Miracle-dz/UniFAR-FTUs huggingface dataset:
Discriminative Prototype Learning for Few-Shot Object Detection in Remote-Sensing Images

Few-shot object detection (FSOD) in remote-sensing images (RSIs), which aims to detect never-seen objects with few training samples, has attracted wide attention. Some recent works leverage meta-learning to tackle this challenging task and achieve promising performance. However, information attenuation during feature extraction and simple prototype representation hamper further improvement in detecting novel classes. In this article, we propose a novel meta-learning-based FSOD approach named DPL-Net. Specifically, within the meta-learning-based framework, performing role-specific feature extraction in query and support branches, DPL-Net adopts a fine-grained information fusion (FIF) module to capture scale-aware information within query regions of interest (RoIs) and a multifrequency information enhancement (MIE) module to retain the spectral information of support samples. Moreover, considering the variability of remote-sensing objects, a discriminative prototype learning (DPL) strategy is developed to rectify the ambiguous distribution of support samples for more representative class-aware prototypes (CPs). Experiments on two benchmark datasets (NWPU VHR-10 and DIOR) demonstrate that our method effectively improves the performance of meta-learning in detecting RSIs with limited training data.
Understanding the seagrass‐sediment‐light feedback to guide restoration planning: a case study using Zostera muelleri

Seagrass ecosystems are ecologically important but are declining worldwide, prompting restoration efforts. However, restoration success is partially reliant on ensuring that donor material and planting methods are suitable under the environmental conditions expected within restoration sites. This may require planting at critical densities needed to initiate favorable environmental feedbacks, ensuring plants are pre‐adapted to local conditions, and setting realistic restoration goals. An understanding of the relationship between local environmental conditions (e.g. the local light environment and seagrass structural complexity) can be particularly important in guiding restoration decision‐making. Here, we investigate how sediment‐light conditions interact with seagrass structural complexity (density and above‐ground morphology) to guide restoration planting approaches for the intertidal seagrass Zostera muelleri. Using generalized additive models and empirical data, we identified significant relationships between the local sediment‐light environment and the structural complexity of Z. muelleri meadows present in Western Port, Victoria, southeast Australia. We found a decrease in shoot density and leaf length with decreasing light availability, potentially reflective of an adaptive change in the species morphology in response to low‐light environments. We also found a decrease in sediment sorting and increased fine particles with increasing meadow structural complexity, suggesting that seagrass structural complexity increases sediment stability, and accretion, and may contribute to local water clarity via ecological feedbacks. These findings suggest that understanding both environmental drivers and the potential for ecological feedbacks to occur is needed before large‐scale planting begins and that restoration targets should reflect the meadow form most likely to occur under the environmental conditions present.
Hybrid Deep Reinforcement Learning Model for Safe and Efficient Autonomous Vehicles at Pedestrian Crossings

The autonomous vehicle is a particularly promising area of application for machine learning algorithms. Autonomous driving is challenging due to the complexity of the road network, the hardly predictable human behaviors, either pedestrians or drivers and the imperative need to ensure the safety of all road users. Deep Reinforcement Learning algorithms have garnered significant interest as a viable option for controlling autonomous vehicles. They allow defining a control policy by solely describing the observation space, the action space, and a reward function. Yet, the most common solutions face difficulties when the action space is hybrid (simultaneously continuous and discrete), and the reward is sparse, which is the case when a selection between choices must be made (like yielding or not), and the trajectory of the vehicle must be adapted according to this decision. This paper addresses these challenges by focusing on controlling an autonomous vehicle at pedestrian crossings. The vehicle adapts its speed (continuous decision) and the displayed signal (discrete decision) according to the pedestrian's state. The unique characteristic of the speed profile is to facilitate safe pedestrian crossings without requiring the vehicle to come to a complete stop. This raises the consistency problem between the speed profile and the signals issued by the vehicle. This paper introduces a new hybrid DRL model capable of making both decisions. It discusses the training method, the construction and necessary constraints on the reward, as well as the success of generalization to different speed limitations.
Feature Engineering Approach to Detect Retinal Vein Occlusion Using Ultra-Wide Field Fundus Images

Retinal Vein Occlusion (RVO) symptoms can be identified through analysis of fundus image capturing the retinal area of an eye. One of the symptoms associated with RVO is haemorrhage lesions due to rapture or damage of blood vessels in the retina. This paper investigates the feature values associated with haemorrhage symptoms in wide-angle fundus images. The feature values are used to construct a classifier to label an image into RVO or non-RVO. A total of 80 feature values are extracted based on various image properties. Four classifiers are built by using the feature vectors to compare their performance. A total of 87 wide-angle images are used in the evaluation. It is found that shape- and colour-based features are useful for separating the RVO images from non-RVO images with a sensitivity of 0.80 and specificity of 0.85. Given the accuracy rate of 0.90, specificity of 0.92, and sensitivity of 0.88 on RVO detection, SVM performed best compared to other classifiers. The traditional feature-based approach can achieve performance levels close to the deep learning approaches using UWF images for haemorrhage prediction.
A Trained Fuzzy Expert System to Detect Emergent Behavior

The concept of emergence has gained attention due to its relevance in understanding complex systems, its cross-disciplinary nature, advancements in computational tools, challenges to reductionism, its connections to artificial intelligence and machine learning, philosophical implications, and potential real-world applications. As our understanding of emergent phenomena grows, it continues to shape our perspective on how the world works at various scales. In this article, we propose a novel method for detecting emerging behavior in a multi-agent system based on a fuzzy expert system. In addition, The article explores how interactions can be used as a metric to identify emergent behaviors in the Boids model and outlines the initial effects of a three-step process: (1) Representation and acquisition of simulation data,(2) Building a fuzzy expert system,(3)Learning process and emergence detection. Since this is a part of ongoing research, future direction is also discussed.
AmBC-Aided Integrated Sensing and Communication Systems for V2X Networks

In this paper, we consider a novel Ambient Backscatter Communication-aided Integrated Sensing and Communication (ISAC) system for the Vehicle to Everything (V2X) networks. Specifically, the Backscatter Devices (BDs) attached to the targets passively transmit their information to the mobile vehicular ISAC terminal by backscattering the ISAC signals. Through the backscattered signals, the ISAC terminal simultaneously estimates the radar parameters, including delays, doppler shifts and the azimuths, of the targets including the BDs, and demodulates the BDs. The radar parameters and BD signals are however coupled with each other, which makes the above joint estimation and demodulation very challenging. Hence, a transmission frame structure of the BDs is designed to solve the coupling issue, and the 3D-Newtonized Orthogonal Matching Pursuit (3D-NOMP) estimator is utilized to realize the parameter estimates and the BDs' information demodulations. Moreover, we calculate the performance bounds of the parameter estimates. Finally, the simulation results are provided to validate that the BDs attached to the targets are allowed to transmit information without any degradation of the estimation performance, and the 3D-NOMP estimator performs better than the benchmark in both the parameters estimation and the demodulation.
Tick-Borne Diseases of Humans and Animals in West Africa

Ticks are a significant group of arthropod vectors that transmit a large variety of pathogens responsible for human and animal diseases worldwide. Ticks are the second biggest transmitters of vector-borne diseases, behind mosquitoes. However, in West Africa, there is often only limited knowledge of tick-borne diseases. With the scarcity of appropriate diagnostic services, the prevalence of tick-borne diseases is generally underestimated in humans. In this review, we provide an update on tick-borne pathogens reported in people, animals and ticks in West Africa by microscopic, immunological and molecular methods. A systematic search was conducted in PubMed and Google Scholar. The selection criteria included all studies conducted in West Africa reporting the presence of Rickettsia, Borrelia, Anaplasma, Ehrlichia, Bartonella, Coxiella burnetii, Theileria, Babesia, Hepatozoon and Crimean–Congo haemorrhagic fever viruses in humans, animals or ticks. Our intention is to raise awareness of tick-borne diseases amongst human and animal health workers in West Africa, and also physicians working with tourists who have travelled to the region.
Computational Thinking Research in Mathematics Learning in the Last Decade: A Bibliometric Review

This study aims to map the landscape of the importance of previous research on computational thinking (CT) in mathematics learning over the last decade. This research is a literature review research, there are 113 publications collected from the Scopus database which are then analyzed using the bibliometric analysis method assisted by the Vosviewer application. The data taken from the Scopus database was refined so that it became 113 publications. The results of the study show that publications are spread across various continents, including America, Asia, Africa, and Europe. The United States and the United Kingdom are the most influential countries and have high cooperation with other countries in this field. The main conclusion from this study is that computational thinking has an important role in learning mathematics and STEM. The integration of computational thinking in the mathematics curriculum can improve students' understanding of mathematics and help them face the challenges of the digital age. In addition, the development of effective training methods for mathematics teachers as well as the assessment of good computational thinking learning tools and platforms can help improve the effectiveness of computational thinking teaching in mathematics contexts. These innovations will help better understand how computational thinking can be effectively integrated in mathematics learning and prepare students for a future filled with digital technologies.
Wave uplift pressures on horizontal platforms

The major objective of the study has been to investigate in detail the rapidly-varying peak uplift pressure and the slowly-varying positive and negative uplift pressures that are known to be exerted by waves against the underside of a horizontal pier or platform located above the still water level, but not higher than the crests of the incident waves. In a "two-dimensional" laboratory study conducted in a 100-ft long by 15-in. -wide by 2-ft-deep wave tank with a horizontal smooth bottom, individually generated solitary waves struck a rigid, fixed, horizontal platform extending the width of the tank. Pressure transducers were mounted flush with the smooth soffit, or underside, of the platform. The location of the transducers could be varied. The problem of a d equate dynamic and spatial response of the transducers was investigated in detail. It was found that unless the radius of the sensitive area of a pressure transducer is smaller than about one-third of the characteristic width of the pressure distribution, the peak pressure and the rise-time will not be recorded accurately. A procedure was devised to correct peak pressures and rise-times for this transducer defect. The hydrodynamics of the flow beneath the platform are described qualitatively by a si1nple analysis, which relates peak pressure and positive slowly-varying pressure to the celerity of the wave front propagating beneath the platform, and relates negative slowly-varying pressure to the process by which fluid recedes from the platform after the wave has passed. As the wave front propagates beneath the platform, its celerity increases to a maximum, then decreases. The peak pressure similarly increases with distance from the seaward edge of the platform, then decreases. Measured peak pressure head, always found to be less than five times the incident wave height above still water level, is an order of magnitude less than reported shock pressures due to waves breaking against vertical walls; the product of peak pressure and rise-time, considered as peak impulse, is of the order of 20% of reported shock impulse due to waves breaking against vertical walls. The maximum measured slowly-varying uplift pressure head is approximately equal to the incident wave height less the soffit clearance above still water level. The normalized magnitude and duration of negative pressure appears to depend principally on the ratio of soffit clearance to still water depth and on the ratio of platform length to still water depth.
Nyiragongo Crater Collapses Measured by Multi‐Sensor SAR Amplitude Time Series

Crater morphology undergoes rapid changes at active volcanoes, and quantifying these changes during volcanic unrest episodes is crucial for assessing volcanic activity levels. However, various limitations, including restricted crater access, cloud cover, haze, and intra‐crater eruptive activity, often impede regular optical or on‐site crater monitoring. To overcome these challenges, we utilize multi‐sensor satellite Synthetic Aperture Radar (SAR) imagery to generate dense time series of quantitative indicators for monitoring crater morphological changes. By combining images from diverse satellites and acquisition modes, we achieve high temporal resolution. Nevertheless, due to variations in acquisition geometries, direct image comparisons become impractical. To address this, we develop PickCraterSAR, an open‐access Python tool that employs basic trigonometry assumptions to measure crater radius and depth from SAR amplitude images in radar geometry. We apply our methodology to study the crater collapse associated with the May 2021 and January 2002 eruptions of Nyiragongo volcano. Following the 2021 collapse, we estimate the maximum depth of the crater to be 850 m below the rim, with a total volume of 84 ± 10 Mm3. Notably, the post‐2021 eruption crater was 270 m deeper but only 15%–20% more voluminous compared to the post‐2002 eruption crater. Additionally, we demonstrate that the 2021 crater collapse occurred progressively while a dike intrusion migrated southward as a consequence of the drainage of the lava lake system. Overall, our study showcases the utility of multi‐sensor SAR imagery and introduces PickCraterSAR as a valuable tool for monitoring and analyzing crater morphological changes, providing insights into the dynamics of volcanic activity.
U-Shaped Terahertz Microstrip Patch Antenna for 6G Future Communications

In this article a terahertz antenna has been proposed which is designed on Rogers RO3010 substrate, with a standard thickness $10\mu\mathrm{m}$ having relative dielectric constant ($\mathcal{E r}$) $=11.2$ and thermal conductivity of $0.66\mathrm{~W}/\mathrm{K}/\mathrm{m}$. A dielectric material with a high epsilon value is used, the effective wavelength of the electromagnetic wave is reduced within the material, resulting in a reduction of the physical size of the antenna. The designed antenna is compact light weight and is fed using a microstrip line feeding technique which provides ease of integration inside the device. The resonating frequency of the designed antenna is 0.854THz with a return loss $-19.5\mathrm{~dB}$ offering a high bandwidth of 44GHz. High bandwidth application of the future communication requirements is achieved by the modelled antenna.
Compact Mobile Terminal Antenna With Endfire Circularly Polarized Beam for Satellite Communications

In this communication, a compact endfire circularly polarized (CP) antenna designed for satellite communication on mobile terminals is proposed. In our design, a rectangular loop antenna is proposed with a compressed vertical dimension which is suitable to be integrated into the plastic bezel of the mobile terminals. The folded dipole mode and loop mode of the rectangular loop antenna provide orthogonal radiation components. An L-shaped stub is introduced to excite the two modes by capacitive coupling. The directivity of the horizontally polarized (HP) radiation is improved to be comparable to that of the vertically polarized (VP) radiation when integrating with the chassis ground. Independent mode tuning can be achieved with capacitive loading on the antenna. A prototype was fabricated to validate the design. The measured results indicate that a −6 dB impedance bandwidth of 10.0%, axial ratio (AR) bandwidth of 0.76% (covering the $S$ band of the BeiDou Navigation Satellite System (BDS) of 2483.5–2500 MHz), and a peak gain of 4.80 dBic are achieved with a compact size of $0.47\lambda _{0} \times 0.06\lambda _{0} \times 0.004\lambda _{0}$ . The proposed CP antenna has the merits of compact size, improved gain, and quasi-planar structure which is a good candidate for mobile terminals supporting satellite communication.
The Characteristics and the Dominant Factor of Fracture Development of the Lower Silurian Longmaxi Formation Black Shale in Sangzhi

Based on the analysis of tectonic faults in Sangzhi area, black shales fracture characteristics and controlling factors of the Longmaxi formation of Lower Silurian are studied. That fracture distribution of the study area is mainly controlled by fault, where the NE-trending fault system plays the most significant role. Fracture zones are located in the anticlinal axis, as well as at the stress concentration ,such as near the fault or in intersection of different fault zone.Dispersion of fracture strike and its density are controlled by the fault line, fracture develops near the fault line, which strike is mainly north-east. It contains high content of brittle mineral which becomes friable rock and has well-developed fracture. Keywords-characteristics; dominant factor; fracture development; the Lower Silurian Longmaxi formation; Sangzhi; black shale
Mitigating Energy Depletion Attack In Wireless Sensor Network Using Signaling Game

Nowadays, with the evolution of technology, sensor networks have experienced a real boom. Due to their constitutions, sensors suffer from low security and are therefore susceptible to different types of attacks. Wireless sensor networks (WSNs) deployed in hostile environments suffer particularly from energetic attacks, i.e. attacks aimed at shortening the life cycle of sensors. Sensors have limited energy resources; replacing or recharging nodes in hostile environments is difficult. Attacks that cause a drain on the energy level are the most common attacks in a hostile environment and can lead to the death of sensors such as sleep denial attacks. In this paper, we design a game model using a signaling game within clusters that enables both detection and defense against attackers. In this paper, we identify and impose penalties on nodes that practice sleep deprivation torture in WSNs. The simulations showed that the model is able to force the attacker to behave normally in a WSN.
Heuristic-Deep Q-Network-Based Network Slicing in LoRaWAN

Due to the increase in the number of Internet of Things (IoT) devices in recent years, managing and supporting the diversity of services is becoming more difficult. Network Slicing will be the solution, in which the network slices are tailored to the requirements of the services. In this paper, network slicing is investigated in LoRaWAN networks using the Heuristic-Deep Q-Network (H-DQN) solution that manages the network resource allocation. We propose an intra-service allocation based on the deep Q-Network (DQN) algorithm by allocating virtual resource blocks to services. In addition, the intra-service allocation is based on a heuristic algorithm that assigns the transmission probability to the LoRa nodes of each service for each block in a way to maximizes the Packet Delivery Rate (PDR) of the network while ensuring that the priority of services is maintained. Simulation results show that the proposed approach improves the PDR, and ensures prioritization among services.
A methodology for modelling and retrieving satellite images basing on spatial knowledge: application to natural risks

In this paper, we present a methodology for modelling and retrieving satellite images basing on their spatial knowledge. The main idea of our approach is that the use of spatial knowledge, reasoning and inference technique, can contribute to deduce the susceptibility of the scene at natural risks (erosion, flooding, fires, etc.). Our methodology takes in input a set of multi-sensor images representing a scene. It contains four modules: (1) Modelling of the scene, (2) fusion of image annotations, (3) similar case retrieval, and (4) reasoning and interpretation.
Breast MRI Segmentation by Deep Learning: Key Gaps and Challenges

Breast segmentation in magnetic resonance imaging (MRI) slices plays a vital role in early diagnosis and treatment planning of breast anomalies. Convolutional neural networks with deep learning have indicated promise in automating this process, but significant gaps and challenges remain to address. This PubMed-based review provides a comprehensive literature overview of the latest deep learning models used for breast segmentation. The article categorizes the literature on deep learning based on input modalities, use of labeled/unlabeled data during training, and different architectures. Additionally, it describes more complex frameworks structured using hierarchical, ensemble, or fused learning. Then, MRI processing approaches, key aspects of convolutional neural networks, and key gaps and challenges are focused. The need for large breast MRI datasets with accurate annotations and the generalization of the proposed structures to diverse and comprehensive datasets are among the gaps.