Miracle-dz/UniFAR-FTUs huggingface dataset:
Occurrence and levels of selected compounds in European Sewage Sludge Samples

This report describes the work conducted by the European Commission’s Joint Research Centre in the context of an Administrative Arrangement between DG Environment and the JRC. The work aimed at technical support in the establishment of an approach to identify and prioritise among relevant compounds that have to be considered in European regulation dealing with Sewage Sludge. Particular emphasis was given with regard to resilience in soil or the ability to compromise ecosystems adjacent to sludge-receiving soils. The work includes the results of a targeted and independent screening of typical European situations of sewage sludges with regard to the occurrence and levels of compounds of concern, many of which never assessed in a pan-European dimension. In total, 63 samples, mostly taken as grab samples and originating from 15 countries, were assessed for 22 minor and trace elements and 92 organic compounds including ingredients of personal care products and pharmaceuticals. The underlying analytical methods are carefully documented with regards to their performance characteristics. Obtained results are assessed statistically and where possible compared to other findings. Although the analysed single samples are insufficient to make any statement on the performance of the treatment processes leading to the sewage sludge, the collective of data allows having a glance at the panEuropean situation as regards the studies compounds. Background information from literature describing the situation before the survey is included. To assess the availability of selected persistent organic pollutants, findings of long-term case study are included, too. Participating laboratories and roles The findings presented in this report are the result of a large collaborative effort. For confidentiality reasons, the identities of the participating Waste Water Treatment Plants cannot be revealed in this public report, but are known to the European Commission. This anonymity shall not minder our acknowledgment of the considerable support and in-kind contribution behind the exercise. As regards the practical execution of the project organization and the work in the laboratories the following persons contributed actively. European Commission Joint Research Centre, Institute for Environment and Sustainability, 21027 Ispra, Italy • Giovanni Locoro (Coordination of sampling operations and Logistics, CV-AAS) • Michela Ghiani (Technical correspondence and liaison with sites) • Sara Comero (ICP-AES, CV-AAS, Statistical evaluation) • Robert Loos (Perfluoroalkyl substances; PFAS) • Simona Tavazzi, Bruno Paracchini (LC-MS/MS) • Gert Suurkuusk, Giulio Mariani (GC-MS) • Gunther Umlauf (long-term study on POPs) • Carmen Cristache, Isabelle Fissiaux, Agustín Alonso-Ruiz (Analytical support) Technical University of Lodz Institute of General Food Chemistry, ul Stefanowskiego 4/10, 90-924 Lodz, Poland • Elzbieta Sobiecka (Literature review), Umweltbundesamt GmbH Austrian Federal Agency for Environment, Spittelauer Lände 5, 1090 Vienna, Austria • Oliver Gans (Deputy Head-of-Unit) • Werner Hartl (GC-MS)
Effects of Discharge in Residence Time Distributions in a Small Headwaters Wetland in the Ipswich River Watershed

The Ipswich River Watershed, a 401 km2 watershed located in northeastern Massachusetts, has been observed to be undergoing increasing urbanization with resulting increases in nutrient loading, in particular, nitrogen. Nitrogen uptake occurs in a 1st-order process which is dependent on the concentration of nitrogen as well as the amount of time the water containing nitrogen remains within the wetland, which is described as the residence time distribution (RTD). To better understand how discharge affects the RTD of the wetland, a number of tracer studies were conducted between May 2011 and August 2011. Additionally, fluxes into and out of the wetland were calculated for this same period to estimate groundwater flow into or out of the wetland in order to understand interactions of groundwater with the wetland. The RTDs calculated from four tracer studies suggest that lower discharges result in longer detention times and higher discharges result in shorter detention times, though the results are not conclusive. Estimates of water budget fluxes suggest that the direction and magnitude of groundwater flow may change depending on whether the wetland is at base flow or flood flow.
Water use efficiency in an arid watershed： a case study

Integrated Water Resource Management (IWRM) becomes increasingly important in arid watersheds, where water resources are scarce and demands are rising under increased population pressure. In this case study, we present the results from the Elqui Valley in Chile, where water resources are shared between agriculture, mining operations, hydropower generation and drinking water for nearby cities. An IWRM model was applied to visualize water resources distribution in the catchment and determine irrigation and water use efficiencies. After calibration with observed surface and groundwater measurements, the model showed large differences in efficiencies between different irrigation sectors. Most notably, irrigation sectors with the highest irrigation security, i.e., higher water rights per hectare, were characterized with lower water use efficiencies. Frequent water deficits were present in sectors downstream of the main reservoir, which was related to limited water rights per hectare, lower irrigation efficiencies and higher water demands due to larger agricultural areas. In subsequent scenario analyses, the model was used to evaluate the impact of improved irrigation and water use efficiencies in these downstream sectors, to confirm an important reduction in water deficits, except for drought years when surface water resources are insufficient. In a third scenario, groundwater resources were allowed to compensate for water deficits, effectively reducing these in most irrigation sectors, especially during droughts. Expansion of the current agricultural area by 37% was evaluated, as a future scenario, and was found to be unsustainable, as aquifer levels are lowered to such levels that make annual recharge insufficient.
Response of beams resting on linear and nonlinear elastic foundations and subjected to harmonic high-speed moving loading

Analytical solution technique that provides fast, efficient and accurate results is developed for the fourth-order nonlinear differential equations describing the transverse vibrations of a beam on an elastic foundation. The method of solution developed is based on a recent novel method of calculation, the Adomian Modified Decomposition Method (AMDM). AMDM has advantages of solving without discretization, linearization, perturbation, or a priori assumptions, all of which has the potential to change the physics of the problem. The accuracy and the convergence speed of the method are validated against exact solutions. The beam is analyzed using two existing beam theories, namely, Euler-Bernoulli and Timoshenko beam theories. For the foundation widely used one-parameter Winkler and two-parameter Pasternak foundations are used. Numerical calculations of vibration frequencies and mode shape functions are performed. Effect of a foundation parameters and loadings on beam vibrations are analyzed and discussed.
Calculation of Capacitance of the Rectangular Coaxial Lines with Offset Inner Conductor by Strong FEM

In this paper, capacitance per unit length of rectangular coaxial transmission lines with offset nonzerothickness inner conductor, having an isotropic and anisotropic dielectric, using strong FEM formulation is calculated. The results were compared with the results obtained by the weak FEM and commercial software FEMM, which uses node-based first-order basis function. Based on that, appropriate conclusions are made.
Evaluation of the Dosimetric Accuracy of Brain Stereotactic Radiotherapy by Using a Hybrid Quality Assurance (QA) Toolkit

We evaluated the feasibility of a magnetic resonance (MR) tumor model and the accuracy of 3D dosimetry for evaluating brain stereotactic radiotherapy (SRT). We first validated the stability of the BANG3 polymer gel dosimeter and performed a pre-clinical verification of this device for brain SRT. Gel-filled vials were irradiated with 6-MV beams to generate a dose calibration curve in relation to the R2 (1/T2) values on 9.4 T MR images. The dose linearity, inter-reproducibility and intra-reproducibility, and dose-rate dependence of the gel dosimeters were evaluated. We also developed an image tumor model for pre-clinical testing. A calibration kit for the gel dosimeter and two spherical tumor representations were fabricated by using 3D printing. Two planning target volumes (PTVs) were contoured onto the MR images of the tumor models comprised of dosimetric gel. To evaluate the geometric and dosimetric accuracy, we created a treatment plan such that the D95s for PTV1 and shifted PTV2 were more than the prescribed dose. The shifted PTV2 was produced by an intentional shift of 5 mm from the true target position. A 2 arc VMAT plan was created to deliver 35 Gy in 5 fractions. After irradiation, calibration vials and tumor model phantoms were scanned by using 9.4 T MRI, and the acquired images were then analyzed using ImageJ and DCMTK software libraries. Scanned MRI images of the tumor models were imported into a treatment planning system and registered to CT images. We also compared the agreement of results between the planned and the measured data in 1D (ion chamber), 2D (2D film), and 3D (gel dosimeter). The best dose linearity achieved was 0.99 (R2) at 180 TE (ms) under which conditions the reproducibility and dose rate dependence were less than 2.2% and 3.5%, respectively. The mean dose differences between the treatment plan and the ion-chamber results were 3.3%, 0.4%, and 4.5%, respectively, for PTV1 and 2 and for the shifted PTV. Moreover, the point dose differences between the treatment plan and the gel results were 0.9%, 2.6% and 3.7%, respectively, for PTV1 and 2 and for the shifted PTV. The gamma passing rates with 3%/3 mm criteria were greater than 99% for all plans. The isodose distributions and profiles showed good qualitative agreement between the gel dosimeter, EBT film and RTP data for all PTVs. Our present study results, thus, indicate that an MR tumor model can effectively evaluate the geometric and dosimetric accuracy of brain SRT. Image tumor models using gel dosimeters are, therefore, useful for validating the 3D dose distributions for patient-specific whole treatment processes. We are planning to improve this image tumor model by facilitating respiratory mobility and utilizing more complex tumor shapes for liver stereotactic body radiotherapy (SBRT) cases.
Software Tools For Large Scale Interactive Hydrodynamic Modeling

Developing easy-to-use software that combines components for simultaneous visualization, simulation and interaction is a great challenge. Mainly, because it involves a number of disciplines, like computational fluid dynamics, computer graphics, high-performance computing. One of the main characteristics of an interactive model is that it should provide immediate feedback to the user, for example respond to changes in model state or view settings. Features involving interaction during simulation are usually available for models with a relatively small number of computational cells and are used mainly for demonstration and educational purposes. The reason for that is that the time required to compute a single time step and render model results become significant when comparing to a simple model. It would be useful if interactive modeling would also work for models typically used in consultancy projects involving large scale simulations. This results in a number of technical challenges related to the combination of the model itself and the visualization tools (scalability, implementation of an appropriate API for control and access to the internal state). While model parallelization is increasingly addressed by the environmental modeling community, little effort has been spent on developing a highperformance interactive environment. What can we learn from the other domains where visualizations plays crucial role, such as 3D animation, gaming, virtual globes (Autodesk 3ds Max, Google Earth) that also focus on efficient interaction with 3D environments? In these domains high efficiency is usually achieved by the use of computer graphics algorithms such as surface simplification depending on current view, distance to objects, and efficient caching of the aggregated representation of object meshes. We investigate how these algorithms can be re-used in the context of interactive hydrodynamic modeling without significant changes to the model code and allowing model operation on both multi-core CPU personal computers and high-performance computer clusters.
ZeroFlow: Scalable Scene Flow via Distillation

Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process full-size point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feedforward methods are considerably faster, running on the order of tens to hundreds of milliseconds for full-size point clouds, but require expensive human supervision. To address both limitations, we propose Scene Flow via Distillation, a simple, scalable distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feedforward model. Our instantiation of this framework, ZeroFlow, achieves state-of-the-art performance on the Argoverse 2 Self-Supervised Scene Flow Challenge while using zero human labels by simply training on large-scale, diverse unlabeled data. At test-time, ZeroFlow is over 1000x faster than label-free state-of-the-art optimization-based methods on full-size point clouds (34 FPS vs 0.028 FPS) and over 1000x cheaper to train on unlabeled data compared to the cost of human annotation (\$394 vs ~\$750,000). To facilitate further research, we release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets at https://vedder.io/zeroflow.html
Identification of Ghost Targets for Automotive Radar in the Presence of Multipath

Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows forming a larger virtual array for angle finding. However, multiple paths impinging the receiver is a major limiting factor, in that radar signals may bounce off obstacles, creating echoes for which the DOD does not equal the DOA. Thus, in complex scenarios with multiple scatterers, the direct paths of the intended targets may be corrupted by indirect paths from other objects, which leads to inaccurate angle estimation or ghost targets. In this paper, we focus on detecting the presence of ghosts due to multipath by regarding it as the problem of deciding between a composite hypothesis, ${\cal H}_0$ say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD's and DOA's, and a composite alternative, ${\cal H}_1$ say, that the observations also contain an unknown number of indirect paths, for which DOD's and DOA's do not coincide. We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure, wherein the unknown parameters are replaced by carefully designed estimators. The angles of both the active direct paths and of the multi-paths are indeed estimated through a sparsity-enforced Compressed Sensing (CS) approach with Levenberg-Marquardt (LM) optimization to estimate the angular parameters in the continuous domain. An extensive performance analysis is finally offered in order to validate the proposed solution.
Students Success Modeling: Most Important Factors

The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. The present study, motivated by the same encouragement, proposes a deep learning model trained with 121 features of diverse categories extracted or engineered out of the records of 60,822 postsecondary students. The model undertakes to identify students likely to graduate, the ones likely to transfer to a different school, and the ones likely to drop out and leave their higher education unfinished. This study undertakes to adjust its predictive methods for different stages of curricular progress of students. The temporal aspects introduced for this purpose are accounted for by incorporating layers of LSTM in the model. Our experiments demonstrate that distinguishing between to-be-graduate and at-risk students is reasonably achievable in the earliest stages, and then it rapidly improves, but the resolution within the latter category (dropout vs. transfer) depends on data accumulated over time. However, the model remarkably foresees the fate of students who stay in the school for three years. The model is also assigned to present the weightiest features in the procedure of prediction, both on institutional and student levels. A large, diverse sample size along with the investigation of more than one hundred extracted or engineered features in our study provide new insights into variables that affect students success, predict dropouts with reasonable accuracy, and shed light on the less investigated issue of transfer between colleges. More importantly, by providing individual-level predictions (as opposed to school-level predictions) and addressing the outcomes of transfers, this study improves the use of ML in the prediction of educational outcomes.
Investigating Efficient Deep Learning Architectures For Side-Channel Attacks on AES

Over the past few years, deep learning has been getting progressively more popular for the exploitation of side-channel vulnerabilities in embedded cryptographic applications, as it offers advantages in terms of the amount of attack traces required for effective key recovery. A number of effective attacks using neural networks have already been published, but reducing their cost in terms of the amount of computing resources and data required is an ever-present goal, which we pursue in this work. We focus on the ANSSI Side-Channel Attack Database (ASCAD), and produce a JAX-based framework for deep-learning-based SCA, with which we reproduce a selection of previous results and build upon them in an attempt to improve their performance. We also investigate the effectiveness of various Transformer-based models.
Communication-Aware Map Compression for Online Path-Planning

This paper addresses the problem of the communication of optimally compressed information for mobile robot path-planning. In this context, mobile robots compress their current local maps to assist another robot in reaching a target in an unknown environment. We propose a framework that sequentially selects the optimal compression, guided by the robot's path, by balancing the map resolution and communication cost. Our approach is tractable in close-to-real scenarios and does not necessitate prior environment knowledge. We design a novel decoder that leverages compressed information to estimate the unknown environment via convex optimization with linear constraints and an encoder that utilizes the decoder to select the optimal compression. Numerical simulations are conducted in a large close-to-real map and a maze map and compared with two alternative approaches. The results confirm the effectiveness of our framework in assisting the robot reach its target by reducing transmitted information, on average, by approximately 50% while maintaining satisfactory performance.
GGL-PPI: Geometric Graph Learning to Predict Mutation-Induced Binding Free Energy Changes

Protein-protein interactions (PPIs) are critical for various biological processes, and understanding their dynamics is essential for decoding molecular mechanisms and advancing fields such as cancer research and drug discovery. Mutations in PPIs can disrupt protein binding affinity and lead to functional changes and disease. Predicting the impact of mutations on binding affinity is valuable but experimentally challenging. Computational methods, including physics-based and machine learning-based approaches, have been developed to address this challenge. Machine learning-based methods, fueled by extensive PPI datasets such as Ab-Bind, PINT, SKEMPI, and others, have shown promise in predicting binding affinity changes. However, accurate predictions and generalization of these models across different datasets remain challenging. Geometric graph learning has emerged as a powerful approach, combining graph theory and machine learning, to capture structural features of biomolecules. We present GGL-PPI, a novel method that integrates geometric graph learning and machine learning to predict mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multi-scale weighted colored geometric subgraphs to extract informative features, demonstrating superior performance on three validation datasets, namely AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 datasets. Evaluation on a blind test set highlights the unbiased predictions of GGL-PPI for both direct and reverse mutations. The findings underscore the potential of GGL-PPI in accurately predicting binding free energy changes, contributing to our understanding of PPIs and aiding drug design efforts.
Self-Tuning Hamiltonian Monte Carlo for Accelerated Sampling

The performance of Hamiltonian Monte Carlo simulations crucially depends on both the integration timestep and the number of integration steps. We present an adaptive general-purpose framework to automatically tune such parameters based on a local loss function that promotes the fast exploration of phase space. We show that a good correspondence between loss and autocorrelation time can be established, allowing for gradient-based optimization using a fully differentiable set-up. The loss is constructed in such a way that it also allows for gradient-driven learning of a distribution over the number of integration steps. Our approach is demonstrated for the one-dimensional harmonic oscillator and alanine dipeptide, a small protein commonly used as a test case for simulation methods. Through the application to the harmonic oscillator, we highlight the importance of not using a fixed timestep to avoid a rugged loss surface with many local minima, otherwise trapping the optimization. In the case of alanine dipeptide, by tuning the only free parameter of our loss definition, we find a good correspondence between it and the autocorrelation times, resulting in a >100 fold speedup in the optimization of simulation parameters compared to a grid search. For this system, we also extend the integrator to allow for atom-dependent timesteps, providing a further reduction of 25% in autocorrelation times.