Miracle-dz/UniFAR-FTUs huggingface dataset:
On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.
balanced learning-based method for remote sensing aircraft detection

Using optical remote sensing image to detect airport aircraft quickly has a wide range of application prospects in many fields. The traditional method based on artificial interpretation has limitations, and has been gradually replaced by the method based on machine learning. With the development of machine learning, convolutional neural network (CNN) has been found to be effective in feature extraction. However, the aircraft target in the optical remote sensing image has some problems, such as the imbalance of samples and the small size of targets. For this reason, we propose a balanced learning for aircraft detection method based on optical remote sensing image, which focuses on the following aspects: (1) A lightweight network is used to improve the detection efficiency. (2) Balanced learning module is implemented to enhance the accuracy. (3) For network learning, a Balanced Orientation method is introduced into our network. Experimental results show that compared with other methods, our method has slightly improved detection performance on the dataset.
Adversarial Learning of Mappings Onto Regularized Spaces for Biometric Authentication

We present AuthNet: a novel framework for generic biometric authentication which, by learning a regularized mapping instead of a classification boundary, leads to higher performance and improved robustness. The biometric traits are mapped onto a latent space in which authorized and unauthorized users follow simple and well-behaved distributions. In turn, this enables simple and tunable decision boundaries to be employed in order to make a decision. We show that, differently from the deep learning and traditional template-based authentication systems, regularizing the latent space to simple target distributions leads to improved performance as measured in terms of Equal Error Rate (EER), accuracy, False Acceptance Rate (FAR) and Genuine Acceptance Rate (GAR). Extensive experiments on publicly available datasets of faces and fingerprints confirm the superiority of AuthNet over existing methods.
Improving the Performance of Deduplication-Based Storage Cache via Content-Driven Cache Management Methods

Data deduplication, as a proven technology for effective data reduction in backup and archiving storage systems, is also showing promises in increasing the logical space capacity for storage caches by removing redundant data. However, our in-depth evaluation of the existing deduplication-aware caching algorithms reveals that they only work well when the cached block size is set to 4 KB. Unfortunately, modern storage systems often set the block size to be much larger than 4 KB, and in this scenario, the overall performance of these caching schemes drops below that of the conventional replacement algorithms without any deduplication. There are several reasons for this performance degradation. The first reason is the deduplication overhead, which is the time spent on generating the data fingerprints and their use to identify duplicate data. Such overhead offsets the benefits of deduplication. The second reason is the extremely low cache space utilization caused by read and write alignment. The third reason is that existing algorithms only exploit access locality to identify block replacement. There is a lost opportunity to effectively leverage the content usage patterns such as intensity of content redundancy and sharing in deduplication-based storage caches to further improve performance. We propose CDAC, a Content-driven Deduplication-Aware Cache, to address this problem. CDAC focuses on exploiting the content redundancy in blocks and intensity of content sharing among source addresses in cache management strategies. We have implemented CDAC based on LRU and ARC algorithms, called CDAC-LRU and CDAC-ARC respectively. Our extensive experimental results show that CDAC-LRU and CDAC-ARC outperform the state-of-the-art deduplication-aware caching algorithms, D-LRU, and D-ARC, by up to 23.83X in read cache hit ratio, with an average of 3.23X, and up to 53.3 percent in IOPS, with an average of 49.8 percent, under a real-world mixed workload when the cache size ranges from 20 to 50 percent of the workload size and the block size ranges from 4KB to 32 KB.
Digital Multi-Value Logic Gates for Monolithic GaN Power ICs

In this work we propose a first attempt in the fabrication of a family of monolithic GaN-based quaternary multi-value logic gates (MVL), including QNOT, QNOR, and QNAND in a process flow compatible with enhancement mode (E-mode) power HEMT (BV > 650 V). Integration of MVL with GaN power HEMTs increases logic-state density and allows for the design of smart gate drive circuits with reduced switching loss and ringing when compared to circuits using discrete devices. Using TCAD and SPICE device models calibrated against experimental data we demonstrate the functionality of a quaternary inverter with noise margins NML and NMH above 60% and 50% of the logic state voltage range, respectively. Quaternary logic gates show an average reduction of 27% in area utilization when compared to binary logic gates. Additionally, the layout of a quaternary 16-bit DEMUX circuit is projected to occupy 22.5% less area than its binary equivalent. The application of quaternary MVL to digital control circuits for GaN power HEMTs is expected to reduce total circuit area and consume less power than a binary counterpart.
A Fast-Automated Scheduling Method Based on Genetic Algorithm for Final Assembly Workshop

In this paper, we proposed a fast-automated scheduling method for the final assembly workshop based on the Genetic Algorithm. This method belongs to the technical field of production planning management involving the application of the genetic algorithm and computer simulation in advanced planning and scheduling. Comprehensively, we take into account realistic factors including order priority, lines workload balance and time-consuming to design our three-level scheme evaluation systems. Moreover, the order priority is decided by the Decision Tree model we train. Considering the efficiency, we redesign the coding scheme, variation operator and fitness function to make the Genetic Algorithm serve as an exclusive accelerator for the approximation of optimal schemes. As a result, this fast-automated scheduling method is practical and conducive to production efficiency improvement as well as enterprise benefits maximization.
Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data mining (EDM) and learning analytics (LA), neglects this critical task. This shortcoming could in many cases have a negative impact on the prediction power of both the EDM and LA based approaches. To address such issues, this work proposes an evaluation approach that automatically compares several clustering methods using multiple internal and external performance measures on 9 real-world educational datasets of different sizes, created from the University of Tartu’s Moodle system, to produce two-way clustering. Moreover, to investigate the possible effect of normalization on the performance of the clustering algorithms, this work performs the same experiment on a normalized version of the datasets. Since such an exhaustive evaluation includes multiple criteria, the proposed approach employs a multiple criteria decision-making method (i.e., TOPSIS) to rank the most suitable methods for each dataset. Our results reveal that the proposed approach can automatically compare the performance of the clustering methods and accordingly recommend the most suitable method for each dataset. Furthermore, our results show that in both normalized and nonnormalized datasets of different sizes with 10 features, DBSCAN and k-medoids are the best clustering methods, whereas agglomerative and spectral methods appear to be among the most stable and highly performing clustering methods for such datasets with 15 features. Regarding datasets with more than 15 features, OPTICS is among the top-ranked algorithms among the nonnormalized datasets, and k-medoids is the best among the normalized datasets. Interestingly, our findings reveal that normalization may have a negative effect on the performance of certain methods, e.g., spectral clustering and OPTICS; however, it appears to mostly have a positive impact on all of the other clustering methods.
Analysis of channel properties at extremely high temperature in 3.3-kV SiC trench-etched double diffused MOS (TED-MOS®) using temperaturesensitive electrical parameters (TSEPs)

We have developed a measumement technique that combines a double pulse switching test with a short-circuit (SC) test to analyze SC behavior. Our proposed technique was utilized to analyze the channel properties of 3.3-kV SiC trench-etched double diffused MOS (TED-MOS®) at high junction temperatures (Tj) up to 600°C. SC was used to increase Tj rapidly and locally, and turn-on waveforms were used to extract channel properties and identify the Tj of TED-MOS® using temperature-sensitive electrical parameters (TSEPs). Results showed that, in contrast to SiC double diffused MOS (DMOS), the saturation current of TED-MOS® was suppressed at Tj above 200°C due to its stable channel characteristics at high temperature. These superior properties of the trench-sidewall channels of TED-MOS® at high temperature are promising for expanding SC capability.
Effects of Photoperiod and Temperature on Postnuptial Molt in Captive White-Crowned Sparrows

The annual cycle of the White-crowned Sparrow (Zonotrichia leucophrys) and its regulation have been intensively studied with respect to reproduction and migration (e.g., DeWolfe 1967, 1968, Farner and Lewis 1971, King 1972a, Morton et al. 1972, Lewis and Farner 1973). Nevertheless, the environmental control of molt is poorly understood. Several experimental studies of molt in captive birds have been performed under artificial conditions or in natural environments distant
An Audio Classification Approach using Feature extraction neural network classification Approach

Audio signal and audio data is an important information data. It make useful to understand behaviour and understanding of existing or past scenario. There are multiple features in sound signal which help in such analysis. There are many approach such as wavelet analysis, chroma, centroid based approach and MFCC is the main approach for such feature extraction. In this paper there is a sound classification using the proper feature extraction then followed by proper classification using fast efficient neural network based approach is proposed. This algorithm process and classify the sound input dataset and help in high classification rate classification. The execution of this system is performed using Matlab 2018b and result execution performance shows the efficiency of algorithm over traditional classification solution.
Automatic Reconstruction of 3-D Building Structures for TomoSAR Using Neural Networks

Tomographic Synthetic Aperture Radar (TomoSAR) has become a competitive remote sensing method of three-dimensional (3-D) reconstruction over urban areas. Raw TomoSAR point clouds are inevitably noise-corrupted, which would severely obstruct the reconstruction of building structures. Whereas, data segmentation and parameter tuning are required in current methods of 3-D building reconstruction, which influences the precision and efficiency of the reconstruction process. In this paper we propose a novel method using neural networks to reconstruct 3-D building structures from TomoSAR data. By using the proposed method, the precise 3-D surface of the building structure can be retrieved quickly. The proposed method also performs commendably in point cloud denoising. More importantly, our method achieves full automation of the reconstruction process, which does not require data segmentation or complex parameter adjusting. Experiment results demonstrate the effectiveness of the proposed method.
Bearing health indicator construction scheme based on a hybrid deep learning structure methods

The monotonicity and trendability of health indicator constructed by traditional method are not very ideal. To solve these problems, a bearing health indicator construction scheme based on a hybrid deep learning structure method is proposed. The proposed scheme consists of two parts: features extractor part and health indicator constructor parts. In features extractor part, the deep convolutional neural network is used to extract numerous features from the time-frequency maps transformed from the vibration signal. Then, the extracted features are evaluated and selected by the health indicator metrics. In health indicator constructor part, the selected features are fused to construct a health indicator through a stacked auto-encoder neural network. Performance of other health indicator construction methods, such as RMS, PCA, ELM-AE and SDAE-SOM are tested and contrasted. The experiment results show that the HI constructed by the proposed health indicator construction method has the best trendability and monotonicity.
The research on acoustic communication anti-doppler shift technology based on FrFFT

Underwater acoustic communication technology plays an important role in underwater applications, but the sound velocity v0 in underwater acoustic channel is much lower than the propagation velocity c0 in electromagnetic wave in the air. It is assumed that the velocity of the platform is v, i.e. v/v0>> v/c0 makes the influence of the velocity of the platform in the underwater acoustic channel on the doppler shift of the receiver far greater than the influence of the electromagnetic wave mobile platform in the air on the receiver channel. In this paper, a joint channel coding and decoding method based on LFM-FrFFT (Linear Frequency Modulation- Fast Fourier Transform) is proposed. The receiver system decodes the communication system by detecting the up-and-down frequency sweep of the LFM. Due to the doppler insensitivity of the LFM signal, the FrFft can effectively detect the LFM signal through the fractional-order domain to solve the Doppler-induced receiver channel mismatch problem.
Earth Observation Satellite Learning to Video Compressor complexity reduction

Earth Observation (EO) missions in the new era of satellite applications encounter a tangible growth. Capturing live video of the Earth satellites are new trends that would be the next customers' demands for this industry. These rapid changes are having serious effects on studies on customizing this type of mission. The having predictable motion of a satellite in an orbit is a significant advantage for video capturing missions that current study is focusing on. This paper gave a brief overview of some general studies and then discussed the Motion Estimation (ME) technique by means of which Video Compressors are able to detect and eliminate spatial redundancy. The required configuration sections of video capturing satellites in connection with video compression were described as well. To address the main question in this paper which is how to teach video capturing satellites for enhancing video compressor techniques, evidence for the necessity of such complexity reduction was presented. Detecting dominated Motion Vectors (MVs) in statistical aspect for a recorded video sample of an EO satellite put emphasis on the title of this article. The final stage was proposing an algorithm by considering the repetitive manner of EO satellite to scan the earth or any other targets. A simulated scenario in STK software helped us to extend this idea in detailed. Cumulative MVs that stem from the average of the previous MVs repetition round of satellite sweeping, suggest the default MVs for the next round. This procedure could reduce the calculation time that is consumed to find MVs in video compression algorithms. The required memory size based on the defined scenario was calculated.
Reconstruction of the Shape of a Tumbling Target from a Chaser in Close Orbit

Operations involving two or more spacecraft, including approach, rendezvous and servicing, are not always cooperative. The lack of cooperation means a limited set of information initially available to the approaching spacecraft. Still, the determination by the chaser of the relative kinematic state of the target spacecraft stands as a required step to continue the approach. Completing this first fundamental information about relative motion, also the reconstruction of the target's shape can be considered an important part of the rendezvous and a pre-requisite for safe docking. In fact, shape reconstruction enables the chaser to understand target's configuration, to assess its integrity and eventually to compare it with already known spacecraft's models. The reconstruction needs to be accurate even when starting from the limited number of images captured only from the points of view attained during relative motion, and is indeed quite a challenging task. Due to the extremely wide range of possible relative poses and light conditions, an extensive test campaign based on numerical simulations is required to validate possible algorithms to carry on this operational phase. Only after simulations are successfully passed, would it be possible to move towards experiments in a ground-based testbed and finally to in-flight qualification. This paper details the experience gained with the simulation phase at the Guidance and Navigation Lab of Sapienza University of Rome. A software suite is developed in order to simulate in orbit acquisition of the target image, managing its 3D CAD model according to the relative dynamics and lighting conditions. Several spacecraft configurations, different in terms of shapes and relative pose, are assumed for the target, and the relevant images are captured by the chaser during its relative motion around it. An effective process for the identification and match of the features, capable to manage their appearance and disappearance during the sequence of images has been implemented. Following these steps, it is possible to gain an educated guess of the shape of the target. Advanced filtering techniques are applied, significantly contributing to the final result of the process. The post-facto comparison between the actual CAD model and the estimated target's shape shows appealing success rates in the recognition task for the proposed technique.
Stable-Nitrogen Isotope Enrichment in Avian Tissues Due to Fasting and Nutritional Stress: Implications for Isotopic Analyses of Diet

Studies using measurements of61'5N to delineate diet or trophic level in natural ecosystems are based on the premise that 6'5N values in consumer tissues can be reliably correlated with those in the diet. However, juvenile Japanese Quail (Coturnixjaponica) fed a rationed diet designed to maintain, but not increase, body mass showed significantly enriched tissue 6'5N values over a control group fed the same diet ad libitum. We tested the hypothesis that fasting or nutritional stress can also cause elevated 6'5N values in tissues of wild birds by examining tissues of Arctic-nesting female Ross' Geese (Chen rossii) before and after their period of fasting during egg laying and incubation. Significant declines in body, pectoral muscle, liver and abdominal fat mass occurred from arrival through incubation. Post-incubating geese showed significantly higher pectoral muscle and liver 6'5N values compared to geese taken before clutch initiation but 6a3C values in these tissues were unchanged. We hypothesize a mechanism of tissue 6'5N enrichment due to reduced nutrient intake and discuss the implications of these results to ecosystem studies using stable-nitrogen isotope analysis.
A Measurable Framework for Run-time Data Sampling in Large-scale Datacenter

In large-scale data center, collecting run-time data is a very effective method which can be used to analyze and monitor the performance of data centers. But due to the huge size of data centers, limited computing resources and the requirement of low delay, it is very difficult and unrealistic to collect all the data in large-scale data centers. Therefore, to solve the serious problem, sampling partial data from all data is a common method at present. However, existing researches only focus on designing some efficient data sampling methods to reduce resource and time overhead in datacenters, but these works do not provide a unified and measurable framework to quantity the quality and practicability of other sampling methods. In this paper, we propose a measurable framework for general run-time data sampling in large-scale data center by modeling underlying recovering hypothesis explicitly. The proposed framework is mainly composed of four processes: sampling, collecting, recovering, and comparing. It could measure sampling bias degree accurately. And we design and implement three sampling methods with different recovering hypothesis. The experimental results demonstrate that the proposed framework can help us find a better run-time data sampling method effectively which has a lower sampling bias degree with same sampling rate.