Miracle-dz/UniFAR-FTUs huggingface dataset:
A Zero-Watermark Hybrid Algorithm for Remote Sensing Images Based on DCT and DFT

Remote sensing image has significant advantages such as large detection range and fast acquisition speed, so it has been used in military, environmental, meteorological and other fields. In order to improve the robustness of the digital watermarking technology on the premise that the remote sensing image information is not modified, a remote sensing image zero watermarking algorithm based on Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) is proposed in this paper. First of all, the color remote sensing image is converted into grayscale image. The transformed coefficient matrix is obtained by DFT, to the grayed host image. Secondly, in order to compress the amount of information and improve the efficiency of the algorithm, the high frequency coefficient image is transformed by DCT transform. The low frequency coefficient image which is consistent with the size of the watermark image after DCT is selected as the feature image of the host image. After that, the watermark information is scrambled by Arnold transform to improve the security of the watermark information. Finally, the zero-watermark image is obtained by XOR operation between the scrambled watermark information and the feature image. The zero-watermark image is saved in a third-party database for copyright verification. In order to verify the robustness of digital watermarking, this paper carries out sharpening, histogram equalization, Karma transform, noise, filtering and geometric attacks on the host image, and compares it with other algorithms. The NC value between the extracted watermark image and the original watermark image after attacking the host image show that the algorithm has a strong ability to resist common attacks.
AAR-Unet: Blood vessels segmentation in ultra-wide-angle fundus images

The structural information of blood vessels in ultra-wide-angle fundus images has important guiding significance for diagnosing ophthalmic diseases. Besides, efficient and correct segmentation of ultra-wide-angle fundus vascular images has become an urgent clinical need. However, manual diagnosis is a time-consuming and laborious work. Because of the limitation of primary medical resources, the situation of missed diagnosis and misdiagnosis would probably appear in n the diagnosis of artificial experience. In this paper, we proposed an automatic vascular segmentation algorithm based on attention gate based atrous residual (AAR) Unet combined with Top-Hat transform vascular enhancement. In AAR-Unet, we integrated atrous convolution into Res-Unet to expand the receptive field and improve the correlation between objects. To improve the segmentation effect of small blood vessels under low contrast, the attention gate module is introduced. The experimental results show the overall accuracy, sensitivity, specificity and DSC of the proposed algorithm are 0.988, 0.890, 0.993 and 0.875 respectively. The results indicate the algorithm can segment the blood vessels of ultra-wide-angle fundus images accurately.
Research on The Method of Grass Mouse Hole Target Detection Based on Deep Learning

Due to climate warming, increasing soil erosion and overloading of grassland grazing, grasslands around Taipusi Banner in Inner Mongolia have been degraded to varying degrees, causing widespread rodent damage. Aiming at the problem of rodent damage in the grassland surrounding Taipusi Banner, the UAV visible light image is the main research object, and the precise detection of grass mouse holes is the research goal. Computer vision technology is used to study grass mouse hole detection methods, in order to achieve rapid and accurate mouse detection. Hole function.
Determination Method of Contact Ablation Degree of Power System Circuit Breaker Based on Power Load Property

In the field of electrical engineering, the stable operation of circuit breaker is one of the important indexes to ensure the safety and stability of power system. Because the influence of load property on electromagnetic wave waveform is not considered, the method to determine the ablation degree of circuit breaker by using the characteristics of radiated electromagnetic wave is not completely accurate. Therefore, this paper proposes a method to determine the ablation degree of circuit breaker considering the load properties. By adding load factor, the ablation degree of circuit breaker contact can be determined more accurately. When the load is resistive load, the load factor is 1, when the load is inductive load, the load factor is 2.5, when the load is capacitive load, the load factor is 3. When the duration of the radiated electromagnetic wave exceeds the corresponding time, it can be judged that the contact is seriously ablated. The method proposed in this paper mainly uses electrical detection means to realize the rapid determination of the ablation degree of the circuit breaker contact and avoid the misjudgment caused by not considering the nature of the load.
The Calculation and Mathematical Modelling of Melbourne Crown Risk by Linear Regression Analysis

To improve the performance and meet the rising tourist demand of luxury hotels, Crown Resorts Ltd. proposed to implement a new project, which is supposed to build a 6-star hotel resort with VIP gaming facilities in an iconic building at Barangaroo South (Jones Lang LaSalle 2013). Because the goal of Crown plans to build a luxury hotel with a casino, so the company requires a large amount of capital to launch the new project. The best financial solution to raise capital is to issue the bond. Therefore, according to the current market condition and previous experience of issuing Crown Note I & Crown Note II, here are our recommendations for the most beneficial strategy to issue a new bond--Crown Note III.
Research on Data Mining Method of TCM Prescription Based on Machine Learning

This paper uses conditional random field model to mine TCM data. In order to identify the information of prescriptions in ancient Chinese medicine books, the model firstly cleaned and segmented the original text data, then, labels are added according to the selection of TCM characteristics to mark part of the corpus, and according to the demand divided into two parts, the training corpus was used to adjust the parameters of the custom CRF feature template, and the model was generated after adjusting the parameters. The accuracy of the model for entity recognition was verified by using the test corpus. In the end, the feasibility of the conditional random field model in the identification of TCM prescriptions was verified experimentally.
Recurrent Pattern Aware Multiphase Flow Soft Sensor Model

Given data captured from multiphase flow industrial process which has non-stationary and dynamic characteristics, it’s difficult to predict the dominant variables precisely via traditional soft sensor models. In this paper, we propose RP-LSTM (Recurrent Pattern Aware Long Short Term Memory) model, a soft sensing method that takes both dynamic of time-sequence and pattern mining of industrial process into account. In this way, we can fully utilize limit model capacity, and enhance model’s knowledge on recurrent data stream patterns. Experiments on real data set demonstrate that RP-LSTM out-performs traditional sequence deep learning models by a significant margin.
Application of 5G Communication Technology in Power Communication and Research on Key Technologies

With the rapid development of power system and the deepening construction of smart grid, 5G communication technology is favored by all walks of life because of its ultra-low delay, ultra-high reliability, deep coverage, extreme capacity and ultra-low power consumption. It has become the key technology to lead scientific and technological innovation and promote the sustainable development of power system, providing a solid guarantee for the realization of human-computer interaction and Internet of Everything. This paper first introduces the network structure, basic principle and core features of 5G communication, and expounds the core slice of 5G communication and its three application scenarios; secondly, the potential demand scenarios of 5G communication in power system are analyzed in depth, that is, high capacity, high bandwidth and low delay business scenarios; finally, the key technologies of 5G communication, such as large-scale MIMO communication technology, ultra-dense heterogeneous network technology, green communication technology and millimeter wave communication technology, are discussed, and the linkage between current communication requirements and communication technologies is studied, which provides theoretical support for the application of 5G communication in power system.
Improved High Precision Aircraft Target Detection Method of YOLT

The first remote sensing dataset that can be used for aircraft target detection is created and a 3D model simulation method for data augmentation is proposed. After statistical analysis of aircraft sizes, a high-precision improved YOLT method for target detection is proposed. YOLT is the first light algorithm focusing on target detection in remote sensing images. We improve its network structure, design a creative method based on receptive field improvement and adopt an optimized non-maximum suppression strategy. The results show that our method has better performance than other main target detection algorithms, especially for small targets and cross-scale samples.
Conﬁgurable RISC-V softcore processor for FPGA implementation

— Over the past years, the processor market has been dominated by proprietary architectures that implement instruction sets that require licensing and the payment of fees to receive permission so they can be used. ARM is an example of one of those companies that sell its microarchitectures to the manufactures so they can implement them into their own products, and it does not allow the use of its instruction set (ISA) in other implementations without licensing. The RISC-V instruction set appeared proposing the hardware and software development without costs, through the creation of an open-source ISA. This way, it is possible that any project that implements the RISC-V ISA can be made available open-source or even implemented in commercial products. However, the RISC-V solutions that have been developed do not present the needed requirements so they can be included in projects, especially the research projects, because they offer poor documentation, and their performances are not suitable. With this work, the main goal was the development of a RISC-V processor that has as characteristics the adaptability, ﬂexibility, and features that are not yet present in the current solutions. As the base of this work, it was used a non-RISC-V processor, the MB-Lite, that was modiﬁed to implement this ISA, and it was improved with new functionalities, such as caches, data transfer through the PCIe bus, a serial communication module (UART), counters and timers, and multi-cycle functional units. The proposed solution was implemented and tested on an FPGA in order to validate the system’s correct operation and to obtain its experimental characterization.
Incidental Finding of Renal Impairment Leading to a Diagnosis of Nephronophthisis

The patient was born 2 months preterm but was reportedly a well-baby and was never hospitalised as a child. Past medical history was unremarkable. In particular, there was no history of childhood enuresis, recurrent urinary tract infections or renal calculi. He denied intake of any maintenance or over-the-counter medications and denied tobacco, alcohol and recreational drug use. He had no known drug or food allergies. He also denied chronic NSAID or analgesia use.
A Comparison of Digital Droop Detection Techniques in ASAP7 FinFET

This report compares current techniques for digital supply droop detection. While there are many droop detection circuits in use, they have been thus far studied in isolation; it is not clear what the design merits and trade-offs of different topologies are. This project presents design methodologies for two single-cycle digital droop detectors, which are used to design and compare droop detector sensitivity and selectivity for two representative example specifications in ASAP7.
Word-final /ɔ̃/ in Greater Poland Polish: A Cumulative Context Effect?

An empirical corpus-based study of the likelihood of realizing the Polish nasal vowel /ɔ̃/ word-finally as [ɔm] (i.e. of 'nasal stopping') is presented. The goal was to verify whether the phenomenon exhibits a cumulative context effect, with words typically occurring in an environment favoring a particular phonetic variant showing higher rates of that variant regardless of environment. The results show that nasal stopping is more likely before stop-initial words than before words beginning in other sounds, if there is no intervening pause. Results with regard to the hypothesis that words typically followed by stops will show higher likelihood of nasal stopping, however, remain inconclusive.
Access and Core Transmission Joint Planning Including Interconnection Disaster Recovery

The main objective of this dissertation is the design and implementation of an algorithm that seeks to resize the network architecture, in a disaster recovery context, i.e. after the occurrence of network element fault and/or connection breakdown in the network. In response, traffic is redirected, in case of redundancy existence, and/or the calculation of a new interconnection plan, for elements (or set of elements) that may have become disconnected from the network, is done. Regarding the interconnection context, a recursive function of searching for disconnected network elements was developed, forming sub-networks for subsequent re-interconnection to the network. The process of sub-networks forming depends on conditions such as existence of line of sight and distance (searching for geographically closer elements), emulating microwave connections. It is also considered optical fibre connections, having into account the existence of fibre ducts nearby. However, microwave connections were assumed to have greater importance, since it has a faster implementation solution and, consequently, a shorter response time in the network replacement. Regarding traffic redirection, the use of multi-criteria cost functions allowed a joint evaluation on each connection, in terms of available bandwidth and associated latency. Both redirection and interconnection replanning methods were implemented over a Python built network simulator using the NetworkX tool, along with real network Key Performance Indexes (KPIs) given by Celfinet Enterprise. To test the algorithm, the following scenarios were considered: maintenance, earthquake and computer attack called Distributed Denial of Service (DDoS).
A Four-Layer Odd Function Symmetrical Dual-Polarization Equilateral Right Triangle Slot Printed Broadband Directional Antenna for Wireless Lab Measurement Applications

A four-layer odd function symmetrical dual-polarization coplanar waveguide- (CPW-) fed equilateral right triangle slot printed broadband directional antenna for wireless lab measurement applications is presented. The proposed antenna consists of two stacked perpendicular odd function symmetrical equilateral right triangle slot antenna structures with two stacked reflector ground planes. It is miniaturized, simple, and easy to be fabricated. The measured results demonstrate that the proposed antenna can achieve wide impedance bandwidth that is almost 520% for |S11| and |S22|; VSWR ≦ 3 which has implemented the operating band from 1.37 to 7.12 GHz for Global Positioning System (GPS, 1.575 GHz), Wireless Local Area Networks band (2.4 GHz and 5.8 GHz), 6 GHz unlicensed spectrum (5.925 GHz∼7.125 GHz), Long Term Evolution Upper Band (LTE, 1710–2690 MHz), and sub-6 5 G band (3.5 GHz and 4.7 GHz) applications. The measured in-band isolation performance between the two input ports |S12| ≦ −17 dB is significantly achieved, and meanwhile, the antenna radiation pattern, peak gain, and efficiency of the proposed antenna are measured as well. In the end, the radiation pattern data are compared and analyzed with simulation results.
Automated Fitting of X-Ray Powder Diffraction Patterns from Interstratified Phyllosilicates

NEWMOD®, developed by R.C. Reynolds, Jr., has been an important tool for evaluating quantitatively X-ray diffraction (XRD) patterns from interstratified clay minerals for more than 20 years. However, a significant drawback to the NEWMOD® approach is that analyses are done by forward simulation, making results sensitive to user input and starting-model assumptions. In the present study, a reverse-fitting procedure was implemented in a new program, FITMOD, which automatically minimizes the differences between experimental and simulated XRD patterns. The differences are minimized by varying model parameters (such as Reichweite, crystal-size distribution, cation content, type of disorder, etc.) using the downhill simplex method. The downhill simplex method is a non-linear optimization technique for determining minima of functions. This method does not require calculation of the derivatives of the functions being minimized, an important consideration with many of the parameters in NEWMOD- type simulations. Instead, the downhill simplex method calculates pseudo-derivatives by evaluating sufficient points to define a derivative for each independent variable. The performance of FITMOD was evaluated by fitting a series of synthetic XRD patterns generated by NEWMOD+, yielding agreement factors, Rwp, of <0.3%. As long as the correct interstratified system was specified (e.g. illite-smectite), excellent fits were obtained irrespective of the starting parameters for the simulations. FITMOD was also tested using experimental XRD patterns, which gave very good fits, in agreement with previously published results. The optimization routine yields good fits for both synthetic and experimental XRD profiles in a reasonable time, with the possibility of varying all important structural parameters. FITMOD automatically provides optimum fits to experimental XRD data without operator bias, and fitting efficiency and accuracy were, therefore, significantly improved.
CHANGE DETECTION USING MODELS DERIVED FROM POINT CLOUDS

Change Detection using Models derived from Point Clouds This thesis examines the detection of geometric changes in 3D data based on models derived from the point clouds. The process chain consists of the registration of point clouds, the derivation of a model and the detection of changes in these models. For the registration AprilTags are used, which, in combination with the trajectories of the respective measuring runs, allow the estimation of a transformation between two local coordinate systems without a preceding assessment of the tag position. The point cloud is sampled down to a uniform distribution for faster processing and the normals and curvatures are calculated for the remaining points. A region growing process uses this additional information to divide the point clouds into planar and nonplanar areas. The former are modeled as planes through simplification and meshing using a modified quadtree structure. The non-planar points are clustered by supervoxels and the extracted clusters are approximated by Axis Aligned Bounding Boxes. Both planar and non-planar modeling is performed for all datasets which are to be compared. The bounding boxes are used for the detection of changes. The box model of a dataset can easily be examined for intersections with a second dataset, due to the axis alignment. The intersections allow the detection of changed areas in the derived models. The methods proposed are suitable for both indoor and outdoor applications, provided that the changed objects are well separated and the compared datasets cover overlapping areas. The accuracy depends on the chosen size of the bounding boxes as well as the size of the changed objects. The evaluation has also shown that the proposed method can be integrated into a realtime-capable system.
Computational Analysis of the Thermoelastic Damping

Quantifying damping phenomenons is of utmost importance, once they have a crucial influence on every dynamic system. However, depending on each situation, these phenomenons can be associated with different objectives. For instance, in most micro-mechanical systems the goal is to dissipate the least energy, whereas in large structures the suppression of undesirable vibration can be advantageous. A possible model to describe structural damping is the thermoelastic one, as an alternative to the commonly used hysteretic one. In this work a finite element program was developed in order to evaluate the thermoelastic damping of thin models. A serendipity shell element was formulated through Reissner Mindlin’s plate theory, three-dimensional heat conduction and linear thermoelasticity, based on certain assumptions. The consequent computational implementation was described in order to enlighten the user regarding the allocation of the variables and the general functioning of the program, which was validated with simple problems of elasticity and heat-transfer to study the reliability of the meshes. Finally, the thermoelastic damping was evaluated for three cases, beginning with a silicon micro-beam that has already been studied, moving on to an aluminium beam and concluding with an aluminium plate. Although the plate results slightly diverted from the theoretical models, the beam ones were satisfactory.
Community ecology of the metazoan parasites of red porgy, Pagrus pagrus (L., 1758) (Osteichthyes, Sparidae), from the coastal zone, state of Rio de Janeiro, Brazil

Ninety specimens of red porgy, Pagrus pagrus (Linnaeus, 1758) (Osteichthyes: Sparidae) collected from the coastal zone of the State of Rio de Janeiro, Brazil (21-23o S, 4145oW), from October 1998 to March 2000, were necropsied for parasite studying. All fish were parasitized by metazoan. Twenty-two species of parasites were collected. The nematodes were 59% of the total parasitic specimens number. Contracaecum sp. larvae were the dominant species with highest prevalence and abundance. Abundance and prevalence of cymothoid isopod were positively correlated with host total length, while the prevalence of Parahemiurus merus (Linton) and Polymorphus sp. were negatively correlated with host total length. The host gender did not influence any parasite species prevalence or abundance. The mean diversity in the P. pagrus infracommunities was H=0.306±0.119, with no correlation with the host’s total length and no significant difference between male and female fish. Three pairs of species shared a significant positive covariation, and two pairs shared significant positive association between their abundances and prevalences, respectively. Negative parasite species association or covariation were not found. Qualitative similarity was observed between the ectoparasite infracommunities of P. pagrus, at the genus level, with other sparid fishes of the world.