Miracle-dz/UniFAR-FTUs huggingface dataset:
Ice drift and regional meteorology in the southern Bering Sea: Results from MIZEX West

In February 1983 a multifaceted study of the air-ice-ocean interaction was made in the southern Bering Sea. The study, MIZEX West, addressed a broad spectrum of physical problems related to the Bering Sea marginal ice zone (MIZ). As a part of that study an array of eight Argos-tracked floes on a scale of 50 km provided information on the mesoscale behavior of the ice pack. Wind and current measuring platforms on two of the floes gave detailed information on the forces on individual floes which were compared with the larger-scale motions. Under relatively steady northeast winds and with weak or negligible regional currents, the floes accelerated considerably as they crossed the MIZ. The array showed little distortion, even though it skirted around St. Matthew Island and changed trajectory direction by over 90° during the 12-day study period. Similarly, individual floes remained within 20° of their original orientation, although their angular motions were erratic and often rapid. A baroclinicly forced acceleration of the wind over the MIZ contributed to floe acceleration. The relative currents at 2 and 6 m under the ice were highly variable and random in time, although well correlated in depth. One of the floes was much smoother than the other, and higher winds and currents were noted at the smoother floe. The motion of the floes reflect strong coupling to the currents at tidal and lower frequencies and low-frequency (greater than 6 hours) response to the wind. Both floes drifted to the right of the mean wind by approximately 30° at about 4% of the wind speed at 3 m, and relative wind and current directions were within 20° of being colinear, traits that are consistent with free drift hypothesis. Using quadratic drag representations for wind and current stress, the near colinearity allowed calculation of the ratio of the air/ice to ice/water drag coefficients, Ca/Cw, relative to measurement heights of 3 m and −2 m. For the rough floe this ratio changed in mean from 0.06 to 0.2 in its movement to the MIZ. For the smooth floe the variation in the ratio was less, from 0.14 to 0.2. However, for the rough flow the angle between wind and current was generally less than 180°, and for the smooth flow it was greater. It is possible that another type of drag related to the bow effect of the floe pushing through the water can account for this observation. Finally, the motion of the ice floes and ice edge are compared by examination of satellite photos. Two regions of the ice pack—one thick with rafting and rubble, one thin and broken—showed a ratio of edge velocity to floe velocity of 0.64 and 0.43, respectively. Thinner ice overtakes the edge and melts more rapidly than the thicker ice. A simple thermodynamic model explains this observation.
Research on lightweight safety protection methods for vehicle bus communication

This paper analyzes and studies the problem of large consumption of computing and bandwidth resources in the application of the security onboard communication scheme in the vehicle-mounted embedded system, proposes a possible solution, and uses software encryption in the vehicle-mounted embedded ECU to verify and test the proposed security protection scheme, and compares the performance of applying several commonly used encryption algorithms to secure the bus communication data, At last, the paper gives a relatively more suitable protection scheme for the classical CAN and CAN-FD communication.
Research on SDN traffic anomaly detection technology based on knowledge graph

SDN (Software Defined Networking) is a novel network architecture that allows for flexible configuration and centralized control of resources. However, it also presents new fault problems, particularly in high data volumes and complex topologies where traditional anomaly detection algorithms often need to be revised. To address this issue, we propose a knowledge graph-based approach to SDN fault detection. By leveraging the interpretability and expressiveness of knowledge graphs and the SDN controller’s global information, we construct and continuously update a knowledge graph that enables real-time monitoring and analysis of the network state. Finally, we detect and diagnose the abnormal conditions in the network through graph inference. The experimental results indicate that the knowledge graph-based SDN fault detection algorithm demonstrates high accuracy and efficiency, effectively enhancing SDN networks’ operational stability and security.
IM-AEPCI: iterative measurement using automatic exposure point cloud imaging for autonomous refueling robot vision guide

With the ever-growing number of vehicles in our world, enhancing fuel efficiency has become a paramount concern for gas stations. Traditional manual refueling process, which typically takes 5 to 6 minutes, or even longer during peak hours, no longer meets the demands of today’s fast-paced life. In light of this challenge, this paper proposes a 2D-3D fusion adaptive visual perception algorithm named iterative measurement using automatic exposure point cloud imaging (IM-AEPCI) to obtain the vehicle position. Specifically, we first construct an adaptive exposure prediction network (AEP-Net) based on deep convolutional network and attention mechanism to generate highly reliable 3D point clouds, and then propose the iterative-based point cloud registration algorithm to obtain 6D pose measurement. Experimentally, the maximum error of our method in the three directions of XYZ does not exceed 1.8mm under different lighting conditions, even with difficult measurement highly reflective vehicles, which proves that IM-AEPCI can greatly improve the accuracy and robustness of 6D pose measurement for robotic arm. Besides, our entire visual measurement process takes only 5s, which results in the entire robotic arm refueling system only takes 2-3 minutes to complete. Our research makes it possible to implement a non-contact refueling system, which prominently improves refueling efficiency and safety, and greatly saves labor costs.
Genetic Studies on Salt Tolerance in Rice (O. Sativa) Using Conventional and Molecular Methods

ii DEDICATION . iv ACKNOWLEDGEMENTS . v CHAPTER ONE . 1 1.0. GENERAL INTRODUCTION . 1 CHAPTER TWO . 5 2.0. LITERATURE REVIEW . 5 2.1. Taxonomy and origin of rice. 5 2.2. Ecology of rice . 6 2.3. Characteristics of salt affected soils . 6 2.3. 1.Saline soils . 7 2.3. 2.Sodic soils . 7 2.4. Effects of soil salinity on plant growth . 8 2.5. Salt-affected soils management for rice Production . 9 2.5. 1. Physical and hydro-technical amelioration . 9 University of Ghana http://ugspace.ug. edu.gh
Prediction of existing tunnel deformation induced by shield undercrossing based on BO-Bi-LSTM

To accurately predict the existing tunnel deformation induced by shield undercrossing, a BO-Bi-LSTM deep learning network model is developed and trained using shield construction parameters, geological condition parameters, tunnel design parameters and existing tunnel deformation data. By using Bayesian Optimization (BO) algorithm to optimize hyperparameters of Bi-directional Long Short-Term Memory (Bi-LSTM), a prediction model of existing tunnel deformation induced by shield undercrossing based on BO-Bi-LSTM is established and compared with other neural network models. The main findings of this study are as follows: (1) The prediction models established for horizontal and vertical displacement induced by shield undercrossing have high accuracy, and the test sets R2 are 0.935 and 0.924, RMSE are 0.504 and 0.903, and MAE are 0.415 and 0.824, respectively. (2) Compared with Bi-LSTM and GRU models, the proposed existing tunnel deformation prediction model has the highest accuracy and accuracy, and realizes real-time and reliable prediction of tunnel deformation.
Lithology identification algorithm of cuttings based on one-dimensional convolutional neural network for mineral element content data

The lithology identification of cuttings based on mineral element content data plays an important role in oil and gas exploration. Currently, the method for acquiring the element content of cuttings is to air-dry cuttings obtained in the mud logging process, and then use x-ray fluorescence (XRF) technology to obtain the types and contents of the main elements in the cuttings. In this paper, a method for identifying the lithology of cuttings based on channel attention mechanism is proposed for the mineral element content data of cuttings obtained by XRF technology. Specifically, the existing one-dimensional data composed of mineral elements are input into the network model. The channels are first expanded to introduce more features. Then, the features obtained by the multi-channels are fused to obtain features that are more conducive to the identification of cuttings lithology. To avoid introducing too much noise during channel change, the SE module is improved and applied to the one-dimensional convolutional neural network in this paper. Additionally, the features of different channels are weighted by autonomous learning, ensuring that the features related to the current task have a higher contribution to the network. By reducing the influence of invalid features caused by changing channels, this method safeguards the reliability of the features used for debris classification. The experiment results show that the cuttings recognition algorithm proposed in this paper has higher accuracy than the comparison algorithm.
Preliminary results of mass-balance observations of Yala Glacier and analysis of temperature and precipitation gradients in Langtang Valley, Nepal

Abstract Monitoring the glacier mass balance of summer-accumulation-type Himalayan glaciers is critical to not only assess the impact of climate change on the volume of such glaciers but also predict the downstream water availability and the global sea-level change in future. To better understand the change in meteorological parameters related to glacier mass balance and runoff in a glacierized basin and to assess the highly heterogeneous glacier responses to climate change in the Nepal Himalaya and nearby ranges, the Cryosphere Monitoring Project (CMP) carries out meteorological observations in Langtang Valley and mass-balance measurements on Yala Glacier, a debris-free glacier in the same valley. A negative annual mass balance of –0.89m w.e. and the rising equilibrium-line altitude of Yala Glacier indicate a continuation of a secular trend toward more negative mass balances. Lower temperature lapse rate during the monsoon, the effect of convective precipitation associated with mesoscale thermal circulation in the local precipitation and the occurrence of distinct diurnal cycles of temperature and precipitation at different stations in the valley are other conclusions of this comprehensive scientific study initiated by CMP which aims to yield multi-year glaciological, hydrological and meteorological observations in the glacierized Langtang River basin.
Non-line-of-sight reconstruction based on filtered back projection

Traditional optical imaging techniques are limited in terms of information acquisition and processing methods, as they can only image targets within the line of sight. Non-Line-of-Sight Imaging (NLOS) aims to enable imaging beyond the line of sight. One important method in NLOS imaging is based on the measurement of photon time-of-flight. It involves analyzing the paths of light propagation before reaching the detector to reconstruct the surface of hidden objects. In non-line-of-sight scenarios, light from non-line-of-sight objects can be imaged after being reflected by visible objects in the scene. However, the multiple reflected light signals in traditional imaging are typically considered as noise due to their low intensity. In contrast, non-line-of-sight imaging captures the weak and scattered light signals that have undergone multiple reflections, and uses the time of photon arrival to infer the shape of objects hidden from the line of sight. This paper explores the relationship between the shape of hidden object surfaces and the sensed light signals under multiple bounces. A model is constructed to recover the three-dimensional shape from diffuse reflections and infer the shape of objects outside the line of sight. The reconstructed model and method are validated using a dataset collected from real-world scenarios.
Constructing the prediction model for oil well wax deposition and its application

In the process of oil field production, wax deposition in oil well will lead to lower pumping efficiency and even wax sticking in well. In response to the problem of low accuracy in determining the flushing cycle of oil well wax removal and prevention work relying on manual experience, the degree of wax deposition in the oil well is obtained by analyzing the indicator diagram of the oil well working conditions and using the load difference. The Pearson correlation coefficient is used to analyze the correlation of oil well parameters and establish a dataset of oil well load. On this basis, the oil well wax deposition prediction model is constructed by using the Long Short-Term Memory network, and the optimal parameters matching the network structure are searched by Particle Swarm Optimization algorithm. The prediction model is utilized to train the sample dataset, obtain the prediction data and realize the pre-warning of oil well wax deposition. Finally, the least square curve fitting method is used to obtain the growth rate of load difference and the wax deposition state of the well. The experimental results indicate that the constructed oil well wax deposition prediction model can obtain the wax deposition status in advance, guide the wax removal and prevention work, and provide decision support for the formulation of the well flushing cycle in the oil field.
Multi-stage context-guided semantic segmentation in construction sites

Semantic segmentation refers to the segmentation of different objects within images to obtain a semantic interpretation of each pixel position based on semantic information. With the development of artificial intelligence and computer vision, intelligent visual surveillance solutions for construction sites are playing an increasingly prominent role in site monitoring. For this purpose, we propose a semantic segmentation network for intelligent scene perception in construction sites. The features are extracted with the residual block and channel-wise feature aggregation module, thus obtaining multi-scale features with rich information. Then, high-level futures are further enhanced with long-range contextual information, which is adopted as guidance for the decoding process. Experimental results show that the proposed network can efficiently process construction site scenes in real-time and has important practical applications.
Tunable multi-wavelength Brillouin–erbium random distributed feedback fiber laser

A tunable multi-wavelength Brillouin–erbium random fiber laser with a half-open cavity is proposed and experimentally demonstrated. In this simple laser device, stabilized 15-order Stokes lines and 3-order anti-Stokes lines can be obtained by adjusting the pumping power. By adjusting the wavelength of Brillouin pump, the random laser wavelength tuning is realized, which can be tuned to 1550.5 nm-1565.5 nm. In addition, the laser has high wavelength and power stability. The wavelength fluctuation range of 1–10 orders Stokes light is less than 0.01 nm, and the corresponding peak power fluctuation is less than 1.8 dB. The results show that the laser has the advantages of simple structure, many spectral line orders, wide tunable wavelength range and high stability, which makes it have broad application prospects in many fields, such as fiber sensing, microwave photonics, optical imaging, optical communication systems, precision metrology and so on.
Observations and Speculations on the Reasons for Recent Increases in Pink Salmon Production

“I for one am ready to give up research for a unique cause of dominance and concentrate rather on identifying which cause or causes operate on each individual stock” (Ricker 1962). This statement by one of the world’s foremost fisheries scientists summarizes the difficulty that researchers have understanding the biology and population dynamics of pink salmon. Made almost 50 years ago, the statement is mostly still true today. This workshop brings experts together to discuss why pink salmon throughout their distribution are either increasing in abundance or are at least not declining in abundance as are some other species of Pacific salmon. Bill Ricker ’s thoughts identify the complexity of understanding the dynamics of pink salmon production. Thus, we are encouraging participants to think differently and to speculate, an exercise that is usually frowned upon by reviewers and editors. I will present information that was not published, mostly because it did not show much. However, I will speculate that what it does not show is the message. Catches of Pacific salmon throughout the subarctic Pacific have increased in the past two decades with historic high catches in 1995, 2007 and 2009. The percentages of pink and chum salmon in these catches is 80%; with pink, chum, and sockeye salmon representing 96%. Since 1993, pink salmon represented 43% of the total catch and chum salmon 37%. There has been a dramatic increasing trend in the catches of pink salmon that spawn in odd-numbered years (odd-year pink salmon) but no apparent trend in the catch of even-year pink salmon (Fig. 1). In this paper, I look at the dynamics of pink salmon that enter the Strait of Georgia from the Fraser River and return as adults in the following year along with information on the population dynamics of pink salmon from the central coast area of British Columbia to speculate on why pink salmon are increasing in abundance and why one line of pink salmon is increasing and the other line is not. Methods
Generating Realistic Attack Data for Microservices: Framework and Case Study

Microservice applications have gained significant popularity due to their capability to decrease the complexity of developing highly scalable, manageable, and flexible systems. However, the microservices distributed nature, fine service granularity, and large attack surface introduce new security challenges, making it crucial to develop effective mechanisms for protecting those applications at runtime. Nevertheless, developing novel solutions relies on the availability of suitable datasets, which are currently lacking and need to be generated through new research. This research paper presents a comprehensive framework designed to support research on monitoring and analyzing microservices, aiding in the development of novel solutions for detecting attacks in the context of microservice applications. We present an implementation of the framework for generating data for high- and low-volume Denial of Service (DoS) attacks, highlighting its key components and features. Furthermore, we present a series of attack profiles and evaluate the framework’s performance in a case study, demonstrating the framework’s usefulness for generating data for DoS attacks by presenting a model that successfully detects the attacks.
Influence of the Iceland mantle plume on crustal accretion at the inflated Reykjanes Ridge: Magma lens and low hydrothermal activity?

[1] A simple ridge model is applied to the hot spot-influenced, slow spreading Reykjanes Ridge in the North Atlantic to explore the effects of both mantle temperatures and hydrothermal cooling due to the Iceland mantle plume on the thermal structure and crustal accretion at this ridge. The objective of this study is not only to fit existing observations but also to raise important questions for future field programs to test. Does a magma chamber similar to that at the East Pacific Rise exist beneath the inflated Reykjanes Ridge north of the transition site at 59°N and if it does, at what depths? Modeling results of this study show that with increased magma supply a long-lived magma chamber could be present at the lower crust if hydrothermal activity is comparable to elsewhere of the Mid-Atlantic Ridge. More importantly, modeling results predict a shallow magma chamber beneath the inflated Reykjanes Ridge north of 59°N if it is associated with low hydrothermal activity. Since the recent observations of the extremely low incidence of high-temperature vents along the Reykjanes Ridge was interpreted as the indication for the diffusive flow being the dominant mode of hydrothermal circulation there, not as the evidence for low hydrothermal activity, the questions raised here call for more field observations at the inflated Reykjanes Ridge north of 59°N against these testable model predictions and thus to resolve these important issues.
Test volume mitigation for mobile devices software development: An improvement approach considering shared requirements

The number of software variations that are embedded in mobile devices is growing. Due to the high demand for software adaptation, manufacturers need to find strategies to ensure the quality of the software they embed. In an attempt to reduce software test workload after requirements implementations, we propose an improvement to a process we use at SIDIA R&D Institute. Our approach is based on testing each requirement, instead of the current process, which consists of tests repeated for each client. Our experiments’ results are based on a small set of device models and show that gains obtained can be expanded to the entire device models portfolio. Additionally, the proposed improvement can greatly reduce requirements testing time. We performed our experiments in a real scenario, so the results may be affected by variables out of study scope, specially for the fourth experiment, which was performed on new device models, where some stability work still may be required.
ATF - An end-to-end testing framework: experience report

Software products nowadays are becoming increasingly complex, containing modules directed for different devices, such as mobile and web. To maintain the quality of these products, development companies may adopt software testing techniques, such as end-to-end functional adequacy tests. However, a challenge arises when these companies serve diverse clients employing various technologies, requiring their developers and testers to possess knowledge across all these technologies. In this paper, we report our experience using the Automated Testing Framework (ATF), which makes the development of end-to-end functional adequacy tests easier for various types of devices in a standardized and organized manner. We also discuss its use on projects from two real clients presenting productivity data and feedback collected from the testers. Based on their feedback, we present the challenges they face and how we intend to tackle them.