Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault and graben growth along active magmatic divergent plate boundaries in Iceland and Ethiopia

Recent studies highlight the importance of annual‐scale dike‐induced rifting episodes in developing normal faults and graben along the active axis of magmatic divergent plate boundaries (MDPB). However, the longer‐term (102–105 years) role of diking on the cumulative surface deformation and evolution of MDPB is not yet well understood. To better understand the longer‐term normal faults and graben along the axis of MDPB, we analyze fissure swarms in Iceland and Ethiopia. We first focus on the simplest case of immature fissure swarms, with single dike‐fed eruptive fissures; these consist of a <1 km wide graben bordered by normal faults with displacement up to a few meters, consistent with theoretical models and geodetic data. A similar structural pattern is found, with asymmetric and multiple graben, within wider mature fissure swarms, formed by several dike‐fed eruptive fissures. We then consider the lateral termination of normal faults along these grabens to detect their upward or downward propagation. Most faults terminate as open fractures on flat surface, suggesting downward fault propagation; this is consistent with recent experiments showing dike‐induced normal faults propagating downward from the surface. However, some normal faults also terminate as open fractures on monoclines, which resemble fault propagation folds; this suggests upward propagation of reactivated buried faults, promoted by diking. These results suggest that fault growth and graben development, as well as the longer‐term evolution of the axis of MDPB, may be explained only through dike emplacement and that any amagmatic faulting is not necessary.
Federated $k$-Dominant Skyline: An Efficient Approach Under Differential Privacy

$k$-dominant skyline queries have been widely used in many applications, such as multi-criteria decision making and recommendation. In recent years, a new computing paradigm, called data federation, has emerged to provide collaborative data analytics among several data owners who would like to share their data under privacy protections. This paradigm brings a new challenge to the $k$-dominant skyline query, where data is distributed among these owners and privacy leakages should be prevented during the query processing. Accordingly, we define the problem of federated $k$-dominant skyline query and show that existing solutions cannot be extended to answer this query with satisfactory efficiency. To improve the efficiency, we present an observation of a vector aggregation based re-formulation and design an efficient solution under differential privacy. Finally, extensive experiments are conducted to demonstrate that our solution is notably faster than the state-of-the-arts.
Building the Future of Intelligence, Surveillance, and Reconnaissance (ISR) Collections: The Development and Evaluation of a Collaborative ISR Tool to Support Intel Analysts

Sensors capture an abundance amount of information that must be deciphered regarding adversary capabilities. Once critical information is extracted, it must be aligned with a Priority Intelligence Requirement (PIR) and disseminated to the intelligence community (IC). Currently, there's not a tool available to host this information. Therefore, the 711th Human Performance Wing developed a collaborative Intelligence, Surveillance, and Reconnaissance (ISR) tool - Sphinx. Sphinx allows analysts the ability to explore collected intelligence to answer PIRs. Moreover, analysts can add to the body of knowledge. The study objective was to (1) evaluate usability and benefit of Sphinx (2) determine if providing analysts the opportunity to input information strengthens predictions (3) determine if providing misleading information causes biases. Sixteen analysts were provided a scenario and asked to breakdown a PIR into Essential Elements of Information (EEI), Indicators, and Observables. Findings discovered that Sphinx systematically supported the breakdown of PIRs leading to consistent predictions.
Sequential Multiuser Scheduling and Power Allocation for Clustered Cell-Free Massive MIMO Networks

Resource allocation is a fundamental task in cell- free (CF) massive multi-input multi-output (MIMO) systems, which can effectively improve the network performance. In this paper, we study the downlink of CF MIMO networks with network clustering and linear precoding, and develop a sequential multiuser scheduling and power allocation scheme. In particular, we present a multiuser scheduling algorithm based on greedy techniques and a gradient ascent (GA) power allocation algorithm for sum-rate maximization when imperfect channel state information (CSI) is considered. Numerical results show the superiority of the proposed sequential scheduling and power allocation scheme and algorithms to existing approaches while reducing the computational complexity and the signaling load.
Integrated Dynamic Control of Process and Cost in Complicated R&D Projects: A Reinforcement Learning Approach

This study addresses the integrated dynamic control problem of process and cost in an complicated R&D project based on earned value theory. It investigates optimal correlative efforts for correcting project's process and cost performance when confronted with performance deviations from the baseline at sequential checkpoints. The objective is to minimize the total costs, including actual and progress costs. To tackle this problem, we employ a stochastic Markov process to model the stochastic state transitions effected by the level of the efforts and exogenous random noises throughout the project's lifespan. We use Proximal Policy Optimization (PPO), a reinforcement learning technique, to identify a near-optimal control policy. Experimental results demonstrate the effectiveness of our proposed algorithm and managerial insights are derived from the results.
Stability of Unfolded Forward-Backward to Perturbations in Observed Data

We consider a neural network architecture to solve inverse problems, which is built by unfolding a forward-backward algorithm. This algorithm is based on the minimization of an objective function which corresponds to a penalized least squares problem. In this context, ensuring stability is consistent with inverse problem theory since it guarantees both the continuity of the inversion method and its insensitivity to small noise. The latter is a critical property as deep neural networks have been shown to be vulnerable to adversarial perturbations. The main novelty of our work is to analyze the robustness of this inversion method with respect to a perturbation of the bias parameter of the network. In our architecture, the bias accounts for the observed data in the inverse problem. The analysis is performed by using tools of fixed point theory. Our theoretical results are illustrated by numerical simulations on a problem of signal restoration.
A Multi-Hop Industrial IoT Network at THz Bands Using Contention-Based Access

This paper addresses the problem of enabling intra-machine communication in an Industrial Internet of Things (IIoT) scenario using Terahertz (THz) frequencies. To mitigate the path loss of such high frequencies, a tree-based topology is built: each automation machine of the factory is controlled by a Router that collects data measured by wireless sensor devices located on the machine and forwards them to a final Gateway (GW) for elaboration purposes. Routers are equipped with multiple antenna elements, while sensor devices have only a single antenna element. In this scenario, we study the applicability of a contention-based access protocol, based on Carrier Sense Multiple Access, and we demonstrate its effectiveness when compared to Aloha. The performance of an uplink communication is evaluated in terms of Packet Success Probability and Network Throughput. Results demonstrate the impact of different parameters, like the sensing range of devices, the number of radiating elements at the Routers, the size and number of machines, and demonstrate that propagation delays cannot be neglected at THz frequencies.
Analysis of communication overheads for DNN inference offloading techniques in homogeneous edge networks

Computational offloading is used to augment the capabilities of edge devices by delegating highly complex tasks (e.g. Deep Neural Network - DNN - inference) to remote devices. This allows devices to complete workloads beyond their limited computational resources. DNN partitioning allows a DNN inference task to be divided so that the partitioned segments can be executed on separate devices. How a DNN is partitioned significantly impacts the communication delay an inference task will incur. An interesting scenario is offloading in homogeneous networks (e.g. IoT networks, smart cameras), where all devices have the same computational resources. Here there is no processing reduction to be obtained by offloading. The most important factor for a partitioning technique in this context is the ability to minimise communication delay. This paper compares six different DNN partitioning techniques (fused tile partitioning, multi-split vertical partitioning, single-split vertical partitioning with/without horizontal partitioning and finally, no vertical partitioning with/without horizontal partitioning) under various levels of network load when offloading VGG16, YoloV2 and MobileNetV2 inference tasks. Based on our findings a single-split approach with horizontal partitioning provides the least communication delay while also providing a small reduction to processing time through parallelising the processing of the DNN task.
A Novel Approach for Transfer Learning Motor Imagery Classification Based on IVA

Motor imagery (MI) classification based on electroencephalogram (EEG) signals performs an important role in neurological rehabilitation for therapeutic proposes. Independent Component Analysis (ICA) is a set of techniques with a solid framework and is widely used in the signal processing area. Inspired by ICA, Independent Vector Analysis (IVA) is an extension of the problem for multiple datasets and explores the correlation between different datasets through the use of Mutual Information (Mutinf). The statistical dependency between datasets through Mutinf could help in MI classification since it allows a generic and homogeneous treatment of the whole data and a possible knowledge transfer between patients. This paper proposes an innovative approach for the Transfer Learning MI task by exploring the minimization of mutual information through IVA applied to motor imagery. The results show a high correlation and small standard deviation cross-subjects.
Leveraging the Internet of Things and Cloud Computing: A Crowdsourcing Web Application for Reporting Hazard Incidents

Crowdsourcing Web Application has not been utilized effectively for hazard identification. This paper presents the development of a Web-based crowdsourcing platform for hazard identification. Hence, a Web Application based on the Model- View-Controller framework is presented in this paper. The Web Application provides a platform through which data from IoT devices monitoring the environment for hazardous air pollutants can be visualized. Also, human users are enabled to report other forms of hazard around them through the crowdsourcing capability of the Web Application. The Laravel PHP framework is employed for the backend part of the Application while a combination of HyperText Markup Language, Tailwind Cascading Style Sheets and J avaScript were employed in the frontend part. JavaScript Object Notation is used as the data exchange format between the Internet of Things devices, cloud database and Web Application. Upon testing, the Application was confirmed to be working in line with the requirements gathered at the beginning of the development process. Notably, the IoT sensor data was visualizable through the Web Application in a user-friendly manner and users were able to communicate hazardous incidents using the App. Thus, the Web Application facilitates the hazard identification task, thereby improving the risk management processes.
Graph Algorithms for Topology Identification in Electrical Medium Voltage Grids

The availability of realistic grid topologies is of central importance for the usability of grid models in calculations. However, especially in distribution grids, poor documentation, limited digitization and insufficient data quality can lead to faulty topologies, which result in errors in subsequent calculations. Methods of topology identification (TI) offer possibilities to use measurement data of grid quantities such as voltages to validate and, if necessary, correct the topology used for calculations. This paper presents robust methods for TI in medium voltage networks based on an adapted form of Prim's algorithm, which is based on measurements of node voltage magnitudes. Methods to include spatial priors in the process are investigated. The developed method is applied to realistic models of medium-voltage networks and its robustness to unadapted graph-based methods is shown. The correlation of injection correlations, as well as noise on the measured data, is addressed to show that the proposed methods can handle these conditions.
Construction and Processing Method of Industrial Internet Attack Behavior Dataset

The number of industrial control safety incidents around the world shows a rising trend, causing massive losses and threats to social economy and enterprise costs. Industrial internet security has becomed an urgent problem to be solved. In order to accurately analyze the attack behavior of industrial internet, it is urgent to build a dataset of industrial internet attack behavior and establish a set of data processing methods. In this paper, we analyze the framework of industrial internet, and then propose the industrial internet attack behavior data cleaning scheme as well as code quabtization scheme to construct and process the dataset of industrial internet attack behavior.
Graph-based Multivariate Multiscale Permutation Entropy: Study of Robustness to Noise and Application to Two-Phase Flow Data

We propose a novel technique for exploring the complexity of multivariate time series (possibly with different lengths) across multiple time scales using a new graph-based approach. Our method, called multivariate multiscale permutation entropy, $\text{MMPE}_{\mathrm{G}}$, incorporates the interactions between channels by constructing an underlying graph for each coarsegrained time series and then applying the recent permutation entropy for graph signals. This approach enables the analysis of multivariate time series with varying lengths, providing insights into the dynamics and relationships between different channels. To address the challenge posed by noise in real-world data analysis, we evaluate the robustness of $\text{MMPE}_{\mathrm{G}}$ to noise using synthetic time series with varying levels of noise. Our results show that $\text{MMPE}_{\mathrm{G}}$ exhibits better performance than similar multivariate entropy metrics. We also apply $\text{MMPE}_{\mathrm{G}}$ to study two-phase flow data, an important industrial process characterised by complex and dynamic behaviour. Specifically, we process multivariate Electrical Resistance Tomography (ERT) data and extract multivariate multiscale permutation entropy values. The results indicate that $\text{MMPE}_{\mathrm{G}}$ characterises the flow behaviour transition of two-phase flow by incorporating information from different scales and is sensitive to the dynamics of different flow patterns. The noise-robustness of $\text{MMPE}_{\mathrm{G}}$ makes it a suitable approach for analysing the complexity of multivariate time series and characterising two-phase flow recordings.
Deep Learning Based DOA Estimation With Trainable-Step-Size LMS Algorithm

In this paper, we investigate the trainable-step-size least mean square (TSS-LMS) algorithm, which combines the LMS algorithm model with deep learning for the direction of arrival (DOA) estimation using adaptive filtering. Although the existing variable-step-size LMS approaches have been proposed to improve the DOA estimation accuracy, they are generally unsuitable for scenarios with the limited number of snapshots. Therefore, we propose a deep learning-based algorithm driven by the LMS model in this work, which incorporates the trainable step size. More specifically, the iterative mathematical model based on the LMS algorithm is unfolded into a deep network, where each iteration corresponds to a layer of this network. Based on this structure, the iteration step size in each layer is set as a trainable variable, allowing for adaptation during the training of the network. Through deep learning with the received signal dataset, the TSS-LMS network improves the adaptability and DOA estimation accuracy of the LMS algorithm for the limited snapshots scenario. Simulation results show that our proposed method is effective and outperforms existing algorithms in terms of DOA estimation performance and computational complexity trade-off.
1997 IUCN red list of threatened plants.

This represents the most comprehensive compilation of data on threatened vascular plants ever published. It includes the names of some 33,000 plant species determined to be rare or threatened on a global scale. Conservation assessments were provided by the IUCN Species Survival Commission, the National Botanical Institute (South Africa), Environment Australia and CSIRO,. The Nature Conservancy, the Smithsonian Institution, and the Royal Botanic Gardens, Kew together with hundreds of botanic gardens and botanists throughout the world. The Royal Botanic Gardens Edinburgh and New York Botanical Garden have made major in-kind contributions. The result of 20 years work by botanists and conservationists around the world, it is intended as a conservation tool, as provider of baseline information to measure conservation progress and as a primary source of data on plant species. Most importantly, however, it provides the building blocks on which to base a worldwide effort to conserve plant species. This publication has been scanned by the Biodiversity Heritage Library and is available online: http://biodiversitylibrary.org/page/31085360
Nanowires in Terahertz Photonics: Harder, Better, Stronger, Faster

By virtue of their quasi one-dimensional geometries, III–V semiconductor nanowires present unique capabilities for terahertz photonic devices. Ultrafast terahertz polarisation modulators and miniature terahertz photoconductive detectors are two examples of such nanowire-based devices. By the same token, terahertz methods such as terahertz conductivity spectroscopy offer unparalleled insight into the electronic processes that dictate the performance of nanowire-based devices.
Adapting terahertz spintronic emitters towards maximum performance

The boom in terahertz technologies of the last few decades is now not limited to the use of electric charge, but also fully exploits spin. Revolutionary spintronic terahertz emitters (STEs) offer versatile usage under a wide range of visible and infrared excitation wavelengths, arbitrary reflection/transmission excitation geometries, and utilization with most femtosecond oscillators and amplifiers. STE emission results in a 30THz-broad and gapless spectrum with easily controllable polarization. However, the optical-to-terahertz conversion efficiency is lower than traditional THz sources. Here, we demonstrate experimentally the enhancement of factor 6 in STEs power, which is the highest improvement nowadays. We integrate STEs with our optimized photonic cavities and provide procedures on how to dramatically increase efficiency.
Modelling and Performance Analysis of the Coexisting NR-U and WiGig Networks

The 5G New Radio-based in unlicensed spectrum (NR-U) has been proposed to harmoniously coexist with the Wireless Gigabyte (WiGig) network in the 60 GHz unlicensed spectrum. It employs the directional listen-before-talk (dirLBT) mechanism to improve the throughput of the coexisting NR-U and WiGig networks (CNWNs). In this paper, we are the first to leverage the stochastic geometry to analyze the performance of the large-scale CNWNs. The medium access probabilities (MAPs) of NR-U base station (NBS) and WiGig access point (WAP) are both derived in closed-form. Based on these MAPs, the downlink successful transmission probabilities (STPs) of NR-U and WiGig networks, which is determined by the retaining probability of the serving NBS/WAP and the downlink coverage probability of NR-U/WiGig network, are given in analytical expressions. All these MAPs and STPs are validated by Monte Carlo simulations to verify the correctness of our proposed model. Moreover, the effect of NBS and WAP density on the mean STP of the large-scale CNWNs are analyzed numerically. The results show that the dirLBT adopted by NR-U outperforms omnidirectional LBT in terms of the mean STP, especially in ultra-densely deployed CNWNs scenario. Furthermore, there exists an optimal NBS density to maximize the mean STP. The results indicate that the dirLBT mechanism should be adopted in the densely deployed CNWNs with proper chosen of NBS density.
Adjustable Stiffness Techniques Endoscope for Minimally Invasive Surgery

Soft robotics has several benefits over standard rigid robotics in situations requiring safety and dexterity, especially in minimally invasive surgery (MIS). The STIFF-FLOP manipulation has previously been proposed as a new approach for developing endoscopic instruments with great dexterity and inherent safety utilising soft substances. However, because of its intrinsic low stiffness, the capacity to create larger forces and stabilisation when needed has to be researched further. There is currently no ideal solution with current technology that fulfils all of the required stiffness variation and dexterity standards. This research offers a novel Parallel Chamber Endoscope (PCE) module design that can perform the needed bending and improving the variation of the endoscope stiffness. The suggested design is based on finite element (FE) modelling with ANSYS programme. The PCE module dimensions sensitivities are analysed. Then the sensitive dimensions are optimized by employing a gradient-based optimization approach. The results reveal that the novel PCE module outperforms earlier techniques that used comparable designs with a larger stiffness variation paired with a considerable degree of flexibility.
Accelerated beam searching for optical wireless communication system with slow feedback channel

1In Optical Wireless Communication (OWC), the use of narrow beams can enhance the achievable bit rates and reduces interference. However, this requires a mechanism to find the direction in which the transmitter must send its beam towards the receiver. It turns out that it may be counterproductive to widen the beam during the direction search phase. This paper analyzes a system with a feedback loop that allows the receiver to report back, once it sees an incoming signal. However, this feedback channel may not have a high time resolution and may have a relatively long and random delay. This paper studies the appropriate beam width adaptation during direction search and acquisition, taking into account the received (electrical) energy per bit, and noise limitations of the transimpedance amplifier. The paper also introduces a new address coding scheme. It uses continuous addressing, thereby eliminating overhead from packet headers, error correction coding or synchronisation words, by adopting maximum-length Linear Feedback Shift Register sequences. This accelerates the beam direction search.