dataesr/scientific-paragraphs-categorization huggingface dataset:
As per standard practice, the selected groups will meet on a weekly basis during the first several months to learn about the process through 7 IRC led modules, make decisions as to how they want to implement the savings program, and select a management committee to assist with the running of the program. The next period (approximately months 3-12) will cover the actual running of the savings and loans program, when people give money on a weekly basis and apply for loans. The program is complete when the pay-out is provided, typically starting in months 9-11. The PSAs who did not originally receive the adapted mental health training and who provided services to the waitlist control condition will be provided with the training once the maintenance data has been reviewed. After receiving the training, any control study women who still want services will be invited to participate in the group therapy.
Data Collection There are 4 designated data collection points: 1. Baseline Quantitative Assessment -prior to initiation of any of the intervention strategies. If we assume that approximately 75% of the women assessed will meet eligibility criteria and will agree to participate in the study, we will assess approximately 500 women within the 16 PSAs areas and 480 women in the 9 VSLA locations. This will allow us to meet our goal of 360 study women in the VSLA program (40 women per location; 180 intervention; 180 control) and 380 study women in the mental health program (24 women per PSA; 180 intervention; 180 control). 2. Qualitative Post-program -after approximately 10-11 months, following the pay-out period for the VSLA program and the end of the treatment period for the mental health treatment, a brief qualitative assessment will be done to identify any unexpected outcomes.
Monitoring Over the course of the implementation of both the VSLA and mental health program, IRC will systematically monitor the outputs and outcomes of each program. We will monitor progress of the project on two levels: individual-level and group-level. 1. VSLA Program: IRC staff will make regular monitoring visits to each VSLA group. During these visit they will collect the following quantitative data for monitoring purposes: attendance rate, dropout rate, accumulated value of savings, number and value of current loans, and the value of cash not in circulation. This data will be inputted and analyzed in the standard VSLA Monitoring Information System. The frequency of monitoring visits will be determined by the phase of the VSLA. During the intensive phase, groups will be monitored on a weekly basis at every meeting.
Instrument development and validation report: This report will provide information on the selection, adaptation and validation of the mental health and psychosocial assessments and will present the complete instrument to be used for all subsequent quantitative data collections. Baseline Quantitative data report: The baseline report will provide background characteristics of the women in the evaluation; detailed information about the pre-intervention status of the main outcomes that the programs aim to improve and an analysis of whether or not there are systematic differences between treatment and control individuals. Qualitative Post-program report: This report will provide data on any unexpected outcomes associated with participating in the programs and information about how these outcomes will be incorporated into the subsequent quantitative assessments.
Study area and methods This is the first study to compile data on all known archaeological sites in the Central African rainforest region. The area under study includes six countries of Atlantic Central Africa (the southern half of Cameroon, mainland Equatorial Guinea (the island of Bioko has a different history), Gabon, Republic of Congo, the western part of the Democratic Republic of Congo and the southwest of the Republic of Central Africa). We divide the region into two generic biomes: forest and savanna/savanna-forest mosaic (figure  1 ). We identified 328 archaeological sites with at least one reliable carbon date from the last 5000 years. Of these, 32% (106 of 328) were sites where one of the authors had been involved in research, including 20 sites for which no data have previously been published; the remainder were from the literature  [38, 40,.
Funding statement. This research was funded by the  ERA-Net Biodi-vERsA  ( CoForChange  project), with the national funders  ANR (France)  and  NERC (UK) , part of the 2008 BiodivERsA call for research proposals. Other funding was also provided by the  EU programme ECOFAC  ( 1992-1998 ),  IRD  Projects (ECOFIT 1992-1996, PALEOFORGA 1997-1999, ADHENTRO 2000-2005, PALOC since 2008, PPR-FTH since 2011) and  CNRS  projects ( PNEDC PRIMUS 2002-2004  and  ECLIPSE REGAB 2005-2009 ). L. W. was supported by the  Wildlife Conservation Society  and the  Centre International de Recherches Medicales de Franceville. rstb. royalsocietypublishing. org Phil Trans R Soc B 368: 20120304.
Data set description In the following the building damage assessments derived from satellite and aerial imagery are presented. Then, we present, the different reference data sets used for the validation including oblique aerial imagery (pictometry) and field assessments. Academy of Sciences (CAS), respectively (the datasets can be downloaded from this website: http://wiki. openstreetmap. org/wiki/WikiProject Haiti/VectorAndMapData. The OSM set is a point set identifying collapsed buildings, which is a combination of an early damage assessment made by Information Technology for Humanitarian Assistance, Cooperation and Action (ITHACA) and extended with OSM contributions. It covers the complete impact area, including the cities outside greater Port-au-Prince, i. e. Gressier, Leogane and Jacmel.
Aerial-based damage assessments The PDNA point data set designated here by UWJ (UNOSAT-World Bank-JRC) was generated by computer aided visual interpretation of pre-earthquake satellite imagery and post-earthquake airborne imagery with a spatial resolution of 0. 15 m. The methodology consisted in assignment of different damage grades to individual buildings, using the European Macro-seismic Scale (EMS) 1998  (Gr ünthal and Levret, 2001). This scale defines 5 damage grades: grades 1 to 5 should ideally represent a progressive increase in the strength of shaking for different types of masonry and reinforced-concrete buildings. The damage descriptions for these two building types were specifically selected because they most closely represented the types of construction in Haiti. The main qualitative criteria used for recognizing damage grades on overhead or nadir imagery include, but is not limited to, the shifting of building perimeters, a lack of definition of perimeter walls, or obvious debris surrounding the damaged buildings.
Block-sampling using Pictometry data Pictometry imagery with a spatial resolution of better than 25 cm was acquired in early February 2010 from an aircraft flown at a height of around 1 km with multiple cameras at nadir and oblique angles. A set of 60 locations corresponding to urban blocks (defined by street intersections) were selected in the Port-au-Prince area so that approximately equal numbers of locations were in each of following land use categories: downtown; commercial, residential and informal (shanty) settlements. At each location, 20 buildings approximately (1247 buildings in total) were identified on the Pictometry images and assessed by Cambridge Architectural Research as part of an exercise funded by the World Bank  (Spence and Saito, 2010). The Pictometry dataset has been validated in the field  (Booth et al.
, 2011). This level of investigation was considered the most reliable and most accurate indicator of damage and thus represents the benchmark to which the remote-sensing assessment should be compared. This inventory has however some quality problems that must be taken into account when using this dataset as a reference for validation purposes. Some typical examples of quality issues were encountered in the informal neighbourhoods where the inventory seems to have overlooked heavily damaged houses (Fig. 3a  and b ). Among the reasons for the underestimation of the damage we could possibly cite, the presence of rubble that prevented access to field visits. On some other occasions, several labels were assigned to the same building while neighboring buildings were not assessed (Fig. 3c ). Apparently some visits were only conducted from the outside or from the road (Fig.
3d ). Another quality issue that was identified during the analysis of this dataset, was the presence of inconsistencies between the assignments of the color codes and the percentages of damages. Figure  4  shows that 7 % and 10 % of the buildings falling respectively in the red and yellow categories, have been labeled with a damage percentage value of 0-1 %. All these highlighted inconsistencies demonstrate the lack of Figures a comprehensive quality control of the collected information before its on-line publication and the necessity to carefully handle this dataset especially when using it as a reference within a validation exercise.
Intercomparison of satellite and aerial based damage assessments The purpose of the intercomparison is to provide an objective measure of the quality of the satellite based damage assessments and to develop an understanding of the validity and comparability of the satellite (OSM and CAS datasets) and aerial (UWJ) derived damages to buildings. Given that the three datasets have different coverage, it was decided to focus the analysis on a subset comprising all the three assessments and covering an area of approximately 25×15 km, which is enclosing the area depicted in Fig. 5. Since the aerial based damage assessment represents the most detailed data, it was considered here as the reference against which the intercomparison was made. While the UWJ dataset consists of four different damage grades (Grade 1: no visible damage, Grade 3: substantial to heavy damage, Grade 4: very heavy damage, Grade 5: destruction), the OSM and CAS datasets contain only collapsed and heavily affected buildings that actually correspond to Grades 5 and 4, respectively according to the EMS-scale.
Therefore, for the intercomparison, only points labelled with Grades 5 and 4 were considered from the UWJ dataset. The differences in the spatial resolutions between the satellite (50 cm) and the aerial (15 cm) imagery combined with displacement error between them do not allow an accurate point-to-point matching. To circumvent this problem, the point counts for grid cells of 50 × 50 m were taken as a reference for the subsequent analysis. The window size of 50 meters showed to be a reasonable compromise between (i) the displacement errors, (ii) the building sizes and the spacing between 16 them and (iii) the requirements for the following statistical analysis. Overall statistics in Table  1  show that the detection of damage classes 4 and 5 in the aerial imagery is approximately a factor 8 greater than that in the satellite imagery.
However, differences in mean and standard deviations already indicate that relative changes in the sets are not equally distributed. The study of the pattern of matching between the aerial and the satellite derived damage assessments (UWJ versus CAS and UWJ versus OSM) was performed using a 2-D cross-correlation  (Gonzalez and Woods, 1992)  on the gridded datasets. The point sets corresponding to grades 5 and 4 were aggregated for a direct comparison with the OSM and CAS datasets. For each 3× 3 window of neighbouring grid cells, the normalised correlation coefficient is calculated as follows: γ = 2 i ,j =0 (f (i , j ) -f )(g (i , j ) -ḡ) 2 i ,j =0 (f (i , j ) -f ) 2 2 i ,j =0 (g (i , j ) -ḡ) 2 (2) where: f (i , j ) , g(i , j ) is the point count for each grid sample in the window (i , j = 0.
2 for a 3 × 3 window) for the first set (e. g. UWJ) and the second set (e. g. OSM); f , ḡ is the average point count for all grid samples in the window. The γ-value for each window is assigned to the central pixel of each 3 × 3 window. For two datasets showing a good matching of the point counts in the 50 × 50 m grid cell, the resulting cross-correlation (γ) coefficient will be close to 1. Inversely a low correspondence between the counting of damaged buildings will result in a value γ of close 0. Note that we assign γ = 0 to the central pixel of windows for which grid counts in either set are zero, and for which the normalised correlation coefficient is undetermined. A graphical impression of the overall correlation is shown in Fig. 6. The images on the left provide a simple tool to inspect pattern matching directly.
Review of sample-based validation conducted in previous studies Several sample-based validation activities were set up to review the quality of the visual interpretation of building damage on the aerial imagery. The results of these validation activities are also useful to extrapolate the damage for lower damage grades using a set of statistical models. These validation methods differ in terms of the sampling designs, the sample sizes and the type of reference data acquired. The purpose here is not to describe the full methodologies and the detailed results, but to establish common patterns in the results obtained from these different methods. Low Cost Mobile Mapping System (LCMMS) and  Booth et al. (2011)  and  Spence and Saito (2010)  for the block-sampling using Pictometry data. In the three sample-based validation studies, contingency matrices are used to measure the agreement/disagreement between the aerial based damage assessment (UWJ) and the different reference sources (ground-surveys, LCMMS georeferenced frames and Pictometry).
Buildings with light or moderate damage states (grade 2) and substantial to heavy damage (grade 3) could not be established with any certainty from remotely sensed images as confirmed by  Booth et al. (2011). Even with the Pictometry data, the interpreters encountered difficulties identifying grade 3 buildings. Therefore, it was decided to merge this damage grade with grades 0 and 1 for a more consistent comparison between the different datasets (i. e. field data versus remote sensing data). Comparing the three accuracy assessments of the UWJ, it can be seen that the higher the sample size (N), the lower the overall accuracy (73 % for N = 4329, 79 % for N = 1247 and 88 % for N = 168). However the contingency table using Pictometry data with a "moderate" sample size selected on the basis of an urban block sampling (Table  4 ) provides accuracy values close to those reached with a much larger sample size obtained from a ground-survey (Table  2 ).
Damage statistics by type of land use In this section, we extend the validation approaches discussed previously, with an analysis of the UWJ set versus the exhaustive field survey (MTPTC). According to the teams of structural engineers who led the exhaustive damage assessment program  (Miyamoto et al. , 2011) , the majority of evaluated buildings (about 75 % of the total number of assessed buildings) were single-family residential structures. On the basis of this information, it is reasonable to consider that household-dwelling units are comparable to independent building units. Due to the ambiguity of the color assignments in the MTPTC dataset (Sect. 2. 3. 4), it was decided to use instead the percentage of damage (0-1 %, 1-10 %, 10-30 %, 30-60 %, 60-100 % and 100 %) for the comparison with UWJ.
In order to ensure the maximum semantic correspondence between the two point sets under comparison and due the low confidence in the assignments of damage grades 2 and 3 using remotely sensed data, only buildings assigned to damage levels 4 and 5 were considered from UWJ set and those assigned with damage percentages of 60-100 % and 100 % were selected from MTPTC. Hence, only buildings considered as heavily affected or destroyed were retained from each of two point datasets. For the analysis a subset covering an area of 25 × 15 km and extending from Carrefour to Port-au-Prince was defined. A point-to-point comparison of the two survey results is only partly feasible, but does not result in very meaningful indicators. One of the issues is due to the placement of building labels: the UWJ aerial interpretation typically places the damage label at the center of what is perceived as the building roof.
Using the grid cells, it is relatively straightforward to inspect specific cases of relative under-and over-estimations between the two data sets. These are based on spatial queries in the data base in which all points and grid outlines have been stored. In this way, we can find grid cells that have a much larger (e. g. 5×) count of one set of points versus the other set. This analysis is very useful to provide an understanding of the underlying characteristics of the deviation between the sets. Figures Two examples are given in Fig. 7. On the left side of the figure, the example shows an overestimation of MTPTC surveyed points, with damage categories 60-100 % and 100 % with respect to UWJ damage points for categories 4 and 5. The main reason for this deviation is, apparently, that the damage artefacts registered in the MTPTC survey are likely to be related to internal damage, which is not shown in the aerial view, as little external signs of damage are visible.
Back Close Full almost 40 % of cells, an underestimation of the heavily damaged and affected buildings has been detected). It is important to note that these results do not take into account the magnitude of the differences between the counting, which means that even if Diff MTPTC-UWJ = 1, it is considered as an underestimation. Figure  9  shows the overall number of heavily affected and damaged buildings per category of land use for both datasets considering all the 50 × 50 m cells under analysis (30 922 in total). From this graph, it can be seen that despite the high level of disagreement detected in the previous table for shanty zones, especially for the cases of underestimation (2090 cells), in terms of absolute values, the number of heavily affected and damaged buildings in the aerial point set is slightly higher than that obtained from the field survey (13 449 against 13 334).
Besides, in almost all major land use categories, except in the industrial areas, the assessment from aerial imagery detects more damaged building than the field inventory. In the downtown, there is an agreement in the absolute number of damaged buildings between the two datasets (ΣCount MTPTC = 1112 and ΣCount UWJ = 1082). On the basis of the results shown in Table  5  and Fig. 6 , we can detect a dependency between the counting of the damaged buildings in the two points datasets and the type of land use. To confirm these findings, we conduct a Tobit analysis with the purpose of identifying the land use categories that account for the marked differences observed between the damage assessments obtained from MTPTC and UWJ. Since we are interested in analyzing here the absolute differences in the counts between MTPTC and UWJ for cells of 50 × 50 m (|Diff MTPTC-UWJ |) Tobit was chosen instead of common Ordinary Least Squares regression analysis because it accounts for the differences that are equal to 0 (i.
9  and the results of Tobit, indicates both the agreement (in downtown) and disagreement (in residential high density and shanty) between UWJ and MTPTC are influenced by the type of land use: in highly populated areas, especially in shanty zones, the overestimation of building damage from aerial imagery could be simply a result of an incomplete field survey while, in downtown, where field surveys seem to have been conducted in a more systematic way, the damage assessment from aerial imagery matches very well the one obtained from the field. Despite the differences observed between the two datasets, the damage inventory from aerial imagery demonstrates a pattern and has a total size which can be considered as a reasonable accurate in the field.
To verify this assumption, the total floor area and associated repair/replacement costs were computed using the MTPTC dataset and compared to the remotely sensed estimates based on the UWJ dataset Table  7  gives an overview of the number of heavily affected and damaged buildings per category of land use in the area of interest (extending from Carrefour to Port-au-Prince), the derived floor areas and the related replacement/repair costs for both datasets. The results show that despite the differences in the damage counts observed in the two datasets, the overall cost estimated from remote sensing is a good approximation of the costs obtained from the detailed field survey (2425 versus 2258 Million US$). Knowing that the primary objective of the PDNA is to support the elaboration of the Recovery Framework -normally within five to six weeks following the onset of a disaster, the joint damage assessment from aerial imagery currently represents the best possible compromise between timeliness and accuracy.
Concluding remarks In this paper, the quality of damage assessments derived from different sources following the 2010 Haiti Earthquake was analyzed. The following conclusions are made with respect to the cost-effectiveness of the different assessments: -The intercomparison of the satellite and the aerial based damage assessments, showed that the damage derived from satellite imagery (CAS and OSM datasets) was underestimated by a factor of eight, compared to the damage derived from aerial imagery. These results suggest that despite the fast availability of very high resolution satellite imagery for rapid generation of damage assessment maps, the spatial resolution of 0. 5 m is not sufficient for an accurate interpretation of building damage as already observed in  Lemoine (2010) ,  Corbane et al.
Examples of the core barrier are shown in Fig. 3  where the azimuthal wavenumber becomes large in the core and a caustic is formed. Caustics are regions with a high-spatial density of rays where the corresponding wave equations yield a high-wave energy density. Early work by Bonoli and Ott  13  and Andrews and Perkins 28 point out that the scattering of the waves reduces the intensity of the waves predicted at caustics calculate from rays in quiescent or turbulence-free plasma. Following the model developed in Bonoli and Ott,  13  simulations for the RF power scattering from density-fluctuations scattering were developed by Bertelli et al. 14  for the GENRAY/CQL3D code and applied to two Alcator C-Mod discharges. The simulations compared power deposition profiles for low-and high-density C-Mod discharges.
V. SUMMARY AND CONCLUSIONS The Tore Supra experiments show that LH waves launched from the high-power wave guide matrix antenna shown in Fig. 1  with 96-phased elements designed to radiate a directional RF wave spectrum with hn k i ¼ ck RF k =x RF ¼ À1:7 transfers wave momentum to resonant electrons producing a steady state toroidal plasma current. The resonant absorption by the electrons of the RF waves provides a method to control the shape of the toroidal plasma current making possible to search for transport barriers formed with minimum q(r) profiles. A database of 66 Tore Supra discharges and 4 JET pulses with LHCD has established the efficiency of LHCD  38  in fusion grade tokamak plasmas. An issue of a lack of penetration of the lower hybrid current drive in divertor discharges was reported by Wallace et al.
ACKNOWLEDGMENTS The authors acknowledge valuable discussions with L. Colas,  D. Moreau ,  J. Hillairet ,  W. Helou , and  Y. Elskens. This work was supported by  CEA at Cadarache ,  Aix-Marseille University  through the  Institute for Mediterranean Advanced Research (IMERA). The first author (Horton) was partially supported by the  US-DoE  through a grant to the  Institute for Fusion Studies at The University of Texas at Austin.