dataesr/scientific-paragraphs-categorization huggingface dataset:
The median is 13. 6 ka. Based on the data from this study alone, this distribution also shows a lack of meteorites with terrestrial ages of <5 ka. One strewn field (SaU 001) falls in the 5-10 ka bin. In addition, the distribution shows a low abundance of strewn fields with terrestrial ages larger than 25 ka. Connected to this, while the previously published 14 C terrestrial age of SaU 163 was very high, >50 ka, the new 14 C and 14 C-10 Be terrestrial ages are significantly lower, that is, 22. 8 AE 2. 2 ka and 23. 3 AE 2. 0 ka, respectively. However, by including the Ghubara and RaF 032 strewn fields (both L ordinary chondrites) in the statistics, the lack of young strewn fields disappears and there might even be a slight peak at 0-15 ka. The Ghubara strewn field (total mass > 1750 kg, Meteoritical Society Database) has a young terrestrial age of 0.
Comparison of 14 C and 14 C-10 Be Terrestrial Ages Currently only a small database of 14 C-10 Be terrestrial ages for ordinary chondrites from Oman exists. Before our study, 14 C-10 Be terrestrial ages were only available for samples from the JaH 073 strewn field, and unfortunately, those are less meaningful because of the complex exposure history of JaH 073. Considering that 14 C-10 Be terrestrial ages are more reliable than 14 C terrestrial ages (but sometimes less accurate, see also below) and that the interpretation of the terrestrial age histogram of strewn fields suffers from very low statistics, the database should definitely be extended. Figure  4  depicts the mean 14 C-10 Be terrestrial ages as a function of the mean 14 C terrestrial ages for the samples studied by us (strewn field samples and individuals).
Acknowledgments-This study was supported by  Swiss National Science Foundation  grant  200020_166251. We thank the  Public Authority for Mining  and the  Ministry of Heritage and Tourism of the Sultanate of Oman  for the support and necessary permissions during fieldwork. The ASTER AMS national facility (CEREGE, Aix-en-Provence) is supported by the  INSU/CNRS , the  ANR  through the  "Projets thématiques d'excellence" program for the "Equipements d'excellence" ASTER-CEREGE action , and  IRD. Very constructive reviews by  Kees Welten  and  Hamed Pourkhorsandi  helped to significantly improve the manuscript. Data Availability Statement-Data available on request from the authors. Editorial Handling-Dr. A. J. Timothy Jull.
Time-variable gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions have opened up a new avenue of opportunities for studying large-scale mass redistribution and transport in the Earth system. Over the past 19 years, GRACE/GRACE-FO time-variable gravity measurements have been widely used to study mass variations in different components of the Earth system, including the hydrosphere, ocean, cryosphere, and solid Earth, and significantly improved our understanding of long-term variability of the climate system. We carry out a comprehensive review of GRACE/GRACE-FO satellite gravimetry, time-variable gravity fields, data processing methods, and major applications in several different fields, including terrestrial water storage change, global ocean mass variation, ice sheets and glaciers mass balance, and deformation of the solid Earth.
The combined GRACE and GRACE-FO observations provide an extended record of precise measurements of the Earth's time-variable gravity field, which will continuously improve our understanding of mass variations in the climate system, especially at long-term time scales. So far, over three years of GRACE-FO time-variable gravity solutions with comparable accuracy as from GRACE have been released  (Landerer et al. 2020). There is an about 1-year gap  (July 2017 -May 2018)  between the GRACE and GRACE-FO missions, which is not ideal, but would not affect most of related applications focusing on seasonal and long-term time scales. Initial analyses using GRACE-FO data show encouraging results with accuracy mostly consistent with pre-launch expectations  (Landerer et al. 2020; Velicogna et al. 2020; Boergens et al.
2020). Despite the tremendous success of GRACE and GRACE-FO (noted as GRACE/ GRACE-FO hereafter unless for separate discussions), accurate quantification of mass variations using GRACE/GRACE-FO gravity measurements has been challenging due to its coarse spatial resolution and limited accuracy. In addition to the KBR system, each GRACE/GRACE-FO satellite is equipped with a SuperSTAR Accelerometer (ACC), 1 3 GPS receiver/antenna, Star Cameras, and Laser Retro Reflectors to complement the science instruments. The GRACE/GRACE-FO Science Data System (SDS) consisting of the Center for Space Research at the University of Texas at Austin (CSR), NASA's Jet Propulsion Laboratory (JPL), and the German Research Centre for Geosciences (GFZ) uses the ranging and ancillary data to estimate a new gravity field every month, in the form of corrections to a background gravity model used in the data processing procedure  (Bettadpur 2018; Yuan 2018; Dahle et al.
GRACE/GRACE-FO Time-Variable Gravity Field and Mass Variation GRACE/GRACE-FO time-variable gravity solutions are provided by the three GRACE/ GRACE-FO SDS data processing centers CSR, JPL, and GFZ, and other institutions, e. g. , TU Graz in Austria, Tongji University in China, the International Combination Service (1) V( ) = G ∭ A 0 0 | | -0 | | dA (2) U(r, , ) = GM a ∞ ∑ l=0 l ∑ m=0 a r l+1 P lm (cos )(C lm cos m + S lm sin m ) (3) C lm S lm = 1 (2l + 1)Ma l ∭ V 0 ( )r l P lm (cos ) cos m sin m dV 1 3 for Time-variable Gravity Fields (COST-G), etc. The current SDS Release 6 (RL06) solutions are expressed in the form of SH coefficients up to a maximum degree and order of 60 (degree and order 96 products are also available). Atmospheric and dynamical oceanic mass variations are largely removed during GRACE/GRACE-FO gravity field processing by using a non-tidal atmosphere and ocean de-aliasing model  (Dobslaw et al.
2017a ). The high-frequency atmospheric and oceanic signals, if not removed, will introduce artifacts (at lower frequencies) in the GRACE/ GRACE-FO monthly fields. For each monthly GRACE/GRACE-FO gravity solution (the so-called GSM product), the processing centers provide some supplementary datasets that contain the monthly means of the removed atmosphere and ocean de-aliasing model. For example, the supplementary GAC product represents the combined non-tidal atmospheric and oceanic mass changes removed from GRACE/GRACE-FO GSM solutions. Please see  Dobslaw et al. (2017b)  for detailed definitions of the supplementary fields (e. g. , GAC, GAD, and GAA). These removed signals need to be restored to GRACE/GRACE-FO GSM products for certain applications (e. g. , total gravity change over land or bottom pressure change over the ocean).
2008). This method was later improved by combining GRACE data over land and barystatic sea level change constrained by sea level equation  (Sun et al. 2016; Jeon et al. 2018 ). The updated method leads to significantly improved geocenter motion determinations at long-term time scales. For the GRACE/GRACE-FO SDS RL06 gravity solutions, geocenter motion series are estimated by the SDS using the improved method  (Sun et al. 2016) , and provided as supplementary datasets (GRACE Technical Note 13, Landerer 2019).
Uncertainty Assessment Quantification of the uncertainty of GRACE/GRACE-FO-estimated mass change is difficult due to the lack of adequate independent observations at scales comparable to the GRACE/GRACE-FO observations. The formal errors provided along with GRACE/ GRACE-FO GSM SH coefficients appear too optimistic for quantifying the real uncertainty of GRACE/GRACE-FO mass change estimates, as errors of the background geophysical models and processing methods are mostly unknown. The characterization of errors and error correlations of GRACE gravity solutions has improved significantly over the years  (Kvas et al. 2019). A limited number of available other independent datasets are useful for validating GRACE/GRACE-FO observations (see Sect. 2. 3. 3), but not accurate enough to provide an accurate quantitative assessment of GRACE/GRACE-FO uncertainty level.
Acknowledgements The authors are grateful to the two reviewers ( Martin Horwath  and  Bert Wouters ) and Associate Editor  Nico Sneeuw  for their comprehensive and insightful comments, which have led to improved organization and presentation of the results. This paper is an outcome of the workshop "Probing the Deep Earth Interior by using in synergy observations of the Earth's gravity and magnetic fields, and of the Earth's rotation" held at the  International Space Science Institute (ISSI, Bern, Switzerland)  on.
Assessment of visual encryption of video and image content requires a reliable estimation of content recognizability and low quality. As pointed out in the literature, current methods are insufficient and research into this topic, as well as into the relation between low quality and recognizability, is still lacking. This lack of research is primarily due to a lack of data. To improve on the status-quo we have taken a recognizability database and performed a subjective quality evaluation on a subset of the images. This gives us a new database with both subjective recognizability and quality information and allows to delve into the relation between low quality and recognizability. We analyze the relationship between quality and recognizability as well as the predictive quality of state of the art visual quality indices.
Therefore, an analysis of the remaining visual information, which can be extracted from the non-encrypted parts of the data, is necessary. There is a special case for selective encryption which is called "format compliant encryption". The definition is that a selective encryption scheme is format compliant if a standard compliant decoder can decode the encrypted format without crashing (this is the source of images in the database published with this paper). The benefit of that is that the encrypted data can be used just like regular data. For example, with a careful selection it is possible to use an encrypted video as a low quality preview which can be upgraded to full quality with the key. Depending on the application scenario, the resulting quality can range from "preview quality" to the content should be "unrecognizable".
This is where the notion of quality and recognizability by human observers becomes a primary concern. The typical use cases for selective encryption, providing previews or preventing an enjoyable consumption by human observers (see  [6]  for typical use cases), directly aim the consumption by humans. Subjective quality and recognizability assessment is thus needed to ascertain the proper protection by the encryption. To constantly evaluate such systems by actual human observers would quickly become overly time consuming and costly. The obvious alternative is to utilize visual quality indices (VQIs) which are built to emulate the way the human visual system (HVS) assesses the quality. For the development of such VQIs, databases of distorted images with a quality score provided by actual human observers are utilized.
There exist plenty of subjective (human assessed) and objective (issued by computer programs) datasets for still, natural image quality assessment  [7]  or for video quality assessment  [8]. Subjective and objective quality assessment studies have also been devoted to Depth Image Based Rendering (DIBR)  [9] , others have focused on 3D watermarking quality assessment  [10]. All these works concern very high quality images evaluation, but only very few works have been conducted on the quality evaluation of selective encryption. This in turn means that VQIs are trained primarily on high quality databases (because that is what is available) to emulate the human perception of high quality content. It has been shown,  [6] ,  [11]  that traditional visual quality indices are not well equipped to handle images that move too far away from high quality, and consequently fail at the task of evaluating the content and quality of selectively encrypted data.
Indeed, the traditional VQIs are designed in such a way that a special weighting is applied onto some components of the image where the perception is more sensitive. In other words, these quality metrics are tuned to look for differences nearby the visibility threshold, and hence are not adapted to handle very strong distortions. Producing new visual quality indices which can handle selective encryption is also a field of active research  [12] -  [15]. The development of visual quality indices requires either expensive human observer experimentation or a solid database of such observations for evaluation and design. So the current state of affairs is that the available VQIs are a poor fit for evaluating the low quality of images which are encrypted in a medium to strong fashion. Further, there is little to no work done on the recognizability of strong to very strong selective encryption.
Even worse, we do not even know the relationship between quality and recognizability. Still, VQIs (mostly PSNR and SSIM because they are widely known and readily available) are used as a primary tool for evaluating the performances of all encryption strengths, primarily because they are the only tools available. For the development of visual quality indices for mid-to lowquality images there are, to the best of the authors knowledge, two databases:  [16]  and  [17]. This is not an optimal situation but at least allows for the development on one database and the testing on the other. For the development of indices which can handle content intelligibility the situation is somewhat more complicated as shown in  [18]  which also introduces the first database with a recognition score for images encrypted on the border of content recognition.
The database presented in  [17]  also contains a recognizability score (denoted content leak information), which is directly rated by observers when comparing the original to the encrypted image, in addition to the visual quality data. The presentation of the original coupled with the tendency of the human visual system to find patterns strongly influences the content leak information. The translation of this content leak information  [17]  to the recognition index  [18]  is difficult at best. Specifically, both are susceptible to pareidolia, i. e. , the tendency for incorrect perception of a stimulus as an object, but the method in  [18]  is designed to catch that (multiple originals) while the setup in  [17]  is not (single original). The data in  [17]  is still useful for the security evaluation of encrypted images, but not the evaluation of the recognition threshold.
So there is a lack of data which makes it hard to produce quality or recognizability indices for strong to very strong distortions. In addition, we do not know if the VQIs which are available could properly evaluate recognizability, primarily because the relation of quality and recognizability for the HVS in this context is not researched at all. What we do know however, from  [18] , is that the VQIs most frequently used for this task are not able to perform well in this capacity. In this paper we present a subset of the database from  [18]  to a panel of human observers to get an evaluation of perceived quality for encrypted images at the recognition threshold. This is not only a further database for the development of quality based indices for selective encryption but also allows us to study the differences and commonalities between quality and recognizability, a hitherto unstudied subject.
The rest of the paper is structured as follows: The database, how it was collected, its content, and where to get it, is described in Section II; The analysis of the relations between quality and recognizability, i. e. , the conformance of quality, perceptibility, and various visual quality and recognition indices, is described in detail in Section III; Some topics are not directly related to the experiments, but are still important to discuss, those are presented in Section A; The conclusion, Section IV, gives a recap of our findings and concludes the paper.
A. Images Contained in the Database This database is a subset of the USEE database  [18]. A subset was chosen so we could perform the subjective experiments with a large enough number of observers. The primary driver behind the decision was how many images we could handle and the cost and time involved. The USEE database contains 14 images, 12 color and two grayscale, with 6 encryption types in 9 distinct steps for a total of 770 images (including the originals). We reduced the number of images for the USEE Quality (USEEQ) database by removing the grayscale images and two of the encryption types (fake, jpg). Further, we reduced the number of steps between highest and lowest quality to 6 (from 9 in the USEE database). This means the USEEQ contains a total of 288 images (12 × 6 × 4). Fig. 1  shows examples of the encryption types for the lighthouse image.
The observers were asked to modulate the scroll bar according to their perception of the distorted image quality as compared with the original. On average, the allocated scores were rather low with an average score of 30. 57 and a standard deviation of 5. 61. At this point, thanks to the previously collected recognizability scores (  [18] ), and the quality assessment presented here, we have at our disposal, for each image of the USEEQ dataset, two subjective scores: The recognizability Mean Opinion Score (MOS R )  [18]  and the quality score (MOS Q ), collected in the experiments as described above.
III. ANALYSIS OF THE RELATION BETWEEN QUALITY AND RECOGNIZABILITY In  [6]  the authors showed that the usual visual quality indices, which are built for high quality imagery, are not well suited to assess low quality, as in strongly encrypted, images. Furthermore, they pointed out that the lack of a recognizability database prevents any evaluation for the recognition of image content, which is important for confidential encryption. This led to the generation of a recognizability database in  [18] , which was then used to evaluate visual quality indices to be used as recognition indices. The authors showed that traditional visual quality indices are poor recognition indices. They also proposed using a structure based index (the NCC), in an attempt to create a better index. Overall, the result from  [18]  is that the consolidation of recognizability and (low) quality is difficult.
However, in  [18]  no human evaluation of the quality of the low quality is available, meaning the assessment of quality and recognizability is only speculative. The relation between high and low quality has to some extent been looked at in  [6] , but the relation of quality and recognition has not been looked into yet due to a lack of data. We now have a database which has images at the recognition threshold annotated with quality information. This allows us to look into the relation of quality and recognition, with the main goal of understanding why visual quality indices perform so poorly. As the discussion and analysis can be long, and at times very detailed, we endeavor to succinctly summarize the most relevant results from the longer subsections into an "in brief" paragraph at the end.
Two reported values are operating points on the well known receiver operating characteristic  [28]. The equal error rate (EER) is the operating point where the false positive and the false negative rates are equal, it is primarily useful for the comparison of methods and significance calculation  [29]. The other is the false positive rate at the threshold where zero false negatives are reported (0FNR) as this is the point of interest (no insecure images are reported as secure) when assessing the security for encryption. Finally, Matthews correlation coefficient (MCC)  [30] ,  [31]  is related to the chi-squared statistics for the contingency table. It is an especially good fit for classification problems where the class size is unbalanced, as is the case here. Since USEEQ is a subset of the USEE database we also repeat the VIF, LEG, SSIM, PSNR, and NCC evaluation on this reduced subset to be able to properly compare them.
The results are given in Table  I. The VQIs show the same overall behaviour as in  [18] , they still are only a weak predictor of recognizability. The MOS Q based image quality on the other hand performs rather well which is kind of surprising. The VQIs are built based on the HVS, although for high quality images, but work poorly on this data while the subjective quality scores (MOS Q ) themselves work decently well. This indicates that there is a difference in how human observers perceive high and low quality data. For example in  [17]  even for low quality images the content leakage score can be high, meaning the human observer is well capable of differentiating between content and quality. Further, the human visual system is adept at noticing differences of a sufficient magnitude, but that changes with the overall variance in the image (this is usually known as contrast masking).
2  which represents two cases of this phenomenon. Each pair of images has either a similar quality score (red squares) or a similar recognizability score (blue circles). The corresponding images are shown in Fig. 4. Clearly, quality and recognizability can be highly decorrelated, i. e. , for two distinct images a similar perceived quality might result in a significantly different recognizability score (and vice versa). Fig. 3  also shows the separation of the data into high quality, where the images are clearly recognizable, and low quality data, where a distinct drop in quality happens. The range between low and high quality is denoted as medium quality. The quality drop at the border between low and high quality is not very visible in the combined plot but can be easily seen if the plots are separated by encryption type, shown in Fig.
In contrast, the NCC, which does not attempt to conform to the human visual system, exhibits a lower error rate than the quality indices. Obviously this was calculated on the whole set and is the best result. The question is how well are the different VQIs able to generalize, i. e. , what would happen if we applied this to unseen data. The database is composed of four subsets, so we can calculate the threshold for a subset and apply it to the other three sets. This gives us the best result that can be achieved with a given VQI on a single set as well as the generalization performance when applied to a different set. Results are given in Table  III. For each VQI the threshold is calculated for one testset, given in T. Source, and the total error is given when evaluating classification into recognizable and non-recognizable images of the testset given in the column.
72%. This would suggest that the best we can hope for in an unseen data set would be ≈ 16. 66% total classification error. Another important result from this test, and the results from Table  IIIa , is that the quality estimation by human observers is also not a good base for the classification of images into recognizable/non-recognizable classes, even though Table II might give that impression. In brief: We can state two main results: 1) the quality as given by human observers (MOS Q ) is a poor source for the classification of image into recognizable and unrecognizable images, and 2) current VQIs are also not well suited to operate this classification (which comes as less of a surprise given that most are built specifically to model the quality estimation by the HVS).