Miracle-dz/UniFAR-FTUs huggingface dataset:
Droughts can Rreduce the Nnitrogen Rretention Ccapacity of Ccatchments

In 2018 – 2019, Central Europe experienced an unprecedented two-year multi-year drought with severe impacts on society and ecosystems. In this study, we analyzed the impact of this drought on water quality by comparing long-term (1997-2017) nitrate export with 2018-2019 export in a heterogeneous mesoscale catchment. We combined data-driven analysis with process-based modelling to analyze nitrogen retention and the underlying mechanisms in the soils and during subsurface 15 transport. We found a drought-induced shift in concentration-discharge relationships, reflecting exceptionally low riverine nitrate concentrations during dry periods and exceptionally high concentrations during subsequent wet periods. Nitrate loads were up to 730% higher, compared to the long-term load-discharge relationship. Model simulations confirmed that this increase was driven by decreased denitrification and plant uptake and subsequent flushing of accumulated nitrogen during rewetting. Fast transit times (<2 months) during wet periods in the upstream sub-catchments enabled a fast water quality response to 20 drought. In contrast, longer transit times downstream (>20 years) inhibited a fast response but potentially contribute to a long-term drought legacy. Overall, our study reveals that severe multi-year droughts, which are predicted to become more frequent across Europe, can reduce the nitrogen retention capacity of catchments, thereby intensifying nitrate pollution and threatening water quality.
Modeling and control of a wing at low Reynolds number with high amplitude aeroelastic oscillations

At high angles of attack or low Reynolds number, aircraft wings or blades of helicopters or even wind turbines may encounter separation of the ow which can eventually lead to aeroelastic couplings such as utter. These instabilities can be particularly destructive and are limiting for numerous applications. This thesis aims at considering the aeroelastic modeling and control of a pitching wing in utter conditions and at providing a general approach to tackle this problem. First, an aeroelastic model is developed based on previous works. This model provides an extension of the model proposed by Goman-Khrabrov, and modi ed by Williams, using the ONERA BH model. If the rst component of the model captures the hysteresis of the aerodynamic load of a pitching wing, the second one allows us to capture the vortex shedding and dynamic stall model which can be observed. This second component is particularly challenging to predict, while it plays an important role in the dynamics of the wing. The aerodynamic model is then trained and successfully compared to experimental data for a NACA 0018 rigid wing undergoing pitch oscillations at low Reynolds number. This model, like many aeroelastic or aerodynamic models, su ers from its inherent complexity and nonlinearities which make its analysis and control highly complicated with respect to the automatic control point of view. For this reason, the set of equations is conveniently manipulated to encapsulate the nonlinearities in a polytopic formulation with unknown parameters. Then, control strategies dedicated to linear time invariant systems are derived to account for this polytopic formulation. In addition, rate and magnitudes saturations are a major and recurrent issue in ight control and are also considered as an additional constraint in the control loop. Based on linear quadratic regulation theory, several theorems are developed using framework of linear matrix inequalities and allow not only to synthesize a stabilizing controller but also to de ne the region of attraction. The theorems are then applied to solve the problem of stall utter and successfully stabilize the closed-loop system in presence of rate and magnitude saturations, which demonstrate the potential of the contributions developed within this PhD approach.
Drought and Political Trust

Droughts can affect people’s political trust positively, through rallying effects, or negatively, through blame attribution. We examine how drought conditions affect political trust in the context of Africa. We link high-precision exogenous climate data to survey respondents, 2002–2018, and report moderate negative effects of drought conditions on people’s trust in their president. These negative effects increase with the severity of drought conditions. The political economy of favoritism, where some regions are preferentially treated by rulers, should result in heterogeneous effects across territories. We find that trust increases in capital regions and in leader birth regions during dry conditions. In contrast, when droughts take place in such regions, trust levels fall in other regions. This is in line with the idea that capital regions and leader birth regions could be preferentially treated in the aftermath of droughts. Understanding these processes further is important given their salience because of global warming.
Initialization Noise in Image Gradients and Saliency Maps

In this paper, we examine gradients of logits of image classification CNNs by input pixel values. We observe that these fluctuate considerably with training randomness, such as the random initialization of the networks. We extend our study to gradients of intermediate layers, obtained via GradCAM, as well as popular network saliency estimators such as DeepLIFT, SHAP, LIME, Integrated Gradients, and SmoothGrad. While empirical noise levels vary, qualitatively different attributions to image features are still possible with all of these, which comes with implications for interpreting such attributions, in particular when seeking data-driven explanations of the phenomenon generating the data. Finally, we demonstrate that the observed artefacts can be removed by marginalization over the initialization distribution by simple stochastic integration.
Inﬂuence of the Temperature of Salt Brine on Salt Uptake by Ragusano Cheese 1

The inﬂuence of temperature (12, 15, 18, 21, and 24 ° C) of saturated brine on salt uptake by 3.8-kg experimental blocks of Ragusano cheese during 24 d of brining was determined. Twenty-six 3.8-kg blocks were made on each of three different days. All blocks were labeled and weighed prior to brining. One block was sampled and analyzed prior to brine salting. Five blocks were placed into each of ﬁve different brine tanks at different temperatures. One block was removed from each brine tank after 1, 4, 8, 16, and 24 d of brining, weighed, sampled, and analyzed for salt and moisture content. The weight loss by blocks of cheese after 24 d of brining was higher, with increasing brine temperature, and represented the net effect of moisture loss and salt uptake. The total salt uptake and moisture loss increased with increasing brine temperature. Salt penetrates into cheese through the moisture phase within the pore structure of the cheese. Porosity of the cheese structure and viscosity of the water phase within the pores inﬂu-enced the rate and extent of salt penetration during 24 d of brining. In a previous study, it was determined that salt uptake at 18 ° C was faster in 18% brine than in saturated brine due to higher moisture and porosity of the exterior portion of the cheese. In the present study, moisture loss occurred from all cheeses at all temperatures and most of the loss was from the exterior portion of the block during the ﬁrst 4 d of brining. This loss in moisture would be expected to decrease porosity of the exterior portion and act as a barrier to salt pene-Received
Debiased Learning from Naturally Imbalanced Remote Sensing Data

Deep learning has shown remarkable success in analyzing grounded imagery, such as consumer photos due to large-scale human annotations are available for dataset, e.g., ImageNet. However, such extensive supervision is not the case for remote sensing data. We propose a highly effective semi-supervised approach tailored specifically for remote sensing data. Our approach encompasses two key contributions. We adapt the framework from semi-supervised learning approach, such as Fix-Match, to remote sensing data by designing a set of robust strong and weak augmentations suitable for this domain. By learning from actual labeled data, combining with pseudo-labeled data, yet address the pseudo-labeling imbalance, we leverage a recently proposed debiased learning approach to mitigate the bias in pseudo-labeling. Validated by extensive experimentation, our simple semi-supervised framework with 30% annotations delivers significant accuracy gains over the supervised learning baseline by 7.1%, and over recent supervised state-of-the-art, CDS by 2.1% on remote sensing Xview data.
Semiglobal Nonmemoryless Attitude Controls on the Special Orthogonal Group

This paper presents tracking strategies for the attitude dynamics of a rigid body that are global on the configuration space SO(3) and semiglobal over the phase space SO(3)×ℝ3. It is well known that global attractivity is prohibited for continuous attitude control systems on the special orthogonal group. Such topological restriction has been dealt with either by constructing smooth attitude control systems that exclude a set of zero measure in the region of attraction or by introducing discontinuities in the control input. This paper proposes nonmemoryless attitude control systems that are continuous in time, where the region of attraction guaranteeing exponential convergence completely covers the special orthogonal group. This provides a new framework to address the topological restriction in attitude controls. The efficacy of the proposed methods is illustrated by numerical simulations and an experiment.
The first joint experimental results between SURA and CSES

In June 2018, for the first time, the SURA heating facility in Russia , together with the in‐orbit China Seismo‐Electromagnetic Satellite (CSES), carried out a series of experiments in emitting high frequency (HF) O‐mode radio waves to disturb the ionosphere. This paper reports data from those experiments, collected onboard CSES, including electric field, in‐situ plasma parameters, and energetic particle flux. Five cases are analyzed, two cases in local daytime and three in local nighttime. We find that the pumping wave frequencies f0 in local daytime were close to the critical frequency of the F2 layer foF2, but no pumping waves were detected by the electric field detector (EFD) on CSES even when the emitted power reached 90 MW, and no obvious plasma disturbances were observed from CSES in those two daytime cases. But on June 16, there existed a spread F phenomena when f0 was lower than foF2 at that local daytime period. During the three cases in local nighttime, the pumping waves were clearly distinguished in the HF‐band electric field at the emitted frequency with the emitted power only 30 MW; the power spectrum density of the electric field was larger by an order of magnitude than the normal background, with the propagating radius exceeding 200 km. Due to the small foF2 over SURA in June at that local nighttime period,f0 in these three cases were significantly higher than foF2, all belonging to under‐dense heating conditions. As for the plasma parameters, only an increase of about 100 K in ion temperature was observed on June 12; in the other two cases (with one orbit without plasma data on June 17), no obvious plasma disturbances were found. This first joint SURA‐CSES experiment illustrates that the present orbit of CSES can cross quite close to the SURA facility, which can insure an effective heating time from SURA so that CSES can observe the perturbations at the topside ionosphere excited by SURA in the near region. The detection of plasma disturbances on June 12 with under‐dense heating mode in local nighttime provides evidence for likely success of future related experiments between CSES and SURA, or with other HF facilities.
Mechanisms of dynamic wetting failure in the presence of soluble surfactants

A hydrodynamic model and flow visualization experiments are used to understand the mechanisms through which soluble surfactants can influence the onset of dynamic wetting failure. In the model, a Newtonian liquid displaces air in a rectangular channel in the absence of inertia. A Navier-slip boundary condition and constant contact angle are used to describe the dynamic contact line, and surfactants are allowed to adsorb to the interface and moving channel wall (substrate). The Galerkin finite element method is used to calculate steady states and identify the critical capillary number $Ca^{crit}$ at which wetting failure occurs. It is found that surfactant solubility weakens the influence of Marangoni stresses, which tend to promote the onset of wetting failure. Adsorption of surfactants to the substrate can delay the onset of wetting failure due to the emergence of Marangoni stresses that thicken the air film near the dynamic contact line. The experiments indicate that $Ca^{crit}$ increases with surfactant concentration. For the more viscous solutions used, this behaviour can largely be explained by accounting for changes to the mean surface tension and static contact angle produced by surfactants. For the lowest-viscosity solution used, comparison between the model predictions and experimental observations suggests that other surfactant-induced phenomena such as Marangoni stresses may play a more important role.
The Study of Early Selection Method through Vigor and Viability Parameter in Citrus amblycarpa (Hassk) under Salinity and Drought Stress Condition

The study was aimed to obtain information regarding the early selection method of Limau lime under saline and drought condition including the proper method for seed extraction, observation period in germination testing, and type of germination media. The experiment was conducted from March 2015 to January 2016 at the Laboratory of Seed Science and Technology, Department of Agronomy and Horticulture, Faculty of Agriculture, Bogor Agricultural University. It consisted of four main experiments. The first experiment was the extraction method on Limaulime seed using distilled water, ash from the husk, talcum powder, and HCl 1%. The second experiment was germination testing of lime seed using three media i.e. sand, the combination of sand: soil (1:1) (v:v), and towel tissue. The third experiment has tested the vigor of Limau lime seeds in different saline conditions by using NaCl, i.e. 1500 ppm, 3000 ppm, and 4500 ppm. The fourth experiment, tested the vigor of Limau lime seeds in drought stress condition using PEG 6000, i.e. 0, - 0.75 bar, -1.5 bar, and -2.25 bar. The result of the first experiment showed that the treatments had no significantly different effect on all variables observed except for the seed moisture content. The second experiment revealed that the best germination media was elucidated from sand treatment and sand: soil (1:1) with the first and the second count at 20th and 29th day respectively. The result of the third and four experiment elucidated that the higher concentration of NaCl (Salinity stress condition) or PEG 6000 (drought stress condition), the smaller the germination percentage of Limau Lime’s seeds.
Active Learning for Semantic Segmentation of Point Clouds

The primary objective of this thesis is to address the challenge of minimizing the annotation cost associated with point cloud datasets used in semantic segmentation tasks through active learning techniques. Within this research, we present a novel active learning framework tailored explicitly for multi-view LiDAR-based datasets, which provide diverse viewpoints of objects. We thoroughly investigate the impact of incorporating multiple viewpoints on the performance of commonly employed uncertainty selection strategies in active learning. Moreover, we introduce an innovative uncertainty selection strategy based on the variance of the model’s outputs within our framework, offering a comparative analysis of its effectiveness against stateof-the-art methods. Additionally, we explore the significance of filtering out unreliable predictions when selecting annotated data for active learning. To evaluate the efficacy of our proposed active learning framework, we conduct comprehensive experiments on widely used automotive datasets for LiDARbased semantic segmentation. Through these evaluations, we effectively demonstrate how active learning can significantly improve the annotation efficiency of point cloud datasets.
Pace v0.1

Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore’s Law driving forward rapid specialization of hardware architectures, building simulation codes on a low-level language with hardware specific optimizations is a significant risk. As a solution, we present Pace, an implementation of the nonhydrostatic FV3 dynamical core (cid:58)(cid:58)(cid:58) and (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) GFDL (cid:58)(cid:58)(cid:58)(cid:58) cloud (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) microphysics (cid:58)(cid:58)(cid:58)(cid:58)(cid:58)(cid:58) scheme which is entirely Python-based. In order to achieve 5 high performance on a diverse set of hardware architectures, Pace is written using the GT4Py domain-specific language. We demonstrate that with this approach we can achieve portability and performance, while significantly improving the readability and maintainability of the code as compared to the Fortran reference implementation. We show that Pace can run at scale on leadership-class supercomputers and achieve performance speeds 3.5-4 times faster than the Fortran code on GPU-accelerated supercomputers. Furthermore, we demonstrate how a Python-based simulation code facilitates existing or enables entirely new 10 use-cases and workflows. Pace demonstrates how a high-level language can insulate us from disruptive changes, provide a more productive development environment, and facilitate the integration with new technologies such as machine learning.
Spatial stability for the monolithic and sequential methods with various space discretizations in poroelasticity

We investigate spatial stability with various numerical discretizations in displacement and pressure fields for poroelasticity. We study 2 sources of the early time instability: discontinuity of pressure and violation of the inf‐sup condition. We consider both compressible and incompressible fluids by employing the monolithic, stabilized monolithic, and fixed‐stress sequential methods. Four different spatial discretization schemes are used: Q1Q1, Q2Q1, Q1P0, and Q2P0. From mathematic analysis and numerical tests, the piecewise constant finite volume method for flow provides stability at the early time for the case of the pressure discontinuity. On the other hand, a piecewise continuous (or higher‐order) interpolation of pressure shows spatial oscillation, having lower limits of time step size, although lower approximations of pressure than displacement can alleviate the oscillation. For an incompressible fluid, Q2Q1 can be better than Q1P0, because Q1P0 might not satisfy the inf‐sup condition. However, regardless of fluid compressibility and the pressure discontinuity, the fixed‐stress method can effectively stabilize the oscillation without an artificial stabilizer. Even when Q1P0 and Q1Q1 with the monolithic method cannot satisfy the inf‐sup condition, the fixed‐stress method can yield the full‐rank linear system, providing stability. Thus, the fixed‐stress method with Q1P0 can effectively circumvent the aforementioned 2 types of instability.
Ion Implantation Doping of Inertial Confinement Fusion Targets

Abstract Controlled doping of inertial confinement fusion (ICF) targets is needed to enable nuclear diagnostics of implosions. Here, we demonstrate that ion implantation with a custom-designed carousel holder can be used for azimuthally uniform doping of ICF fuel capsules made from a glow discharge polymer (GDP). Particular emphasis is given to the selection of the initial wall thickness of GDP capsules as well as implantation and postimplantation annealing parameters in order to minimize capsule deformation during a postimplantation thermal treatment step. In contrast to GDP, ion-implanted high-density carbon exhibits excellent thermal stability and ~100% implantation efficiency for the entire range of ion doses studied (2 × 1014 to 1 × 1016 cm−2) and for annealing temperatures up to 700°C. Finally, we demonstrate a successful doping of planar Al targets with isotopes of Kr and Xe to doses of ~1017 cm−2.
Revisiting the relationship between the South Asian summer monsoon drought and El Niño warming pattern

Based on the observational and reanalysis data, El Niño warming patterns associated with the South Asian summer monsoon droughts are investigated. While the inverse relationship between the eastern Pacific (EP) type of El Niño‐Southern Oscillation (ENSO) and the Indian monsoon rainfall weakened significantly, the correlation between the central Pacific (CP) type of ENSO and the monsoon rainfall strengthened after the late 1970s. Moreover, the drought‐producing El Niño warming pattern also exhibits a notable decadal modulation associated with the climate shift. The analysis results indicate that both the EP type of El Niño with positive sea surface temperature (SST) anomalies extended to the date line and the CP type of El Niño with the maximum warming located in the central equatorial Pacific may produce severe droughts over the Indian subcontinent. Although the CP warming is more effective in driving anomalous rising motion in the central equatorial Pacific and consequently producing anomalous subsidence over South Asia, the position and strength of the anomalous ascending and descending branches of the Walker circulation are sensitive to the detailed distributions of tropical SST anomalies and determined by the competing effects of the CP and EP warming.
A Feynman-Kac type formula for a fixed delay CIR model

Abstract Stochastic delay differential equations (SDDE’s) have been used for financial modeling. In this article, we study a SDDE obtained by the equation of a CIR process, with an additional fixed delay term in drift; in particular, we prove that there exists a unique strong solution (positive and integrable) which we call fixed delay CIR process. Moreover, for the fixed delay CIR process, we derive a Feynman-Kac type formula, leading to a generalized exponential-affine formula, which is used to determine a bond pricing formula when the interest rate follows the delay’s equation. It turns out that, for each maturity time T, the instantaneous forward rate is an affine function (with time dependent coefficients) of the rate process and of an auxiliary process (also depending on T). The coefficients satisfy a system of deterministic differential equations.
Bore-ing into Nocturnal Convection

There has been a recent wave of attention given to atmospheric bores in order to understand how they evolve and initiate and maintain convection during the night. This surge is attributable to data collected during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. A salient aspect of the PECAN project is its focus on using multiple observational platforms to better understand convective outflow boundaries that intrude into the stable boundary layer and induce the development of atmospheric bores. The intent of this article is threefold: 1) to educate the reader on current and future foci of bore research, 2) to present how PECAN observations will facilitate aforementioned research, and 3) to stimulate multidisciplinary collaborative efforts across other closely related fields in an effort to push the limitations of prediction of nocturnal convection.