Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on energy hub configuration optimization problem based on analytic hierarchy and grey correlation analysis

For the energy hub system configuration, there are problems such as high operation and maintenance costs, poor system safety, low efficiency of renewable energy utilization and large pollution emissions. This paper proposes an energy hub configuration optimization method based on analytic hierarchy and grey correlation analysis. First, configure the energy hub model. Second, establish a configuration optimization model that includes operation and maintenance costs, installation costs, renewable energy utilization efficiency, system failure rate and environmental protection. Finally, through the hierarchical decomposition and gray correlation analysis and the use of Matlab soft pair model calculation, the effectiveness of the algorithm is proved, and the simulation results show that the proposed algorithm can improve the consumption performance of renewable energy, enhance the safety of the system, and make the system run economically.
Active inference, preference learning and adaptive behaviour

The ability to adapt to a changing environment underwrites sentient behaviour e.g., wearing a raincoat when walking in the rain but removing it when indoors. In such instances, agents act to satisfy some preferred mode of behaviour that leads to predictable states necessary for survival, i.e., states that are characteristic of that agent. In this chapter, we describe how active inference agents, equipped with preference learning, can exhibit these distinct behavioural modes – influenced by environment dynamics – to aptly trade-off between preference satisfaction and exploration. We validate this in a modified OpenAI Gym FrozenLake environment (without any extrinsic signal) with and without volatility under a fixed model of the environment. In a static (i.e., without volatility) environment, preference-learning agents accumulate confident (Bayesian) beliefs about their behaviour and act to satisfy them. In contrast, volatile dynamics led to preference uncertainty and exploratory behaviour. This demonstrates that active inference agents, equipped with preference learning, have the appropriate machinery to (i) engage in adaptive behaviour under appropriate levels of volatility, and (ii) learn context-dependent subjective preferences.
Object-oriented extraction research under multiple scenarios based on Sentinel-2A satellite images

In order to solve the problem of information extraction from Sentinel-2A satellite remote sensing images, four scene images of mountainous landscape with complex forests, flat cultivated rural landscape suburban coastal landscape and complex urban landscape were selected as experimental objects, and three algorithms of KNN, CART and SVM of object-oriented method are used to extract the main feature information, so as to compare the extraction accuracy of different methods in different scenes. The experimental results show that the overall accuracy of object-oriented feature classification of Sentinel-2A image in four scenes is greater than 75.47%, and the kappa coefficient is greater than 0.72 except that the overall accuracy of KNN in Scenes 4 is 0.66. Through the comprehensive comparative analysis of the four scenes, it can be seen that the average overall accuracy of rural landscape of Sentinel-2A image is 84.65%, the average kappa coefficient is 0.79, which is better than the average overall accuracy of urban landscape classification of 78.1%, the average kappa coefficient is 0.7, and the flat terrain scene is better than the mountainous area with large topographic elevation difference. In terms of classification methods, SVM algorithm has better classification accuracy than CART algorithm, and CART has better classification accuracy than KNN.
Modeling the Lunar plasma wake

Bodies that lack a significant atmosphere and internal magnetic fields, such as the Moon and asteroids, can to a first approximation be considered passive absorbers of the solar wind. The solar wind ions and electrons directly impact the surface of these bodies due to the lack of atmosphere, and the interplanetary magnetic field passes through the obstacle relatively undisturbed because the bodies are assumed to be non-conductive. Since the solar wind is absorbed by the body, a wake is created behind the object. This wake is gradually filled by solar wind plasma downstream of the body, through thermal expansion and the resulting ambipolar electric field, along the magnetic field lines. Here we study this plasma expansion into a vacuum using a hybrid plasma solver. In the hybrid approximation, ions are treated as particles, and electrons as a massless fluid. We also derive corresponding one- and two-dimensional model problems that account for the absorbing obstacle. It is found that the absorbing obstacle create ion velocity distributions that are far from the assumed upstream thermal Maxwellian distributions. Such distributions can give rise to different plasma instabilities that has been observed in the Lunar wake.
Embodied Intelligence in soft robotics: joys and sorrows

Embodied intelligence and soft robotics are strictly interconnected, as soft bodyware is key to obtain emergent behaviours from external interactions. The overall advantages of embodied intelligence in robotics are balanced by additional challenges in robot design and control. A main contribution of embodied intelligence to robotics consists of simplifying principles for robot control, in terms of control parameters. At the same time, robot design becomes more complex, involving the complex dynamic interaction of the robot with the environment and the task. These two form the ecological niche where embodied intelligence is effective, but they also limit the robot task space. To what extent can we enlarge the ecological niche without missing the benefits of embodied intelligence? Emergent behaviours are what we expect to simplify control, but what level of controllability can we still achieve? And how far are we from a mathematical description of embodied intelligence, to the benefit of soft robot design and control? Few trade-offs between contrasting needs in the use of embodied intelligence in soft robotics are discussed here, in view of directions of future progress.
Design of EL defect detection system for photovoltaic power station modules

In recent years, the photovoltaic power generation industry has been vigorously promoted and developed, while the solar cell as its core component may have micro-crack defects, which directly affect the power generation efficiency and stability of the photovoltaic system. Therefore, it is necessary to adopt a low-cost, efficient and flexible method to detect defects in solar cells. At present, most of the existing micro-cracks detection is carried out in the laboratory, and there is no EL micro-cracks detection in the actual photovoltaic power station. The main purpose of this paper is to design a set of EL defect detection system that can be used for actual photovoltaic power station modules, which is different from the traditional laboratory-level or module-level defect detection, and to carry the designed detection algorithm to the integrated device to operate. Experiments have shown that the system can perform real-time detection of photovoltaic module defects based on the string level, which improves the detection efficiency while saving time and cost, and shows good performance.
Towards a modeling of the time dependence of contact area between solid bodies

I present a simple model of the time dependence of the contact area between solid bodies, assuming either a totally uncorrelated surface topography or a self-affine surface roughness. Time-dependent deformation due to ‘creep’ processes is incorporated using a recently proposed model, which produces the time increase of the contact area A(t). For an uncorrelated surface topography, A(t) is numerically found to be well fitted by expressions of the form , where the exponent q depends on the normal load FN as q ∼ FNβ, with β close to 0.5. In particular, when the contact area is much smaller than the nominal area I obtain A(t)/A(0) ∼ 1 + Cln(t/t0 + 1), i.e. a logarithmic time increase of the contact area, in accordance with experimental observations. The logarithmic increase for low loads is also obtained analytically in this case. For the more realistic case of self-affine surfaces, the results are qualitatively similar.
The mass-concentration relationship of virialized haloes and its impact on cosmological observables

A generic property of the cuspy simulated virialized haloes in cold dark matter (CDM) cosmogenies is that their concentration is inversely correlated with their mass. This behaviour has also been confirmed in observations, although differences in the exact form and dispersion of this so-called mass–concentration relationship have been reported. Some observational studies of massive haloes suggest that they are statistically overconcentrated with respect to the expectations of ΛCDM. Here we investigate the impact that various published mass–concentration relationships, both from simulations and derived from observations, would have on other cosmological observables, in particular considering upcoming surveys. We find that an integral measure of lensing shear, such as counts of peaks from haloes, is very sensitive to the relationship between mass and concentration at fixed σ8, and the disparity between some reported fits is much larger than the impact of uncertainty in σ8 itself. We also briefly assess the impact of baryonic physics on cluster scale observables, using state-of-the-art simulations, concluding that it is unlikely to give rise to the high concentrations reported for some clusters.
Optimal path following for underactuated marine vessels

This report presents two optimization problems, where three cost-functions are suggested for each. The goal for the first optimization problem is to find a time variant look-ahead distance which improves the performance of the vessel in terms of the cross-track error, relative to constant look-ahead distances. The second optimization problem is an extension of the first, where a time variant surge velocity reference is also calculated. This results in smaller cross-track errors, at the cost of increased calculation time. It is assumed that the path and the desired surge velocity on the path is supplied by some external source. Existing kappa-exponential control laws are used to track the references resulting from the calculated optimal look-ahead distance and surge velocity reference. The predictions needed to solve the optimization problem are made from a model where the control laws are inserted into the dynamics to simplify the model. The optimization problems are solved for two different approaches. The first approach uses an LTV model for predictions and a QP-solver to solve the optimization problem. The second approach forms predictions of the states by numerical integration of the system dynamics and uses an optimization problem solver for general non-linear functions to solve the optimization problem. The latter approach generally results in longer calculation times but better accuracy, while the first approach yields convexity of the optimization problem. A passive observer is used to estimate the current such that it can be included in the predictions. Four of the six suggested cost-functions lead to significantly improved performance of the vessel in terms of the cross-track error. This is true both in the case of no disturbances and in the presence of a constant irrotational current. That is, the position of the vessel converges faster to the path, the vessel stays closer to the path and has less over-shoot in the cross-track error, for the optimal inputs than for constant inputs. The cost-functions includes weights for tuning where the tuning process is easy for some of the cost-functions. One of the cost-functions where only the optimal look-ahead distance is calculated, results in calculation times shorter than the time between samples, after a few time steps. This is a promising result since the application of MPC to fast-dynamic systems such as marine vessels is desired, but often problematic due to too long calculation times for solving the optimization problem. It takes more time to solve the optimization problem when the optimal surge velocity reference is found in addition to the optimal look-ahead distance.
Risk Aware Ranking for Top-k Recommendations

Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-$k$ items from the estimated preference order is recommended to the end user in the decreasing order of preference for final consumption. We analyze this model and observe that such a ranking model results in suboptimal performance when the payoff associated with the recommended items is different. We propose a novel and very efficient algorithm for the preference ranking considering the uncertainty regarding the payoffs of the items. Once the preference scores for the users are obtained using any preference learning algorithm, we show that ranking the items using a risk seeking utility function results in the best ranking performance.
Bayesian Spatial Classification

In analyses of spatially-referenced data, researchers often have one of two goals: to quantify relationships between a response variable and covariates while accounting for residual spatial dependence or to predict the value of a response variable at unobserved locations. In this second case, when the response variable is categorical, prediction can be viewed as a classification problem. Many classification methods either ignore response-variable/covariate relationships and rely only on spatially proximate observations for classification, or they ignore spatial dependence and use only the covariates for classification. The Bayesian spatial generalized linear (mixed) model offers a tool to accommodate both spatial and covariate sources of information in classification problems. In this paper, we formally define spatial classification rules based on these models in order to provide a principled empirical comparison to other spatial/non-spatial classification rules in the literature. We also take a close look at two different versions of these models that have been proposed in the literature, namely the Bayesian spatial generalized linear model (SGLM) and the Bayesian spatial generalized linear mixed model (SGLMM). We briefly describe the implications of the seemingly slight differences between these models for spatial classification and explore the issue of robustness to model misspecification through a simulation study. We also provide a comprehensive review of alternatives to the SGLM/SGLMM-based classifiers and illustrate the various methods using satellite-derived land cover data from Southeast Asia.
The intriguing life of star-forming galaxies in the redshift range 1 ≤ z ≤ 2 using MASSIV

Abstract MASSIV (Mass Assembly Survey with SINFONI in VVDS) is an ESO large program which consists of 84 star-forming galaxies, spanning a wide range of stellar masses, observed with the IFU SINFONI on the VLT, in the redshift range 1 ≤ z ≤ 2. To be representative of the normal galaxy population, the sample has been selected from a well-defined, complete and representative parent sample. The kinematics of individual galaxies reveals that 58% of the galaxies are slow rotators, which means that a high fraction of these galaxies should probably be formed through major merger processes which might have produced gaseous thick or spheroidal structures supported by velocity dispersion rather than by rotation. Computations on the major merger rate from close pairs indicate that a typical star-forming galaxy underwent ~0.4 major mergers in the last ~9.5 Gyr, showing that merging is a major process driving mass assembly into the red sequence galaxies. These objects are also intriguing due to the fact that more than one galaxy over four is more metal-rich in its outskirts than in its center.
Step aside! A fuzzy trip down side-channel lane Side-channel assisted fuzzing in embedded systems

This thesis investigates how to integrate side-channel information of an embedded system into the feedback loop of a fuzzer. Fuzzers, such as afl, try to find many execution paths of a fuzzing target by mutating input values such that the target’s output changes. Side-channels, such as execution time, expose additional information about a running system. We developed a custom communication interface program that operates between afl and the smartcard, to provide input filtering and report execution results to afl. In this thesis, we perform experiments using differential fuzzing with DifFuzz, and mutational/evolutionary fuzzing with afl. We present two case studies: a custom, password checking applet on a Java Card smartcard, and an unprovisioned sim (Subscriber Identity Module) card. The password checker implements both a timing-sensitive, and a constant-time password checker. First, we find that, for execution distinguishability in differential fuzzing with DifFuzz, the timing side-channel’s measurements need to have nanosecond precision. Next, we fuzz the password checking applet with status words as a pseudo side-channel in afl. The password checker reports three status words: length correctness using (1) overall message length and (2) the value of the length parameter in the message, and overall password correctness (3). We show that afl finds the correct password length (2), but is not able to find the correct password (3) directly. Next, we use status words in afl to find all possible instructions of a sim card (instruction set discovery). We show that afl finds the instruction set in about 30 minutes of fuzzing time, which is twice as slow as a brute-force approach under the same conditions. Finally, we fuzz the password checking applet with timing as side-channel information in afl, in addition to status words. We show that afl uses the timing side-channel, because it focuses on the timing-sensitive password checker, and that afl finds the correct password length (2). However, afl does not find the correct password (3) due to the password’s complexity and the misinterpretation of the overall message length (1).
Epidemic graph diagrams as analytics for epidemic control in the data-rich era

COVID-19 highlighted how modeling is an integral part of pandemic response. But it also exposed fundamental methodological challenges. As high-resolution data on disease progression, epidemic surveillance, and host behavior are now available, can models turn them into accurate epidemic estimates and reliable public health recommendations? Take the epidemic threshold, which estimates the potential for an infection to spread in a host population, quantifying epidemic risk throughout epidemic emergence, mitigation, and control. While models increasingly integrated realistic host contacts, no parallel development occurred with matching detail in disease progression and interventions. This narrowed the use of the epidemic threshold to oversimplified disease and control descriptions. Here, we introduce the epidemic graph diagrams (EGDs), novel representations to compute the epidemic threshold directly from arbitrarily complex data on contacts, disease and control efforts. We define a grammar of diagram operations to decompose, compare, simplify models, extracting new theoretical understanding and improving computational efficiency. We test EGDs on two public health challenges, influenza and sexually-transmitted infections, to (i) explain the emergence of resistant influenza variants in the 2007-2008 season, and (ii) demonstrate that neglecting non-infectious prodromic stages biases the predicted epidemic risk, compromising control. EGDs are however general, and increase the performance of mathematical modeling to respond to present and future public health challenges.
Meson spectroscopy in a confining theory via AdS/CFT

Instanton effects may have implications for hadronization of quark-gluon plasma as it cools. Here we study dispersion relations of mesons in a strongly coupled plasma with an instanton background present. It will be shown that at higher energies the instanton effect diminishes and some comments on the limiting velocity of mesons in the plasma. the profile function of mesons on the gravity side is considered also because of its relevance to energy loss of quark in plasma.
Scaling Directed Controller Synthesis via Reinforcement Learning

Directed Controller Synthesis technique ﬁnds solutions for the non-blocking property in discrete event systems by exploring a reduced portion of the exponentially big state space, using best-ﬁrst search. Aiming to minimize the explored states, it is currently guided by a domain-independent handcrafted heuristic, with which it reaches state-of-the-art performance. In this work, we propose a new method for obtaining heuristics based on Reinforcement Learning. The synthesis algorithm is framed as an RL task with an unbounded action space and a modiﬁed version of DQN is used. With a simple and general set of features, we show that it is possible to learn heuristics on small versions of a problem in a way that generalizes to the larger instances. Our agents learn from scratch and outperform the existing heuristic overall, in instances unseen during training.
PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction

Keyphrase extraction is the process of automatically selecting a small set of most relevant phrases from a given text. Supervised keyphrase extraction approaches need large amounts of labeled training data and perform poorly outside the domain of the training data. In this paper, we present PatternRank, which leverages pretrained language models and part-of-speech for unsupervised keyphrase extraction from single documents. Our experiments show PatternRank achieves higher precision, recall and F1-scores than previous state-of-the-art approaches. In addition, we present the KeyphraseVectorizers package, which allows easy modification of part-of-speech patterns for candidate keyphrase selection, and hence adaptation of our approach to any domain.
Computer Vision Based Inspection on Post-Earthquake With UAV Synthetic Dataset

The area affected by the earthquake is vast and often difficult to entirely cover, and the earthquake itself is a sudden event that causes multiple defects simultaneously, that cannot be effectively traced using traditional, manual methods. This article presents an innovative approach to the problem of detecting damage after sudden events by using an interconnected set of deep machine learning models organized in a single pipeline and allowing for easy modification and swapping models seamlessly. Models in the pipeline were trained with a synthetic dataset and were adapted to be further evaluated and used with unmanned aerial vehicles (UAVs) in real-world conditions. Thanks to the methods presented in the article, it is possible to obtain high accuracy in detecting buildings defects, segmenting constructions into their components and estimating their technical condition based on a single drone flight.
Current injection and voltage insertion attacks against the VMG-KLJN secure key exchanger

: In this paper, the vulnerability of the Vadai, Mingesz and Gingl (VMG)-Kirchhoff-Law-Johnson-Noise (KLJN) Key Exchanger (Nature, Science Report 5 (2015) 13653) against two active attacks is demonstrated. The security vulnerability arises from the fact that the effective driving impedances are different between the HL and LH cases for the VMG-KLJN scheme; whereas for the ideal KLJN scheme they are same. Two defense schemes are shown against these attacks but each of them can protect against only one of the attack types; but not against the two attacks simultaneously. The theoretical results are confirmed by computer simulations.