dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the effect of lithology, while expected and confirmed at some locations, still remains insufficiently constrained at a larger spatial scale. In this study, in order to draw perspectives and identify future ways to improve our understanding of radon emissions at increasing spatial scales, we present a comprehensive database of available direct RF measurements in one of the largest countries, continental China, from the 1980s to 2021. We considered all reliable RF data obtained on the ground surface using various techniques without discrimination. This database includes geographical data, RF values and other key information such as geology or lithology. From this detailed inventory, after we present a critical evaluation and the limitations of the data in some regions, reference RF values can be established for different substratum materials and, consequently, a revised national mean RF for China can be proposed.
Data collection and database production To build a comprehensive RF database covering the time period from the 1980s to 2021, we collected and summarized all published documents, available from open surveys and databases, where RF was obtained and sometimes complemented with soil-gas 222 Rn concentration (RC) or soil radium-226 concentration (RAC) measurements. The primary administrative provinces in China were considered except Macao and Hong Kong (Table  1 ). In some provinces, no data records were found. A total of 69 publications were selected from a pool containing documents, including 85 theses, found in Chinese academic literature databases (China National Knowledge Infrastructure (CNKI), Wanfang Data and CqVIP). Except for a few studies, published in English or with an English abstract, all the documents were only in Chinese language.
Data processing and analysis In our literature pool, a large number of studies dealt with the RF measurement methods (133 papers; thus 27%), rather than the obtained results themselves. We also found that 172 studies (35%) focusing on the RF measurement were performed in places related to uranium industry (e. g. , uranium mining and milling sites, uranium tailings), either studying the radon migration (53 papers; 11%) or assessing the RF level (36 papers; 7%). In this category, we selected 898 RF data from publications. A large fraction of studies (26%) dealt with radon-222 emission (RE) from building materials (e. g. , bricks and granite decorations), performed for radioprotection issues. In a lesser proportion (≤10%), we found studies about the RF from fault zones (3 papers with 832 data), and geological studies in metamorphic and igneous context (8 papers with 95 data), sedimentary context (3 papers with 24 data) and Quaternary deposits context (28 papers with 585 data).
Building materials and indoor radon studies In our database, few RF locations were found inside cities, but a large number of data are available for two types of building materials (i. e. , the normal and decoration building materials) from the literature pool (Fig. 4a ). Emanation results and radon studies of the building materials are not our main subject of interest in this paper. However, it is interesting to summarize briefly these data in order to calculate RE in the urban areas. Two main building materials could be found in our database. One is normal building material used to build home (e. g. , brick, sand and gravel), ranging from 0. 2 to 45 mBq m -2 s -1 with a mean RF of 11. 5±2. 6 mBq m -2 s -1 (n=592), while the other is the indoor decoration rocks (e. g. , granite) varying from 180 to 7050 mBq m -2 s -1 with a mean of 1130±300 mBq m -2 s -1 (n=29)  (Lin et al.
RF results versus lithology In the following, we have separated the RF results according to three different lithological groups (Table  3 ): sedimentary rocks, metamorphic and igneous rocks and Quaternary deposits. While substratum is considered as a first order control factor, it is important to keep in mind that the local nature of the soil, the soil thickness, its water content or the vegetation cover also have an effect on RF  (Ielsch et al. , 2002; Kozak et al. , 2003). The RF values from those follow a log-normal distribution with a mean of 2. 04 and a sigma of 0. 79 (Fig. 4b ). In our database, most of RF values were obtained in Beijing City, Guangdong Province, Tianjin City and Hunan Province (Table  1 ), which all fall in Quaternary deposits. Overall, the RF in Quaternary deposits ranged from 2 to 320 mBq m -2 s -1 with a mean of 31.
1±1. 0 mBq m -2 s -1 (Fig. 3  and Table  2 ). This value is close to the national mean RF (i. e. , 29. 7 mBq m -2 s -1 ) on surficial soil of China calculated by  Zhuo et al. (2008). In metamorphic and igneous context, 71 RF data were collected, while 24 data were selected in sedimentary rocks. The mean RF depends on the different rock groups considered; it is significantly larger, with a mean of 180±32 mBq m -2 s -1 , in the metamorphic and igneous context than in sedimentary context with a mean of 36. 9±4. 8 mBq m -2 s -1. Generally, RF is always the highest in metamorphic and igneous context and the lowest in Quaternary context  (Ielsch et al. , 2002; Manohar et al. , 2013).
RF results near active faults Measuring RF in areas of active faults and volcanoes is considered important in numerous locations around the world  (Biraud et al. , 2000; Richon et al. , 2003; Manohar et al. , 2013; Neri et al. , 2016; Girault et al. , 2018 Girault et al. , , 2022)). However, while radon studies were popular in China at the time of the 1975 Haicheng and the 1976 Tangshan earthquakes  (Raleigh et al. , 1977; Jin and Aki, 1986; Wang et al. , 2006b) , such studies have not been performed much after 1980. In our database, we selected only 3 publications on this topic, but these references provided a large number of RF data (832), corresponding to 32% of our samples, with a higher contribution rate to our study than expected. Most of RF values were measured in fault zones around the Beijing City and were smaller than 670 mBq m -2 s -1.
Spatial variations of RF and the effect of latitude While numerous RF records were incorporated into our database, it is still limited for the whole country. The number of available references in each province (Fig. 5 ) provides the first information about the situation of the current RF data set. In particular, Fig. 5  shows that the available results for direct RF mainly come from Beijing City, Guangdong and Hunan provinces. Few studies were found in the Tibet area, Nixia, Jiangsu and Zhejiang provinces (marked in gray in Fig. 5 ). It is unreasonable to assess the RF level of different provinces with limited data (Table  3 ). However, it is still acceptable to use the data available in those places and find rules with those measured points, keeping the limitations from Fig. 5  in mind. The highest mean RF value was observed at Lantian uranium area in Shaanxi Province, with a value of 9700±200 mBq m - s -1 (Table  1 ), and the second highest RF mean values were found in Liaoning, Inner Mongolia (Neimenggu), Hunan, Jiangxi and Guangdong provinces, with the values of 1500±140, 763±81, 1900±210, ±110 and 1230±80 mBq m -2 s -1 (Table  1 ), respectively.
Estimating a mean RF value for China and the significance of RF versus lithological context Summarizing in the current knowledge of RF in China from our database with only one number, i. e. , a representative mean RF value, appears extremely difficult, and this problem illustrates the limitations of our current knowledge. Indeed, the RF values given in Table  1  cannot be taken as the representative value for the given province, because they are only average values of the RF measurements available in that particular province, possibly subject to site bias. This is particularly true for provinces, such as Liaoning or Shaanxi provinces, where measurements were only carried out in or close to mining areas, which can be considered as anomalous locations, not reflecting the average geological context of the province.
, 2008; Griffiths et al. , 2010). However, some estimates, except in the presence of wet soils, tend to underestimate values from direct RF measurements  (Manohar et al. , 2013)  and correction factors need to be incorporated. The relationship between RF values and lithology, however, is not necessarily robust, even within our database, and we need to discuss the effect of active faults. In previous studies, numerous RC in soil were measured in the areas of active faults, e. g. , in Mt. Qilian  (Li et al. , 2016; Li et al. , 2017) , Gansu Province  (Xiaolong et al. , 2016; Zhou et al. , 2017; Lei et al. , 2020; Su et al. , 2020) , Sichuan Province  (Guangxi and Zhen, 2009; Zhou et al. , 2010; Yang et al. , 2018 ), Hong Kong (Tung et al. , 2013) , Fujian Province  (Wang et al. , 2006a)  and Tangshan in Hebei Province  (Wang et al.
, 2014). In our database, while significantly different, mean RF is about two times higher in faulted areas than in Quaternary and sedimentary contexts. Higher RF were also observed at other tectonically active faults abroad  (Steinitz et al. , 2003; Papastefanou, 2010; Sciarra et al. , 2018; Albert et al. , 2021). In China, however, only few faulted areas have been investigated, mostly in the vicinity of Beijing City and the Kunlun Fault  (Li et al. , 2007; Richon et al. , 2010; Chen et al. , 2018). Fig. 6. Simplified geological map of China separating the lithological categories used in this study, with the available RF results shown as dots (see legend). Zones of potential uranium mining are shown as shaded areas. The Quaternary, sedimentary, and igneous and metamorphic contexts are compiled from the geological map of the People's Republic of China with a scale of 1:250,000,000 (https://g eocloud.
cgs. gov. cn/). The granitic, faulted and uranium-related areas were modified according to the work done by  Xu et al. (2021). Map projection EPSG:4326. B. Lei et al. The relationship between the samples size and the geological structures and the lithology was complex. The more complex lithology the region has, the more RF studies should be performed. For example, 319 RF data were recorded near Beijing City, around clear faults, while, from the literature pool, there were a little records in Shenzhen, Zhuhai and Zhongshan cities of Guangdong Province (see database in the Supp. Mat. ) with a higher RC background caused by the granite encasing rocks  (Liu et al. , 2016). Therefore, it is not completely clear how much of the enhancement is due to the geological background and how much is due to the active faults themselves.
Mat. Text S1. 1). When soil thickness is not known (faulted areas), we set it to 1 m. These corrected RF values are given separately in Table  3. Based only on   (Wu and Zhou, 1994) , Guangdong  (Tan et al. , 2012) , Liaoning  (Hu et al. , 2021)  and Inner Mongolia provinces  (Cui et al. , 2021). Zhou et al. (2015)  measured RF at a REE site in Sichuan Province. Our data were a combination of the RF records from the Quaternary context in natural sites from the database and the soil thickness from  Xu et al. (2015). (b) Box plot of the ratio of given RF measured over mean RF value (Ratio RF ) as a function of (top)soil thickness for natural sites only, divided into four groups. A total of 81 RF data records were selected from the Quaternary context in natural sites with the soil thickness taken from  Xu et al.
Relationships between RF and RC or RAC Here, we evaluate in which respect additional data such as RC or RAC can complement RF values. We discuss more precisely the relationship between RF, RC and RAC when considering different background soil types, for the whole dataset for faults and other lithological types as we did previously (Figs. 8  and 9 ). RC in soil does not appear as a clear proxy of RF in China (Fig. 8 ), when all data are considered together. When lithological types are considered separately, by contrast, domains can be identified more clearly. Quaternary context, in particular, while able to sustain high RC in some cases (>10 4 Bq m -3 ), is characterized by lower RF values (Table  3 ). Within a given lithology, clearer correlation patterns also appear. Metamorphic and igneous context and especially uranium-rich context, while RC concentration does appear necessarily anomalous, are definitely characterized by larger RF values.
Summary: proposing a RF value representative of China mainland While this calculation is rather difficult, nevertheless, we believe that a reasonable value can be proposed and the uncertainties can be discussed, so that progress perspectives emerge. Taking the corrected value for each lithological context (Table  3 ), an average is calculated by weighting with the corresponding surface area for each lithology, evaluating separately the minor contribution of faults as discussed above. A reasonable mean RF for China is, at the best of our current knowledge using our heuristic database, 20. 2±6. 1 mBq m -2 s - (Table  3 ) with the contribution of lithology, soil thickness, faults and, for the first time, urban areas. To compute an uncertainty, given as a one-σ standard deviation, the following contributions are considered.
g. , granite) in our database enhanced significantly the predicted mean RF value. Second, by contrast to  Zhuo et al. (2008) , we took into account faulted areas, but their effect on mean RF was relatively small. However, it is likely that areas in active tectonic contexts should exhale more radon. Future work should be dedicated to the characterization of radon release at active faults in continental China. Third, our calculation of the influence of urban areas was a first-order estimate. We considered the mean RF value for building materials, but the number of data on bricks was for example smaller than that on granite or other higher radon emitter material. Finally, the (top) soil may be thicker than we estimated based on the work by  Xu et al. (2015). Our approach could be improved for example using future detailed soil thickness maps.
Conclusion We have presented an updated comprehensive database on direct RF measurements in China from research records in the CNKI, Wangfang Data and CqVIP literature databases. Our database includes 2622 RF data records from the 1980s to 2021, and shows that RF research has proceeded in China in two stages, with most work performed before 2000 and between 2000 and 2010. The obtained records have been divided into North China (e. g. , Beijing City, Tianjin City and Hebei Province), South China (e. g. , Hunan, Jiangxi and Guangxi provinces) and West China groups (e. g. , Xinjiang, Qinghai and Gansu provinces). In our database, there were few RF records in some developed areas (e. g. , Jiangsu, Fujian and Zhejiang provinces, and Shanghai City) and undeveloped areas (e. g. , Xizang, Yunan, Guangxi and Ningxia provinces).
Our work highlights the lack of data in a large number of provinces of China, especially in less developed provinces. From our database, mean values can be calculated depending on different geological context: the largest value of 180±32 mBq m -2 s -1 was obtained in metamorphic and igneous rocks, while the mean RF was lower in sedimentary rocks (36. 9±4. 8 mBq m -2 s -1 ) and in Quaternary context (31. 1±1. 0 mBq m -2 s -1 ). RF is significantly different around active faults, with a mean of 66. 1±1. 6 mBq m -2 s -1. From these measured values, extrapolating to a representative value for continental China, essential for large-scale transport studies  (Zahorowski et al. , 2004; Chambers et al. , 2018)  and global health issues, is difficult. Indeed, sites where intensive RF measurement campaigns are carried out induce biased estimates, and the obtained local measurements are not guaranteed to provide a meaningful extrapolation at large scale.
Thus, direct RF measurements, while interesting, are not necessarily better than extrapolation from other mapped parameter, such as soil properties or radioactive surveys. To try to circumvent this problem, we tried, first, to evaluate from our database a scaling law versus soil thickness and, second, to obtain results from large-scale soil thickness maps. An exponential scaling law seems to emerge and reliable mean soil thickness values can be obtained separately for each lithological category. However, the soil thickness can play an important role to reduce the RF level, leading to a tentative and reasonably corrected mean RF value of 20. 2±6. 1 mBq m -2 s -1 at the scale of continental China with the contribution of lithology, soil thickness, faulted areas and, for the first time, urban areas.
This mean RF value remains larger than other mean continental estimates, such as in Europe, but agrees well with a previous estimate given for China  (Zhuo et al. , 2008)  and with global estimates. Honestly, our results are based on direct RF records from the literature pool selected from three Chinese Academic Databases over the past 30 years. However, this issue needs to be studied in more details in the future, as the atmospheric signature of radon could be wrong by a factor of two around China. First, though experts have done some works (e. g. ,  Zhuo et al. , 2008) , a better mapping of RF should be undertaken in China, with a regular grid, and avoiding local bias. A first step towards this goal could be a trial mapping experiment, with a tentative 10 km grid, in a selected province where the logistical problems could be easily managed.
(SM1. 1) to (SM1. 4) in the Supp. Mat. Text S1. The corrected RF value and uncertainty deduced from the corrected soil thickness regression were calculated by the Eqs. (SM1. 5) to (SM1. 8) (Supp. Mat. Text S1). The radon emission values were estimated based on the Eqs. (SM1. 9) to (SM1. 12) in the Supp. Mat. Text S1. The total surface area values of main land in China were taken from the Wikipedia database (https://en. wikipedia. org/wiki/List_of_Chinese_administrative_divisions_by_area). *Determined using radon flux from building materials (see main text and Supp. Mat. Text S1).
4 km 2 ) n Refs Overall dataset Natural sites N d Mean RF (mBq m ¡2 s ¡1 ) Radon emission (10 9 Bq s ¡1 ) Area density (10 ¡4 km ¡2 ) RSY (yr ¡1 ) N d Mean RF (mBq m ¡2 s ¡1 ) Radon emission (10 9 Bq s ¡1 ) Beijing 1. 6 14 319 29. 3±1. 2 0. 5±0. 1 199. 4 199. 4 247 22. 4±0. 8 0. 4±0. 1 Hebei 18. 9 3 438 45. 2±0. 9 8. 5±0. 2 23. 2 13. 8 126 32. 5±0. 3 6. 1±0. 1 Heilongjiang 45. 3 1 11 87±40 39±18 0. 7 0. 3 Henan 16. 6 1 6 720±250 119±42 0. 4 0. 2 Liaoning 14. 8 1 30 1500±140 222±21 2 0. 9 Inner Mongolia 114. 5 7 56 763±81 874±93 0. 5 1 3 14. 0±6. 5 16. 0±8. 0 Shaanxi 20. 6 1 20 9700±2200 2000±450 1. 4 0. 9 Shandong 15. 8 1 14 28. 2±1. 6 4. 5±0. 3 0. 9 0. 6 14 28. 2±1. 6 4. 4±0. 3 Shanxi 15. 7 1 96 42. 7±1. 6 6. 7±0. 3 6. 1 3 Tianjin 1. 2 2 328 74. 5±1. 5 0. 9±0. 1 273. 3 273. 3 265 32 1318 3270±460 Ahui 14 1 20 71±17 9.
7 NA NA NA NA NA Zhejiang 10. 6 NA NA NA NA NA Jilin 19. 1 NA NA NA NA NA Fujian 12. 4 NA NA NA NA NA Shanghai 0. 8 1* NA NA NA NA Total 948 81 2622 5420±590 Note: Hong Kong and Macao were not included in our investigation. N d is the number of the data records from our overall dataset and natural sites selected. *Radon flux from building materials. NA: not available. B. Lei et al.
For these comparisons, both the baseline FCC-hh detector magnetic system  [4] , and the magnetic system with the minimal steel yoke, are calculated with the program TOSCA from Cobham CTS Limited  [6]. A part of this study was presented in 2018 at 6th International Conference on Superconductivity and Magnetism-ISCM2018 in Antalya, Turkey. At the time, the minimal steel yoke option included exact shapes of the radiation protection shields around the forward coils in correspondence with the baseline magnetic system. In the baseline option, these shields are assumed to be constructed from a nonmagnetic material. In the minimal steel yoke option, the shields are the parts of the steel flux return yoke. The original shapes of these shields include the large cones propagated inside the main coil, that in the minimal steel yoke design creates the enormous magnetic flux density inside the cones and, as a result, the large axial electromagnetic forces towards the center of the main coil.
Synthesis assumptions and constraints To obtain as much realistic as possible complexity figures, in an attempt to produce systematic and fairly comparable descriptions, all converters were implemented in parameterized structural Verilog, following the identical coding guidelines. In all cases, the multi-operand adder mod 2 2n -1 was implemented using the CSA with EAC, followed by the final 2-operand adder mod 2 2n -1 of  [19]. The FAs used in the CSAs have been implemented using register transfer level (RTL) 1-bit adder ("+" operator in Verilog RTL code), except for the special FAs proposed in  [12]. In the latter case, we have implemented two versions, using the special and regular (RTL) FAs. All converters were synthesized using Cadence RTL Compiler v. 8. 1 over the commercial CMOS065LP 65-nm low-power library from ST Microelectronics.
The objective of this study is to confirm the possibility of tracing sources of airborne mineral dust using elemental ratios. The region of study is the north-eastern part of the Sahara where dust compositional data were lacking; 272 mineral dust samples collected at 3 experimental stations of Egypt were analyzed by X-ray fluorescence. Based on the mineral dust concentration, 65% of the samples correspond to moderate (3-10 μg m -3 ), 22% to medium (10-25 μg m -3 ), and 13% to intense (> 25 μg m -3 ) dust events. The Ca/Al and (Mg + Ca)/Fe ratios were found to be strongly correlated and their ranges of variation to cover are more than one order of magnitude, which confirms their interest for the tracing of the dust sources. Using a combination of MODIS satellite observations, HYSPLIT back-trajectory analysis, and simulations with dust emission models, the large (> 6) and intermediate (2-6) Ca/Al ratios were shown to correspond to dust originating from saline playas (chotts) and calcareous surfaces, respectively.
To supplement this dataset, two dust samples (SR and WD, respectively) were also generated in the laboratory from soils collected along the Mers Matruh to Siwa road (at lat. : 29. 95°N, lon. : 25. 99°E) and in the White Desert (lat. :27. 20°N, lon. : 28. 06°E), north of the El Farafra Oasis. In a second stage, we propose to establish a link between the elemental composition of the dust and the type of source soil from which it was produced. Information on the locations of the experimental sites, and on the dust sampling and generation protocols is provided in "Materials and methods" section. It also details the elemental analysis by XRF. Then, an in-depth analysis of the variabilities of both the dust concentration and composition is developed in "Results and discussion" section. In discussion, the results obtained in Egypt are compared with those of other Saharan regions, and a typology of dust sources based on the Ca/Al ratio is proposed.
(2006)  and  Abed et al. (2009). In their study, the former authors quantified the elemental compositions of the fine (PM20) fractions of (  1 ) one sample of soil collected 75 km north-east of Cairo and (2) one sample of dust deposited in Cairo after an intense dust event. Following the original publication, these two samples will be referred to (Table  3 ) as Cairo 2 and Cairo 3, respectively. Abed et al. (2009)  analyzed the composition of dust deposits collected in 10 localities of Jordan after at least 5 major Khamaseen storms having reached the country in spring 2006. Thus, the analyzed dust was assumedly of Saharan origin. In addition, the composition of the dust sampled onsite can be compared to that of aerosols generated with GAMEL (Table  3 ) from the Egyptian soils collected at two different locations of the Western Desert (Siwa Road and White Desert; this work) and from the Tunisian soil (Douz; data from  Saad et al.