Miracle-dz/UniFAR-FTUs huggingface dataset:
A High Duty Cycle Transmitter Unit for Steady-State Surface NMR Instruments

Groundwater measurements using surface nuclear magnetic resonance (NMR) have been notoriously challenged by a poor signal-to-noise ratio (SNR), but a new steady-state methodology based on long, high duty cycle, phase-locked pulse trains have demonstrated huge SNR increases. The hardware requirements for transmitters for steady-state surface NMR are significantly increased compared to transmitters for standard surface NMR use, due to the need for very high pulse-to-pulse stability over long survey times and the increased thermal load caused by a much higher duty cycle. Further, the increased SNR 5 leads to increased production rates, which necessitates lightweight equipment, that can be carried easily between many field sites during surveys. Here we demonstrate a novel steady-state surface NMR transmitter with a maximum 93 A peak current. The stability of the transmitter is evaluated on 10 minutes, 10% duty cycle, pulse trains containing pulses of either 5 ms, 10 ms, 20 ms, or 40 ms duration, and low or high current. We observe less than 150 ns pulse-to-pulse timing jitter and amplitude variations below 0.4% between pulses for all pulse durations and currents. During tests, we observed no temperature effects 10 on the timing and current stability. We have designed a customized heatsink, which reduces the transmitter weight by 30% and size by 16% without compromising safe thermal operating conditions. We evaluate the capacitor bank size and current stability and demonstrate that a 10 mF capacitor bank is an appropriate trade-off with insignificant current drooping in measurements. The extensive analysis and verification demonstrate that the transmitter generates highly stable pulse trains resulting in high SNR signals.
Bargaining, Forward-Looking Buyers and Dynamic Competition

In many industries where supply-side scale economies or demand-side network effects may allow a single firm to become dominant, buyers may not be the atomistic price-takers that are typically assumed. We extend the dynamic “learning-by-doing and forgetting” model of Besanko, Doraszelski, Kryukov, and Satterthwaite (2010) to analyze how two types of buyer sophistication, bargaining power and forward-looking behavior, affect market structure, welfare and the effects of stylized policies that are designed to preserve competition. Absent distortionary policies, even modest buyer sophistication of either type leads to unique equilibria. We show how dynamics can lead to the redistribution of bargaining power between buyers and sellers having non-linear and non-monotonic effects on outcomes, welfare and the impact of policies. We also show how buyer bargaining power and forward-looking behavior interact.
A FS-MPC Analytical Study of the MPPT Gridded PV System

The solar system's vitality is a fantastic supply for the current fashion. The photovoltaic system has been calculated for energy harvesting and is capable of gathering numerous PV with the best efficiency. When PV is operating at its peak efficiency, which depends on both temperature and radiation, the effectiveness is higher. The PV system is capable of tracing the greatest point of power and has to be introduced in order to generate more energy because the temperature and radiation vary constantly with time. Also, the goal of this research is to improve the power point system's tracing using the INC (increased conductance) technique. This algorithm has been devised for regulating the buck boost duty cycle converted for assuring the highest effectiveness. This algorithm performance has been compared with an extensively utilizing P and O on an environment. It has been envisioned from the simulation outcomes that IC model performs better and is having oscillation minimum. The projected model has been compared with contemporary MPPT model under diversified variations of irradiance.
3D Semantic Mapping: A Registration Based Approach

Vision-based scene understanding is a common task in the fields of robotics and computer vision in which visual systems attempt to understand their environment, both from a semantic and geometric perspective. This task was the base of the Robotic Vision Scene Understanding Challenge [8], which assesses the ability of a robotic vision system to comprehend semantic and geometric aspects of its surroundings. As such, this report proposes a 3D semantic mapping pipeline to solve the Object-based Semantic SLAM (Simultaneous Localization and Mapping) task presented in the challenge. The core of our method is the creation of a 3D point cloud map of a simulated environment with the use of fast global registration. The semantic map is then created using a pre-trained 3D object detection model that assigns bounding boxes around objects in the original 3D map. The results indicate that, while very accurate in terms of predicting the label of an object, our method is not robust regarding other aspects important to the resulting map, such as the number of objects detected and their spatial quality. Our pipeline is impaired by components related to the estimation of coordinate transformations and limitations of the fast global registration method.
Dependence Modelling and Testing: Copula and Varying Coefficient Model with Missing Data

This thesis investigates three topics in theoretical econometrics: goodness-of-fit tests for copulas, copula density estimators which preserve the copula property, and bias-correction for the naive kernel local linear estimators in the two-sample varying coefficient model with missing data. In the first topic a family of goodness-of-fit tests for copulas is proposed. The tests use generalizations of the information matrix equality of White (1982). The asymptotic distribution of the generalized tests is derived. In Monte Carlo simulations, the behavior of the new tests is compared with several Cramer-von Mises type tests and the desired properties of the new tests are confirmed in high dimensions. In the second topic, a semi-parametric copula density estimation procedure that guarantees that the estimator is a genuine copula density is outlined. A simulation-based study is constructed to examine the performance of the proposed copula density estimation method and compare it with the leading copula density estimators in the literature. The method is also applied to estimate copula densities in two empirical cases. The third topic shows that the naive kernel estimator using matching data is not consistent in the two-sample varying coefficient model with missing data. A bias-corrected consistent estimator is proposed and the asymptotic theory is discussed. A simulation study is conducted to support the theoretical results.
Monitoring glacier calving using underwater sound

Abstract. Climate shifts are particularly conspicuous in glaciated areas. Satellite and terrestrial observations show significant increases in the melting and breakup of tidewater glaciers and their influence on sea level rise and ocean mixing. Increasing melt rates are creating an urgency to better understand the link between atmospheric and oceanic conditions and glacier frontal ablation through iceberg calving and melting. Elucidating this link requires a combination of short- and long-timescale measurements of terminus activity. Recent work has demonstrated the potential of using underwater sound to quantify the time and scale of calving events to yield integrated estimates of ice mass loss (Glowacki and Deane, 2020). Here, we present estimates of subaerial calving flux using underwater sound recorded at Hansbreen, Svalbard, in September 2013 combined with an algorithm for the automatic detection of calving events. The method is compared with ice calving volumes estimated from geodetic measurements of the movement of the glacier terminus and an analysis of satellite images. The total volume of above-water calving during the 26 d of acoustical observation is estimated to be 1.7±0.7×107 m3, whereas the subaerial calving flux estimated by traditional methods is 7±2×106 m3. The results suggest that passive cryoacoustics is a viable technique for long-term monitoring of mass loss from marine-terminating glaciers.
Capillary hysteresis in sloshing dynamics: a weakly nonlinear analysis

The sloshing of water waves in a vertical cylindrical tank is an archetypal damped oscillator in fluid mechanics. The wave frequency is traditionally derived in the potential flow limit (Lamb, Hydrodynamics, Cambridge University Press, 1932), and the damping rate results from the combined effects of the viscous dissipation at the wall, in the bulk and at the free surface (Case & Parkinson, J. Fluid Mech., vol. 2, 1957, pp. 172–184). Still, the classic theoretical prediction accounting for these effects significantly underestimates the damping rate when compared to careful laboratory experiments (Cocciaro et al., J. Fluid Mech., vol. 246, 1993, pp. 43–66). Specifically, theory provides a unique value for the damping rate, while experiments reveal that the damping increases as the sloshing amplitude decreases. Here, we investigate theoretically the effects of capillarity at the contact line on the decay time of capillary–gravity waves. To this end, we marry a model for the inviscid waves to a nonlinear empiric law for the contact line that incorporates contact angle hysteresis. The resulting system of equations is solved by means of a weakly nonlinear analysis using the method of multiple scales. Capillary effects have a dramatic influence on the calculated damping rate, especially when the sloshing amplitude gets small: this nonlinear interfacial term increases in the limit of zero wave amplitude. In contrast to viscous damping, where the wave motion decays exponentially, the contact angle hysteresis can act as Coulomb solid friction, thus yielding the arrest of the contact line in a finite time.
Knowledge Model for Disaster Dataset Navigation

In a situation where there are multiple diverse datasets, it is essential to have an efficient method to provide users with the datasets they require. To address this suggestion, necessary datasets should be selected on the basis of the relationships between the datasets. In particular, in order to discover the necessary datasets for disaster resolution, we need to consider the disaster resolution stage. In this paper, in order to provide the necessary datasets for each stage of disaster resolution, we constructed a disaster type and disaster management process ontology and designed a method to determine the necessary datasets for each disaster type and disaster management process step. In addition, we introduce a method to determine relationships between datasets necessary for disaster response. We propose a method for discovering datasets based on minimal relationships such as “isA,” “sameAs,” and “subclassOf. ” To discover suitable datasets, we designed a knowledge exploration model and collected 651 disaster-related datasets for improving our method. These datasets were categorized by disaster type from the perspective of disaster management. Categorizing actual datasets into disaster types and disaster management types allows a single dataset to be classified as multiple types in both categories. We built a knowledge exploration model on the basis of disaster examples to ensure the configuration of our model.
Improving Description-based Q-learning

Deep reinforcement learning (DRL) has made promising advances in recent years solving complex sequential decision-making problems, e.g. by beating the grandmasters in the game of Go, predicting protein folding or speeding up matrix multiplication. However, applying DRL to new problems successfully requires careful tuning and adaptation to find a suitable algorithm. This was demonstrated by the creators of MoGym, a tool which allows training DRL agents on any problem formulated as Markov decision process in the JANI specification language. They applied Deep Q-learning (DQN) on a set of problems of the Quantitative Verification Benchmark Set (QVBS) to test their tool and noticed that the DRL algorithm could not successfully learn on some instances. This thesis aims to find the main challenges of such instances loaded with MoGym, provide the community with DRL algorithms and techniques to address these issues and especially aims to contribute to the community by improving on existing approaches. It is shown that the DQN agent was likely unable to learn due to an exploding action-space (as a result of loading a problem using MoGym) and the lack on sophisticated exploration during training. Description-based Q-learning is proposed as a possible solution to replace DQN, because this variant can mitigate the effect of the action-space dimension by learning state-values instead of action-values. To solve the exploration problem, the thesis proposes a new version of the Go-Explore framework called Stochastic Go-Explore. The new variant drops some limiting assumptions on the training environment, providing the DRL community with a powerful exploration technique that is applicable to a larger variety of training environments than its predecessor. The experiments show that this new technique is the only one of all tested exploration techniques which enables the agents to learn on all tested problems. In addition, DBQL is extended to use n-step learning, which aims to improve sample efficiency by proposing three new n-step DQBL agents. Experiment findings establish their feasibility and provide some evidence to show that their use can lead to sample efficiency gain. However, the experiment results further suggest that this improvement might lead to higher training times. A discussion on when to use n-step learning when training time is a limiting factor is included. All findings are based on experiments made on a subset of QVBS problems and are evaluated using deep statistical model checking (DSMC) and training returns. It is shown that there exists a discrepancy in the measurements of both approaches, hinting that DSMC can be a valuable tool for the DRL community when evaluating experiments.
CONFLICT RESOLUTION WITH TIME CONSTRAINTS IN THE TERMINAL MANEUVERING AREA USING A DISTRIBUTED Q-LEARNING ALGORITHM

—With the growing number of flights, more and more conflicts have to be solved in Terminal Manoeuvring Areas (TMAs). In order to keep a fluid flow of aircraft arriving on an airport, air traffic controllers use softwares to help them to solve conflicts and sequence aircraft on runways. This paper faces the sequencing and merging problem using a reinforcement learning algorithm (Q-Learning) in order to measure its performance. This algorithm has been run on a scenario representing a regular day at Paris Charles de Gaulle airport (CDG), and gives satisfying results. Then, it has been benchmarked on heavily-loaded scenarios, with more aircraft than the previous ones in order to see the limits of reinforcement learning efficiency. The Q-Learning algorithm can not only solve conflicts on this heavily- loaded scenario but it also has a reasonable computational time. By using a Q-learning algorithm in a distributed way, we aim to find an optimized solution on heavily-loaded scenarios without compromising the computational time.
Ground-based noontime D-region electron density climatology over northern Norway

Abstract. The bottom part of the Earth's ionosphere is the so-called D region, which is typically less dense than the upper regions. Despite the comparably lower electron density, the ionization state of the D region has a significant influence on signal absorption for propagating lower to medium radio frequencies. We present local noon climatologies of electron densities in the upper middle atmosphere (50–90 km) at high latitudes as observed by an active radar experiment. The radar measurements cover 9 years (2014–2022) from the solar maximum of cycle 24 to the beginning of cycle 25. Reliable electron densities are derived by employing signal processing, applying interferometry methods, and applying the Faraday-International Reference Ionosphere (FIRI) model. For all years a consistent spring–fall asymmetry of the electron density pattern with a gradual increase during summer as well as a sharp decrease at the beginning of October was found. These findings are consistent with very low frequency (VLF) studies showing equivalent signatures for nearby propagation paths. It is suggested that the meridional circulation associated with downwelling in winter could cause enhanced electron densities through NO transport. However, this mechanism can not explain the reduction in electron density in early October.
Ideology and the collective construction of knowledge of unfolding events on Wikipedia

In this work, we share preliminary findings on the role played by political ideology in the editing of events pages on Wikipedia. We aim to estimate the ideological alignment of editors of articles that appeared during 2022 on the Portal ”Cur-rent events” of the English version of Wikipedia, using a small sample of editors and their self-disclosed ideological labels. We use a left-right ideological scaling to assign an ideological score to 2,131 editors based on political ”userbox” tags they used on their personal pages. We integrate additional structural layers such as URL, interest, language, and prolific editor features to max-imize the classifier’s performance. The resulting ideological score obtained from the classifier is then used to answer research questions related to homophily in user contributions, adversarial engagement, and the impact of knowledge production on the diversity of referenced sources. The study provides insights into the ideological distribution of Wikipedia editors and its impli-cations for knowledge production.
Optimal quantization of rank-one matrices in floating-point arithmetic—with applications to butterfly factorizations

We consider the problem of optimally quantizing rank-one matrices to low precision ﬂoating-point arithmetic. We ﬁrst explain that the naive strategy of separately quantizing the two rank-one factors can be far from optimal, and we provide worst case error bounds to support this observation. We characterize the optimal solution as the quantization of suitably scaled factors of the rank-one matrix and we develop an algorithm of tractable complexity to ﬁnd the optimal scaling parameters. Using random rank-one matrices, we show experimentally that our algorithm can signiﬁcantly reduce the quantization error. We then apply this algorithm to the quantization of butterﬂy factorizations, a fundamental tool that appears in many fast linear transforms. We show how the properties of butterﬂy supports can be exploited to approach the problem via a series of rank-one quantization problems and we employ our algorithm as a building block in a heuristic procedure to quantize a product of butterﬂy factors. We show that, despite being only heuristic, this strategy can be much more accurate than quantizing each factor independently or, equivalently, can achieve storage reductions of up to 30% with no loss of accuracy.
1 Examination of varying mixed-phase stratocumulus clouds in terms of their 1 properties, ice processes and aerosol-cloud interactions between polar and 2 midlatitude cases: An attempt to propose a microphysical factor to explain the 3 variation

53 54 This study examines the ratio of ice crystal number concentration (ICNC) to cloud droplet 55 number concentration (CDNC), which is ICNC/CDNC, as a microphysical factor that 56 induces differences in cloud development, its interactions with aerosols and impacts of ice 57 processes on them among cases of mixed-phase clouds. This examination is performed 58 using a large-eddy simulation (LES) framework and one of efforts toward a more general 59 understanding of mechanisms controlling those development and impacts in mixed-phase 60 clouds. For the examination, this study compares a case of polar mixed-phase clouds to 61 that of midlatitude mixed-phase clouds with weak precipitation. It is found that 62 ICNC/CDNC plays a critical role in making differences in cloud development with respect 63 to the relative proportion of liquid and ice mass between the cases by affecting in-cloud 64 latent-heat processes. Note that this proportion has an important implication for cloud 65 radiative properties and thus climate. It is also found that ICNC/CDNC plays a critical role 66 in making differences in clouds and their interactions with aerosols and impacts of ice 67 processes on them between the cases by affecting in-cloud latent-heat processes. Findings 68 of this study suggest that ICNC/CDNC can be a simplified general factor that contributes 69 to a more general understanding of mixed-phase clouds and roles of ice processes and 70 aerosols in them and thus, to the development of more general parameterizations of those 71 clouds and roles. 72 73 74
Open-Ended 3D Object Recognition: Investigating the Eﬀect of Deep Features, Color Spaces, and Similarity Measures

: Object recognition is a challenging task in unpredictable environments, where service robots need to react fast, and cooperate with human-users. Lack of training data and limited use of object information, such as only shape information, reduces the robustness of object recognition algorithms. This study investigates the inﬂuences of shape information, color spaces, and similarity measures in open-ended 3D object recognition. Towards this end, three experimental setups, color-only, shape-only, and color-shape, were evaluated in both oﬄine and online setups. Following the OrtohraphicNet construction, the experiments were conducted using two deep learning networks, Mobilenet-v2, and VGG16 in combination with four parameters: ortho-graphic image resolution, similarity distance functions, k-values (in a KNN algorithm), and three color spaces. In the online evaluation, extensive experiments showed that the k-nearest neighbor algorithm had a neglectable inﬂuence over the system’s performance. It was also observed that the color information improves the performance over shape information alone, color-only RGB obtaining the best result. The online evaluation showed a decrease in accuracy compared with the oﬄine results, predicting the lack of robustness of classical train-test experiments in open-ended domains. The results’ hierarchy from oﬄine followed in the online evaluation as well.
Spatiotemporal bedload transport patterns over two-dimensional bedforms

Abstract. Despite a rich history of studies investigating fluid dynamics over bedforms and dunes in rivers, the spatiotemporal patterns of sub-bedform bedload transport remain poorly understood. Previous experiments assessing the effects of flow separation on downstream fluid turbulent structures and bedload transport suggest that localized, intermittent, high-magnitude transport events (i.e., permeable splat events) play an important role in both downstream and cross-stream bedload transport near flow reattachment. Here, we report results from flume experiments that assess the combined effects of flow separation–reattachment and flow re-acceleration over fixed two-dimensional bedforms (1.7 cm high; 30 cm long). A high-speed camera observed bedload transport along the entirety of the bedform at 250 frames per second. Grain trajectories, grain velocities, and grain transport directions were acquired from bedload images using semiautomated particle-tracking techniques. Downstream and vertical fluid velocities were measured 3 mm above the bed using laser Doppler velocimetry (LDV) at 15 distances along the bedform profile. Mean downstream fluid velocity increases nonlinearly with increasing distance along the bedform. However, observed bedload transport increases linearly with increasing distance along the bedform, except at the crest of the bedform, where both mean downstream fluid velocity and bedload transport decrease substantially. Bedload transport time series and manual particle-tracking data show a zone of high-magnitude, cross-stream transport near flow reattachment, suggesting that permeable splat events play an essential role in the region downstream of flow reattachment.