Miracle-dz/UniFAR-FTUs huggingface dataset:
Routing in FD-Enabled Cognitive Radio Networks with Minimum Time-Sharing for Improved Performance

The growing demand for wireless applications has driven research on technologies that can meet these needs. Cognitive radio (CR) is one such technology that enables shared spectrum usage. Communication in CR networks (CRNs) de-pends on the capabilities of the device, which can be full-duplex (FD) or half-duplex (HD). However, optimal routing for FD CRNs remains a challenge. This paper presents a routing protocol to improve network performance by minimizing time-sharing sessions using out-of-band FD (OBFD) communication. The protocol finds loop-free paths, computes transmission rates, and Probability of Success (POS) for each path, selecting the one with the highest POS. This optimization problem is converted from a binary quadratic problem (BQP) to a more solvable binary linear programming problem (BLP) using a polynomial-time sequential fixing method. The simulation results reveal that the proposed routing protocol significantly outperforms existing routing algorithms in terms of end-to-end throughput.
Formation of Carbon and Hydrogen Species in Magmas at Low Oxygen Fugacity

RECEIVED MAY 30, 2002; ACCEPTED NOVEMBER 18, 2003Studies of iron-bearing silicate melt (ferrobasalt) þ iron metallicphase þ graphite þ hydrogen equilibria show that carbon andhydrogen solubilities in melts are important for the evolution of theupper mantle. In a series of experiments conducted at 3 7GPa and1520--- 1600 C, we have characterized the nature (oxidized vsreduced) and quantified the abundances of C- and H-compoundsdissolvediniron-bearingsilicatemelts.Experimentswerecarried outin an anvil-with-hole apparatus permitting the achievement of equalchemical potentials of H
Multi-Horizon Forecasting of Average Daily $\text{PM}_{2.5}$ Concentration: A Case Study of Air Pollution in Singapore

Understanding the spatial and temporal dynamics of surface concentrations of particulate matter ($\mathbf{PM}_{2.5}$ and PM10) is essential in air quality modelling and climate research. Forecasting of air pollutants is necessary to understand the variation of pollutants and to plan and implement air pollution control measures. This work proposes a methodology for forecasting $\mathbf{PM}_{2.5}$ concentration using various meteorological parameters over multiple time horizons. The proposed deep learning-based models forecast the daily average values concentration for $\mathbf{PM}_{2.5}$ in Singapore. The performance metrics indicate the efficacy of the proposed model in forecasting $\mathbf{PM}_{2.5}$ concentrations over different horizons. The analysis shows that $\mathbf{PM}_{2.5}$ concentrations are best forecasted for the 3-days ahead scenario, with RMSE and MAPE being 3.767 $\mu \mathrm{g}/\mathrm{m}3$ and 6.82%, respectively.
Intellectualization of the Pavement Defect Recognition Process

A classification of machine learning models and algorithms is presented, and convolutional neural networks are reasonably selected, which have a high ability to recognize patterns in images. Mandatory criteria for selecting a deep learning library and software implementation tools were highlighted to reduce the number of possible choices. A comparative analysis of tools for software implementation and training of neural networks was performed, and the Google Colab service was selected. A description of the specialized application “ImageLabeller”, developed by the authors and used in the process of markup, image filtering, and pixel mask generation, is given. The parameters used by the training algorithm are described. A neural network with DeepLabV3 architecture is chosen as the primary neural network model for 1024×1000 grayscale input images. Examples of recognizing pavement defects by a trained neural network are presented.
Multi-component and multi-array TEM detection in karst tunnels

Emerging applications of transient electromagnetic methods (TEM) in tunnelling require higher resolution on the distributions and shapes of low resistivity bodies, such as karst water and karst pipes, using multi-component and multi-array receivers. However, there are no apparent resistivity definitions for both vertical and horizontal components with offsets inside the loop. Although the raw field can show the differences of the earth electric structure, it is not straightforward. Apparent resistivity is very convenient and easy for engineers. We have developed a method for multi-component and multi-array TEM which can be applied in tunnelling and defined the expressions of apparent resistivity. This method takes advantage of the difference in resolution among components. A homogeneous half-space model and four typical three-layered models are used to test the effectiveness of the new definition. A field case history is carried out and analysed to demonstrate the viability of this technique. The results suggest that it is feasible to use the technique in tunnelling, especially for identifying the spatial distribution of karst water and karst pipes.
Micro-Cantilever Array With Electroplating Tin Bumps for Flip-Chip Bonding Technology

A Nickel (Ni) microcantilever beam array with tin (Sn) solder bumps has been designed for flip-chip bonding with high-density electrodes. Micro-electro-mechanical system (MEMS) technology and the micro-electroplating process were used in the fabrication process. Utilizing the out-of-plane deformation of the suspended microcantilever beam, the satisfied heights partially compensate the substrate curvature to ensure a high flip-chip bonding yield, thus, improving package quality in terms of electrical connections. To demonstrate this technique, this study developed a flip-chip carrier for a typical micro-LED array with a beam length of <inline-formula> <tex-math notation="LaTeX">$70~\mu \text{m}$ </tex-math></inline-formula> and a bump height of <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{m}$ </tex-math></inline-formula>. The measurement results revealed that the displacement of the microcantilever beam was approximately <inline-formula> <tex-math notation="LaTeX">$0.8~\mu \text{m}$ </tex-math></inline-formula> when a <inline-formula> <tex-math notation="LaTeX">$500~\mu \text{N}$ </tex-math></inline-formula> force was applied on top of the tin bump. When a glass substrate directly mounts to the flip-chip carrier through the tin bump at a heat reflow of 270 °C, most of the tin bumps successfully melt and solidify, which results in good bonding performance. These results revealed that the proposed approach could be applied to the wafer-level packaging process with high-density ball grid array electrodes.
Accuracy Assessment of Haze Removal of SPOT 6/7 Imagery using Haze Index Algorithm Based on Machine Learning Classification

Multispectral optical remote sensing imagery in tropical regions such as Indonesia is often suffer from haze and cirrus contamination. This will affect to a reduction in image quality and accuracy of data interpretation. To mitigate these issues, a haze removal method has been developed employing the haze index algorithm which is based on reflectance values of the blue and red channels of SPOT 6/7 imagery. However the method must be tested to find out whether the result is sufficient for further implementation or not. The efficacy of the method was determined by testing the data with image interpretation using machine learning classification method to judge the efficiencies of the haze removal algorithm on SPOT 6/7 imagery. Experimental results indicate that the algorithm is proficient in information extraction using haze-free SPOT 6/7 data that has been processed using this method. As a result, data processed with haze removal based on the haze index technique can be useful for remote sensing applications.
BRIEF ANALYSIS ON STUDY DEVELOPMENT FOR THE TRAVAL TIME TOMOGRAPHY OF BODY WAVE IN CHINA REGION

The author has discussed the present situation of travel time tomography of body wave in China region in details by dividing the region into the east and west parts.Some features of the three dimension velocity structure in China have been summarized:the velocity image displays clearly the lateral heterogeneity in the crust and upper mantle,which persists down to 1 100 km deep;there is a correlation between the velocity image of the shallow part and the known geological features;there is also a correlation between the velocity image and seismicity; the lithosphere thickness and the crust thickness change, and so on. Finally, the features of tomography in the present researches in China and it's possible development tendencies have been analysized.
The effect of departures from lower critical temperature on the group postural behaviour of pigs

The postural behaviour of a group of 12 pigs in a pen was studied whilst the air temperature was varied, all physical variables except pig weight being held constant. The air temperature was varied between −4°C and + 5°C of the theoretical lower critical temperature. The range of live weight was from 30kg to 75 kg. An indication of whether the pigs were above or below their lower critical temperature was obtained from the amount of huddling. Pigs of less than 45 kg live weight had a greater tendency to modify their environment by altering their posture. Measurements of floor-area covered showed that pigs over 50 kg live weight occupied an area approximately equal to the recommended minimum whilst smaller pigs occupied up to 20% less area than that recommended, depending upon the temperature: this was due to the tendency of smaller pigs to lie on top of each other when cold.
Crecimiento Económico Y Demográfico Regional En Colombia, 1985-2012 (Regional Economic and Population Growth in Colombia, 1985-2012)

Spanish Abstract: En el crecimiento economico y demografico de Colombia han primado las grandes urbes, y en terminos demograficos hoy es un pais urbano. Este articulo examina la dinamica del crecimiento economico, en especial, la dinamica poblacional desde mediados de los ochenta. El analisis de las matrices de transicion de Markov indica que el comportamiento demografico de los municipios muestra signos de persistencia. Las principales ciudades mantienen su jerarquia y siguen siendo las mas densamente pobladas. Los municipios con bajo nivel de densidad se han mantenido relativamente estancados en terminos demograficos.English Abstract: Economic and demographic growth in Colombia has been dominated by the largest cities. This has resulted in the common belief that Colombia is mainly an urban country. The aim of this paper is to show how economic and demographic growth has evolved, especially in terms of population density, since the mid-eighties. Using Markov transition chains and their spatial counterpart, we show that since the mid-eighties, municipalities have experienced a highly persistent pattern in which the main cities have maintained their position as the most densely populated. In contrast, municipalities that exhibited low levels of population density have remained relatively stagnant.
Temporal Modeling of Instantaneous Interbeat Interval Based on Physical Activity

Heartbeat serves as a vital sign of health, aiding the diagnosis of various health issues. The autonomic nervous system (ANS) is responsible for regulating heartbeat, and physical activity (PA) can influence the heart rhythm. Changes in heart rate can signal alterations in PA. However, poor measurements or extreme conditions can result in the loss of heartbeat data, which can negatively impact the analysis of heartbeats. To accurately monitor the Heart Rate Variability (HRV) and cardiovascular health, a proper model to compensate for lost data is necessary. This study investigated the effect of different PAs on InterBeat Interval (IBI) prediction and the possibility of using models trained on unrelated activities to predict the next IBI. The IBI series is divided into piecewise stationary sections based on PA, that is, running, walking, and sitting, as verified by a statistical test. Various machine and deep learning methods have been used to model the IBIs related to specific activities. The models were then used to predict the next IBI for the testing sets of related and unrelated activities, and the error changes were compared for each permutation of training and testing. The models were tested using a Physionet archived dataset. The findings suggest that linear models offer the least prediction error, whereas PA-relevant training could minimize errors in most scenarios. However, in cases where specific PA data are not available, the proposed CNN model demonstrated superior generalization capabilities. These findings can improve HRV error correction techniques and enhance cardiovascular health and ANS monitoring.
Integrating Deep Learning and Graph Neural Networks for Multimodal Lung Tumor Analysis: A Novel Approach for Improved Classification and Predict

- Lung cancer detection is of paramount importance for improving patient outcomes. This research focuses on the integration of deep learning and graph neural networks (GNNs) for multimodal lung tumour analysis. By leveraging the power of deep learning and GNN s, this study aims to address the limitations of existing approaches and improve the accuracy and interpretability of lung cancer detection. The proposed research first reviews the current state of lung cancer detection and highlights the need for advanced techniques. By integrating high-dimensional image features extracted from CT scans using a convolutional neural network (CNN) with the GNN framework, the model can effectively capture and analyze the relationships between CT images, clinical data, and molecular data. The research includes preprocessing steps, such as normalization and resizing, to ensure compatibility across the multimodal data. Through the construction of a graph structure, the spatial and functional connections between the multimodal data points are established. The GNN component is then designed and trained to process the graph structure and extract meaningful information from the data. The results of this research, as presented in the table, demonstrate the performance of the proposed GNN model compared to other neural network models. The GNN model achieved an accuracy of 85%, outperforming the CNN (80%) and MLP (75%) models. The precision, recall, and F1-score of the GNN model also surpassed those of the other models, indicating its superior predictive capabilities.
Design of Slotted Triangular Microstrip Patch Antenna with Different Feeding Techniques

In recent years, the microstrip patch antenna (MP A) plans an essential role many application such as satellites, mobile communication and aerospace systems. The microstrip patch antenna has several shapes like circular, triangular, rectangular etc. This paper we present the triangular microstrip patch antenna design. In the previous works of MP A, many researchers have proposed different optimization algorithms to enhance the design of MP A. But they face several problems like narrow bandwidth, very low gain and the high return loss. To avoid these problems we use Northern Goshawk Optimization (NGO) algorithm. Then this NGO algorithm gives the optimize length and sides for the triangular microstrip antenna. And we added the slotted array technique in the triangular MP A, because the slotted technique is capable for avoiding the narrow bandwidth and low gain. Then three feeding techniques such as inset feed, coaxial feed and strip line feed are used in this paper for enhancing the antennas input impedance. The performance of the designed patch antenna with the proposed algorithm is calculated based on the radiation pattern, VSWR, input impedance, return loss, gain and bandwidth. Then the proposed methods experimental result shows the better performance with -29.45 dB return loss and 7.23 dB gain.
Robust State Estimation of Integrated Electricity Gas Systems Based on Constrained Iteratively Reweighted Least Squares

Equality constraints of zero-injection buses and operation of coupling devices are essential for accurate state estimation (SE) of integrated energy networks. However, existing robust SE algorithms cannot handle robustness to bad data and equality constraints simultaneously. A robust state estimation method based on constrained iteratively reweighted least squares (IRLS) is proposed in this paper for integrated electricity-gas systems (IEGSs). Firstly, a robust SE model based on Huber M-estimation is established considering zero-injection equality constraints and coupling constraints of gas-fired power generation, power to gas devices, etc. Then, a novel constrained IRLS algorithm is proposed to solve the model, which combines advantages of the Lagrange multiplier method for equality constrained nonlinear programming problems and the IRLS algorithm for unconstrained robust SE problems. The drawback that traditional IRLS algorithm cannot handle equality constraints is therefore solved. The proposed constrained IRLS algorithm can be naturally generalized to robust SE of general energy systems. Test results on a typical IEGS system demonstrate that the proposed model and algorithm is superior than the weighted least absolute value estimation in the aspect of estimation accuracy, robustness and computational speed.
Facies changes across the Triassic–Jurassic boundary in Nevada, USA

A combination of data from litho‐ and biofacies and gamma‐ray spectrometry indicates an upward marine shallowing in the late Rhaetian succession of Nevada followed by deepening in the early Hettangian, which conforms well to a regression–transgression couplet of the sort associated with many marine mass extinctions. This pattern can be discerned widely across the world, most notably in North and South America and in Europe, suggesting the operation of eustatic control of sea level, a global fall being followed by a rise.
Brief communication 'Importance of slope-induced error correction in volume change estimates from radar altimetry'

In deriving elevation change rates (d H /d t ) from radar altimetry, the slope-induced error is usually assumed to cancel out in repeat measurements. These measurements, however, represent a location that can be significantly further upslope than assumed, causing an underestimate of the basin-integrated volume change. In a case-study for the fast-flowing part of Jakobshavn Isbrae, we show that a relatively straightforward correction for slope-induced error increases elevation change rates by up to several metres per year and significantly reduces the volume change error with respect to laser altimetry for the area of interest.
Adaptive Capacity of Water Governance: Cases From the Alps and the Andes

Abstract The Alps and the Andes are both considered water towers in their respective continents and are thus significant not only for their own water needs but also for those of lowland regions farther downstream. As climate change impacts on the hydrology of mountain regions are increasingly observed, attention is turning to the adaptive capacity of the water governance regimes in mountain communities. This paper explores the adaptive capacity of two contrasting water governance regimes in the Swiss Alps and the Chilean Andes. It assesses adaptive capacity by analyzing a set of governance-related adaptive capacity indicators in the context of recent extreme events, which serve as proxies for future climate change. Across these highly contrasted governance contexts, analysis reveals both similar and distinct institutional challenges for developing and mobilizing adaptive capacity in relation to climatic uncertainty and change. It also identifies emergent tensions related to temporal and spatial scales. Conclusions point to the need to focus on challenges relating to trust, integration of hydroclimatic information, and flexibility and iterativity of rules and plans across governance scales to better manage the exacerbating impacts of both climate variability and climate change.
Prácticas mortuorias en la costa norte de Santa Cruz: arqueología de sociedades cazadoras recolectoras en paisajes costeros de la Patagonia argentina

In this thesis the results of research conducted from the year 2011 through 2015, about mortuary contexts present on the northern coast of Santa Cruz province (NCSC) Patagonia Argentina, are presented. Prior to this thesis, the archaeological information available about kind sort of archaeological record was scarce and unsystematic. One exception is an article published by Castro and Moreno in 2000, on which was unveiled the specifics of the mortuary register at the NCSC. The recognition of burials in this publication was conducted from systematic surveys in the field in order to know the characteristics of the archaeological record on the NCSC. As the study area was investigated in its entirety, it was recognized a rich mortuary register, comprised mainly of chenque type structures. In this background, ethnohistoric documents from the area are added, in which recurrent mention of human burials were done. On the other hand, in a manuscript of the mid-twentieth century, Menghin and Bórmida (s/f) noted the presence of a site with a lot of chenques in this part of the coast. These background, along with rescue excavations carried out by Dra. Castro and team allowed start generating a variety research questions. Among the questions may be mentioned: How old are human burials identified in the study area ? ; besides the chenques, are there other forms of burial ? ; the individuals found in burials recorded on the NCSC are people who inhabited these areas for some time during their life, or were only the sites chosen for the realization of the mortuary practices ? ; Why burials were found in certain sectors of the landscape and not others ? ; Why certain burials were found in clusters while others are isolated ? ; How were the societies that performed the burials ? ; the location of burials in space is correlatedwith the nature of the settlements in the mobile hunter gatherers? , These questions