dataesr/scientific-paragraphs-categorization huggingface dataset:
Journal of Advances in Modeling Earth Systems RMSE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ∑ n isub¼1 NE isub WS -NE isub OM ð Þ 2 n ! v u u t (23) where n (n ¼ 28) is the number of training samples, NE isub_WS and NE isub_OM (kg m -2 yr -1 ) are the area-averaged sediment delivery rates in subcatchment isub simulated by WaTEM/SEDEM (subscript WS) and ORCHIDEE-MUSLE (subscript OM), respectively. We also used the area-averaged sediment delivery rates for all of the 28 subcatchment together as training samples to optimize an average value of a, b, c, and d. All simulation results shown in section 3 are based on parameters (a ¼ 43.
79, b ¼ 0. 50, c ¼ -0. 048, d ¼ 1. 78) calibrated against area-averaged sediment delivery rates of the 28 subcatchments together, unless specified otherwise. The simulated runoff from ORCHIDEE-MUSLE was evaluated against observed annual water discharge (m 3 yr -1 ) at 14 hydrological gauging stations corresponding to variable drainage areas (Figure  S1 ). The gauging data were extracted from the Global Runoff Data Centre (GRDC, 56068 Koblenz, Germany). The simulated SOC stocks were evaluated against the SOC stocks from three spatially gridded soil databases: the Harmonized World Soil Database (HWSD, v1. 2), the Topsoil Organic Carbon Content for Europe (OCTOP,  Jones et al. , 2005) , and the Global Soil Dataset for use in Earth System Models (GSDE,  Shangguan et al. , 2014).
6 (±3. 3) g m -2 yr -1 , which is close to the estimated value by WaTEM/SEDEM (71. 7 g m -2 yr -1 ). Although the biases in simulated average sediment delivery rates are large in several small subcatchments (Figure  3 ), they have very limited impact on the estimation error for the total yearly soil delivery over the whole Rhine catchment (9. 1 ± 0. 4 Tg yr -1 , Figure  S6 ). In other words, the estimation errors for the large subcatchments mainly determine the overall estimation accuracy over the Rhine catchment (Figure  S6 ). Comparison of simulated SOC stocks from ORCHIDEE-MUSLE and SOC stocks from the HWSD and OCTOP soil databases indicates that the model overestimates the SOC stocks (in the top 30 cm or 1 m soil layer) in the central portion of the Rhine catchment, while it underestimates the SOC stocks in the northern and southern parts (Figures  4  and S7-S9 ).
In contrast, when the GSDE is used as benchmark, ORCHIDEE mostly underestimates SOC stocks in the whole Rhine catchment (Figures  4c  and 4f ). These results indicate that SOC stock estimates from soil databases are already quite uncertain, as already pointed out by  Tifafi et al. (2018). Not surprisingly, the SOC stocks simulated by ORCHIDEE-MUSLE are generally lower than those obtained with ORCHIDEE-SOM without erosion  (Figures S7d, S7e, S8c, and S8d)  due to the effects of erosion removal in the former model version (Figure  5 ).
The production of alkali-activated materials and geopolymers from the finer fraction of tailings is being increasingly investigated, as they could be used for building and public works, or to retain heavy metals in a consolidated solid matrix. The present study proposes a Pitzer-based geochemical model to reproduce the alkaline leaching for two mine tailing samples and the polymerization reactions. The model fits the results of the leaching tests quite well over the reaction duration and up to NaOH 10 M and 60 °C. Discrepancy for the dissolved Mg concentration indicates that the database can handle ionic strength up to 5 mol. kg -1. A geopolymer solid solution was developed for setting reactions modeling. The results compare favorably with compressive strength tests results, with C-S-H, M-S-H and geopolymers as the main hydration products.
The model relies on the Pitzer thermodynamic formalism for ion activity, associated with the rate laws for the kinetically controlled dissolutions of primary minerals. Setting up this model requires the selection of reliable values for the thermodynamic properties and the kinetic rate laws, followed by their integration into dedicated databases. Models for setting tests are based on contributions by Roosz et al. [8]  for C-S-H (CaO-SiO 2 -H 2 O hydrates) and M-S-H (MgO-SiO 2 -H 2 O hydrates) solubility, and a solid solution model dedicated to geopolymer precipitation was also specifically developed in the present work. The whole modeling process was tested and is in good agreement with the results of the compressive tests.
Database developments Alkali activation involves interactions with high ionic strength solutions. We considered as a starting point the Thermoddem thermodynamic database  [11] , which provides a large set of minerals and includes the temperature dependence for most of the thermodynamic functions. The database was expanded to take into account the main aqueous species at high pH, by considering the Pitzer model for high electrolyte concentrations and the corresponding interaction coefficients between them. Interaction parameters collected by Plummer et al. [12]  were incorporated into the database. Interaction parameters for the main ions of the systems were specifically reviewed, as explained below. Aqueous complexes were removed when their formation could interfere with ion interactions in the Pitzer formalism.
The consistency of the resulting database was checked by comparing the calculation results with the experimental solubilities for several minerals of interest in the alkaline systems. During contact with alkali solution, minerals and especially silicates, progressively dissolve in accordance with their respective kinetic rate laws. To take into account the main minerals forming the tailing materials, we completed the kinetic database  [13] , distributed along with Thermoddem, for dissolution rate laws in high pH solutions. The final goal of the study is to produce a modeling tool capable of assessing the setting potential of a given mine tailing material. Setting occurs when the precipitating silicate hydrates with hydraulic binder properties like C-S-H and M-S-H. For such phases, the model proposed by Roosz et al.
1 MPa) and Tr (298. 15 K) are the reference pressure and temperature and V AB is the molar volume of AB (in cm 3. mol -  ). Finally, the third law entropy necessary for calculating apparent standard properties of any compound at any temperature T is calculated by subtracting the entropy of the elements from the formation entropy. In this work, a solid solution model was developed for geopolymers and used in a geochemical code, which supposes that the database is provided with LogK(T) functions for each end-member. In this work, endmember LogK(T) functions are calculated from formation properties, estimated using a polyhedral decomposition model, as described in the following sections.
Description of solute activity coefficients The Thermoddem database relies on the detailed description of the existing aqueous complexes or, at least, of those identified in the literature. It is used, by default, with the extended Debye-Hückel model for activity coefficients, which depend on the ionic strength of the aqueous solutions only, however, the corresponding range of application is ionic strength-limited. On the other hand, the Pitzer approach  [15] [16] [17] [18] [19] [20]  was developed to describe highly concentrated systems and its validity range is limited by the solubility of salt minerals  [21, 22]. Details are given in Appendix A, together with a description of the different parameters involved. It relies on the use of specific interaction parameters and preferentially considers fully dissociated electrolytes.
However, it is still possible to consider ion pairing with the Pitzer model by including the formation of aqueous complexes  [21] [22] [23] [24]. In addition, the Pitzer equations for activity coefficients also include a Debye-Hückel term that depends on ionic strength. Therefore, there is room to propose a modeling strategy that consistently merges the two approaches: strong major electrolytes (Table  1 ) are described according to the Pitzer formalism, with limited ion pairing, while aqueous complexation reactions are considered for compounds that contain minor or trace elements. This database building strategy depends on the chemical system of interest, in our case it applies to alkaline pH conditions, with Na-dominated solutions. Considering the Thermoddem database  [11]  and interaction.
[12]  as a starting point, a focus was put on refining the collection of interaction parameters for the main ions in our system. The refined collection considers the hydroxylated form of the dissolved elements, as expected in alkaline media. The references used for the refined collection are reported in Table  1  and the parameter values are given in Table  2  for binary and in Table  3  for ternary interactions. The database is developed for use with the PHREEQC software  [25] , therefore, the interaction parameters depend on the temperature according to the following equation: = + + + + + A A T T A T T A T T A T T A T T Y(T) 1 1 ln ( ) ( ) 1 1 r r r r r 0 1 2 3 4 2 2 5 2 2 (5) where Y(T) stands for any interaction parameter listed in Tables  2  and 3 ; T and T r correspond to the temperature (in K) and the reference temperature (298.
NaOH concentration in aqueous solutions at 25 °C, similar results are obtained for KOH solutions -Silica solubility vs. NaOH concentration in aqueous solutions at 90 °C -Hematite solubility vs. NaOH concentration in aqueous solutions at 70 °C. In Fig. 1. f, experimental points correspond to the solubility of quartz obtained by Van Lier et al. [36]  and Schwarzentruber et al. [37]. The calculated solubility of amorphous silica is also displayed for comparison purpose with quartz solubility. Globally, the calculation results displayed in Fig. 1  show good overall agreement with the data from the literature, given the scattering of the datasets that could be gathered.
Kinetic rates database The kinetic part of the database, which includes dissolution and precipitation rates, is based on the compilation proposed by Marty et al. [13]. For the present study, it has been supplemented with several minerals, extracting the dissolution rate in high pH solutions. In the Marty et al. [13]  compilation, the rate law corresponds to a formalism proposed by Lasaga et al. [45] , where the dissolution rate coefficient k (in mol. m -2. s -1 ) is given by: = + + k k A e a a f( G) 0 min E R T H n i i ni r A H (9) with:k 0 : kinetic constant (mol. m -2. s -1 ). A min : surface area of the mineral (m 2. g -1 ). E A : activation energy of the overall reaction (J. mol -1 ). R: gas constant (8. 314 J. mol -1. K -1 ). T: absolute temperature (K). a i and a H+ : activities in solution of species i and H + , respectively.
Leaching and setting experiments Previous developments were then tested by modeling the results of the experiment tests. These results are fully described in companion papers (Obenaus-Emler et al. [10]  and Falah et al. [9] ) and thus, only a short description of the experiment design and of the results is given here. Mine tailing samples were collected from a Cu/Ni mine located in Northern Europe. Samples were ground using ball mill  [10]  or disc mill  [9] , bringing the mean diameter of particles to 7. 5 and 4. 0 μm, respectively. Particle size distributions were measured by laser diffraction. Additionally, mine tailings were characterized by XRD. In the present study, Rietveld refinement was carried out at BRGM using the BGMN/PROFEX software  [56]. The results are reported in Table  8.
Model and specific interface development Based on the Thermoddem  [11]  database (Table  D1  in Appendix D), Pitzer interaction terms are included and kinetic rates of mineral dissolution (Table  4 ) are added as reported previously. Geochemical modeling of both the alkaline leaching and the setting under alkaline activation is carried out. The aim is to reproduce previously described experiments, to provide a tool able to assess the hardening potential of a given mine tailing material with a mafic/ultra-mafic composition. Specialized software, called KITO, is being developed to simulate leaching in alkaline conditions. It consists in a specific interface developed with the Scilab  [57]  programming language, which uses PHREEQC  [25]  as a geochemical calculator and displays the results in pre-determined graphs.
Results and discussion For leaching experiments, the results of the modeling are reported in Fig. 2  which reports the comparison between the modeled and the experimental values for dissolved elements. Calculation results are generally in agreement with Obenaus-Emler et al. [10]  experiments (Fig. 2. A and B) for dissolved Si, Al, Fe, S and Ca. For the Falah et al. [9]  leaching test (Fig. 2. C), the agreement is still correct for Si and Al final concentrations. However, only 2 concentrations (Si and Al) were measured in that case, the verification of the model is somewhat less rigorous at 25 °C than at 40 °C. For dissolved Mg in the NaOH 10 M leaching test, calculated concentrations are much lower than the results from ICP analyses. It should be noted that the modified database (Tables  2  and 3 ) does not include a full set of Mg-OH interaction terms because of a lack in the literature.
Such a gap could explain why an accurate match is not reached for very high ionic strengths like in 10 M NaOH solutions (Fig. 2A ). In that regard, Xiong  [60]  and Altmaier  [61]  have investigated brucite (Mg(OH 2 ) solubility in chloride media, for which Mg-OH specific interaction was not necessary since the maximum pH value reaches 10. 40 and 10. 70, respectively. For the 10 M NaOH leaching, calculated pH reaches 13. 86, far beyond the values investigated by previous authors. Einaga  [62]  postulated the existence of the Mg 3 (OH) 4 +2 complex, based on titration experiments conducted in solutions up to pH 11. 40, at 25 °C. Such pH is still too low to allow extracting relevant parameters to constrain the Mg-OH interaction for our alkaline solutions. However, dissolved Mg concentrations can be correctly reproduced for the 5 M NaOH leaching test, which would set the limit of ionic strength that can be considered with the modified database to 5 mol.
5) to values observed for a Portland cement by Hoshino et al. [74]  (52 MPa at W/ S = 0. 5). Very similar experiments were conducted by Tran et al. [75] , which confirm the similarity between M-S-H and OPC mortars, in terms of compressive strength. Lacking more precise determination, we consider that the relation displayed in Fig. 3 , which is based on C-S-H and geopolymers, also applies for M-S-H or that the bonding capacity of M-S-H is similar to that of C-S-H. The modeling of the hardening tests was carried out using the database developed here, including the L/S ratio, activation solution and initial mineral compositions as reported previously. Calculations are reported in Fig. 4 , as a function of time and compared with compressive strength test results. Fig. D2 (Appendix D ) displays the relative amounts of minerals, dissolved or precipitated during the hydration of mine tailing activated by Na 2 SiO 3 30% at 60 °C.
Conclusions The geochemical modeling of alkali-activated mine tailing materials is presented here. From existing databases, significant developments have been carried out, in order to account for the specific interactions that take place in solutions of high ionic strengths (NaOH or Na 2 SiO 3 ). Six dissolution rate laws were either improved to account for alkaline solution or derived from literature data for minerals not present in the kinetic database (Ferro-actinolite, actinolite). The database was eventually supplied with a binary solid solution model, of which thermodynamic properties are obtained from a polyhedral decomposition method. To facilitate the modeling of the leaching experiments, a specific interface, KITO, is being developed, for use with the PHREEQC software. It uses a version of the Thermoddem database which has been modified by introducing a selection Pitzer interaction coefficients.
Acknowledgements Financial support from the  European community H2020   ITERAMS  project (Grant Agreement No. 730480 ) and from the  French Geological Survey (BRGM)  is gratefully acknowledged. The author would like to thank  P. Kinnunen , as coordinator, for her friendly and efficient management of the project. Many thanks to  Sally Ferguson  for reviewing the text. Anonymous reviewers are also thanked for their decisive help in improving the manuscript.
5 for data encryption, the underlying technical details mandate a constant padding concatenated after the 0x0002 string. Then, we exploit this observation by two means. First, the rigid messages format of GlobalPlatform SCP makes the major part of the encrypted message constant in a discernible way: a big known part and a much smaller unknown part. Here, we apply the Coppersmith attack that solves univariate modular equations to decrypt the whole message. Second, we apply the Bleichenbacher attack that leverages a format oracle to compute modular exponentiation of an arbitrary value over the private exponent without any knowledge of the private key. Attackers can take advantage of this attack in twofold ways: decryption of sensitive messages and signatures forgery to impersonate smart cards.
Responsible Disclosure. Our findings have been timely reported to GlobalPlatform following their responsible disclosure process. The GlobalPlatform Security Task Force was quite responsive and acknowledged our results. They also organized several meetings in order to present the identified vulnerabilities to their concerned industrial partners. We have shared a draft of this paper as well as our PoC code with the interested partners. We also provide our support, so that most of our countermeasures are taken into account in a fix of SCP10 to be rolled out.
Keys Management We recall that any SCP is established between an OCE and an SD. Each entity stores its own RSA key pair. In addition, any OCE is expected to store the public key of the security domain. In case an OCE does not know the SD's public key, it can issue the Get Data [certificate] command. As for the SD, it holds the public keys of all the previously validated OCEs, sent by the command Perform Security Operation [verify certificate]. This command supplies a chain of certificates for verification. Only one public key is active at a time. As a result, before starting an SCP10 connection, the communicating OCE shall ensure that the SD would use the proper key. Otherwise, it asks the use of a different public key with the Manage Security Environment command. This command, noted MSE, can be sent by anyone in order to indicate the setup configurations of SCP10.
Nevertheless, in this particular context, determinism is only a theoretical vulnerability since it would be unlikely that the same session keys (the variable data within Perform Security Operation [decipher]) would be sent more than once. Moreover, the outcome would be limited: the attacker would only recognize previously session keys. This is because a dictionary attack would have a tremendous memory complexity, as for a Meetin-the-Middle attack due to the high number of unknown bits (even if the latter is a far better alternative). These options are not discussed in further details through this paper. A more important consequence of this particular padding is the known structure of the message. Indeed, the biggest part of the messages is constant and publicly known. This allows adversaries to recover the unknown part (i.
Namely, we set up a threshold from which we stop looking for potential trimmers. Empirical measures tend to show that five candidates are enough to significantly reduce the initial interval, reducing the queries to an average of 3962 for an ideal oracle. On constrained devices, such as java cards, the bottleneck of this attack is the number of query we need to make to perform the attack. In order to reduce this number, hence the attack duration, we can stop to reduce the range once there are only a few possible values left for m 0. Indeed, brute-forcing the remaining values may be quicker than continuing to query the oracle. The pseudo-code in Algorithm 2 illustrates the attack implementation. The function Blinding is needed only if the data we are working with does not have the right format (signature forgery for instance).
Thus, the blinding step described in Algorithm 2 line 4 would be necessary to find the first compliant candidate. This additional step induces a major overhead. Considering the relaxed oracle described in Section 4. 3. 2, an approximation of the average query needed to perform the blinding step can be computed with the same method used in [RGG + 19]. Assuming the decrypted message is a random string of 128 bytes, the probability that a candidate is compliant can be expressed as: Pr[O(c) = 1] = 2 -16 × 255 256 8 × 1 - 255 256 118 ≈ 5. 47 • 10 -6. Thus, the expected number of query is (P [O(c) = 1]) -1 which is about 183000 queries. The attack workflow is similar to the one described in Figure  2 , since the same oracle would be used to forge a signature. The only difference is the initial data sent to the oracle (the hash of the certificate being forged in this case).
Setup All our experiments have been performed on a Dell XPS13 7390 running on Fedora 31 with an Intel(R) Core(TM) i7-10510U and 16 GB of RAM. We use a SmartCafe Expert 3. 2 72K smart card supporting JavaCard 2. 1. 2 3. The communication with the card is managed by a contact reader (Omnikey 3121) through a Python v3. 7. 5 script with Pyscard v1. 9. 9 4. Our Python script simulates both the legitimate OCE and the attacker, following the attack workflow described in the previous section. Some computation needed to perform the attacks have been performed using SageMath 5  v8. 8 for the sake of simplicity. The protocol has been implemented and installed as an applet, since we are not able to deploy it at the JCVM level. We converted it to a CAP file using Maven v3. 5. 4 and the antrun 6  v. 1. 8 plugin.
A quantitative analysis is proposed, using data from Swedish statistics about the number of establishments with a NACE code related to logistics, as well as an original method providing a "cleaned" and more comprehensive dataset. We look at data at two different scales, one metropolitan and one regional, for years 2000 and 2014, as to enable a comparative and diachronical analysis. Logistics sprawl is measured by the average distance of warehouses to their common centre of gravity. Finally, interviews with transport and logistics providers as well as real estate investors and public agencies in the region, add qualitative information on the relative importance of different location factors related to logistics facilities and the issues raised. Logistics sprawl in Gothenburg occurs in specific ways, and differently at the two geographical levels of analysis.
Like most port activities, but with more spatial flexibility (as ports retain a certain amount of spatial inertia), logistics facilities have generally been relocated to peripheral areas under the effect of urban transformations. At the same time, many ports in metropolitan areas have retained a key influence for the location of logistics facilities and port-related activities  (Hall, 2013). The consequences of logistics sprawl can be land consumption, increasing distance of final deliveries, and changes in the logistics system, presenting a new challenge for planning the metropolitan area at local and regional scales. In continuity with these works, we propose an analysis of logistics locational patterns in the GMA, Sweden, between 2000 and 2014, based on an original method of collecting a comprehensive dataset of warehouses.