dataesr/scientific-paragraphs-categorization huggingface dataset:
Stellar ages From the D n and H δ values derived above it becomes evident that the obscured AGN population is associated on average with older host galaxies, compared to the unobscured AGN population. It is instructive to derive the approximate stellar ages of these populations. The Cigale code provides stellar ages estimates. However,  Mountrichas et al. (2022b)  caution that the Cigale estimates have limited accuracy if they are not combined with D n and H δ measurements. Hence, they developed a variant of the CIGALE code which uses both these indices to estimate the mass weighted galaxy ages. As this code is not publicly available, we can rely on their galaxy age methodology. This is possible because the LEGA-C COSMOS sample used by  Mountrichas et al. (2022b)  is practically identical to our sample and moreover they use the same parameter grid for their SED fitting.
Summary We examine the ages of the host galaxies of Chandra X-ray selected AGN in the COSMOS field in combination with other properties such as the stellar mass, the SFR and finally the SMBH Eddington ratio. The ages are explored using the calcium break and the Balmer H δ absorption line obtained from the LEGA-C VLT/VIMOS survey. Accurate stellar masses and SFR are derived using the spectral energy distribution code CIGALE. We compare for the first time the ages of the host galaxies of obscured and unobscured X-ray selected AGN. Our sample consists of 50 unobscured or mildly obscured (log N H (cm -2 ) < 23) and 23 heavily obscured AGN (log N H (cm -2 ) > 23) in the redshift range 0. 6 < z < 1. One of our goals is to test the predictions of evolutionary unification models which assert that heavily obscured sources mark the birth phase of the AGN.
As there are rather diverging results on the gas content of AGN  (Maiolino et al. 1997; Perna et al. 2018) , this hypothesis needs to be systematically explored using future ALMA observations of AGN. Acknowledgements. The authors are grateful to the anonymous referee for his careful reading of the manuscript and numerous constructive comments. IG acknowledges financial support by the European Union's Horizon 2020 programme "XMM2ATHENA" under grant agreement No 101004168. The research leading to these results has received funding (EP and IG) from the European Union's Horizon 2020 Programme under the AHEAD2020 project (grant A67, page 15 of 18 agreement n. 871158). GM acknowledges support by the Agencia Estatal de Investigación, Unidad de Excelencia María de Maeztu, ref. MDM-2017-0765. We acknowledge the use of LEGA-C data which have been obtained with the ESO Telescopes at the La Silla Paranal Observatory.
Materials and Methods Figure  2  is a visualization of the material and methods workflow. The materials and methods is divided in three parts. The first one describes the data used for the TPE determination, (a) ICRISAT district level data (DLD); (b) Nasapower synthetic weather data  [30] ; and (c) parameters derived from crop model simulations (Table  1 ). The second part describes the methodology to determine the TPE; and the third part the crop modelling strategy to (a) infer parameters of the system using large-scale simulation, (b) use those simulations to evaluate the crop model prediction (st)ability within the identified TPEs, and (c) determine the relative influence on yield of the tested parameter in the new TPEs. The DLD (http://data. icrisat. org/dld/ (accessed on 1 April 2023)) is an open-source database about Indian agriculture at the district level that contains data originally compiled by the Indian directorate of economics and statistics (https://desagri.
gov. in/ (accessed on 1 April 2023)). From this database, we extracted the most recently available 20 years (1998-2017) of data on pearl millet cultivated area, production, and yield during the rainy season (kharif, Table  1 ). We identified 62 districts representing 90% of the total cultivated area during the period (Figure  1A ). Consequently, the other pearl millet system characteristics were evaluated for these districts over the selected period. In these 62 districts, we used the area, production, and yield average (1998-2017), and further calculated their increase or decrease over a year (i. e. , "trend") as their linear regression coefficient estimate over several years. For area and production, we also estimated their average share as the proportion of pearl millet area (production) compared to other crops.
Weather Data-Nasapower For weather, we used the synthetic data provided by Nasapower  [30] , which has been shown to sufficiently represent the observed weather data  [24, 32]. The selected data contained daily precipitation (Figure  S1 ), minimum and maximum temperature (Figure  S2 ), and solar radiation, which are the only requested crop model input variables. We used the R package apsimx  [33]  to extract the data for a point located at the center of each district. We calculated the cumulative rain during kharif season (15 June-15 September), as well as its trend and variance over years (Table  1 ). We also calculated the temperature trends. The rain and temperature trends were calculated as the linear regression coefficient over years.
Crop Model ExM Parameters Space To evaluate the crop model in different parameter configurations, we defined the ranges of possible values for important ExM parameters (Table  2 ). We determined the relevant ranges after DLD analysis, review of the literature, and discussions with experts. For the soil parameters, we selected generic soil textures that corresponded to the majority soil type information from the DLD  [31] , which potentially constitutes the most precise information about soil type at the district level in India. This information was crossed with data from the ISRIC global database (https://www. isric. org (accessed on 1 April 2023)). We selected three soil textures, sandy, loam, and clay soils, representing the majority soil types (Pssament, Inceptisol, and Vertisol) observed in the pearl millet tract (Figure  S3 ).
Acknowledgments: We thank  Greg McLean ,  Erik van Oosterom  and  Graeme Hammer  for their help with the implementation of the new pearl millet model. We also thank  Janila Pasupuleti ,  Ashok Kumar Are  from  ICRISAT ,  O. P. Yadav  from  ICAR-CAZRI ,  Rajendra Singh Mahala  from  SeedWorks International Private Limited ,  Yogendra Verma  from  Kaveri Seed Company Limited  and  Sanjana P Reddy  from  ICAR-IIMR  for the discussion about the article content.
Numerical Results This section considers the performance of a system employing a three-dimensional single weight zero cross-correlation (3D-SWZCC) code using two terms: the bit error rate (BER), in terms of the number of concurrent users, amount of received power, data bit rate, optical bandwidth, and the quality factor (Q), in terms of the number of concurrent users, with the assistance of MATLAB software. After running MATLAB software, in the workspace window, the mathematical equations obtained in terms of SNR, BER, and Q are rewriter as clear in Section 4 as a function of various parameters. First, the parameters shown in Table  3  are typed in the variable declaration section. We set the data bit rate R b = 1 Gbps , the PD sensitivity = 0. 75, the Optical bandwidth ∆ f = 5 Thz , the effective source power P r = -10 dBm , the load resistor at receiver R L = 1030 Ω, the receiver noise temperature T n = 300 K , the electron charge e = 1.
Finally, both 3D ZCC codes, SWZCC and MD, produce greater SNRs compared to other codes, but the dominant code is always our proposed code. This can be seen from the   As discussed, the system attained the required BER for optical communication networks: BERs of 4. 8, 3, 0. 72, 0. 56, and 0. 51 Gbps were obtained for the 3D codes SWZCC, PTZCC, DCS/MD, PD/MD, and PD, respectively. Consequently, employing our proposed code in an OCDMA system can supply a great data rate, nearly 5 Gbps, while other codes cannot reach 1 Gbps, with the exception of the 3D-MD code. This optimization can be explained by the ZCC property of our code, which positively and effectively optimizes the OCDMA system performance. Additionally, the 1D and 2D SWZCC codes enable each user in the OCDMA system to exploit up to 0.
12 and 0. 67 Gbps, respectively. Note that the differences between 1D and 3D and 2D and 3D in terms of the allowable data rate amount to approximately 40 and 7. 1 times, respectively. Figure  6  shows the BER alteration as a function of the data rate for 200 active users. As discussed, the system attained the required BER for optical communication networks: BERs of 4. 8, 3, 0. 72, 0. 56, and 0. 51 Gbps were obtained for the 3D codes SWZCC, PTZCC, DCS/MD, PD/MD, and PD, respectively. Consequently, employing our proposed code in an OCDMA system can supply a great data rate, nearly 5 Gbps, while other codes cannot reach 1 Gbps, with the exception of the 3D-MD code. This optimization can be explained by the ZCC property of our code, which positively and effectively optimizes the OCDMA system performance.
Additionally, the 1D and 2D SWZCC codes enable each user in the OCDMA system to exploit up to 0. 12 and 0. 67 Gbps, respectively. Note that the differences between 1D and 3D and 2D and 3D in terms of the allowable data rate amount to approximately 40 and 7. 1 times, respectively. Figure  6  shows the BER alteration as a function of the data rate for 200 active users. As discussed, the system attained the required BER for optical communication networks: BERs of 4. 8, 3, 0. 72, 0. 56, and 0. 51 Gbps were obtained for the 3D codes SWZCC, PTZCC, DCS/MD, PD/MD, and PD, respectively. Consequently, employing our proposed code in an OCDMA system can supply a great data rate, nearly 5 Gbps, while other codes cannot reach 1 Gbps, with the exception of the 3D-MD code. This optimization can be explained by the ZCC property of our code, which positively and effectively optimizes the OCDMA system performance.
Additionally, the 1D and 2D SWZCC codes enable each user in the OCDMA system to exploit up to 0. 12 and 0. 67 Gbps, respectively. Note that the differences between 1D and 3D and 2D and 3D in terms of the allowable data rate amount to approximately 40 and 7. 1 times, respectively. due to the intense influence of PIIN. Nonetheless, the latter can achieve the BER flo but with difficulty, at   = -5. 8 dBm. Regarding the other codes, they start in variat until the BER floor is achieved at -6. 6, -10. 3, -14. 9, and-13. 8 dBm for the 1D, 2D, SWZCC, and 3D-MD codes, respectively. In accordance with the above outcomes, e ploying our proposed code enables each user in the OCDMA system to require o minor power at the receiver level and saves around -9. 1 and -1. 1 dBm compared w the 3D-DCS/MD and 3D-MD codes  [24, 25] , respectively.
Simulation Results of 3D-OCDMA-PON First, note that the eye diagrams of eight active users shown in Figure  16  are all different, and none of them are similar to each other. Moreover, the Q-factor values of the users are between 9 and 12 dB, which means that our proposed code offers good results in spite of high data rates, up to 8 Gbps. The 3D-SWZCC code has the ability to detect and restore the desired signals. In accordance with the aforementioned results in Figure  14 , the fact that the eye diagrams of different users are not the same means that the BER and Q-factor values are also different. These phenomena can be explained by several factors: 1. Power waste: All users have equal power on the transmitter side, but during the transmission, they are subject to power losses, and this loss is not necessarily the same for all of the users.
4. Spectrum differential: OCDMA is considered a multi-service transmission technique. This means that it can send data in different formats, such as audio, videos, files, images, etc. Further, each format has its signal type; this means that there are various signal types carried in the same network, and each one has a distinct spectrum. Second, the performance of the 3D-SWZCC code is also estimated at different data rates and characterized by referring to the BER and the eye diagram patterns, as shown in Figure  17. It is clear that the SWZCC code gives the 3D-OCDMA system a good performance; this can be observed through the resulting BER and Q-factor values. The BER values are 3. 48 × 10 -29 , 5. 73 × 10 -18 , and 7. 55 × 10 -13 , and the Q-factor values are 18. 84, 11. 09, 8. 51, and 7.
05 for data rates of 0. 622, 1, 1. 5, and 2 Gbps, respectively. Consequently, we note that both the Q-factor and BER values do not override their sills (Q = 6 dB, BER = 10 -9 ); they should be, respectively, greater than 6 dB and less than 10 -9. Additionally, the system performance can be evaluated through the opening of the eye diagram, because if it extends over a wider range, this denotes the diminishing of MAI and indicates that the system has better performance and vice versa. Second, the performance of the 3D-SWZCC code is also estimated at different data rates and characterized by referring to the BER and the eye diagram patterns, as shown in Figure  17. It is clear that the SWZCC code gives the 3D-OCDMA system a good performance; this can be observed through the resulting BER and Q-factor values.
However, better performance for the 3D-SWZCC setup with L 1 = 2, L 2 = 2, and L 3 = 2 can be achieved at 10 km of distance with a provided data rate of 3. 02 Gbps. Moreover, the BER increases at a distance of 30 km; in this case, the system does not perform as well until the data rate goes from 3. 02 to 2. 06 Gbps. Similarly, the system is impaired until a data rate of 1. 45 Gbps is used at 50 km. Finally, Figure  19  shows the Q-factor variation as a function of the data rate for fixed SMF lengths of up to 20, 30, and 40 km. It is evident that the Q-factor diminishes when the data rate is increased. This indicates the direct relationship between them. The analysis is performed for eight users accessing the milieu simultaneously. It can be seen that the performance of our proposed network lessens when the SMF length is increased.
Conclusions This paper proposes a novel code design, named 3D-SWZCC code, which uses three encoding schemes: spectral, temporal, and spatial for the first, second, and third components, respectively, with the aim of implementing the proposed code in an adapted 3D-OCDMA-PON network. The proposed code overcomes most of the obstacles, mainly the MAI effects and limited capacity, as well as the PIIN effect, which becomes lower using the developed code compared to other 3D systems that use PD, PD/MD, DCS/MD, and PTZCC codes. Additionally, the proposed 3D-SWZCC code offers good simulation results and satisfies the optical communication requirements by presenting a good quality factor of more than 6 dB and a low BER value of less than 10 -9. The suggested SMF length for NG-OCDMA-PON systems can reach up to 122 Km for a data rate of 0.
Figure6shows the BER alteration as a function of the data rate for 200 active users. As discussed, the system attained the required BER for optical communication networks: BERs of 4. 8, 3, 0. 72, 0. 56, and 0. 51 Gbps were obtained for the 3D codes SWZCC, PTZCC, DCS/MD, PD/MD, and PD, respectively. Consequently, employing our proposed code in an OCDMA system can supply a great data rate, nearly 5 Gbps, while other codes cannot reach 1 Gbps, with the exception of the 3D-MD code. This optimization can be explained by the ZCC property of our code, which positively and effectively optimizes the OCDMA system performance. Additionally, the 1D and 2D SWZCC codes enable each user in the OCDMA system to exploit up to 0. 12 and 0. 67 Gbps, respectively. Note that the differences between 1D and 3D and 2D and 3D in terms of the allowable data rate amount to approximately 40 and 7.
Observations, calibration, and data processing 2. 1. JWST data SPT0311-58 JWST data were obtained on July 17, 2022, using the MIRI as part of the European Consortium MIRI Guaranteed Time (proposal ID 1264). The data is composed of imaging observations from the MIRI Imager (MIRIM;  Bouchet et al. 2015)  and integral field spectroscopic observations from the medium resolution spectrograph (MRS;  Wells et al. 2015; Labiano et al. 2021). The image was taken with the F1000W filter centred at 10 µm for a total integration time of 2719. 5 s in FASTR1 read-out mode and using a four-point dither pattern optimised for point source. Each dither was divided into three integrations of 81 groups. The MRS data were taken in SLOWR1 read-out mode with two independent configurations, SHORT and MEDIUM, and each had a total integration time of 7549.
MIRIM F1000W calibration The MIRIM F1000W data were calibrated with version 1. 6. 2 of the JWST pipeline and context 0937 of the Calibration Reference Data System (CRDS). The CRDS context includes inflight dark current and flat-fields reference files. The reduction was done in an iterative way. We first ran the usual pipeline: (stage one) ramp to slopes, (stage two) photometric and astrometry calibration, and (stage three) drizzling, re-mapping, stacking, and mosaicking with default parameters. The mosaic resulting from this iteration presented a background gradient, with the bottom fifth of the field of view (FoV) darkening by up to ∼2% compared to the (much flatter) rest of the image (far away from the SPT0311-58 source in any case). There were also vertical stripes at the <0. 5 background level.
e. the output of stage two of the pipeline) with flat null backgrounds, which we ran through stage three to produce a new mosaic. We then repeated the procedure once more to get better object masking and obtained final background-subtracted mosaics with pixel scales of 0. 1109 (the nominal pixel size) and 0. 06. Another alternative method was used for the stripe removal and background homogenisation. After detecting and masking objects in the different dithered exposures, we calculated a background map to correct a given image. In the construction of the background map, we first removed the median of each image, then combined the images in a stacked background frame, and finally scaled the stack by the median background of the image we were interested in correcting. Although this method has been very successful in dealing with the background in some other datasets (e.
g. , PRIMER data,  Pérez-González et al. , in prep. ) , the limited number of dithering positions in the SPT0311-58 dataset did not allow for a correct background nor for artefact removal using this more advanced method. The image with 0. 06 was used throughout the analysis, as it provides good matching with the ancillary high angular resolution (beam size of 0. 07-0. 08 ) ALMA data  (Spilker et al. 2022). The WCS of these mosaics were off by ∼0. 5 but were corrected when performing the additional astrometry corrections for comparison with ALMA and HST data (see Sect. 2. 5). The 5σ depth of the final mosaic reaches 25. 5 mag for a 0. 32 circular aperture (around our source), the radius being the full width at half maximum (FHWM) of the point spread function (PSF) in the F1000W band, for which we calculated a 30% aperture correction, that is, the data reaches 25.
We found that the standard pipeline identifies and corrects most of the cosmic ray (CR) events, but the so-called CR showers and/or highly energetic CRs still leave some relevant residual effects 2. The residuals of the CR showers and additional detector effects are more relevant in channels one than three due to the differences in the background levels. The second stage of the pipeline, which provides fully calibrated detector images, was run skipping the straylight and background pixel-by-pixel subtraction steps. Our MRS dataset is dominated by the background emission and/or detector effects, and therefore, the straylight correction provides only secondorder corrections. Additionally, the straylight correction could provide over subtraction in some areas of the detector due to the presence of bright or hot pixels.
ALMA and HST ancillary data The [C ii]158 µm observations were carried out with ALMA under program  IDs (2016. 1. 01293. S and 2017. 1. 01423. S: PI Marrone). The data is described in detail in  Spilker et al. (2022) , and we mostly followed the same reduction procedure as described there. A particular difference was that we applied one round of phase-only self-calibration to the data after running it through the ALMA pipeline. We imaged the rest-frame 160 µm continuum (observed at ∼240 GHz) using the line-free spectral channels by excluding a ±1500 km s -1 window around the [C ii]158 µm emission line and using a Briggs weighting scheme with a robust parameter of 0. 5, resulting in a synthesised beam of 0. 093 × 0. 085 and an rms noise of 8. 0 µJy beam -1. We also generated a continuum-subtracted data cube with a channel spacing of 30 MHz (∼37 km s -1 ), which has an rms noise of 0.
JWST-ALMA-HST astrometry To perform a proper comparison of the structures seen at different wavelengths, we needed to put all the different datasets into the same coordinate system. Since we were dealing with very high angular resolution data, this required a precision better than 100 mas in the absolute positioning. The absolute astrometry of the ALMA data was estimated from the position of a secondary source that was observed during the ALMA observations, which has a known position in the sky as tabulated in the ALMA source calibrator catalogue. Observations of this secondary source were interspersed between subsequent phase-target calibrator sequences. Analysis of the position of this secondary source yielded average positional deviations of less than 3 milliarcsec, which even after accounting for the positional uncertainty in the source catalogue, yields absolute astrometric uncertainties on the SPT0311 observation smaller than 10 milliarcsec.
For the dust attenuation, we used the dustatt_ modi f ied_CF00 module of CIGALE  (Boquien et al. 2019). It is based on the  Charlot & Fall (2000)  dust attenuation model where the young stars (<10 Myr) and the nebular emission located in the birth clouds (BC) suffer from additional attenuation compared with the stars (>10 Myr) that have broken out and escaped into the ISM. This module is flexible and allows one to A105, page 7 of 18 set up the slopes of the two components of the dust attenuation curve (BC and ISM), the A V of the ISM component, and the factor that relates the A V of the ISM and BC components. We left all parameters free in the SED fitting analysis, defining exactly the same initial parameters as Lo Faro et al. (  2017 ) in the analysis of the dust attenuation curve of ULIRGs at z ∼ 2 (see the double power-law free case from Table  1  in Lo Faro et al.
Summary SPT0311-58 consists of two gas-rich galaxies at redshift 6. 9 separated by about 8 kpc and experiencing a strong starburst. This paper has presented mid-infrared sub-arcsec imaging and spectroscopy of the SPT0311-58 system taken with MIRI/JWST. The data provide the first resolved kpc-scale rest-frame near-infrared light of an extreme dusty, infrared-luminous star-forming system within the Epoch of Reionization. The data enable the exploration of the stellar and ionised gas structure of the galaxies that form the system. The study also combines the new MIRI data with archival high angular resolution ALMA imaging to derive additional physical properties, such as the SFR, the ratio of the stellar mass to dynamical and gas masses, and the specific SFRs. The main results are summarised below: -The stellar light of the two galaxies (E and W) that belong to the system shows a clumpy structure with a total of nine clumps (five in the E and four in W, plus one tentative clump).