Miracle-dz/UniFAR-FTUs huggingface dataset:
Shelincuo and Bangecuo extensional lake basins in the northern pa rt of Tibet and present chasmic activities

In the regional geologic investigation (1∶250 0 00) in the Duoba area,northern part of Tibet,the water areas of the lake basins of Shelincuo and Bangecuo have increased 19.34% since 1969;the water level has r isen about 50m;in the north bank of Bangecuo and Ganglong Hamuna area,the mid- late Pleistocene stratum is relative risingThe height difference is about 120m ;in the southwest of the Shelincuo,the present slump structure of the north of t he Zeguojiale shows EW trendIn the south bank of Shelincuo,the hopper ground s hows moniliform and net-like structures,which are being formed rapidilyRunning parallel with the south margins of two lake basins,the multitudinous sinter pla tforms,hot springs and cold springs are well developedIt is indicated that the chasmic activities are taking place in the two lake basin areas,i.e,in the back ground of the whole of Qinghai-Tibet plateau uplift, the study area is on the re lative sedimentation.
A Fusion-Based Deep Neural Networks Approach for Face Liveness Detection

Face recognition systems, which are used for access control, security surveillance, and mobile payments, are popular in recent years among biometric systems with their ability to identify individuals quickly and accurately. However, these systems are vulnerable to spoofing attacks using a fake face input for unauthorized access. Liveness detection is a crucial component of face recognition systems that helps to improve their security against spoofing attacks. The paper first presents a comparative study of state-of-the-art liveness detection techniques and their effectiveness in detecting various types of spoofing attacks. Then a match score fusion-based liveness detection algorithm is proposed in which three deep neural networks are combined. As a result of experimental studies using NUAA and CelebA-Spoof datasets to test the performance of the proposed method in face liveness detection, 98.36% accuracy was achieved. The results show that ensemble model can significantly improve the accuracy and robustness of face liveness detection.
Dynamics Personalized Learning Path Based on Triple Criteria using Deep Learning and Rule-Based Method

Personalized learning paths are designed to optimize learning time and improve student learning performance by providing an appropriate learning sequence based on the unique characteristics of each student. A common method for constructing personalized learning paths is based on the student's knowledge but disregards the student's interest in the subject matter. This research employs a deep learning and rule-based approach to recommend suitable material based on the topic's difficulty, student interest, and knowledge level. The difficulty level of the topic is predicted using deep learning. A questionnaire is used to determine the level of student interest, which is then processed using a rule-based approach to generate a learning path. Modeling a dynamic learning path requires measuring student knowledge in each topic and updating the learning path accordingly. Comparing the learning outcomes of students who utilized conventional e-learning versus those who followed a personalized learning path constitutes the evaluation. The results demonstrated that students scored 29% higher, or 15.06 points, than those who utilized conventional e-learning.
MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 220 on server side and 216 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.
An approach to estimate Lower Jurassic seawater oxygen‐isotope composition using δ18O and Mg/Ca ratios of belemnite calcites (Early Pliensbachian, northern Spain)

Palaeotemperature estimates from the oxygen‐isotope compositions of belemnites have been hampered by not knowing ancient seawater isotope compositions well enough. We have tackled this problem using Mg/Ca as a proxy for temperature and here, we present a ~2 Ma record of paired Mg/Ca and δ18O measurements of Jurassic (Early Pliensbachian) belemnites from the Asturian basin as a palaeo‐proxy of seawater oxygen‐isotope composition. From the combined use of the two approaches, we suggest a δ18Ow composition of about −0.1‰ for the Jamesoni–Ibex zones. This value may have been increased by about 0.6‰ during the Davoei Zone due to the effect of waters with a different δ18Ow composition. These findings illustrate the inaccuracy of using a globally homogeneous ice‐free value of δ18Ow = −1‰ for δ18Ocarb‐based palaeotemperature reconstructions. Our data suggest that previous palaeotemperatures calculated in the region from δ18O values of belemnites may have been underestimated as the seawater oxygen isotopic composition could have been higher.
A non-intrusive multivariate analysis for monitoring inattentive driving based on physiological signals, driver inputs and visual features: A hybrid deep learning approach

The widespread adoption of autonomous vehicles is inevitable and imminent, but the presence of human supervision in driving will continue to be essential for many years to come. In order to evaluate a driver’s capability to assume control of the vehicle during crucial situations, the monitoring of inattentive driving behavior can be achieved through the adoption of diverse features. This research aims to address the inquiry by developing a non-intrusive multivariate system that can detect driver inattention using hybrid deep learning techniques based on driver inputs established as throttle and brake pedal positions as well as wheel angle, along with the heart rate variability as a physiological signal, and the eye tracking visual feature collected using a driving simulator. The highest performances results have been acquired using the proposed hybrid deep learning architecture on the basis of MLP and CNN. To the authors’ knowledge, there has been a limited interest at assessing inattentive driving behavior based on physiological signals, driver inputs and visual features. As a whole, the proposed system and findings provide new insights into inattentive driving analysis and can be harnessed as a promising tool to improve intervention efforts related to traffic intelligent transportation systems.
Vertical distribution of the salt marsh invader Spartina alterniflora and native halophytes on the west coast of Korea in relation to tidal regimes

Smooth cordgrass ( Spartina alterniflora Loisel.), an aggressive non-native species worldwide, colonized tidal flats on the west coast of Korea in two regions differing in tidal amplitude between 1990–2004. By the time of our study in 2015, expansion had occurred both clonally and through formation of new patches, providing an opportunity to determine intertidal range, which is a key component of understanding the threat posed by S. alterniflora through competition with native halophytes or transformation of unstructured mudflat. At Ganghwa (5.69 m tidal range), S. alterniflora ranged from 3.52 to 1.34 m above Mean Sea Level (MSL). At Jindo (2.02 m tidal range), S. alterniflora ranged from 1.57 to -0.18 m relative to MSL. Thus, a wider absolute intertidal range was occupied by S. alterniflora at the megatidal vs mesotidal region, but the lower limit of S. alterniflora did not extend below MSL under megatidal conditions, a pattern that now appears to emerge consistently in both the native and introduced range. In both study regions, S. alterniflora occurred at the same elevations as other salt marsh plants, occupying an upper zone with Phragmites australis (non-native) and middle zone with several native species including Suaeda japonica . S. alterniflora occurred below native marsh vegetation at all sites, which would result in transformation of the extensive mudflats along the Korean coast.
Enhanced Traffic Management Through Data Mining: Predictive Models for Congestion Reduction

This research focuses on addressing global traffic congestion issues, encompassing safety and emission concerns. A data mining approach is proposed, combining Multiple Linear Regression, Random Forest Classifier, Decision Tree, and XGBRegressor modules to analyze historical traffic data. The aim of this approach is to identify patterns and characteristics, with the ultimate goal of enhancing traffic flow predictions. Experimental validation demonstrates its efficacy, thereby boosting confidence in its potential as a tool for congestion mitigation and road safety improvement. Additionally, this approach aids intelligent transportation systems in proactive decision-making, optimizing traffic management and safety. The utilization of multiple modules allows for a comprehensive analysis of traffic flow data, capturing diverse insights for accurate predictions. The method also integrates temporal aspects by considering traffic patterns, thereby supporting strategies for congestion alleviation and safety enhancement. In conclusion, this research offers a robust data mining solution that integrates various models to accurately predict traffic flow, thereby contributing to safer and less congested travel experiences while assisting traffic management systems.
RDP-LOAM: Remove-Dynamic-Points LiDAR Odometry and Mapping

Simultaneous Localization and Mapping (SLAM) is a critical technology for autonomous driving and robotics. However, many SLAM algorithms assume a static environment, leading to reduced robustness and accuracy in highly dynamic environments. In this study, we introduce RDP-LOAM, a real-time and robust LiDAR-based SLAM framework designed for dynamic environments. Our approach incorporates a sliding window-based method to retain historical frame information for comparative analysis. We employ probability estimation to detect and eliminate dynamic objects, and we adjust parameters adaptively based on current velocity. Subsequently, we match the static point cloud with a local submap to achieve precise poses and create static maps in highly dynamic environments. To validate our framework, we conduct extensive experiments utilizing both the open-source UrbanLoco dataset and our self-collected dataset. The results conclusively demonstrate that RDP-LOAM effectively removes dynamic points and significantly enhances odometry accuracy.
APTIAN TO CAMPANIAN CALCAREOUS NANNOFOSSILS BIOSTRATIGRAPHY FROM THE BOTTACCIONE SECTION, GUBBIO, CENTRAL ITALY

Late Aptian-Campanian biostratigraphic investigations performed on calcareous nannofossils provided a refined stratigraphic resolution of the Scisti a Fucoidi and Scaglia Formations in the Bottaccione section (Umbria-Marche Basin, Central italy). Additional bioevents such as the Last Occurrence (LO) of both Assipetra infracretacea larsonii and Rucinolithus terebrodentarius youngii and the First Occurrences (FOs) of Pararhabdolithus achlyostaurion and large Biscutum constans were detected and might be used to refine the Late Aptian-Albian biostratigraphy at low latitudes.
THE ANATOMY AND RELATIONSHIPS OF HAASIOPHIS TERRASANCTUS, A FOSSIL SNAKE WITH WELL-DEVELOPED HIND LIMBS FROM THE MID-CRETACEOUS OF THE MIDDLE EAST

Abstract The fossil snake species Haasiophis terrasanctus Tchernov, Rieppel, Zaher, Polcyn, and Jacobs, 2000, from the early Upper Cretaceous of the Middle East, is described and illustrated, following a review of the current debate on snake relationships and origins. The description and discussion presented here adds important detail to the knowledge of this taxon and its phylogenetic significance beyond the limited account presented in the original description of Haasiophis. The species is remarkable in that it shows the skull of a relatively advanced (i.e., macrostomatan) snake, yet preserves well-developed hind limbs. The hind limb includes a femur, tibia, fibula, astragalus, calcaneum, distal tarsal four, and remains of four metatarsals and two phalanges. Haasiophis cannot be considered a juvenile specimen of Pachyrhachis. The implications of the presence of well-developed hind limbs in Haasiophis, Pachyrhachis, and Podophis for the cladistic analysis of the phylogenetic interrelationships of these fossil snakes is discussed. The presence of well-developed hind limbs in Pachyrhachis and Haasiophis also creates methodological problems for the cladistic analysis of the phylogenetic relationships of these fossil snakes. Scenarios of snake origins are reviewed and found to be deficient in the absence of a well-corroborated hypothesis of snake relationships within Squamata.
Personalized OLAP queries under Hierarchical Visualization Constraint*

Decision makers do not evaluate all decision facts in the same way since their requirements differ greatly depending on their specialty, levels of knowledge, and other factors. Everyone has the right to have their own tastes and needs. The multidimensional model is intended for a group of decision-makers and consists of a set of data studied according to different dimensions. It contains all the indicators and analysis axes for a particular domain. They do not take into account the preferences of each decision-maker. A personalization phase of this diagram according to the preferences of these decision-makers would enrich the decision-making process. Another problem for consumers is the inability of mobile devices (smartphones, tablets, etc.) to display the results of OLAP queries. This paper presents a new approach in which preferences are expressed as visualization constraints that are applied sequentially to the schema to represent the result. This approach makes it possible to adapt the output to the profile choices while keeping in mind the constraint of visualization. Our strategy is based on the personalization of concepts and multidimensional data using a quantitative approach. This model interprets new preferences not expressed by the user based on the hierarchical structure of the data in the warehouse. We implemented our approach by considering three standard metrics, namely precision, recall, and F-measure. The results we obtained are very encouraging and confirm the feasibility of our proposed approach.
TopoCommit: A Topological Commit Protocol for Cross-Ledger Transactions in Scientific Computing

While increasingly more applications are tempted to manage their data in decentralized systems, such as blockchains or distributed ledgers, the data exchange across multiple, potentially heterogeneous, decentralized systems remains an open problem: State-of-the-art protocols cannot meet one or more of the core requirements, such as atomicity, liveness, and scalability. Specifically, in the field of scientific computing, although a blockchain service was recently developed for scientific computing environments, the data exchanges and transactions among distinct ledgers are not supported. Observing that many modern scientific applications are collaborated on by multiple teams and the increasingly complicated (in-situ) workflows thereof, we argue that there is a pressing need to realize an efficient and scalable protocol for distinct ledgers to exchange data in scientific computing. This paper proposes a topological approach to enabling atomic, nonblocking, and scalable data exchanges among an arbitrary number of scientific ledgers in the context of collaborative scientific computing. Specifically, we construct a topological space formed by these ledgers—abstracting those nodes in a cross-ledger transaction as topological objects such as abstract simplex and simplicial complex. These topological objects, in turn, serve as the building blocks of a topological protocol, namely TopoCommit, under practical assumptions. We implement TopoCommit and integrate it into SciChain, a recently published distributed ledger for tracking scientific data provenance. The extensive evaluation of up to 1,008 nodes and 144 distinct ledgers on CloudLab shows that TopoCommit outperforms state-of-the-art protocols by up to 70×.
Near Real Time Prediction of Vibration in 3D Printed Flettner Rotor Demonstrator

This paper presents an investigation of the Flettner rotor, a wind propulsion system utilized in ships to decrease fuel consumption and reduce carbon emissions by leveraging the Magnus effect. Proper balancing and stability of the rotor are crucial to ensure high operational efficiency and low energy consumption. In the present work, a 3D printed rotor, which is a scaled-down version of the "Water Taxi", is developed to predict the unbalanced forces and vibrations of the rotor. Two methods are presented in this paper for the determination of the magnitude and direction of unbalanced forces by utilizing strain gauge readings. The force prediction model is implemented using machine learning algorithms. Furthermore, the paper discusses potential solutions for balancing the rotor once vibrations and unbalanced forces are detected. The results of this research could potentially contribute to the development of improved Flettner rotor designs, leading to more efficient and eco-friendly shipping.
Attentive Cross-Domain Few-shot Learning and Domain Adaptation in HSI Classification

This study explores the application of Attentive Cross-Domain Few-Shot Learning (ACDFSL) in Hyperspectral Image (HSI) Classification, specifically addressing challenges associated with environments possessing limited labeled data. Our approach applies the Squeeze-and-Excitation (SE) attention and Residual elements within a deep learning architecture of four convolution blocks. This innovative strategy of integrating attention mechanisms into few-shot learning models represents a significant departure from traditional practices. After rigorous assessment, the ACDFSL model showcased outstanding results, revealing performance rates of 92.14%, 96.23%, and 91.27% in OA, AA, and Kappa, respectively, on the Salinas dataset. Additionally, the model attained rates of 85.67%, 89.66%, and 85.4% on the University of Pavia (PU) dataset. These results indicate an edge over existing state-of-the-art techniques such as SVM, 3D-CNN, SSRN, and other DFSL variants. This considerable progress emphasizes the potential and applicability of the ACDFSL approach in real-world HSI Classification scenarios, especially where labeled data is sparse, and paves the way for future research in this sphere.
Improving the U-Net Segmentation Model for Land Cover Classification in Satellite Image Processing

The development of machine learning methods for onboard satellite processing is important in order to facilitate the filtering of collected data samples to maximize the use of the device's limited resources. Land cover classification can be used to focus the collected data on certain terrain types by utilizing classification methods to determine the class probabilities of individual pixels in a collected satellite image. The importance of the accuracy of the segmentation model used for such a task is important in order to avoid the trashing of data samples that offer significant information and the prioritization of data samples which offer less in terms of usable information, which in the case of land cover classification is determined by which terrain features may be prioritized over others. This study focuses on the U-Net segmentation architecture and performs an experimental study on the effects on two aspects on the training of a segmentation model for increased performance. This includes the division of the images in the dataset into smaller patches and the replacement of the CNN encoder of the segmentation architecture. The changes made to the baseline model introduced an increase in the IoU score from 0.68 to 0.7273.
A Review of Inertia Estimation in Power Systems Using Measurement-Based Approaches

Historically, the power grid has relied on conventional fossil, nuclear, and hydro-power generations, which used large rotating machines that provided ample inertia. However, as the grid transforms with the increasing penetration of inverter-based resources (IBR) like wind, solar photovoltaic, and battery storage systems, which lack inherent inertia, there is a growing need to reassess the role of inertia in the future grid. This shift raises concerns about maintaining system stability and security. In recent years, there has been a significant focus on the importance of inertia estimates in various areas such as robust frequency control, managing the integration of IBR, enhancing system reliability through fast frequency response analysis, and designing synthetic inertia as an ancillary service. This paper aims to provide insight into the impact of various measurement-based inertia estimation approaches on the evolving power grid. The estimation methods are classified into two categories: large disturbance-based methods and ambient data-based methods. This categorization highlights the strengths and weaknesses of different approaches, emphasizing the importance of the various approaches of inertia estimation.
Clay‐mineral distribution in surface sediments of the Eurasian Arctic Ocean and continental margin as indicator for source areas and transport pathways — a synthesis

Clay‐mineral distributions in the Arctic Ocean and the adjacent Eurasian shelf areas are discussed to identify source areas and transport pathways of terrigenous material in the Arctic Ocean. The main clay minerals in Eurasian Arctic Ocean sediments are illite and chlorite. Smectite and kaolinite occur in minor amounts in these sediments, but show strong variations in the shelf areas. These two minerals are therefore reliable in reconstructions of source areas of sediments from the Eurasian Arctic. The Kara Sea and the western part of the Laptev Sea are enriched in smectite, with highest values of up to 70% in the deltas of the Ob and Yenisey rivers. Illite is the dominant clay mineral in all the investigated sediments except for parts of the Kara Sea. The highest concentrations with more than 70% illite occur in the East Siberian Sea and around Svalbard. Chlorite represents the clay mineral with lowest concentration changes in the Eastern Arctic, ranging between 10 and 25%. The main source areas for kaolinite in the Eurasian Arctic are Mesozoic sedimentary rocks on Franz‐Josef Land islands. Based on clay‐mineral data, transport of the clay fraction via sea ice is of minor importance for the modern sedimentary budget in the Arctic basins.