Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparisons between a continuous 3‐year current‐meter observation at the sill of the Yucatan Strait, satellite measurements of Gulf Loop Current area, and regional sea level

From October 1977 through November 1980 a current-meter mooring was maintained in the Yucatan Strait. The meter was moored halfway between Mexico and Cuba, 145 m above the sill or in 1895 m of water. Motions of low frequency (<14−1 cycles/day) are oriented approximately parallel to the isobaths, 021°–030° true. Net drift for 3 years is to the SSW at an average velocity of 1.8 cm/s. Sustained southward flows at intervals of 8 months, which persisted for several months each, have average velocities of 5 cm/s, with randomly spaced bursts as high as 15 cm/s. Energy in subtidal frequency bands has significant peaks near 38−1 and 19−1 cycles/day, with a broad band of energy between 300−1 and 200−1 cycles/day. The latter peak is consistent with the approximately 8-month interval between the southward flow events. Comparison with weekly areal coverage of the Gulf Loop Current from Geostationary Operational Environmental Satellite infrared observations shows little covariation, except that 8 months is typical of some anticyclonic eddy generation. There is little coherence of sill depth velocities with Naples sea level at subtidal frequencies, but with Miami there is coherence at several frequencies, notably 38−1 and 19−1 cycles/day. In the higher frequencies, the principal tidal motions are diurnal and are oriented somewhat across the isobaths toward the northwest, 346°–349° true, with counterrotating O1 and K1 constituents. No semidiurnal, inertial, or fortnightly energy is observed above the background continuum.
A real‐time oceanographic nowcast/forecast system for the Mediterranean Sea

We describe here a nowcast/forecast system for the entire Mediterranean Sea, designed for real-time forecasts and closely resembling operational numerical weather prediction systems. The core of the system is a high-resolution (10 km) three-dimensional primitive equation-based, sigma-coordinate numerical circulation model, assimilating remotely sensed multi-channel sea surface temperature and in situ expendable bathythermograph/conductivity-temperature-depth observational data, using an optimal-interpolational scheme. We present results for 1993 and 1994 from this data-assimilation model, focusing principally on the mesoscale features prevalent in the western and eastern Mediterranean Sea. We show that the model exhibits considerable skill in simulating both the permanent and transient mesoscale features in the Mediterranean. In particular, hitherto less well-known and less well-studied circulation features in the Adriatic and Aegean Seas are carefully examined and presented. Particular care is given to discussing the circulation in the entire Mediterranean, with particular attention to flows through various straits and their variability.
Latency Guarantee for Task Computation in Wireless-Powered Cloud Radio Access Networks

With the massive and rapid deployment of Internet of Things (IoT) devices, the number of IoT devices in the cloud radio access network (C-RAN) has increased dramatically. It is intractable to provide deterministic Quality-of-Service (QoS) guarantee for task processing due to the task burstiness and the time-varying channels. In order to guarantee latency requirements of energy-limited IoT devices in C-RAN, this article proposes a statistical latency guarantee scheme for task computation. With the assistance of densely distributed remote radio heads (RRHs) providing wireless power transfer to the IoT devices, a wireless-powered edge computing C-RAN model is constructed. Then, the problem of minimizing task latency violation probability is formulated. With the help of effective capacity theory, the problem is decoupled into multiple subproblems, which are QoS parameter optimization, task offloading optimization, wireless power transfer, and energy allocation. Thereafter, low-complexity schemes are designed to jointly optimize the wireless power transfer, energy allocation, and task offloading. The effectiveness of the proposed statistical latency guarantee scheme is finally validated by extensive simulations.
Low impact surface hardness testing (Equotip) on porous surfaces – advances in methodology with implications for rock weathering and stone deterioration research

The Equotip surface hardness tester is becoming a popular method for rock and stone weathering research. In order to improve the reliability of Equotip for on‐site application this study tested four porous limestones under laboratory conditions. The range of stone porosity was chosen to represent likely porosities found in weathered limestones in the field. We consider several key issues: (i) its suitability for soft and porous stones; (ii) the type of probe required for specific on‐site applications; (iii) appropriate (non‐parametrical) statistical methods for Equotip data; (iv) sufficient sampling size. This study shows that the Equotip is suitable for soft and porous rock and stone. From the two tested probes the DL probe has some advantages over the D probe as it correlates slightly better with open porosity and allows for more controlled sampling in recessed areas and rough or curved areas. We show that appropriate sampling sizes and robust non‐parametric methods for subsequent data evaluation can produce meaningful measures of rock surface hardness derived from the Equotip. The novel Hybrid dynamic hardness, a combination of two measuring procedures [single impact method (SIM) and repeated impact method (RIM)], has been adapted and is based on median values to provide a more robust data evaluation. For the tested stones in this study we propose a sample size of 45 readings (for a confidence level of 95%). This approach can certainly be transferred to stone and rock with similar porosities and hardness. Our approach also allows for consistent comparisons to be made across a wide variety of studies in the fields of rock weathering and stone deterioration research. Copyright © 2016 John Wiley & Sons, Ltd.
Dynamic variation of the auroral oval during intense magnetic storms

The latitudinal variations of the noon sector polar-cusp region and the nightside auroral oval were examined to investigate the auroral oval dynamics during three intense geomagnetic storms. The variations were compared with the variation in ring-current intensity (i.e., Dst) and with changes of the interplanetary magnetic field Bz component to determine the dominant parameter of the large-scale polar-region configuration changes during magnetic storms. Deviations of the polar cusp and the midnight auroral oval from their normal quiet time position are also compared to determine the occurrence of the large-scale reconfiguration of the polar-region geometry. It is found that (a) the large (>10° geomagnetic latitude) equatorial shift of the auroral oval occurs in coordination with the equatorward motion of the polar-cusp region; (b) the polar cusp is displaced by a few degrees more than the nightside auroral oval near the peak of a magnetic storm; (c) the midnight auroral oval recovers more slowly than the polar-cusp region during the storm recovery phase; and (d) the midnight oval and the noon sector polar-cusp region move coherently with the southward variation of the interplanentary magnetic field Bz, but not necessarily with the Dst intensity variations.
A Robust Data-Based Clock Synchronisation Algorithm for Multi-Channel SAR Systems

We present in this paper an algorithm for the estimation of synchronisation phase errors in bistatic and multistatic SAR systems with azimuth diversity, such as multi-channel architectures or azimuth constellations, solely based on the evaluation of the received bistatic data. The suggested algortihm working with unfocussed SAR data shows more robustness against processing errors when compared to the current literature. The relevance of the approach is enhanced by the current interest in both multi-channel SAR architectures and companion SAR missions.
A Randla-Net-Based Approach to Segment Roadway Objects in Large-Scale Point Clouds

Mobile Laser Scanning (MLS) systems provide fast and easy access to dense and accurate point clouds of roadways. Currently, semantic segmentation of urban roadway objects in these data plays a significant role in urban modelling, autonomous driving, intelligent transportation, etc. However, the semantic segmentation of large-scale urban roadway point clouds is still challenging due to the unstructured and disordered nature of point cloud and the enormousness of points acquired during practical applications. Based on that, we propose an automated target objects semantic segmentation approach based on RandLA-Net. With this procedure, we first perform sample annotation, then train a RandLA-Net for semantic segmentation, and finally evaluate the model on test sets. The mean IoU reaches 76.54%, and the recall score metrics for semantic segmentation of buildings and rods-like objects reach 96.44% and 93.66%, respectively. From the visualized results, we achieved clean building contour segmentation and robust, consistent rods-like object segmentation in up to 108 points urban roadway scenarios.
Preserving Extremes in Satellite Quantitative Precipitation Estimates: A Matter of Scale

Alternative metrics to the mean squared error (MSE) are assessed as optimization criteria for geophysical remote sensing applications. While the MSE promotes smooth solutions with compressed dynamical range, optimization criteria focusing on the preservation of the statistical properties of the target geospatial fields can be used to compensate this effect. These different optimization criteria are included as distance metrics within the objective function during the training of a deep learning algorithm for retrieval and mapping of precipitation rates from space-borne passive radiometric measurements. It is shown than imposing a constraint on the statistical distribution of the target variable at the "pixel" level only is insufficient, and that a multi-scale constraint (applied here through a discrete wavelet transform) is necessary to preserve the statistical properties of the target and the extremes across all scales.
A Reconstruction-Based Visual-Acoustic-Semantic Embedding Method for Speech-Image Retrieval

Speech-image retrieval aims at learning the relevance between image and speech.Prior approaches are mainly based on bi-modal contrastive learning, which can not alleviate the cross-modal heterogeneous issue between visual and acoustic modalities well. To address this issue, we propose a visual-acoustic-semantic embedding (VASE) method. First, we propose a tri-modal ranking loss by taking advantage of semantic information corresponding to the acoustic data, which introduces the auxiliary alignment to enhance the alignment between image and speech. Second, we introduce a cycle-consistency loss based on feature reconstruction. It can further alleviate the heterogeneous issue between different data modalities (e.g., visual-acoustic, visual-textual and acoustic-textual). Extensive experiments have demonstrated the effectiveness of our proposed method. In addition, our VASE model achieves state-of-the-art performance on the speech-image retrieval task on the Flickr8K [Harwath and Glass, 2015]s and Places [Harwath et al., 2018] datasets.
Characterization of Evaporite Minerals on the Surface of the Martian Ritchey Crater Using CRISM Hyperspectral Data

The sulfates, carbonates, and chlorides based minerals have been remotely detected on the Martian surfaces. These evaporite deposits are not only indicative of environmental conditions of their formation, but can provide clues indicating their interaction with water. Due to narrow absorption features, the hyperspectral remote sensing is a powerful technology to identify and discriminat evaporite minerals. This study intends to detect evaporite minerals including gypsum and salt deposits on the Ritchey crater region using hyperspectral data acquired by CRISM carried onboard MRO platform. To achieve this, the data were pre-processed to correct several radiometric, atmospheric, spectral and geometric anomalies. Afterwards, the most popular indices for gypsum and salts detection in arid terrestrial environments such as the NDGI and SSSI were implemented and maps derived. The results obtained indicate that both the indices have mapped the evaporite minerals patterns almost identical. However, the NDGI further highlights the gypsum content, while SSSI product show greater sensitivity to evaporit minerals (i.e., gypsum, sulfate chloride, halite, etc.). Likewise, for validation and comparison procedure, SSSI derived product revealed high similarity with that of chloride published by CRISM-NASA team. Moreover, the spectral signature extracted from the pixels with high evaporite minerals content exhibits the largest hydration signature around 1.95 μm, which is associated with several features typical of gypsum and halite between 1.0 and 2.5 μm. The position, shape, and structure of the absorption features at 1.45, 1.75 and 1.95 μm, as well the gradual decrease in reflectance at 2.4 μm provide some resemblance with the gypsum compared to less hydrated forms of calcium sulfates. However, these absorption characteristics look like a mixture among gypsum, bassanite, anhydrite, and halite.
An Enhanced Unsupervised Feature Learning Framework For Radar Sounder Signal Segmentation

Unsupervised semantic segmentation is the method of discovering meaningful semantic contents within the image domain without using any labelled information. The learned semantic contents are then decomposed into distinct semantic segments with known ontology. The core task of an unsupervised feature learning algorithm is to produce dense features for every pixel with rich semantic content to form distinct clusters with compact information for the downstream task. In this work, we extend the previously developed Self-Supervised Transformer with Energy-based Graph Optimization (STEGO) architecture by integrating a convolution-based Expansive Network in the decoder along with the spatial similarity loss function for radar sounder signal segmentation. Experimental results on the Multi-Channel Coherent Radar Depth Sounder (MCoRDS) data confirm the capability of the proposed unsupervised segmentation method.
Estimating Crop Grain Yield and Seed Composition Using Deep Learning from UAV Multispectral Data

The objective of this work is to predict corn grain yield and seed compositions (i.e., protein, oil and starch concentration) using Unmanned Aerial Vehicles (UAV)-based multispectral imagery and deep learning models. UAV multispectral imagery was acquired throughout the growing season of 2022 over a cornfield near Brookings, South Dakota, USA. Deep learning methods such as Convolutional Neural Network (CNN), Long Short-Term Memory networks (LSTM), and Transformer were used to predict corn grain yield and seed protein, oil and starch compositions based on UAV multispectral imagery. The results show that: (1) both original and attention-based CNN and LSTM, as well as Transformer models are able to successfully predict corn yield and seed compositions. Transformer yielded superior performance over other methods. (2) attention CNN and LSTM consistently outperformed original CNN and LSTM. (3) multitemporal data outperformed each single-day imagery-based estimations.
A Preliminary SAR Simulator on Matlab for System Design

This paper demonstrates a preliminary Synthetic Aperture Radar (SAR) simulator developed on MATLAB. The simulator can involve the parameters of radar transmitters, receivers, antennas, digital processors, and channel fading. This paper summarized some study simulated by this simulator, such as the effect of the receiver noise figure, the phase noise of the oscillator, antenna patterns, side lobes, and multi-receiving digital beam forming (DBF). Most of the design parameters of a SAR can be introduced in the simulator and thus this simulator is suitable for determining system requirements and performance evaluation.
Impact of future land use and land cover changes on atmospheric chemistry-climate interactions

annual soil NO emissions by ∼1.2 TgN yr −1 (9%), whereas isoprene emissions decrease by ∼50 TgC yr −1 (−12%). The analysis shows increases in simulated boundary layer ozone mixing ratios up to ∼9 ppbv and more than a doubling in hydroxyl radical concentrations over deforested areas in Africa. Small changes in global atmosphere‐biosphere fluxes of NOx and ozone point to compensating effects. Decreases in soil NO emissions in deforested regions are counteracted by a larger canopy release of NOx caused by reduced foliage uptake. Despite this decrease in foliage uptake, the ozone deposition flux does not decrease since surface layer mixing ratios increase because of a reduced oxidation of isoprene by ozone. Our study indicates that the simulated impact of land cover and land use changes on atmospheric chemistry depends on a consistent representation of emissions, deposition, and canopy interactions and their dependence on meteorological, hydrological, and biological drivers to account for these compensating effects. It results in negligible changes in the atmospheric oxidizing capacity and, consequently, in the lifetime of methane. Conversely, we expect a pronounced increase in oxidizing capacity as a consequence of anthropogenic emission increases. Citation: Ganzeveld, L. , L. Bouwman, E. Stehfest, D. P. van Vuuren, B. Eickhout, and J. Lelieveld (2010), Impact of future land use and land cover changes on atmospheric chemistry‐climate interactions, J. Geophys. Res., 115, D23301,
"Low Supervision" Deep Cluster Change Detection (CDCluster) On Remote Sensing RGB Data: Towards The Unsupervising Clustering Framework

This paper is concerned with the change detection issue in remote sensing images. This problem is not trivial since the notion of change depends on the application. Moreover, classical supervised deep learning methods have to deal with the limited amount of labelled data available. Based on existing deep learning techniques that exploit unsupervised clustering to assign labels to entire images, we adapt them to the change detection problem by using siamese backbones and extracting pixel-wise results. As fully unsupervised experiments lead to unstable results, we suggest "low supervision" strategy composed of a warm-up stage with few labeled data able to drive the following unsupervised learning through reliable solutions. Preliminary experiments show reliable change maps.
Aquatic fate of aerially applied hexazinone and terbuthylazine in a New Zealand planted forest

Herbicides are used to control competing vegetation during tree establishment, and are often critical to the productivity and economic viability of a planted forest crop. Despite increasing public concern over herbicide use in planted forests and potential impact on the environment, there is limited information on the aquatic fate of many of these herbicides when applied in forests according to standard operational procedures. Two herbicides commonly used in New Zealand’s planted forests, terbuthylazine and hexazinone, were aerially applied during two consecutive years at 7.4 kg and 1.1 kg ha -1 respectively, to a 12.5 ha catchment of newly planted Pinus radiata (Radiata pine) in the Bay of Plenty region of New Zealand. One hundred percent of the area was sprayed during Year 1 while 85% of the area was sprayed during Year 2. The highest concentrations of both herbicides were measured in stream water, sediment and algal samples on the day of herbicide application during Year 1 (September 2012 to June 2013), mainly as a result of spray drift, and in stream water during Year 2 (September 2013 to April 2014) at the time of a rainfall event seven days after herbicide application. Terbuthylazine concentrations exceeded New Zealand and World Health Organisation drinking water standards for <24 hours on both occasions. For the remainder of the trial period, concentrations of both herbicides were below safe drinking water standards. Herbicide concentrations in stream flow declined immediately below the trial site and were close to or below detection limits at the bottom of the catchment. The concentrations of terbuthylazine and hexazinone in stream flow during this trial, posed a short-term and low risk to algae. Peak concentrations of both herbicides were several orders of magnitude lower than Lethal Concentration (LC)50 concentrations for Daphnia and rainbow trout (Oncorhynchus mykiss). These results highlight the need to include data from both field and laboratory based trials when assessing the aquatic risk of these two herbicides when applied under operational conditions in planted forests.
REVIEW OF TEXT BASED PASSWORD AND OTHER AUTHENTICATION METHODS FOR E-COMMERCE DATA PROTECTION

Significant development of the internet has triggered various new technologies. One of them is e-commerce. This causes the security aspect of e-commerce to be one of important factors, especially to prevent unwanted things such as data leaks and financial losses. The authentication method is one example to provide protection, such as protection of user data and protection of transactions in e-commerce. In short, the authentication system prevents access by unauthorized parties. Authentication systems may vary from the simplest, namely the use of passwords, to other methods. The aim of this study is to explore various authentication methods, the advantages, disadvantages, types of attacks that can occur, and the importance of using an authentication system, especially in the e-commerce environment.
The Role of Trust and Satisfaction as Mediators on Users’ Continuous Intention to Use Mobile Payments: Empirical Study

During the period of Corona pandemic, many customers faced some difficulties due to the repeated closures of banks and government institutions, which necessitated them to search for other alternatives like mobile payments to pay their due bills. Consequently, more research is needed to study what factors are important to continue using of such technology. In response, this study used a sample of 880 respondents, then proposed a theoretical model and tested it empirically. Results highlighted the role of trust and satisfaction as mediators which significantly impacted users’ continuous intention to use mobile payments technology. © 2022 Little Lion Scientific