Linux, 2. 9 GHz, 64 GB RAM computer). Users can choose to (i) process their raw Illumina data up to phylogenetic tree inference (RADIS. pl); (ii) perform only data cleaning (RADIS_step1_data_cleaning. pl) or (iii) perform only data analysis (RADIS_step2_data_analysis. pl). RADIS operates on the basis of a configuration file (RADIS. cfg) in which users provide parameters values to be tested and paths to external software. The software comes with a fully annotated configuration file to facilitate the initialization. A correspondence between barcodes and sample codes can be provided to allow file renaming (barcodes_lib_names.
txt). Progress of the analysis can be followed (stdout/stderr files). Output files and necessary subdirectories are automatically created in a directory specified by the user. Data cleaning-Reads that do not pass Illumina's filtering are discarded. RADIS relies on process_radtags from the software pipeline Stacks to demultiplex data. Users can choose to remove nucleotides from the 5 0 and 3 0 ends of forward and reverse reads (e. g. to remove enzyme cut sites or bad quality nucleotides). If barcodes of different sizes are used, reads are automatically trimmed to the same length. To remove PCR duplicates, RADIS then uses clone_filter (Stacks). Finally, sequence files are renamed after the sample codes. At each step of the process, files are created that provide summary statistics on the number of reads removed/kept Data analysis-'Purified read 1' outputs from the first step are processed individually and a set of loci is produced for each sample using ustacks (Stacks).