dataesr/scientific-paragraphs-categorization huggingface dataset:
The algorithm of this minimization problem is directly implemented in Matlab as the function lsqnonnneg. It executes the active-set algorithm of  Lawson et al. (1974). Although it is a greedy algorithm in terms of computing time and resources when confronted to large scale problems, we will show with various simulation tests that its implementation within our methodology is well suited to sparse recovery. Moreover, the sequentialization of its implementation is proposed in section 5. It is shown to performed with significant computing time savings when large scale problems are considered. We also acknowledge many others methods proposed in the literature to solve equation (  4 ), in particular in the context of the volumetric reconstruction of the particles distribution. Among the family of the 'row-action methods', the 'algebraic reconstruction technique' (ART)  (Petra et al.
A c c e p t e d M a n u s c r i p t Once the reconstructed intensity field E in the refined subspace is obtained from the inversion of the linear system, point-like representations of the particles are determined as local maxima of the sub-image. This is performed using a morphological operation (dilatation) with a square structuring element of width L sq. The determination of the optimal size of the structuring element is specifically discussed in sections 4. 2 and 4. 4. The code of our detection algorithm, denoted PIR NNLS in the rest of the paper, is implemented in Matlab and makes use of its high vectorization level and its optimized built-in functions. An other version of the algorithm, hereafter called PIR SMART uses SMART for the inversion procedure. Results obtained by both versions are compared in sections 4.
A c c e p t e d M a n u s c r i p t This source of noise is generally the major contribution in the PIV/PTV applications. A representative illustration of the particle localizations as obtained with DTB, CCM and PIR NNLS in the context of noisy data is presented in figure  6  (bottom row). For this particular case with 98 generated particles, the performance metrics are: TP DTB = 48, TP CCM = 43 and TP PIR NNLS = 90; FP DTB = 20, FP CCM = 12 and FP PIR NNLS = 24. This qualitative observation, again, illustrates the ability of PIR NNLS to efficiently detect in unfavorable conditions. The comparison of the performance metrics as obtained by the proposed PIR NNLS with the conventional DTB and CCM are shown in figure  9  as a function of the particle density, N ppp. Overall, for the highest tested N ppp , the number of TP recovered by PIR NNLS is approximately twice the one obtained with both other methods and a very small number of missed particles returned with PIR NNLS leads to a Yield value of 0.
83. With both other detection methods, the Yield values drop to approximately 0. 4 for the highest particle density. On the other hand, the Reliability coefficient exhibits similar values for each method, whatever the particle density is. This performance points out the fact that PIR NNLS is likely to generate a larger amount of False Positive detections than the conventional CCM and DTB in noisy conditions. This behavior is due to the increasing mathematical difficulty to invert the present ill-posed problem (4) in the context of noisy data. This aspect requires further attention and solutions will be prospected in the future, especially for the application of this method to data collected from experiments. One of the solutions consists in performing a first treatment to the grayscale images by removal of the background noise  (Mendez et al.
Validation of the sequential PIR NNLS algorithm The purpose of this section is to validate the sequential version of the PIR NNLS algorithm. To this end, the same database of synthetic 32×32 pixel images as in section 4. 5 is considered. Statistics presented below are computed from sets of 30 images. The performance metrics (Yield and Reliability) computed from both the sequential and the direct versions of the PIR NNLS algorithm are depicted in figure  10. Clearly, the reliability of our method to detect particles in realistic images is not affected by the sequentialization, whatever the tested particle density is. The robustness preservation of PIR NNLS is moreover obtained with a significant gain in computing time machine: for N ppp ≈ 0. 05 the sequential version of PIR NNLS is on average 5.
MODELING ATTACK FRAMEWORKS FOR EMBEDDED SYSTEMS Embedded systems have faced a growing number of attacks in recent years  (Newman, 2016; Larson, 2017). Combining the ideas in the previous section with the 3 phases of embedded system design in SysML-sec (Analysis, Hardware Software Partitioning, and System/Software Design), we present the new methodology shown in Figure  1. As indicated, we differentiate between the original modeling subphases for embedded systems (in green), the securityrelevant sub-phases (in yellow), and the verification steps (in blue). The solid black arrows show steps the designer must take in developing models manually, the blue arrows show automated steps such as model translation, and purple arrows show steps for reconsidering the model. First, the Analysis phase considers the general needs of the system.
Architectural Countermeasures such as Hardware Security Modules and Firewalls can also be added. Since security operations can be computationallyintensive and require the secure storage of cryptographic elements (e. g. , keys), there exist specialized co-processors Hardware Security Modules which can perform cryptographic operations faster than a normal processor and can store cryptokeys. Firewalls filter communications and can block an attacker without having to attempt to decrypt/analyze the contents of his/her communications. Filtering communications could prevent an attacker from injecting code to a certain extent, if we limit the size of allowed messages. In our first secured model, we only add application countermeasures, in the form of MACs to data that we do not want the attacker to forge, most importantly commands to the ECU.
For example, we could add a firewall to ensure in-car communications cannot be jammed. Furthermore, we could add timestamps to ECU commands before calculating a MAC to prevent replay attacks as we described in section 5. 4. As the root attack is still logically possible, more countermeasures must be added to protect our system. Subsequent iterations through the design process could involve ensuring the sensor data could not be forged, or considering if keys should be periodically distributed. Then, the security verification could evaluate if there exist vulnerabilities in the implementation of those security protocols, which may suggest future attacker scenarios, and consequently, modifications to the displayed Attack Tree or new Attack Trees.
While their approach is also UML-based, they focus on estimating probabilities of success for attacks, while ours focuses on verifying adequate placement of encryption. UMLSec  (Jürjens, 2002)  is a UML profile for expressing security concepts, such as encryption mechanisms and attack scenarios. It provides a modeling framework to define security properties of software components and of their composition within a UML framework. It also features a rather complete framework addressing various stages of model-driven secure software engineering from the specification of security requirements to tests, including logic-based formal verification regarding the composition of software components. However, UMLSec does not take into account the HW/SW Partitioning phase necessary for the design of IoTs.
, 2010). In the lower oases, intensive irrigation is developed, which is hugely reliant on the discharge flowing from the headwater catchments  (Mamitimin et al. , 2014; Ren et al. , 2018) , causing considerable changes in the natural river discharge downstream of the area  (Zhou et al. , 2000; Tao et al. , 2011). In reality, human intervention is very difficult to model as it is policy related and because of the lack of an efficient dataset. The anthropogenic effects on the water cycle, accompanying the climatic and topographic characteristics, make the Tarim one of the most challenging places to apply land surface models. There are three major steps in this study, Firstly, we generated a best-possible forcing dataset for the Tarim domain which reduces as far as possible the biases using refined data sets.
River discharge observations River discharge is a very reliable and integrated observation of the continental water cycle which is always used as a validation variable  (Yang et al. , 2017). Over the headwater catchments, there are 13 hydrological gauges recorded in the Hydrological Yearbooks of China issued by the Ministry of Water Resources, though only six gauges are selected with consideration of their locations and data completeness. Two gauges (1, 2) are in the upper Yarkand River, two (3, 4) are in the upper Hotan River, and two (5, 6) are in the upper Aksu River, while no gauge is found on the Kaxgar River. For the gauges in headwater catchments, the discharge is considered free of human intervention, i. e. , irrigation or reservoir regulation, so to a large extent they represent the natural environment  (Cui et al.
Near-surface atmospheric conditions Near-surface atmospheric conditions are crucial to hydrological responses  (Adam et al. , 2006). However, both the model simulation and gridded forcing generated from observations are proven to have large uncertainties where observations are sparse and heterogeneity is strong  (Harris et al. , 2014; D'Orgeval et al. , 2008) , i. e. , in the arid and mountainous area, so that, in practice, several forcing datasets are always used in parallel to generate an ensemble of climate conditions which hopefully tracks the uncertainties  (Knutti et al. , 2010; Tebaldi and Knutti, 2007). Alternatively, when possible, regional datasets which contain more information than global datasets are used to move the forcing closer to true values  (Ines and Hansen, 2006). In this study, several sets of estimated forcing inputs based on WATCH (Water and Global Change;  Harding et al.
, 2011; Weedon et al. , 2014a)  are developed and then used to drive the land surface model (Fig. 2 ). The best simulations among them will be used to analyze the accompanying bias with its driving forcing. A pair of reference forcing datasets are WFD  (WATCH Forcing Data, 1958 -2001; Weedon et al. , 2010 Weedon et al. , , 2011) )  and WFDEI (WATCH Forcing Data methodology applied to  ERA-Interim data, 1979 -2012; Weedon et al. , 2014a, b). They use the same methodologies but have slight differences in the basic data, processing and formatting  (Weedon et al. , 2014a). In brief, WFDEI is an evolution of WFD where the underlying re-analysis is now ERA-Interim but using the same bias correction methodology. This product has been proven to be superior to WFD. CRU (Climate Research Unit) monthly total precipitation observations were used to biascorrect the precipitation in WFD and WFDEI datasets.
However, the WFD uses a previous version CRU TS 2. 10 before the CRU TS 3. 1 used in WFDEI was released  (Weedon et al. , 2014a). The two CRU datasets differ in the time period  (CRU TS 2. 10: 1901 -2002; CRU TS 3. 1: 1979 -2009; Jones and Harris, 2013) , in the stations used and in the methods employed; see  (Harris et al. , 2014)  for details. Hence, there are still some differences between the two which could further affect the hydrological responses. The two datasets are named WFD-CRU and WFDEI-CRU for later description, and the time step for all the forcing variables is 3 h. The CRU datasets were constructed by monthly observations at meteorological stations across the world's continents. The observations were then interpolated to 0. 5 • longitudelatitude grid cells. Though the CRU compares favorably to some other gridded datasets, it has significant deviations over regions and time periods with sparser observational data  (Harris et al.
, 2014). Moreover, because only the monthly total precipitation was used to correct WFDEI and WFD, it cannot improve the temporal variabilities at smaller time steps (i. e. , daily or sub-daily). However, the precipitation variations in a short period would result in different hydrological responses even in the condition of the total monthly amount remaining the same  (Potter et al. , 2005). Hence, in addition, the WFDEI-CRU dataset was further corrected by the gridded daily precipitation data from the China Meteorological Administration (named CMA). The CMA precipitation product compiles 2416 national meteorological monitoring stations over China, using the climatological optimal interpolation method to generate the gridded 0. 5 • precipitation field from 1951 to 2016  (Zhao et al. , 2018; Shen et al.
, 2010). PRISM (Parameter-elevation Regression on Independent Sloped Methods;  Daly et al. , 2008)  was used to lessen the orographic effects  (Shen et al. , 2010). The density of meteorological stations used in the CMA is much higher than that used for CRU. For instance, there are only six gauges over the Tarim Basin in the CRU database, while 39 gauges are recorded in the CMA system  (Tao et al. , 2011)  so that it can to a certain degree improve the data applicability where precipitation is spatially inhomogeneous compared to CRU datasets. Given that the CMA data provide the daily information, it also improves the temporal variations of precipitation rather than using the total monthly value in CRU datasets. The corrected atmospheric input dataset is hereafter referred to as WFDEI-CMA.
Glacier melt dataset As mentioned in Sect. 2, glacier melt is a vital water input to the Tarim. However, glacier runoff measurement is so diffi-cult for such large regions that model-based estimates of the glacier melt are necessary. In general, a glacier module is not coupled in LSMs  (Fraedrich et al. , 2005). Hence, rather than building a separate glacier module, we use an independent daily glacier melt dataset obtained from the glacier model called HYOGA2 (meaning glacier in Japanese), which has been proven reliable over the globe  (Hirabayashi et al. , 2013). HYOGA2 is a temperature-index-based model utilizing an extensive global-scale glacier inventory and has several improvements compared to its first version (HYOGA) in model parameter simulation as well as the temporal extent. More details can be found in the original papers  (Hirabayashi et al.
, 2010 (Hirabayashi et al. , , 2013)). The glacier melt is added to the rainfall series of WFDEI-CMA as an additional water flux to the system. The melt water hence participates instantaneously in the water cycle without delays such as stores in ice, glacier pack or groundwater recharge beneath the ice being considered. This method was chosen for its simplicity and the lack of knowledge on the details at the transition between the glacier and the soil. Daily values are uniformly distributed over the eight time steps per day of WFDEI. By adding the glacier melt, the fourth new forcing dataset is generated as WFDEI-CCG.
In the Tarim Basin, the irrigation accounts for more than 95 % of the consumed water  (Zhou et al. , 2000) , so that the difference between the simulated discharge and observations can be attributed to neglecting irrigation in the model. In conclusion, as shown in Fig. 2 , four different forcing inputs are prepared to drive the ORCHIDEE simulations, two basic forcing datasets (WFD-CRU and WFDEI-CRU), one after correction by CMA (WFDEI-CMA) and then one after adding glacier melt (WFDEI-CCG). Among them, the input water amounts are different, while WFDEI-CMA and WFDEI-CCG have the same other forcing variables as WFDEI-CRU. Additionally, the experiment with the newly developed snow and soil freezing scheme is named WFDEI-CCG-SF based on the forcing dataset WFDEI-CCG. Because the WFD covers the overlapping period 1901-2002, the WFDEI covers the period 1979-2014, CMA covers years 1951-2016 and the glacier melt dataset covers 1958-2001, all the simulations and analysis in this paper are over the overlap period 1979-2001.
Bias in precipitation The Tarim Basin is one of the areas where significant deviations exist among different modeled precipitation estimates and observation-derived datasets because of the sparse observations and orographic effects  (Fekete et al. , 2004; Wu et al. , 2012). Meanwhile, the precipitation bias might not be well addressed in the CMA system because the number of meteorological gauges is still too limited in the Tarim region to build a reliable interpolation climate field  (Shen et al. , 2010). Although  Xie et al. (2007)  have tried to use other gauges outside China, the density of the gauge distribution is extremely low around the boundaries of the Tarim Basin where most of the precipitation is generated. Furthermore, due to the orographic effects, the precipitation over the mountainous area is larger and with more significant heterogeneity than that in the plains  (Daly et al.
Bias in rainfall and snowfall repartition The differences between WFDEI-CRU and WFDEI-CMA are not only in the total amount of precipitation but also in the proportion of rainfall and snowfall (Table  3 ). Compared to the rainfall, the snowfall is more difficult to observe and affected by a large uncertainty; hence in the CMA dataset, only the rainfall was recorded and then used to scale the CRU dataset but keeping the relative proportion of liquid and solid precipitation provided by WFDEI-CRU. The energy needs for phrases change are considerably different for the liquid from the solid water. Berghuijs et al. (  2014 ) suggested snow will lead to more runoff than rain in similar conditions based on the observations over the US and China. The impact of the precipitation type on the evapotranspiration rate is affected by many factors and hard to measure.
The PET is changed to a "true" PET , and the possible maximum bias in PET is calculated as bias(PET) = (PET -PET )/PET × 100 %. (2) Assumption 3. Only the ET is uncertain. P and PET, which are mainly linked to the forcing, remain the same, while the ET, which is significantly affected by the model structure, is assumed biased. It is essentially relevant to the model processes rather than the forcing dataset. To compensate the discharge bias, the ET should be decreased to point "D" where the ET equals precipitation minus observed discharge (ET'= P -RO). The possible maximum bias in ET is calculated as bias(ET) = (ET -ET )/ET × 100 %. (3) With the target ET , a new Budyko curve can be drawn for new relations between P , PET and the new ET (the blue lines in Fig. 3 ). However, all the assumptions are proposed in conditions of only one variable being uncertain, but in reality any of the three variables can be biased at the same time.
Forcing input comparison among experiments We specify P as the sum of all the water inputs into the system, which include the atmospheric water flux (in its liquid and solid phases) and glacier melt. The precipitation for the three headwater catchments and the upper Tarim is listed in Table  3  for each simulation. The interannual variations and the intra-annual cycle of total precipitation over upper Tarim Basin are plotted for different forcing in Fig. 4. The annual cycle of the two basic forcing datasets WFD-CRU and WFDEI-CRU are similar, while the precipitation in WFDEI-CRU is slightly larger in monthly values (Fig. 4a , red and blue lines). The deviation is the result of their expanding differences after 1990 (Fig. 4b ). The precipitation difference is mainly due to the fact that two different versions of CRU (CRU TS 2.
10 and CRU TS 3. 1) are used for precipitation correction in WFD and WFDEI (see Sect. 3. 1. 2 or  Weedon et al. , 2014a). The precipitation difference between the two CRU datasets is relatively small, while the CMA dataset increases the precipitation to a large extent, by 37. 3 %, compared to CRU for the upper Tarim Basin (Table 3). The changes mostly occur during summer when the peak of CMA precipitation is more than twice as large as precipitation in CRU. The shape of the annual cycle changes greatly as the timing of the peak is shifted from April to July. However, the precipitation amount in winter (DJF) decreases  in CMA, to which the decrease in snowfall is the major contributor. The changes in rainfall and snowfall are similar in all three headwater catchments (Table  3 ) and the upper Tarim Basin.
6b, d, f, 7a)  for CRU datasets. Discharge increases after the precipitation is corrected by the CMA dataset, with the absolute bias decreasing to around 80 %. Adding glacier melt also increases the discharge but by a relatively small amount. Changes in the model structure (new snow and soil freezing scheme) further decrease the bias, especially for the Aksu River. The final biases of the discharge for the three subbasins are -71. 1 %, -47. 8 % and -49. 4 %. The gradual improvements and corresponding magnitude changes are visible in the annual discharge variability in Fig. 6b, d  and f. Besides the increase in the annual mean discharge, the amplitude of the interannual cycle of the discharge is also improved by the progressive changes. The estimated discharge peaks have been shifted from April in CRU simulations to the summer (July or August) by CMA correction and adding glaciers (Fig.
The modification of the snow and soil freezing scheme increases the trend ratio in the upper Aksu River up to 0. 72, while slightly decreasing it for the two other catchments. In brief, the gradual refinement of the forcing datasets (from WFD-CRU to WFDEI-CRU to WFDEI-CMA to X. Zhou et al. :  Retrospecting the estimated discharge bias 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Figure  6. The interannual cycle and intra-annual series of the discharge simulation for three headwater catchments and the upper Tarim Basin: (a, b) Yarkand River, (c, d) Hotan River, (e, f) Aksu River and (g, h) the upper Tarim Basin. Observed discharge for each subbasin was aggregated by the measurements at separated discharge gauges and shown as the black solid line. The simulated discharges at the corresponding grids were extracted from each experiment and plotted as the color lines with markers.
The dotted line and dot-dash line in the interannual cycle plots represent the observed discharge in different periods. WFDEI-CCG) is effective for improving the model performance using different criteria (bias, correlation, proportion to the trend) to compare the observed discharge. The three criteria are independent as they stand for the averages, the variation and the trend, which can capture the various aspects of the model agreement to the observations. The responses are similar for different catchments, but at different magnitude at different stages. The correction of the CMA dataset is Table  5. The annual average values for different water-energy components (P , ET, PET; units: mm yr -1 ) and their relations (P -ET, PET / P and ET / P ) for the three upstream subbasins. The scenarios correspond to the diagnostics of the current model (A) and three bias assumptions listed above from B to D.
Conclusions In this work, we proposed an ORCHIDEE-Budyko framework which is used to attribute the modeled discharge bias to different sources as the forcing and model structure. Bias in the precipitation (P ) and any processes related to the potential evapotranspiration (PET) is considered as bias from forcing and bias in any processes affecting the actual evapotranspiration (ET) estimation is considered as bias from model structures. The discharge simulation was provided by the land surface model ORCHIDEE with latest developments in its modules and driven by the most improved forcing inputs (WFDEI-CCG-SF). However, underestimation in the discharge still exists over the three Tarim headwater catchments, where the biases of P , PET and ET are analyzed with a Budyko analytical approach. With a set of assumptions, we isolated the biases in three variables, and their possibilities were assessed with information from nearby and hydroclimatically similar regions.