Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed BESS Scheduling for Power Demand Reshaping in 5G and Beyond Networks

The mobile network operators are upgrading their network facilities and shifting to the 5G era at an unprecedented pace. The huge operating expense (OPEX), mainly the energy consumption cost, has become the major concern of the operators. In this work, we investigate the energy cost-saving potential by transforming the backup batteries of base stations (BSs) to a distributed battery energy storage system (BESS). Specifically, to minimize the total energy cost, we model the distributed BESS discharge/charge scheduling as an optimization problem by incorporating comprehensive practical considerations. Then, considering the dynamic BS power demands in practice, we propose a multi-agent deep reinforcement learning (MADRL) based approach to make distributed BESS scheduling decisions in real-time. Particularly, QMIX framework is leveraged to learn the partial policy of each agent in the training phase; while in the execution phase, each BS can make scheduling decisions based on local information. The experiments using real-world BS deployment and traffic load data demonstrate that with our QMIX-based distributed BESS scheduling, the peak power demand charge of BSs can be reduced by more than 26.59%, and the yearly OPEX saving for 2282 5G BSs could reach up to U.S. ${\$}$ 196,000.
Slab dehydration and fluid migration at the base of the upper mantle: implications for deep earthquake mechanisms

SUMMARY Water enters the Earth's mantle via subduction of oceanic lithosphere and sediments. A lot of this water immediately returns to the atmosphere through arc volcanism, but part, retained in Dense Hydrous Magnesium Silicates (DHMSs) and Nominally Anhydrous Minerals (NAMs) like olivine, is expected to be subducted as deep as the bottom of the upper mantle (660 km depth). Then, due to its low solubility in lower mantle minerals, water is likely to be released as a hydrated fluid during the spinel–post-spinel phase change. The dynamics of this fluid phase is investigated through a 1-D model of compaction, in which a source term has been introduced to take the fluid precipitation into account. The competition between the advective transport by the descending slab and the buoyant rise of the fluid results in three distinct situations, depending on the properties of the solid and the fluid phases. Low matrix permeability and high fluid viscosity inhibit compaction and favour the entrainment of fluid towards the deep mantle. In this case, the entire slab water content would enter the lower mantle and would be mixed at large scale. However, realistic values of the fluid viscosity and matrix permeability make this possibility unlikely. When effective, compaction results in an accumulation of fluid at and below the phase boundary. Then, depending on the value of the matrix viscosity, the situation evolves differently. Above 1020 Pa s, accumulation of fluid extends below the phase boundary and the pressure difference between the fluid and the matrix increases continuously, exceeding the yield strength of rocks. As a result, cracks would form and evolve towards the formation of dykes. In case of very low mantle viscosity, possibly due to strong grain size reduction during phase change, compaction becomes very efficient and the fluid remains confined within the phase change horizon, without increasing pressure. In the long term, this last situation appears unstable and would also evolve towards the formation of dykes. Thus, it is expected that water returns to the upper mantle by dykes propagating in the direction of the maximum compressive stress. Since maximum compressive stress appears to follow the dip of the slab below 410 km depth, we predict the formation of dykes extending from the 660 km phase change to 410 km depth. The possible existence of such dykes in slabs offers the necessary conditions for strong double couple component earthquake in the deepest part of the upper mantle.
An Efficient and Secure Lightweight Certificateless Hybrid Signcryption Scheme

With the limited resources of the Internet of Things (IoT), security and efficiency have become a challenge. The hybrid signcryption scheme is a proper method to ensure data security and integrity. Recently, through continuous and in-depth research, the signcryption scheme has made many achievements, but there are still some problems. Most proposals use bilinear mapping, which reduces computational efficiency. It brings a heavy burden to the resource-constrained IoT. And many constructions cannot meet the claimed security, causing the leakage of private messages. Therefore, we propose a lightweight certificateless hybrid signcryption (CLHS) scheme with better performance and higher security to solve the above problems. First, we have reduced storage pressure and improved computational efficiency by using certificateless public-key cryptography and elliptic curves instead of bilinear maps to construct a signcryption scheme, and the construction is lightweight. At the same time, to securely transmit messages of different lengths, we employ a hybrid signcryption scheme. Through formal security proof, efficiency, and performance analysis, our proposal has the security requirements of confidentiality and unforgeability and has better computational efficiency and security performance. Finally, we propose a secure data transmission protocol based on our CLHS scheme in Smart Grid, which inherits the advantages of high computational efficiency and better security of the underlying CLHS scheme.
Spatial and temporal evolution of disaster situation based on social media: a case study of the 2018 Beijing rainstorm

Using social media to mine disaster information (situational awareness) is of great significance for disaster management. With the development of technology, researchers began to attempt to use social media data for situational awareness. In this paper, a spatial and temporal evolution framework of disaster situation is proposed. Combining the LDA model, UIE model and kernel density estimation method, the spatial and temporal evolution of rainstorm disaster situation in the interested period is analyzed. The results indicate that the location in social media texts can more accurately reflect disaster information, help to quickly understand disaster dynamics, and provide scientific basis for decision-making by governments and rescue organizations.
How well do 13 satellite-derived precipitation products capture the temporal and spatial variations of precipitation over Chinese mainland?

ABSTRACT Satellite observations of precipitation have greatly improved our understanding of its temporal and spatial distribution. In view of the high spatiotemporal heterogeneity and severely skewed distribution characteristics of precipitation, it is necessary and of considerable application value to understand the ability of satellite-derived precipitation products (SPPs) to characterize the variations of precipitation in the time and space dimensions separately. However, to date, research concerning this is scarce. In this study, we explore the ability of 13 SPPs to characterize the temporal and spatial variations of precipitation based on observations from more than 2400 meteorological stations in Chinese mainland from 2001 to 2018. The results show that (1) SPPs tend to perform better in identifying the temporal than the spatial variation of precipitation. Most SPPs can reliably monitor the temporal and spatial variation of monthly and seasonal precipitation in Chinese mainland, although the identification of the spatial variation of daily precipitation involves larger uncertainty; (2) CMORPH-B, IMERG-F, 3B42, and MSWEP generally had better performance in identifying the temporal and spatial variations of precipitation, while PERSIANN and GSMaP-N performed poorly. However, no single SPP outperformed others in all scenarios; (3) with respect to monitoring the temporal variation of daily precipitation, SPPs performed better in southern China than in the north, with three-quarters and half of median KGE values above 0.5 In comparison, no significant spatial difference was observed in their ability to monitor the spatial variation of daily precipitation; and (4) SPPs had large uncertainties in capturing the temporal and spatial variations of precipitation in winter, while they performed best in identifying the temporal and spatial variations of daily precipitation in summer. The results of this paper provide an important reference for data users needing to select suitable SPPs and for satellite developers desiring to further improve data quality.
Limiting the Spread of COVID-19 Based on Monitoring of PM

In Palestine, the maps of the coronavirus disease 2019 (COVID-19) pandemic issued by the Ministry of Health (MoH), which shows the number of deaths and tested positive cases, have not been able to stop the spread of the disease. Thus; an early warning and decision support system is needed to predict where the infection could happen. In this study, spatial analysis of the number of tested positive cases of COVID-19 indicated that the areas with high levels of Particulate Matter (PM) in the atmosphere were the most affected. PM is closely associated with respiratory diseases and also acts as a carrier of viruses in the air. Accordingly; an affordable mobile system for estimating the levels of PM is designed to warn the user in order to take necessary preventive measures such as wearing a mask or leaving the site. The system consists of a PM sensor, a microcontroller, a power supply, a Bluetooth system, and a mobile application. The findings of this study show that the designed system has a prompt real-time response and a stable operation capable for the protection of human health.
Distributed Nash Equilibrium Seeking With Stochastic Communication Noise

This brief considers a Nash equilibrium seeking problem with stochastic communication noise in non-cooperative games. Each player minimizes its own cost function and estimates the opponents’ actions by utilizing its neighbors’ information. To attenuate the impact of communication noise, two decaying gains are introduced into the estimation and optimization terms, respectively. Moreover, the consensus-based and passivity-based methods are further exploited. In the communication noise scenario, the consensus-based method effectively reduces burrs of noise, while the passivity-based method involves a delicate tradeoff between communication and optimization. By the stochastic Lyapunov analysis, the Nash equilibrium is globally asymptotically stable in mean square sense. Eventually, the two methods are applied to address a line coverage problem, which demonstrates their effectiveness.
SOIL CARBON AND NITROGEN AND GREENHOUSE GAS EMISSIONS AFFECTED BY SHEEP GRAZING UNDER DRYLAND CROPPING SYSTEMS

Sheep grazing to control weeds during fallow may influence soil C and N and greenhouse gas emissions by consuming crop residue and returning feces and urine to the soil. An experiment was conducted to evaluate the effect of sheep grazing compared to tillage and herbicide application for weed control on soil total C, total N, NH4-N, and NO3-N contents at the 0-120 cm depth from 2009 to 2011 and greenhouse gas (CO2, N2O, and CH4) emissions from May to October, 2010 and 2011 under dryland cropping systems in western Montana. Treatments were three fallow management practices (sheep grazing [GRAZ), herbicide application [CHEM], and tillage [MECH]) and three cropping sequences (continuous alfalfa [CA], continuous spring wheat [CSW], and spring wheatpea/barley hay-fallow [W-P/B-F]). Soil samples were collected with a hydraulic probe after crop harvest and greenhouse gas samples at 3 to 14 d intervals with a static chamber. Soil total C was greater in CSW and W-P/B-F than in CA at 5-30 cm but was greater in CA and CSW than in W-P/B-F at 60-90 cm. Soil total N and NO3-N contents were greater in CSW and W-P/B-F than in CA at 5-120 cm. Soil NH4-N content varied with treatments and years. Soil temperature and water content at 0-15 cm were greater in CHEM with W-P/B-F and GRAZ with CA than in other treatments. Greenhouse gas fluxes peaked immediately following substantial precipitation (>12 mm) and/or N fertilization, regardless of treatments. Total CO2 flux from May to October was greater in GRAZ with CA but N2O flux was greater in CHEM and GRAZ with CSW than in other treatments in 2010 and 2011. Total CH4 flux was greater in CA than in CSW and W-P/BF in 2011. Net global warming potential and greenhouse gas intensity were greater in CHEM with CSW than in other treatments. Continuous spring wheat increased soil C and N storage and available N at subsurface layers compared to other cropping sequences. Because of higher N2O emissions and lower C sequestration rate, global warming potential and greenhouse gas intensity increased under continuous spring wheat with herbicide application for weed control.
Positive and cognitive behavioral intervention to control HbA1c in type 2 diabetes: a pilot study

Introduction: the prevalence of diabetes mellitus in Mexican population is 12 %. Hemoglobin A1C (HbA1c) is the most important indicator of control of this disease, so it is required to develop psychological interventions to help people to control HbA1c. Objective: develop and evaluate a positive and cognitive behavioral intervention to control HbA1c in people with type 2 diabetes mellitus (T2DM). Method: eleven patients with T2DM were randomly assigned to an experimental group (EG; n = 6) or control group (CG; n = 5). The intervention consisted of 12 weekly sessions. Self-efficacy, hope, resilience, and optimism were measured with the Diabetes Treatment Self-Efficacy Instrument, Adult Hope Scale, Mexican Resilience Scale, and Life Orientation Test, respectively, along with an assessment of HbA1c, after intervention, and at a three-month follow-up. Results: clinically significant improvements on HbA1c after intervention (g = 0.426) and follow-up (g = 0.749), improvements in self-efficacy after intervention (g = 1.168) and follow-up (g = 0.873) were identified in EG. Discussion: clinically favorable, but not statistically significant, effects of the intervention were discussed; in addition, the role of study variables in HbA1c control is discussed. Conclusions: positive and cognitive behavioral intervention is a promising alternative to improve HbA1c and self-efficacy in people with T2DM. Thus, it is advisable to conduct further evaluation with randomized controlled trials.
Thin layers and shear-wave splitting

The near‐surface weathering layer is considered by many to be strongly anisotropic. Any shear‐wave signal passing through this low‐velocity layer will inherit, to some degree, the anisotropic response of this layer. For thin weathering layers, information about previous anisotropic events may be distorted; when the thickness of this layer approaches some physically defined limit, however, a previous layer’s anisotropic signature is completely overwritten. Hodograms and Alford rotations are typically used to analyze shear‐wave splitting in the presence of azimuthal anisotropy. When the time‐delay generated by an azimuthally anisotropic layer is ⩾τ/8, where τ = one period of the wavelet’s dominant frequency, distortion of a shear‐wave signal is great enough to degrade the accuracy of the interpretation in hodogram analysis. We found that Alford rotations are superior to visual hodogram analysis when the time delay between the fast and slow shear‐waves is less than τ/8. When two azimuthally anisotropic layer.
A New Perleidiform (Actinopterygii, Osteichthyes) from the Middle Anisian (Middle Triassic) of Yunnan, South China

Two complete specimens from the Pelsonian (Middle Anisian, Middle Triassic) of Luoping, Yunnan Province, South China are referred to a new actinopterygian genus, Luopingichthys gen. nov., and ascribed to the perleidiform family Polzbergidae based especially on a typical synapomorphy of a peculiar premaxillary‐maxillary complex, i.e., the fusion between premaxillary and maxillary along the antero‐dorsal margin of the maxillary. The new taxon differs from other deep‐bodied representatives of the family, Felberia and Stoppania, in its fusiform or deep fusiform body shape; sickle‐shaped preopercular with short infraorbital process; lack of modified scales at the base of the anal fin; the short‐based dorsal fin; scarcely ornamented scales; and thin anterior teeth. Based on a redescription of the holotype of the taxon Ctenognathichthys hattichi from the Middle Triassic (Ladinian) Prosanto Formation, Canton Graubünden, eastern Switzerland, the only known specimen, which shows the same fusion between premaxillary and maxillary, the systematic position of the species is clarified and the taxon is proposed to be a further representative of the family Polzbergidae, and is transferred to the new genus.
Effet d'un fluide sur la dynamique d'effondrement d'une colonne de grains

We investigate the effect of an ambient fluid on the dynamics of collapse and spread of a granular column simulated by means of the contact dynamics method interfaced with computational fluid dynamics. The runout distance is found to increase as a power law with the aspect ratio of the column, and, surprisingly, for a given aspect ratio and packing fraction, it may be similar in the grain-inertial and fluid-inertial regimes but with considerably longer duration in the latter case. We show that the effect of fluid in viscous and fluid-inertial regimes is to both reduce the kinetic energy during collapse and enhance the flow by lubrication during spread. Hence, the runout distance in a fluid may be below or equal to that in the absence of fluid due to compensation between those effects. Mots clefs : Milieux granulaires immergés, instabilité gravitaire, distance d’arrêt
Transfer Learning-Based NLOS Identification for UWB in Dynamic Obstructed Settings

Positioning with ultrawideband (UWB) is prominent among industrial localization systems, due to its high-range resolution attributes and lower cost. However, one notable challenge with UWB positioning in industrial environments is the prevalent presence of nonline-of-sight (NLOS) components or signals that degrade localization performance drastically. Coupled with this, industrial settings tend to constantly change making NLOS signals challenging to characterize each time these changes occur. Recently, promising approaches have been proposed to identify NLOS components, however their performances are limited to the specific environment where measurements were performed. Their performance cannot be extended to other unknown environments due to the distribution divergence problem, owing to differences in environments captured by the channel impulse response (CIR) waveforms. This therefore requires laborious processes of data collection and training environment-specific models for NLOS identification. In this article, we propose a robust transfer learning-based NLOS identification approach, which harnesses transition information via cross-domain mappings from both source and target domains, to construct representative homogeneous features of both domains. The representative homogeneous features capture discriminative information of both domains, while reducing the distribution divergence between the domains, making it easy to classify LOS and NLOS components from both environments together. To test the robustness of our approach, we perform extensive simulations with CIR data collected from two distinct environments—“hard NLOS” (characterized by high relative permittivity of surrounding objects, e.g., thick concrete walls, metallic objects, etc.) and “soft NLOS” (characterized by low relative permittivity of surrounding objects, e.g., plasterboard walls). Our proposed approach is not just effective in transferring knowledge between distinct environments, but significantly outperforms state-of-the-art works to NLOS identification in UWB positioning networks, while reducing the laborious process of data collection in the target domain.
Stratified flow over topography: upstream influence and generation of nonlinear internal waves

Results are presented from a recent experiment in Knight Inlet, British Columbia, focusing on the generation and propagation of nonlinear internal waves near the sill during ebb tide. High–quality echo–sounder and velocity measurements were obtained in a novel fashion, using instrumentation carried aboard a small inflatable boat. Complementing these data, a set of photographic images of the surface signature of the internal waves was acquired from both a fixed mountainside location and a helicopter. These photographs situate the observations within the larger–scale structure of the internal response found in the inlet. The observations reveal the development of strongly nonlinear internal waves on a bore occurring upstream of the sill crest. As the tidal flow relaxes, these waves propagate upstream, at first slowly, and later rapidly against the gradually waning flow. A simulation of wave generation with a fully nonlinear numerical model is discussed. The simulation shows that upstream influence, associated with the rapidly increasing tidal forcing, leads to formation of an undular bore upstream of the sill crest. As the tidal flow relaxes, the bore subsequently evolves into a group of upstream–propagating solitary–like internal waves. Taken together, the observations and numerical simulation describe a process for the generation of nonlinear internal waves involving upstream influence, which likely has application in other environments for stratified flow over topography.
Model-based human pose estimation with spatio-temporal inferencing

This thesis presents a computational framework for human pose estimation from depth video sequences. The framework has a potential to achieve interesting applications such as robot motion retargeting, activity recognition, etc, wherever joint motion is an appropriate representation of the human motion. On the one hand, feature points that are informative for pose estimation are tracked with depth image analysis. Human poses are reconstructed from these feature points with kinematic constraints including joint limits and self-collision avoidance. On the other hand, human poses could be estimated based on local optimization using dense correspondences between 3D data and the articulated human model. Both could be unified with temporal motion prediction based on Bayesian information integration. We demonstrate our results for humanoid robot motion learning through a novel collision-free retargeting as well as for an example of the human pose estimation with environmental clutters. We show the computational results on a set of challenging motions where limbs interact with each other.