Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitioning Space Weather Models Into Operations: The Basic Building Blocks

New and improved space weather models that provide real-time or near–real time operational awareness to the long list of customers that the NOAA Space Weather Prediction Center (SWPC) serves are critically needed. Recognizing this, SWPC recently established a Developmental Testbed Center (DTC [see Kumar, 2009]) at which models will be vetted for operational use. What characteristics should models have if they are to survive this transition? The difficulties around the implementation of real-time models are many. From the stability of the data input (frequently coming from third parties) to the elevated information technology (IT) security atmosphere present everywhere, scientists and developers are confronting a series of challenges in the implementation of their models. Quinn et al. [2009] noted that “the transition challenges are numerous and require ongoing interaction between model developers and users. ” However, the 2006 Report of the Assessment Committee for the National Space Weather Program (NSWP; see http://www.nswp.gov/nswp_acreport0706.pdf) found that “there is an absence of suitable connection[s] for ‘academiatooperations’ knowledge transfer and for the transition of research to operations in general. ” To facilitate the process, it is useful to establish a clear distinction between “academic” and “operational” real-time models, and to set specific requirements for transitioning academic models to operations. Academic models are assumed to be intended for the understanding of physical processes, cause-and-effect relationships, etc. They are not designed for production of customer-oriented products, even if they have been implemented as real-time models. By contrast, operational models offer products on which third parties rely to conduct their business. An operational product must be validated, fully documented, and reliable and must adhere to basic standards (among other requirements). Academic models need not fill these conditions—they serve the purposes of the scientists and could be adjusted at will to fit the researchers’ needs. However, by meeting some basic requirements, academic models could be better positioned to transition into operational models. Identifying the Customer “Wish List”
3D evolution of sand fracture under 1D compression

Three-dimensional (3D) synchrotron microtomography (SMT) and the discrete-element method (DEM) were used to investigate the evolution of particle fracture and deformation behaviour of a granular assembly subjected to the k0-loading condition. A series of one-dimensional (1D) compression laboratory experiments were first conducted to better understand the effect of specimen aspect ratio (height-to-diameter ratio, H/D) on deformation behaviour, which demonstrated that changing the specimen aspect ratio influences the stress–strain response of sand. 1D compression was simulated using a 3D DEM model by mapping particle positions from SMT images. Crushable sand particles were modelled in DEM using agglomerates of spherical sub-particles that were bonded by parallel bonds. The DEM model successfully predicted the compressive behaviour of specimens with different aspect ratios. The experimental and DEM results show that the onset of particle fracture and specimen yielding occur at the same strain level. The evol.
Electrical resistivities of crustal fluids

Results of calculations on the electrical resistivities of crustal fluids as a function of temperature, pressure and fluid composition indicate that crustal fluids can range in resistivity from approximately 1 Ω m. The dominant contribution to decreasing the resistivities of crustal fluids comes from increased concentrations of chloride and bicarbonate salts and increased temperature and density of the fluids. Calculations indicate that the sharpest decrease in fluid resistivity occurs at shallow depths 10 km and temperatures > 300°C can not be readily explained as a product of variations in resistivities of crustal fluids. These results indicate that a transition in mineralogy or pore shape geometry is more likely responsible for the observed decrease in electrical resistivity of the middle crust than a major change in fluid resistivity.
Hydrocarbon habitat and classification of inverted rift basins

Abstract This shortened paper presents a brief analysis of the relationship between hydrocarbon distribution and the intensity of inversion in rift basins. For this purpose, three intergradational classes of rift were identified according to the intensity and areal extent of inversion-related uplift. Simple rifts, showing no significant inversion, are characterised by high success rates and dispersed hydrocarbon distributions. Locally inverted rifts, such as those showing restricted areas of uplift along faults, also show a high degree of petroleum retention and success, with the key success factor often being the relationship between timing of trap formation and petroleum charge. Regionally inverted rifts show lower success rates, failures being often attributable to a redistribution of hydrocarbons during inversion, with losses having occurred through biodegradation, surface erosion or seepage on surface penetrating faults. Both types of inverted rift frequently show a concentration of reserves in one large field.
The Mesolithic bone industry from Kula, eastern Serbia

The Iron Gates area (Serbia and Romania) is well known for its extraordinary Mesolithic finds, first discovered almost fifty years ago, which initiated still ongoing debate on the relations between foragers and farmers and the very origin of Neolithic communities in this region. The questions regarding relative and absolute chronology and social relations have received the most attention, while some aspects of the material culture are still not analysed in detail, especially when it comes to the sites on the right (Serbian) bank of the Danube. Most sites are situated in the Upper and Lower Gorge, and the only site with an excavated Mesolithic sequence downstream from the Gorge is Kula near Mihajlovac. In this paper a small collection of about fifty bone and antler artefacts from Kula is analysed. This is the first analysis of the material from the right bank of Danube to be focused on the bone industry only. The analysis incorporates the questions of raw material choice, technique of manufacture and typol.
Aperture correlation of a fractal fracture

A rough-walled fracture is modeled by fractal geometry. In the fractal fracture model, the rock surfaces are characterized by a fractal dimension D between 2 and 3, with lower D for smoother surfaces and higher D for rougher surfaces. The mismatch due to shear displacement between two mirror-image fractal surfaces determines the fracture aperture distribution. An analytic equation is derived for the variogram γƒ(r) describing the spatial correlation of the aperture of a fractal fracture. The aperture of a smooth fracture with low D is highly correlated over distances much larger than the shear displacement. The aperture of a rough fracture with high D becomes uncorrelated within a range shorter than the shear displacement. Near the origin, r = 0, of the variogram, the variogram is proportional to r6–2D. For the special case of Brownian fractal with D = 2.5, the variogram is proportional to r, which is the same r dependence exhibited by the spherical model widely used in geostatistical analyses.
Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef, Australia

Studies investigating the spectral reflectance of coral reef benthos and substrates have focused on the measurement of pure endmembers, where the entire field of view (FOV) of a spectrometer is focused on a single benthos or substrate type. At the spatial scales of the current satellite sensors, the heterogeneity of coral reefs even at a sub-metre scale means that many individual image pixels will be made up of a mixture of benthos and substrate types. If pure endmember spectra are used as training data for image classification, there is a spatial discrepancy, because many pixels will have a mixed endmember spectral reflectance signature. This study investigated the spectral reflectance of coral reef benthos and substrates at a spatial scale directly linked to the pixel size of high spatial resolution imaging systems, by incorporating multiple benthos and substrate types into the spectrometer FOV in situ. A total of 334 spectral reflectance signatures were measured of 19 assemblages of the coral reef benthos and substrate types. The spectra were analysed for separability using first derivative values, and a discrimination decision tree was designed to identify the assemblages. Using the decision tree, it was possible to identify 15 assemblages with a mean overall classification accuracy of 62.6%.
Water repellency of soils; the influence of ambient relative humidity

Adverse effects of soil water repellency (hydrophobicity) are of concern during or following rainfall or irrigation, and are often preceded by conditions of high atmospheric relative humidity (RH). Assessments of repellency are, however, commonly conducted on air-dried samples at ambient laboratory conditions. This study explores the effects of differing antecedent RHs (32-98%) on the water repellency of air-dried soils of wide ranging characteristics under laboratory conditions using water drop penetration time (WDPT) and ethanol-percentage tests. Most samples exhibited considerably higher water repellency after exposure (< 1 d) to 98% RH compared with lower RHs, typical of ambient laboratory conditions. This work suggests that previous studies mayhave incorrectly classified some soils, likely to exhibit water repellency in the field, as wettable, and that tests carried out following exposure of samples to high RH provide assessments that best reflect critical field conditions.
3-D AVO Physical Modeling Experiment Over a Simulated Fracture Medium

Physical modeling, as an adjunct to numerical simulation, is a very useful tool which can provide insights into complex structural and stratigraphic problems. A 3-D physical modeling experiment was conducted over a simulated fracture system. The objective was to understand the quantitative differences in the 3-D AVO signatures for line orientations parallel and perpendicular to fracture orientation.
Heat exchange during snow ablation in plains and mountains of Eurasia

Rates of snow water equivalent (SWE) decrease were obtained as a difference between two successive values of SWE at 189 stations in the former Soviet Union (FSU). The required distribution of duration of snow ablation (snowmelt plus sublimation) was obtained from longterm data on average dates of maximum SWE and dates of snow disappearance. Stations were grouped by dates of snow disappearance and beginning of ablation. The amount of ablated snow for the 10-day periods, normalized by the maximum mean SWE at a station, was averaged throughout stations with the same dates of snow disappearance and beginning of snow ablation. On plains and foothills, up to 80% of snow ablated during the last 10 days of snow cover. At high altitudes in mountains, about 40% of snow ablated during the penultimate 10 days of snow cover. During the ablation season, the amount of energy used to melt snow was of the same order as the combination of other components of the heat budget (e.g‥ heat associated with atmospheric advection, radiation balance, and turbulent heat exchange). Heating of the air would have been 3 times higher if snowmelt had not occurred. Over the continental plains of the FSU, maximum energy used to melt snow occurred in the foothills of the Urals and the northwest portion of the Srednerusskaya upland. Over the mountains, maximum energy to melt snow per unit area was observed in the northern and western Tien Shan where most snow accumulation occurred. A maximum mean energy used to melt snow was observed in the beginning of April over the plains and in June over the mountains.
Anisotropic thermal emission from magnetized neutron stars

Context. The thermal emission from isolated neutron stars is not well understood. The X-ray spectrum is very close to a blackbody but there is a systematic optical excess flux with respect to the extrapolation to low energy of the best blackbody fit. This fact, in combination with the observed pulsations in the X-ray flux, can be explained by anisotropies in the surface temperature distribution. Aims. We study the thermal emission from neutron stars with strong magnetic fields B > 10 13 G in order to explain the origin of the anisotropy. Methods. We find (numerically) stationary solutions in axial symmetry of the heat transport equations in the neutron star crust and the condensed envelope. The anisotropy in the conductivity tensor is included consistently. Results. The presence of magnetic fields of the expected strength leads to anisotropy in the surface temperature. Models with toroidal components similar to or larger than the poloidal field reproduce qualitatively the observed spectral properties and variability of isolated neutron stars. Our models also predict spectral features at energies between 0.2 and 0.6 keV for B = 10 13 -10 14 .
Assessment of Long-Term Trends in Extreme Precipitation: Implications of In-Filled Historical Data and Temporal Window-Based Analysis

Assessment of long-term trends in extreme precipitation data is critical for hydrologic design. Understanding of such trends is also essential from a climate change perspective and requires an extensive evaluation of trends in different temporal slices of historical precipitation data series. The current study focuses on evaluation of these trends via parametric and non-parametric statistical techniques and impact of in-filled missing precipitation data on the biases of long-term trends. A temporal window based approach is adopted for assessment of the trends in extreme values. The temporal windows are selected in such a way that they coincide with Atlantic Multidecadal Oscillation (AMO) cycles. The frequency of occurrence of precipitation extremes over a pre-specified threshold is also analyzed. Long-term historical precipitation data available for 100 years at 53 NOAA rain gage stations in the state of Florida are analyzed using filled and un-filled missing precipitation data. Results indicate that in-filled missing precipitation data introduce biases in the statistical trend analysis and provide insights into the variability of precipitation extremes in AMO cool and warm phases along with the changes in the frequency of occurrence of extreme events over a threshold.
A Structured Prediction Approach for Robot Imitation Learning

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework.
Evidence for Cirque Glaciation in the Colorado Front Range during the Younger Dryas Chronozone

Late Pleistocene glacial chronologies developed for the Front Range Mountains of Colorado include two or more cirque glacier advances, locally known as the Satanta Peak Advances. Sediment cores were recovered from Sky Pond, an alpine lake (3320 m) located less than 100 m downvalley from a moraine that exhibits late Pleistocene to early Holocene relative age features and appears to correlate to the Satanta Peak deposits. One of the cores penetrated 0.5 m of basal diamict and recovered 3.3 m of overlying sediments that are predominantly gyttja. An accelerator mass spectrometry (AMS) age of 12,040 ± 60 yr14C B.P. was obtained from directly above the basal diamict and is similar to other reported ages for cirque deglaciation in the Front Range Mountains. The lower portion of the gyttja contains an interval of clastic sediments that show characteristics consistent with glacial activity in alpine catchments. Radiocarbon ages obtained from below and near the upper contact of this clastic interval are 11,070 ± 50 and 9970 ± 8014C yr B.P., respectively. An additional AMS age of 10,410 ± 9014C yr B.P. was obtained from within the clastic interval in a second core. The most likely source for this interval of clastic sediments is a moraine situated directly upvalley from Sky Pond, and consequently, it appears that the deposition of this moraine was coeval with the European Younger Dryas event (11,000–10,00014C yr B.P.). Similarities in soil development, weathering features, and altitude between this moraine and the type Satanta Peak moraines suggest that the moraines are correlative. These findings are in agreement with a growing body of evidence that suggests a relatively minor advance of alpine glaciers occurred in the North American Rockies during the Younger Dryas Chron.
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch

Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in the data collection process and the reliance on intensive annotations, real-world datasets lack sufficient quantity and diversity to support the increasing demand for data. This work introduces DriveSceneGen, a data-driven driving scenario generation method that learns from the real-world driving dataset and generates entire dynamic driving scenarios from scratch. DriveSceneGen is able to generate novel driving scenarios that align with real-world data distributions with high fidelity and diversity. Experimental results on 5k generated scenarios highlight the generation quality, diversity, and scalability compared to real-world datasets. To the best of our knowledge, DriveSceneGen is the first method that generates novel driving scenarios involving both static map elements and dynamic traffic participants from scratch.
DAnTE: a taxonomy for the automation degree of software engineering tasks

Software engineering researchers and practitioners have pursued manners to reduce the amount of time and effort required to develop code and increase productivity since the emergence of the discipline. Generative language models are just another step in this journey, but it will probably not be the last one. In this chapter, we propose DAnTE, a Degree of Automation Taxonomy for software Engineering, describing several levels of automation based on the idiosyncrasies of the field. Based on the taxonomy, we evaluated several tools used in the past and in the present for software engineering practices. Then, we give particular attention to AI-based tools, including generative language models, discussing how they are located within the proposed taxonomy, and reasoning about possible limitations they currently have. Based on this analysis, we discuss what novel tools could emerge in the middle and long term.
Towards a Novel Ultrasound System Based on Low-Frequency Feature Extraction From a Fully-Printed Flexible Transducer

Ultrasound is a key technology in healthcare, and it is being explored for non-invasive, wearable, continuous monitoring of vital signs. However, its widespread adoption in this scenario is still hindered by the size, complexity, and power consumption of current devices. Moreover, such an application demands adaptability to human anatomy, which is hard to achieve with current transducer technology. This paper presents a novel ultrasound system prototype based on a fully printed, lead-free, and flexible polymer ultrasound transducer, whose bending radius promises good adaptability to the human anatomy. Our application scenario focuses on continuous blood flow monitoring. We implemented a hardware envelope filter to efficiently transpose high-frequency ultrasound signals to a lower-frequency spectrum. This reduces computational and power demands with little to no degradation in the task proposed for this work. We validated our method on a setup that mimics human blood flow by using a flow phantom and a peristaltic pump simulating 3 different heartbeat rhythms: 60, 90 and 120 beats per minute. Our ultrasound setup reconstructs peristaltic pump frequencies with errors of less than 0.05 Hz (3 bpm) from the set pump frequency, both for the raw echo and the enveloped echo. The analog pre-processing showed a promising reduction of signal bandwidth of more than 6x: pulse-echo signals of transducers excited at 12.5 MHz were reduced to about 2 MHz. Thus, allowing consumer MCUs to acquire and elaborate signals within mW-power range in an inexpensive fashion.
Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees

Machine learning models have become increasingly popular for predicting the results of soccer matches, however, the lack of publicly-available benchmark datasets has made model evaluation challenging. The 2023 Soccer Prediction Challenge required the prediction of match results first in terms of the exact goals scored by each team, and second, in terms of the probabilities for a win, draw, and loss. The original training set of matches and features, which was provided for the competition, was augmented with additional matches that were played between 4 April and 13 April 2023, representing the period after which the training set ended, but prior to the first matches that were to be predicted (upon which the performance was evaluated). A CatBoost model was employed using pi-ratings as the features, which were initially identified as the optimal choice for calculating the win/draw/loss probabilities. Notably, deep learning models have frequently been disregarded in this particular task. Therefore, in this study, we aimed to assess the performance of a deep learning model and determine the optimal feature set for a gradient-boosted tree model. The model was trained using the most recent five years of data, and three training and validation sets were used in a hyperparameter grid search. The results from the validation sets show that our model had strong performance and stability compared to previously published models from the 2017 Soccer Prediction Challenge for win/draw/loss prediction.