Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical and chemical hydrogeology

The Origin of Porosity and Permeability. Ground-Water Movement. Main Equations of Flow, Boundary Conditions, and Flow Nets. Ground Water in the Basin Hydrologic Cycle. Hydraulic Testing: Models, Methods, and Applications. Ground Water as a Resource. Stress, Strain, and Pore Fluids. Heat Transport in Ground-Water Flow. Solute Transport. Principles of Aqueous Geochemistry. Chemical Reactions. Colloids and Microorganisms. The Equations of Mass Transport. Mass Transport in Natural Ground-Water Systems. Mass Transport in Ground-Water Flow: Geologic Systems. Introduction to Contaminant Hydrogeology. Modeling the Transport of Dissolved Contaminants. Multiphase Fluid Systems. Remediation: Overview and Removal Options. In Situ Destruction and Risk Assessment. Answers to Problems. Appendices. References. Index.
Swarm Intell Swarm robotics: a review from the swarm engineering perspective

Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.
The Application of Computer-Assisted Tomography in the Analysis of Fracture Geometry

An experimental study was conducted to investigate the surface characteristics of an oil sand fracture. The oil sand fracture was induced in core samples along the axis of the sample using the Brazilian tension test. A computer-assisted tomography scanning analysis was performed to determine the fracture geometry. The fracture was shown to be a variable aperture system that would have significant effects of the permeability of the fracture. The aperture distributions of the fractures were found to follow a log-normal distribution, similar to that of previously studied rock fractures.
Inter‐comparison of high‐resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada

Several different gridded climate data sets have recently been made available with the purpose of providing a consistent set of climatic data for many hydro‐climatic studies. Recent advances in land‐surface schemes and their implementation in fully distributed processes‐based hydrologic models have demanded even higher‐resolution gridded data. It remains, however, a challenge to identify the most reliable gridded climate data for hydrologic modelling, especially in mountainous headwater regions where there is significant spatial variability but few observing stations. Moreover, the accuracy of such climate forcing data applied to alpine headwaters directly affects the modelled hydrologic responses of the lower, downstream portions of river basins. This study evaluates the spatial and temporal differences in precipitation and temperature fields among three high‐resolution climate data sets available in Canada, namely, the North American Regional Reanalysis, the Canadian Precipitation Analysis and the thin‐plate smoothing splines (ANUSPLIN). Inter‐comparison of the quality of these data sets was undertaken for the Athabasca River basin in western Canada. The hydrologic responses of this watershed with respect to each of the three gridded climate data sets were also evaluated using the Variable Infiltration Capacity model. Results indicate that the data sets have systematic differences, which vary with regional characteristics – the largest differences being for mountainous regions. The hydrologic model simulations corresponding to those three forcing data sets also show significant differences and more with North American Regional Reanalysis than those between Canadian Precipitation Analysis and ANUSPLIN. © 2014 Her Majesty the Queen in Right of Canada. Hydrological Processes © 2014 John Wiley & Sons Ltd.
Archaeocyathan limestone blocks of likely Antarctic origin in Gondwanan tillite from the Falkland Islands

Abstract Cambrian limestone clasts containing a rich, well-preserved archaeocyathan fauna have been recovered from the late Carboniferous Fitzroy Tillite Formation of the Falkland Islands. Since neither Cambrian strata nor limestone are present anywhere in the indigenous rock succession, the clasts are regarded as exotic erratics introduced during the Permo-Carboniferous Gondwana-wide glaciation. Most recent reconstructions of Gondwana rotate the Falklands into proximity with the Eastern Cape, South Africa and the Ellsworth Mountains, Antarctica. In both of these areas, Permo-Carboniferous diamictites correlated with the Fitzroy Tillite Formation also contain rare, exotic clasts of archaeocyathan limestone. The Transantarctic Mountains seem the most likely source for all of these unusual erratics. This interpretation sustains the requirement for substantial rotation of the Falklands microplate into Gondwana reconstructions and illustrates the extent of the late Carboniferous ice sheet. Apparent differences in the tillite clast assemblages between East and West Falkland suggest variable provenance within the regional ice-flow regime.
Python Wrapper for Context-based Adaptive Binary Arithmetic Coding

A Python wrapper for Context-based Adaptive Binary Arithmetic Coding (CABAC), extracted from the Test Model (VTM) for Versatile Video Coding (VVC), is presented. Besides providing Python access to CABAC, two extensions are proposed: the probability estimation progress for each context can be traced, and sequences of integer values can be coded without designing a dedicated context model set.
A linearity improvement method for CIS column-parallel SAR ADC using two-step conversion

This paper presents a new 2-step SAR ADC architecture for image sensors in machine vision applications. This structure effectively improves the structural problems of the image sensor caused by the area occupied by the ADC, such as linearity and temporal noise performance. In this work, we designed a two-step SAR ADC using a 6-bit SAR ADC and a PGA generating residue and offset. Since the number of unit capacitor’s is reduced, the common centroid method is applied in the capacitor layout to improve the linearity. As a result, the capacitor mismatch characteristic is improved, and the differential nonlinearity (DNL) obtained is $+0.36/-0.28LSB$. In addition, the temporal noise is about $530\mu V_{\text{rns}}$ due to the small bandwidth of the column-parallel structure in an image sensor. The implemented ADC achieves $250\mathrm{kS}/\mathrm{s}$ as a maximum speed. The maximum frame rate of the sensor is 2500fps. The power consumption of the sensor, except for the LVDS interface, is $37.5\mathrm{~mW}$. This sensor is designed in TowerJazz CIS $180\mathrm{~nm}$ process with one poly and four metal layers. The supply voltage of the analog and digital domains are $3.3\mathrm{~V}$ and $1.8\mathrm{~V}$, respectively.
Analytical Approach to Evaluating the Performance of the OpenFlow Protocol on Software Defined Networks

This work proposes an analytical approach to evaluating OpenFlow performance applied on Software Defined Networks (SDNs). The method can quickly estimate packet waiting time in a controller domain, addressing real world SDN challenges. The effectiveness of the method is demonstrated quantitatively, making it a valuable tool for SDN system designers to improve a network's quality of service. The approach assumes uniform traffic conditions and a variety of offered loads, highlighting the importance of testing under various conditions.
Unlicensed Spectrum Assisted Connection in 5G-NR Enabled Metaverse

As the licensed spectrum has been scarce, the unlicensed spectrum is deemed as a strong supplement to the licensed band, particularly for diversified sensors connected to the metaverse. Moreover, as one of the key components in future communication networks, the massive connection is crucial to the massive sensors connected to the metaverse with object of maximizing the number of connected devices while minimizing their power consumption. Therefore, both licensed and unlicensed bands are used for metaverse users and sensors in this paper. First, a feasible implementation framework is proposed to minimize power consumption and maximize the number of connected sensors while guaranteeing their data rate requirements. Accordingly, a joint power, spectrum allocation and user association scheme is proposed. Finally, the simulation results are provided to verify the effectiveness of the proposed algorithm.
A 22nm 56TOPS/W 6/8-bit Linearly-scalable R-2R Multiply-and-Accumulate Architecture with 2.2ns Latency

This paper presents a current-domain compute-in-memory (CIM) architecture for acceleration of Artfficial Intelligence (AI) edge inferencing. A novel multiply-and-accumulate (MAC) scheme is introduced by exploiting the R-2R resistor ladder as a binary-weighted current recombiner. The area and power of the proposed scheme scale linearly as numerical precision increases for both input activation and weight. Computation latency is maintained single cycle. A prototype in 22nm FDSOI CMOS process achieves 2. 2ns system latency, 56TOPS/W energy efficiency and 4TOPS/m$\mathrm{m}^{2}$ area efficiency with 6-bit input activation and 8-bit weight.
Estimation of water uptake by organic compounds in submicron aerosols measured during the Southeastern Aerosol and Visibility Study

In situ measurements of size-dependent water uptake by atmospheric particles made with a tandem differential mobility analyzer (TDMA) and size-resolved chemical composition of aerosol samples collected with cascade impactors in the Smoky Mountains have been examined in order to ascertain the influence of organic carbon compounds on aerosol hygroscopicity. Particles were dried to ∼5% relative humidity (RH) before entering the TDMA, leading us to believe that salts of ammonium and sulfate were in crystalline states for relative humidities below their expected deliquescent points. TDMA-measured water content was found to be in excess of the sulfate-associated water modeled using laboratory data for binary aqueous solutions and the method of Zdanovskii-Stokes-Robinson for multicomponent solutions over a wide range of humidities (RH = 5–85%). Furthermore, excess water was observed to increase in proportion to the organic fraction of mass associated with each examined size in the range 0.05 to 0.4 μm. These data are used to obtain an empirical relationship between the amount of water associated with particulate organics and relative humidity. This analysis shows that organic-associated water content is considerably less than that of sulfate compounds, on a volume basis, for high RH, but comparable or greater for low RH. These results are consistent with laboratory data for water absorption by a range of organics vis-a-vis ammonium salts of sulfate.
Spatio-Temporal Short Term Load Forecasting Using Graph Neural Networks

Accurate short-term load forecasting (STLF) is essential for the efficient operation of the power sector. Due to heightened volatility and intrinsic stochasticity, forecasting load at a fine resolution, such as weekly load, is difficult. Existing STLF techniques only rely on temporal data and auto-regressive processes to forecast load. However, the power grid has a graphical structure that provides spatial information too. This paper proposes an innovative STLF method fusing both spatial and temporal information. We propose a creative way to convert load data into graphical form, which is fed into graph convolutional networks (GCN) to learn spatial embeddings. The GCN embeddings are used along with temporal features to predict the load. We perform extensive experiments using state-of-the-art machine learning and deep learning techniques to validate our approach. The results demonstrate that by using spatial information, we can sub-stantially improve the forecasting performance.
A Scalable Multi-Chip Self-Aligning Ka-Band Phased Array

This work presents a scalable phased array architecture based on background phase alignment between its nearest neighbors. We implement the proposed interferometric phase monitoring scheme in a multi-chip array configuration with chip-to-chip bonding between adjacent modules. The prototype $1\times 7$ phased array consists of 7 transmitter chips and 6 control chips all fabricated in the TSMC $65\mathrm{n}\mathrm{m}$ CMOS process. The die areas of the two modules are $0.86\mathrm{m}\mathrm{m}^{2}$ and $0.41\mathrm{m}\mathrm{m}^{2}$, respectively. The array demonstrates a measured ± 30° beam steering consistent with 210° measured phase tuning range between adjacent neighbors. The transmitter phase noise is-114.6dBc/Hz at 1MHz offset at 30GHz and operates across a 16.7% fractional bandwidth. To the best of our knowledge, this work presents the first scalable self-aligning and beam steering mm-wave phased array.
Mesh Deformation Scheme for High Quality 3D Model Reconstruction

This paper proposes a new mesh deformation scheme in a 3D reconstruction in which the performance deteriorates when an object of an unlearned category is input in a 3D model generation network. The proposed method uses a new loss function to generate appropriate 3D reconstruction outputs even for inputs of unlearned categories of 3D models. We compare the performance with the existing method so as to prove the validity of this method. The simulation results show that the proposed loss function improves the generalization ability of the 3D reconstruction with good performance even for inputs of unlearned categories.
SiC MOSFET Inverter Design, Considering Unplanned Events for Electric Aviation

In this paper, distributed electric propulsion system has been reviewed using virtual concept urban air mobility with 18 propulsion motors and inverters on each side. For distributed power, a demo inverter board with a SiC MOSFET was used and it is functionally tested with permanent magnetic synchronous motor. This board's dynamic loss was measured by double-pulse test and these measured values were used for measurement-based loss simulation. For evaluating total system redundancy, unplanned events, such as changed trajectory, accelerated voltage drop and extended flight time were reviewed. Additionally, each power-distributed inverter with increased current by failure event was simulated in a virtual profile. For maximum power loss, defined cooling condition with heat-sink were evaluated by thermal simulation. Finally, over current test by failure event was checked by silicon on insulator gate driver in the inverter.
Implementation of Digital Twin-Assisted Condition Monitoring and Fault Diagnosis for Wind Turbines

To achieve higher efficiency and reduce the wind energy cost, the size of wind turbines is getting bigger and offshore wind is getting more attraction. Therefore, the Operation & Maintenance of a single wind turbine and a large-scale wind farm are becoming more important. This paper presents the implementation of digital twin-assisted condition monitoring and fault diagnosis for wind turbines. Based on a 5-dimension modeling approach, we design and implement a digital twin for a 2 MW class double-fed induction generator-based wind turbine system. Then, we suggest an algorithm based on the digital twin framework to virtually monitor wind turbine conditions and form an initial fault detection algorithm. The digital twin shows high accuracy and the digital twin-based diagnosis algorithm shows a high performance.
A 100x100 CMOS SPAD Array with In-Pixel Correlation Techniques for Fast Quantum Ghost Imaging Applications

In this paper, a 100x100 CMOS Single Photon Avalanche Diode (SPAD) array designed for quantum imaging applications requiring synchronization with an external unpredictable and aperiodic trigger is presented. The targeted application takes advantage of non-classical quantum states of light to achieve quantum ghost imaging in the middle-infrared (MIR) up to 7 $\mu \mathrm{m}$ wavelength by detecting the corresponding entangled photons in the visible spectrum with the SPAD imager. For this purpose, a SPAD array with in-pixel backward-looking temporal correlation capabilities has been implemented to spatially resolve the entangled photons in a ghost imaging setup. The correlation is performed through a pixel-wise asynchronous delayed time window, whose width can be tuned to maximize the correlation efficiency. Two pixel variants, without and with resource sharing, have been designed: the resulting imager has been implemented in a 110 nm CIS FSI technology, achieving a correlation window width between 2 ns and 27 ns and a delay range between 5 ns and 40 ns, with 17 $\mu \mathrm{m}$ pixel pitch and fill-factor of 19.3% and 31.3%. A smart readout architecture has been implemented to improve the acquisition duty cycle, given the expected sparsity of the detected events.
Comparison of Adoption Factors toward Instant Messenger Applications in Indonesia and Malaysia using the Extended UTAUT Model 2

This study identifies, measures, and compares factors which influence the Behavioural Intention (BI) and usage of instant messenger applications (IMA) of Indonesians and Malaysian customers centred on the UTAUT 2 model. The objectives are first, to find out the proposed fit model for measuring instant messenger application both in Indonesia and Malaysia. Secondly, to develop a measurement tool (a set of questionnaires) to assess the proposed model. Data accumulated from Indonesia and Malaysia using questionnaires were distributed by using the purposive sampling technique. 1123 respondents from Indonesia and 415 respondents from Malaysia were collected. Data analysis was performed by utilizing smart PLS software. R² values of 0.683 (BI) and 0.631 (UB) were obtained for Indonesian respondents. R² values of 0.771(BI) and 0.571 (UB) were obtained for Malaysian respondents. Results have indicated that the model has significantly described the variation of consumer’s behavioural intention and UB to utilize instant messenger application both in Indonesia and Malaysia. Time constraint did not permit the study to be extended to other nations in the regions. This is an attempt to understand significant usage characteristics between two countries are very similar culturally.
A 3.7mW 11b 1GS/s Time-Interleaved SAR ADC with Robust One-Stage Correlation-Based Background Timing-Skew Calibration

This work proposes a one-stage correlation-based background timing-skew calibration method. It detects timing-skew errors of all channels simultaneously and relaxes the input bandwidth limitation up to the Nyquist frequency, ensuring fast and robust convergence. It enables an 11b1GS/s4-channel time-interleaved SAR ADC that achieves the best FoMs of l70.6dB and FoMw of 4. 9fJ/conv-step among all reported ADCs with $\mathrm{f}_{\mathrm{s}}$>600MS/s.
Assessing the Socio-Economic Potential of Electric Vehicle Charging Infrastructure: A Machine Learning based Approach for Marrakech-Safi Region, Morocco

The evaluation of socio-economic potential plays a pivotal role in advancing sustainable transportation systems, particularly in the field of the infrastructure for electric vehicle charging. In this study, we present a comprehensive methodology that integrates the Analytic Hierarchy Process (AHP) and machine learning techniques to evaluate the socio-economic potential of the Marrakech-Safi region. By employing AHP, we determine the target variable, and subsequently apply RF (Random Forest) and SVM (Support Vector Machine) models incorporating 11 key factors such as demographics, road network, public facilities, typology, and power grid. The findings of our study reveal that the RF model, with an accuracy of 96.37%, outperforms the SVM model, which achieved an accuracy of 94.81%, in accurately predicting the socio-economic potential of our region. Building upon these results, we employ the RF model to project the potential of the Casablanca-Settat region, uncovering promising opportunities for the construction of an infrastructure for electric vehicle charging, notably in the city of Casablanca. The information provided by this study hold significant implications for decision-makers and policymakers involved in the planning and promotion of sustainable transportation infrastructure. By leveraging the combination of AHP and machine learning techniques, our methodology provides a solid framework for evaluating a region's socioeconomic potential, contributing to the formulation of informed strategies for sustainable transportation systems.
A Fully Synthesizable DPLL with Background Gain Mismatch Calibrated Feedforward Phase Noise Cancellation Path

This work presents a fully synthesizable DPLL with a feedforward phase noise cancellation (FPNC) path. The gain mismatch between the FPNC path components has been calibrated digitally in the background against process, voltage, and temperature (PVT) variations. As a result, the rms value of the absolute jitter was reduced over different conditions beyond the DPLL stability barrier. A prototype chip with an area of 0.105mm2 was fabricated in 65nm CMOS technology. The measurements showed a jitter reduction at 1. 0GHz output frequency from 1. 0ps to 753fs at the cost of 0. 7mW extra power consumption and FoM improvement from -234.4dB to -236dB. Also, similar improvements were measured under different voltages, chips, and output frequencies.
Community Detection Algorithm for Mitigating Eclipse Attacks on Blockchain-enabled Metaverse

The Metaverse has recently attracted a lot of attention from academia and the industry. Blockchain technology is a critical coordinator for the Metaverse, providing a secure and decentralized infrastructure to create and manage digital assets. Nonetheless, several security threats arise from using blockchain in the Metaverse such as Distributed Denial of Service (DDoS) in peer-to-peer networks. The eclipse attack is one of the most common and easy-to-launch DDoS attacks on blockchain networks and can further lead to 51%, selfish mining, double-spending, and race attacks. However, eclipse detection remains a challenge for light nodes that cannot deploy complex intrusion detection systems. In this paper, we design an intrusion detection system based on a community detection algorithm (CDA) to detect every attempt to launch an eclipse attack through blockchain-enabled metaverse systems. First, the blockchain traffic data is captured from peers participating in the blockchain network. Then, one of the most efficient CDAs, namely the Leiden algorithm, is applied to detect eclipse attempts. The experimental results show that the proposed approach can identify the potential malicious nodes with high accuracy in order to monitor their behaviors and connections through the network and mitigate the attack.