Miracle-dz/UniFAR-FTUs huggingface dataset:
Satellite Anomaly Detection based on Improved Transformer Method

In this paper, we present an Improved Transformer model and a self-supervised framework for detecting anomalies in satellite telemetry data. Previous approaches based on RNN and LSTM variants have shown some progress in anomaly detection, but still require improvement in detection performance and context parallelism. To address these challenges, we propose an innovative Improved Transformer model that incorporates correlation modeling, long-range modeling, and parallel reasoning capabilities. This model features a token split structure, position embedding, and mask unit, which enhance its overall performance. Our self-supervised framework consists of two stages: pre-training and fine-tuning. We conduct extensive experiments on NASA’s public dataset and demonstrate the effectiveness of our proposed framework. Using our self-supervised approach, we achieved a significant 95.7% (3-class) F1-score on the validation set, outperforming supervised counterparts that utilized the same training data.
Lidar measurements of Asian dust storms and dust cloud interactions

[1] Long-range transport of Asian dust storms has been investigated by using lidar at Chungli (24.6°N, 121.1°E) in 2002–2004. One of the dust storms that occurred in the period of 27–31 March 2002 was also observed by lidar at Hefei (31.9°N, 117.16°E). The lidar height distributions of dust extinction were compared with back trajectory calculation and atmospheric data including First Global GARP Experiment (FGGE)-type ground observations and National Center for Environment Prediction/Atmospheric Research (NCEP/NCAR) reanalysis. Dust layers had an average total (aerosol and molecular) depolarization ratio (DR) about 0.2; however, formation of droplets with near-zero depolarization ratio (0.02) was also measured. The cloud condensation nuclei (CCN) activities of dust particles were considered based on chemical properties observed from the ground. We observed that dust transport to the south may interact strongly with the moisture from the active weather system in the region producing significant climate effects.
The Role of E-Marketing in the Development of Internet User Attitudes Toward Tourist Sites in Saudi Arabia

The Internet has become just a vital part of the contemporary social life. It has gained importance in tourism sectors and institutions since it has served as a means to reach people who are interested and to influence their attitudes and behavior towards tourist sites. This research intended to identify the role of e-marketing in the developing attitudes of Internet users to tourist sites in Saudi Arabia. The research aimed also to explore the e-marketing mix: electronic service, cost, promotion, website, the accuracy of the information, payment, and personalization. The paper reviewed the previous studies of the electronic marketing of tourism, and worked on and developed a tool for field data collection which has given acceptable Validity and Reliability indicators. The research has been carried out with 478 participants (Internet users) chosen from various regions of the Kingdom. This paper presents methods to improve the role of e-marketing attitudes on Internet users in relation to tourism and through touching the needs and desires of tourists and its impact on the design of the website. The results of the research indicated that the level of using e-marketing towards the tourist sites in Saudi Arabia has increased, the attitudes of Internet users to tourist sites in Saudi Arabia were positive, and the dimensions of the e-marketing mix (e-service, price, promotion, website, the accuracy of the information, and payment) have impacted on Internet users’ attitudes towards the tourist sites in Saudi Arabia. Keywords: E-marketing, Tourism; Internet; Attitudes.
Transfer learning of recurrent neural network‐based plasticity models

Mechanics‐specific recurrent neural network (RNN) models are known for their ability to describe the complex three‐dimensional stress–strain response of elasto‐plastic solids for arbitrary loading paths. To apply RNN models to real materials, it is crucial to identify a strategy that allows for their training from small datasets that could be obtained from robot‐assisted experiments. It is demonstrated that regular training with datasets comprising random walks (RWs) in strain space yield a significantly higher generalization ability than the same number of sequences for smooth loading paths. Moreover, it is found that transfer learning, that is, initializing the weights and biases with the parameters from an already trained material, improves the convergence rates and reduces the required number of stress–strain sequences for training. When leveraging the experience gained for multiple materials through ensemble transfer learning, even more substantial improvements are obtained. For example, the same model accuracy and generalization ability is obtained from training with 400 smooth stress–strain sequences after ensemble transfer as from training with 10,000 RW sequences after regular training.
A Method for Parallelized Fast Dynamic Cascading Failure Simulation of Power System

A new approach called Backward Euler method with Predictor-Corrector (BEM-PC) for simulating cascading failure in dynamic models of power systems was recently proposed. It applied Backward Euler integration method (BEM) with stiff decay property while overcoming its so-called hyperstability issue. The method led to a significant simulation speedup without sacrificing accuracy in tracking cascading path when compared with traditional solution techniques like Trapezoidal integration method (TM). In this paper, we demonstrate that a further speedup can be achieved by a parallelized version of BEM-PC, which we call BEM-PC-parallel (BEM-PCP). In this version, the predictor subprocess of BEM-PC is run in multiple parallel processors for identification of oscillatory instability using eigen-decomposition of the system matrix at post-disturbance unstable equilibria. Monte-Carlo studies on a 2,383-bus Polish system confirm that BEM-PCP is on average 17% faster than BEM-PC and $\approx 40$ times as fast as TM while maintaining the same accuracy as BEM-PC.
Design and evaluation of moringa gum-based hydrogel dressings for cutaneous wound healing

The study aimed to evaluate moringa gum (MG) and carboxymethyl moringa (CMG) for their possible use in hydrogel-based dressings to treat cutaneous wounds. Initially, MG was characterized for its physical, functional, morphological, and thermal properties. The MG and CMG have shown limited toxicity as indicated by hemolytic assay where values were less than 2.5%. Furthermore, these samples also showed antimicrobial potential. The antioxidant potential of MG was slightly greater than its carboxymethyl derivative. Afterward, MG and CMG were encapsulated in sodium alginate-based hydrogel films. These films showed good physical attributes necessary for the end application. These films were tested in an open wound model in rats. The MG containing films showed the highest % wound contraction on the 10th day, that is,79.42 ± 0.46 with low levels of IL6 and TNF alfa. Out of MG and CMG films, the native moringa gum-based thin film showed comparatively higher potential for wound healing and in the future could be a potential candidate for wound healing dressings.
Overview of the geology, petrology and tectonic framework of the high‐pressure–ultrahigh‐pressure metamorphic belt of the Kokchetav Massif, Kazakhstan

Abstract High‐ to ultrahigh‐pressure metamorphic (HP–UHPM) rocks crop out over 150 km along an east–west axis in the Kokchetav Massif of northern Kazakhstan. They are disposed within the Massif as a 2 km thick, subhorizontal pile of sheet‐like nappes, predominantly composed of interlayered pelitic and psammitic schists and gneisses, amphibolite and orthogneiss, with discontinuous boudins and lenses of eclogite, dolomitic marble, whiteschist and garnet pyroxenite. On the basis of predominating lithologies, we subdivided the nappe group into four north‐dipping, fault‐bounded orogen‐parallel units (I–IV, from base to top). Constituent metabasic rocks exhibit a systematic progression of metamorphic grades, from high‐pressure amphibolite through quartz–eclogite and coesite–eclogite to diamond–eclogite facies. Coesite, diamond and other mineral inclusions within zircon offer the best means by which to clarify the regional extent of UHPM, as they are effectively sequestered from the effects of fluids during retrogression. Inclusion distribution and conventional geothermobarometric determinations demonstrate that the highest grade metamorphic rocks (Unit II: T = 780–1000°C, P = 37–60 kbar) are restricted to a medial position within the nappe group, and metamorphic grade decreases towards both the top (Unit III: T = 730–750°C, P = 11–14 kbar; Unit IV: T = 530°C, P = 7.5–9 kbar) and bottom (Unit I: T = 570–680°C; P = 7–13.5 kbar). Metamorphic zonal boundaries and internal structural fabrics are subhorizontal, and the latter exhibit opposing senses of shear at the bottom (top‐to‐the‐north) and top (top‐to‐the‐south) of the pile. The orogen‐scale architecture of the massif is sandwich‐like, with the HP–UHPM nappe group juxtaposed across large‐scale subhorizontal faults, against underlying low P–T metapelites (Daulet Suite) at the base, and overlying feebly metamorphosed clastic and carbonate rocks (Unit V). The available structural and petrologic data strongly suggest that the HP–UHPM rocks were extruded as a sequence of thin sheets, from a root zone in the south toward the foreland in the north, and juxtaposed into the adjacent lower‐grade units at shallow crustal levels of around 10 km. The nappe pile suffered considerable differential internal displacements, as the 2 km thick sequence contains rocks exhumed from depths of up to 200 km in the core, and around 30–40 km at the margins. Consequently, wedge extrusion, perhaps triggered by slab‐breakoff, is the most likely tectonic mechanism to exhume the Kokchetav HP–UHPM rocks.
Role of Vegetation on Beach Run-up due to Regular and Cnoidal Waves

Abstract The effectiveness of measures in protecting the coast against natural hazards depends on the extent of run-up by ocean waves, which is an essential parameter in their planning and design. Although, several formulae have been derived in the past for estimation of run-up in laboratory studies for a variety of wave front structures, certain uncertainties still remain pertaining to flat slopes and long waves. To understand the performance characteristics of vegetation in attenuating run-up, a comprehensive experimental investigation was taken up. The studies were carried out with regular and cnoidal waves propagating over a plane slope of 1 ∶ 30 in the presence and absence of vegetation of different characteristics. The results on the variation of dimensionless run-up as a function of wave and vegetal parameters are presented and discussed in this paper.
Relationships between Malaysians Cultivars of Tongkat Ali (Eurycoma longifolia Jack) Obtained through RAPD Analysis

Random Amplified Polymorphic DNA (RAPD) analysis was done on Eurycoma Longifolia Jack (ELJ) cultivars originating from 7 different locations in Malaysia to determine whether the originality of ELJ cultivars can be preserved in an uncontrolled cultivation area. Out of 60 arbitrary 10-mer primers, a total of 320 DNA fragments were amplified with an average of 53.3 RAPD markers per primer. 71 (22%) of the 320 DNA-fragments was found to be monomorphic. The remaining 249 (78%) were polymorphic. The approximate size of the largest fragment produced was 3000 base pair (bp) and the easily recognizable fragment produced was 300 bp. Similarity index of RAPD banding patterns was used to investigate the relationships between ELJ cultivars. Mantel test showed that the correlation between Jaccard and Dice similarity matrices was high and significant (0.9907). The dendogram showed that ELJ cultivars can be clustered into three main clusters. Cluster 1 consisted of Sabah cultivars (East Malaysia), Cluster 2 consisted of Pahang and Terengganu cultivars (middle part of West Malaysia) while cluster 3 consisted of Kedah and Kelantan cultivars (east part of West Malaysia). The results of Principle Coordinates Analysis (PCA) were comparable to the Dendogram by UPGMA. The first two principle coordinates explained 53.62% of total variations and separated the 37 ELJ cultivars into three main clusters with a slight overlap between each other.
Evaluation and Comparison of MODIS and IMS Snow-Cover Estimates for the Continental United States Using Station Data

Satellite-derived information on fractional snow cover is essential to resource monitoring, hydrologic modeling, and climate change assessment. Evaluating the accuracy of remotely sensed snow-cover products is important but difficult, largely because point-scale surface observations are spatially sparse and generally nonrepresentative of the remote sensor footprint. In this study, two remotely sensed snow-cover products [the Interactive Multisensor Snow and Ice Mapping System (IMS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG), v.3] are evaluated against ground observations from the Cooperative Observing Network and SNOTEL on a daily basis over the continental United States for calendar year 2000. Ground observations are treated as points in space and time; no physical modeling or statistical interpolation is applied. Hypothesis tests based on discrete and continuous distributions are developed to assess agreement between ground observations and the remotely sensed snow-cover products at 0.25° resolution. (The MODIS CMG product was degraded from 0.05° for this study, thus its potential is not fully evaluated.) As overall snow extent increases in the course of the season, both MODIS and IMS improve in identifying snow-covered areas (fewer errors of omission), but deteriorate in identifying snow-free areas (more errors of commission). The detection of scattered areas of snow is generally better during ablation than during accumulation. Weaknesses of the statistical methods and assumptions are discussed. This work will help to identify areas for improvement in snow-cover detection algorithms and provides a framework to assess the accuracy of remotely sensed snow cover used as model input and/or confirmation.
TOM-TEIDEVS, Seismic Tomography of Teide Volcano, Spain

The TOM-TEIDEVS project is an active seismic experiment carried out on the volcanic Island of Tenerife, Canary Islands, Spain, to study the internal structure of Teide Volcano. The multinational experiment involved institutes from Spain, United Kingdom, Italy, Ireland and Mexico. The main objective of TOM-TEIDEVS is to obtain a 3-D structural image of Teide Volcano using seismic tomography, seismic reflection/refraction, and receiver function imaging techniques. During the active seismic experiment in January 2007 more than 6,500 air gun shots were recorded on a dense local seismic network consisting of 150 independent seismic stations (Figure 1). The Spanish oceanographic research vessel “Hespérides” fired shots, which were recorded (with a sampling rate of 200 Hz) on Guralp 6TD short period instruments provided by SeisUK. In total ~75, 000 seismic traces have been recorded during the active experiment and first arrival times are currently being determined. Additionally, recordings from one teleseismic event at 26 sites were used to infer significant variations in the upper most structure of the
Volcanic biotite-sanidine 40Ar/39Ar age discordances reflect Ar partitioning and pre-eruption closure in biotite

The 40 Ar/ 39 Ar radioisotope system is widely used to date eruption and cooling of volcanic tephra–marker horizons that commonly provide the only means of correlating and assigning numerical ages to stratigraphy in which they are contained. This chronometer bridges the gap between 14 C and longer-lived isotopic systems that are too imprecise for dating young samples. However, 40 Ar/ 39 Ar ages obtained from coevally erupted biotite and sanidine do not always match. Here, we use an independent chronometer, 238 U- 230 Th disequilibrium, to demonstrate that 40 Ar/ 39 Ar age disparity is not caused by differences in pre-eruption crystallization times. Our findings indicate that the presence of extraneous 40 Ar in biotite, and its absence in sanidine, may result from violations of two assumptions implicit in 40 Ar/ 39 Ar geochronology on volcanic samples: (1) Prior to eruption, minerals are devoid of 40 Ar due to rapid loss to an “infinite reservoir” such as the atmosphere, and (2) closure to volume diffusion is geologically instantaneous and coincident with eruption. We propose a mechanism whereby the presence of extraneous Ar in certain minerals is explained by the relative sequence of four events in a magmatic system: (1) crystallization, (2) mineral closure with respect to Ar diffusion, (3) isotopic equilibration of magmatic and atmospheric Ar, and (4) quenching of the system by eruption. These data have potentially far-reaching implications for studies that depend on geochronological data, necessitating re-evaluation of interpretations based solely on biotite with no independent age control, particularly in young samples where the effects are most pronounced.
Master Computer Science Semi-Supervised Iterative Learning on hiPSC-CMs

Cardiotoxicity can be defined as any damage to the heart or cardiovascular system that emerges from cancer treatment. It is a common side effect of various classes of drugs used to treat cancer. Adverse effects can occur years after the treatment, they can vary from high blood pressure to heart failure, leading to increased mortality among patients. Cardiotoxicity has to be analysed during drug development. For that purpose, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were designed and reprogrammed from human somatic cells. The analysis of microscopy image data acquired from cardiotoxicity studies can be time-consuming for experts because they can obtain a significant amount of image data from high-throughput imaging. Many existing machine learning techniques are capable of identifying objects from images for further analysis, such as Mask R-CNN. However, a considerable amount of labelled data is necessary to train such models. In this paper, we propose a semi-supervised learning method of iterative training. In this method, less annotated data is needed, as the model also trains on annotations predicted from the model trained in previous iterations. The results of the iterative learning method with the use of Mask R-CNN as an inner model had less accurate results in terms of mAP and mF 1 metrics, compared to supervised learning on Mask R-CNN with all data annotated. However, thanks to the new iterative learning method, we found out that Mask R-CNN trained on half of the data annotated and half without annotations can achieve 98% of the precision compared to the model trained on fully annotated data.
A Model-Based Approach to Automotive Feature Development for Updates and Upgrades

The automotive industry faces challenges due to the increasing demand for customization and individualization by consumers. With a wide range of equipment levels and feature options, it has become impossible to test all possible configurations for a single vehicle during development and especially for vehicles already in service. As a basis for solving these challenges, integrated approaches for the development of upgradeable vehicles are required. In this context, future upgrade options should be considered and planned already in the design stage to enable sustainable business models for next vehicle generations. This paper proposes a refined variability model that combines two existing approaches to address the challenges faced by the industry. The same model can perform buildability or upgradeability checks and dynamic allocation optimization. The model is evaluated qualitatively by three exemplary use cases.
Sensor Networks, Data Processing, and Inference: The Hydrology Challenge

In the last years, many European countries have experienced the effects of climate change, in the form of a scarcity of drinking water resources, prolonged periods of drought, and extremely heavy rainfall, with unprecedented dramatic environmental, economic, and social costs. Therefore, understanding, modeling, and predicting the movement and distribution of water on Earth, and effectively managing water resources are problems of paramount importance. In this article, we discuss the fundamental role that sensing technologies, data processing algorithms, and inference based on machine learning techniques can have in modern hydrology and the many challenges that still need to be addressed to improve the accuracy and reduce the complexity of current hydrology models. More specifically, we overview the main solutions proposed in the literature to monitor, analyze and predict hydrological processes, and present a selection of results obtained from empirical data sets to ground the main concepts and substantiate the dissertation. Finally, we conclude our article by discussing open problems and possible avenues for future research.
Efficient spatiotemporal matching algorithm for stereo videos

An efficient stereo matching algorithm for computing stable disparity map sequence from video footage is presented. The algorithm is based on both the spatial and temporal consistency in the stereo sequences, and high quality disparity maps are achieved. Weber local descriptors (WLD) are extracted for each color channel from current stereo pairs, and the raw matching costs between the images are initialized by WLD. Orthogonal integral image (OII) technique along with minimum spanning tree (MST) is used to aggregate the similar pixels and preserve disparity edges adaptively. MST takes place of the process of voting support regions in OII technique and provides a specific support region for each pixel. The nodes of MST are all the image pixels, and the weight of edges are absolute difference between the nearest neighboring pixels. It’s a global method, and can achieve more accurate disparity maps than traditional OII technique. Three-frame subtraction is used to determine the temporal consistency between adjacent frames. The motion region is extracted and the disparity map of motive region is renewed. The disparity of current frame with the renewed disparity and the one for last frame is confirmed. The proposed approach has been tested on the real stereo sequences, and the results are satisfactory.
Communication Jamming-Aware Robot Path Adaptation

Robot networks are necessary for a variety of monitoring and surveillance applications where robots must communicate observations. This work considers robot networks that must operate in an area affected by potential communication jamming and excessive radio frequency noise. Specifically, we look at deploying a reinforcement robot to strengthen an existing network of robots that are already performing a monitoring task. The robot's objective is to find a location that maximizes the network's signal-to-noise ratio. We present a Gaussian process approach to predict areas of excessive radio frequency noise, enabling the robot to avoid these areas and relay more data. We evaluate our algorithm's performance in physical robot experiments using a Clearpath Jackal robot and a USRP software-defined radio to broadcast a jamming signal. Our results show major improvements in networking metrics over a baseline and demonstrate the need for jamming-aware robot planning.