Mine Plug Integrity Evaluation

The integrity of a mine plug impounding more than 30 million gallons of acidic mine drainage water (AMD) was investigated using a combination of technologies. A two-phase investigation program was adopted which allowed the full depth of the mine plug to be explored without release of detrimental AMD. The condition of the concrete, support rock, rock/concrete interface, and drainage pipes and valves was evaluated. Phase 1 included (1) a review of plug design documents and construction data, (2) review of data from other mines on acid attack of concrete, (3) detailed visual inspections, (4) use of nondestructive testing techniques to assess the condition of the concrete, and (5) geochemical testing of seepage and drain pipe waters. Phase 2 explored uncertainties identified during Phase 1 and included (1) coring into the concrete plug and adjacent rock, (2) cross-hole sonic logging, (3) laboratory testing of concrete and rock samples, (4) operational testing of valves, and (5) measurements of the thickness of pressurized piping components. While several minor defects were detected, none were significant enough to affect the mine plug\u27s performance. The investigation confirmed the integrity of the mine plug after 13 years of operation

Combined analytical and numerical approach to study magnetization plateaux in doped quasi-one-dimensional antiferromagnets

We investigate the magnetic properties of quasi-one-dimensional quantum spin-S antiferromagnets. We use a combination of analytical and numerical techniques to study the presence of plateaux in the magnetization curve. The analytical technique consists in a path integral formulation in terms of coherent states. This technique can be extended to the presence of doping and has the advantage of a much better control for large spins than the usual bosonization technique. We discuss the appearance of doping-dependent plateaux in the magnetization curves for spin-S chains and ladders. The analytical results are complemented by a density matrix renormalization group (DMRG) study for a trimerized spin-1/2 and anisotropic spin-3/2 doped chains.Instituto de Física La Plat

Towards more sustainable material formulations: a comparative assessment of PA11-SGW flexural performance versus oil-based composites

The replacement of commodity polyolefin, reinforced with glass fiber (GF), by greener alternatives has been a topic of research in recent years. Cellulose fibers have shown, under certain conditions, enough tensile capacities to replace GF, achieving competitive mechanical properties. However, if the objective is the production of environmentally friendlier composites, it is necessary to replace oil-derived polymer matrices by bio-based or biodegradable ones, depending on the application. Polyamide 11 (PA11) is a totally bio-based polyamide that can be reinforced with cellulosic fibers. Composites based on this polymer have demonstrated enough tensile strength, as well as stiffness, to replace GF-reinforced polypropylene (PP). However, flexural properties are of high interest for engineering applications. Due to the specific character of short-fiber-reinforced composites, significant differences are expected between the tensile and flexural properties. These differences encourage the study of the flexural properties of a material prior to the design or development of a new product. Despite the importance of the flexural strength, there are few works devoted to its study in the case of PA11-based composites. In this work, an in-depth study of the flexural strength of PA11 composites, reinforced with Stoneground wood (SGW) from softwood, is presented. Additionally, the results are compared with those of PP-based composites. The results showed that the SGW fibers had lower strengthening capacity reinforcing PA11 than PP. Moreover, the flexural strength of PA11-SGW composites was similar to that of PP-GF compositesPostprint (published version

A Novel PMSM Hybrid Sensorless Control Strategy for EV Applications Based on PLL and HFI

In this paper, a novel hybrid sensorless control strategy for Permanent Magnet Synchronous Machine (PMSM) drives applied to Electric Vehicles (EV) is presented. This sensorless strategy covers the EV full speed range and also has speed reversal capability. It combines a High Frequency Injection (HFI) technique for low and zero speeds, and a Phase-Locked Loop (PLL) for the medium and high speed regions. A solution to achieve smooth transitions between the PLL and the HFI strategies is also proposed, allowing to correctly detect the rotor position polarity when HFI takes part. Wide speed and torque four-quadrant simulation results are provided, which validate the proposed sensorless strategy for being further implemented in EV.Peer ReviewedPostprint (author's final draft

IPMSM torque control strategies based on LUTs and VCT feedback for robust control under machine parameter variations

In recent years, Interior Permanent Magnet Synchronous Machines (IPMSMs) have attracted a considerable attention in the scientific community and industry for Electric and Hybrid Electric Vehicle (HEV) propulsion systems. Lookup Table (LUT) based Field Oriented Control (FOC) strategies are widely used for IPMSM torque control. However, LUTs strongly depend on machine parameters. Deviations of these parameters due to machine ageing, temperature or manufacturing inaccuracies can lead to control instabilities in the field weakening region. In this paper, two novel hybrid IPMSM control strategies combining the usage of LUTs and Voltage Constraint Tracking (VCT) feedbacks are proposed in order to overcome the aforementioned controllability issues. Simulation results that demonstrate the validity of the proposed approaches are presented.Postprint (author's final draft

Current jumps in flat-band ladders with Dzyaloshinskii-Moriya interactions

Localized magnons states, due to flat bands in the spectrum, is an intensely studied phenomenon and can be found in many frustrated magnets of different spatial dimensionality. The presence of Dzyaloshinskii-Moriya (DM) interactions may change radically the behavior in such systems. In this context, we study a paradigmatic example of a one-dimensional frustrated antiferromagnet, the sawtooth chain in the presence of DM interactions. Using both path integrals methods and numerical Density Matrix Renormalization Group, we revisit the physics of localized magnons and determine the consequences of the DM interaction on the ground state. We have studied the spin current behavior, finding three different regimes. First, a Luttinger-liquid regime where the spin current shows a step behavior as a function of parameter D, at a low magnetic field. Increasing the magnetic field, the system is in the Meissner phase at the m = 1/2 plateau, where the spin current is proportional to the DM parameter. Finally, further increasing the magnetic field and for finite D there is a small stiffness regime where the spin current shows, at fixed magnetization, a jump to large values at D = 0, a phenomenon also due to the flat band.Fil: Acevedo, Santiago Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Pujol, P.. Université Paul Sabatier; Francia. Université de Toulouse; FranciaFil: Lamas, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; Argentin

Wide area network autoscaling for cloud applications

Modern cloud orchestrators like Kubernetes provide a versatile and robust way to host applications at scale. One of their key features is autoscaling, which automatically adjusts cloud resources (compute, memory, storage) in order to adapt to the demands of applications. However, the scope of cloud autoscaling is limited to the datacenter hosting the cloud and it doesn't apply uniformly to the allocation of network resources. In I/O-constrained or data-in-motion use cases this can lead to severe performance degradation for the application. For example, when the load on a cloud service increases and the Wide Area Network (WAN) connecting the datacenter to the Internet becomes saturated, the application flows experience an increase in delay and loss. In many cases this is dealt with overprovisioning network capacity, which introduces additional costs and inefficiencies. On the other hand, thanks to the concept of "Network as Code", the WAN exposes a set of APIs that can be used to dynamically allocate and de-allocate capacity on-demand. In this paper we propose extending the concept of cloud autoscaling into the network to address this limitation. This way, applications running in the cloud can communicate their networking requirements, like bandwidth or traffic profile, to a Software-Defined Networking (SDN) controller or Network as a Service (NaaS) platform. Moreover, we aim to define the concepts of vertical and horizontal autoscaling applied to networking. We present a prototype that automatically allocates bandwidth to the underlay network, according to the requirements of the applications hosted in Kubernetes. Finally, we discuss open research challenges.This work was supported by the Spanish MINECO under contract TEC2017-90034-C2-1-R (ALLIANCE), the Catalan Institution for Research and Advanced Studies (ICREA).Peer ReviewedPostprint (author's final draft

Magnon crystals and magnetic phases in a kagome-stripe antiferromagnet

In this paper we analyze the magnetization properties of an antiferromagnetic kagome-stripe lattice, motivated by the recent synthesis of materials exhibiting this structure. By employing a variety of techniques that include numerical methods such as density-matrix renormalization-group and Monte Carlo simulations, as well as analytical techniques such as perturbative low-energy effective models and exact solutions, we characterize the magnetization process and magnetic phase diagram of a kagome-stripe lattice. The model captures a variety of behaviors present in the two-dimensional kagome lattice, which are described here by analytical models and numerically corroborated. In addition to the characterization of semiclassical intermediate plateaus, it is worth noting the determination of an exact magnon crystal phase which breaks the underlying symmetry of the lattice. This magnon crystal phase generalizes previous findingsFil: Acevedo, Santiago Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Lamas, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Arlego, Marcelo José Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Pujol, Pierre. Universitè de Toulouse; Franci

Discurso que en la pompa fúnebre efectuada por Decreto de S.M. en ... Barcelona, a la ... memoria del Excmo. Señor Don Mariano Alvarez de Castro ... el dia 26 de octubre de 1816 ...

Copia digital. Madrid : Ministerio de Cultura. Subdirección General de Coordinación Bibliotecaria, 200

Unveiling the potential of graph neural networks for robust intrusion detection

The last few years have seen an increasing wave of attacks with serious economic and privacy damages, which evinces the need for accurate Network Intrusion Detection Systems (NIDS). Recent works propose the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks). However, existing ML-based NIDS are barely robust to common adversarial attacks, which limits their applicability to real networks. A fundamental problem of these solutions is that they treat and classify flows independently. In contrast, in this paper we argue the importance of focusing on the structural patterns of attacks, by capturing not only the individual flow features, but also the relations between different flows (e.g., the source/destination hosts they share). To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information. In our evaluation, we first show that the proposed GNN model achieves state-of-the-art results in the well-known CIC-IDS2017 dataset. Moreover, we assess the robustness of our solution under two common adversarial attacks, that intentionally modify the packet size and interarrival times to avoid detection. The results show that our model is able to maintain the same level of accuracy as in previous experiments, while state-of-the-art ML techniques degrade up to 50% their accuracy (F1-score) under these attacks. This unprecedented level of robustness is mainly induced by the capability of our GNN model to learn flow patterns of attacks structured as graphs.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA), and by the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI-POINTER Project, funded under grant agreement No. 871528. This article reflects only the authors’ view; the European Commission is not responsible for any use that may be made of the information it contains.Peer ReviewedPostprint (published version