Miracle-dz/UniFAR-FTUs huggingface dataset:
Stream macroinvertebrate community responses to fire: are they the same in different fire-prone biogeographic regions?

Droughts, fires, and floods are natural disturbances influencing aquatic ecosystems. If drought is accompanied by fires, and if fires are closely followed by floods, teasing apart their distinctive and potentially interactive responses can be difficult. We compared the responses of macroinvertebrate communities to fire via comparisons of streams in burned and unburned catchments in 3 fire-prone biomes that differ biogeographically and climatically (northwestern Mediterranean, southeastern Australia, and northwestern intermountain USA). The responses of macroinvertebrate communities in streams in burned catchments were similar in all biogeographic regions, but the magnitude of these responses varied. Fire combined with high seasonal stream flows, flooding, or drought was associated with reduced measures of taxonomic richness and increased abundance, especially of r-strategist taxa. Differences between sites in burned and unburned catchments were consistently stronger in southeastern Australia than in northwestern intermountain USA and northwestern Mediterranean regions. Our observations suggest that the timing and magnitude of postfire flows (snowmelt, seasonal high flows) may substantially alter the recolonization process and override fire effects, and that drought may play a strong role in limiting the resilience and resistance of macroinvertebrate communities in streams in catchments that have experienced wildfire.
A new species of the Genus Harpadon (Aulopiformes, Synodontidae) from the north-eastern Arabian Sea, India

A new species of Harpadon Lesueur, 1825 captured from the continental slope area 19° 53’ N - 69° 23’E (north-eastern Arabian Sea) is described. The species Harpadon nudus sp. nov. was identified possessing a prominent trilobed caudal fin and a wide mouth with strong fang like barbed teeth and has been distinguished from its congeners by the following combination of characters: slender, completely scaleless body with very short pectoral (8.6 - 13.8% SL) and pelvic (16.4 - 21.6% SL) fins, large eyes (9.1 - 15.1% HL) with narrow inter-orbital width (15% of HL) and single pair of nares, situated close to the tip of the narrow snout. Morphologically it differs from the partially scaled species such as Harpadon nehereus in having very short pectoral fins not reaching up to its dorsal fin, single pair of nares and a pointed snout in dorsal view. It can be distinguished from Harpadon microchir by its greater head length of 22 - 26% SL; absence of scales at the base of the adipose fin and on the lateral line as well as the dorsal fin count of 12 rays. It is differentiated from Harpadon mortenseni by its lower lateral line pores count (40 - 43), higher number of branchiostegals (17 to 20), absence of scales on the lateral line or caudal peduncle and absence of vomerine teeth. It is distinct from Harpadon translucens which has a translucent body, partially covered with scales in the caudal region and protruding bristle like teeth on the jaw symphysis. It is distinguished from Harpadon squamosus which has scales on its head, trunk and caudal region with a relatively deeper body (5.8 in SL), by its slender and elongate body (8 - 10 in SL), short pelvic fins (16.4 - 21.6% SL) not extending to the anal vent and a narrow snout. It is also distinguished from Harpadon erythraeus which has a dark body entirely covered with scales and black fins, short head and rounded snout. H. nudus sp. nov. stand apart from H. nehereus (Kimura 2 Parameter distance 20%), the only species reported from the Arabian Sea and H. microchir (K2P 19%) by forming a distinct clade with 100% bootstrap support.
On-Road Object Identification with Time Series Automotive Millimeter-wave Radar Information

Identifying objects with radar is in great demand to avoid road accidents. Recent research tried to identify moving objects on a road by inputting radar information to a machine learning classifier. In the conventional method, the features used in the machine learning are extracted from the observed radar information with a short time interval. Since the movement of the objects is different depending on the objects, time series information is effective for classification, which has not been exploited before. In this paper, we propose an on-road object identification considering time series of radar information. We measured objects with 79.5 GHz millimeter-wave radar and extract features from a series of time windows by calculating the mean and variance of object information, i.e., velocity, distance, and signal power. The classification performance was evaluated with a dataset obtained by on-road experiments. It is shown that our method outperforms the conventional one and the proposed features significantly contribute to the accurate identification.
Impact of Imperfect Communication on Cooperative Vehicular Maneuvering at Intersections

Cooperative Vehicular Maneuvering (CVM) significantly improves traffic safety and efficiency, but relies on wireless information exchange between cooperating vehicles. As wireless links are unreliable and the communication resources are limited, the performance of CVM degrades depending on the communication quality. We propose a novel model to represent the imperfectness of communication, formalize CVM at an intersection, and evaluate the performance degradation of CVM under varying communication conditions. From our analytical perspective, we can derive the requirements for maneuver coordination. Our results provide a valuable resource for the design of future communication technologies and dissemination strategies.
Ancient Korean Neural Machine Translation

Translation of the languages of ancient times can serve as a source for the content of various digital media and can be helpful in various fields such as natural phenomena, medicine, and science. Owing to these needs, there has been a global movement to translate ancient languages, but expert minds are required for this purpose. It is difficult to train language experts, and more importantly, manual translation is a slow process. Consequently, the recovery of ancient characters using machine translation has been recently investigated, but there is currently no literature on the machine translation of ancient Korean. This paper proposes the first ancient Korean neural machine translation model using a Transformer. This model can improve the efficiency of a translator by quickly providing a draft translation for a number of untranslated ancient documents. Furthermore, a new subword tokenization method called the Share Vocabulary and Entity Restriction Byte Pair Encoding is proposed based on the characteristics of ancient Korean sentences. This proposed method yields an increase in the performance of the original conventional subword tokenization methods such as byte pair encoding by 5.25 BLEU points. In addition, various decoding strategies such as n-gram blocking and ensemble models further improve the performance by 2.89 BLEU points. The model has been made publicly available as a software application.
Hyperspectral Image Dimension Reduction Using Weight Modified Tensor-Patch-Based Methods

Dimension reduction (DR) addresses the problem known as the curse of dimensionality in myriad hyperspectral imagery applications. Although the spatial pattern may assist in the distinction between different land covers that have close spectral signatures, it is often neglected by the current DR methods. In order to overcome this defect, two solutions: patch-based and tensor-patch-based, are studied in this article for a group of graph-based DR methods. To date, only a few attempts have been made in the patch- and tensor-patch-based variations for the graph-based DR methods. This article proposed two weight modified tensor-patch-based methods, namely weight modified tensor locality preserving projections and weight modified tensor neighborhood preserving embedding. Specifically, as graph-based DR methods heavily rely on the construction of adjacency graphs, this paper proposes a new use of the weighted region covariance matrix in the calculation of adjacency graphs. We found that the two proposed tensor-patch methods outperform the up-to-date methods.
Protecting Location Privacy for Crowd Workers in Spatial Crowdsourcing Using a Novel Dummy-Based Mechanism

Spatial Crowdsourcing (SC) is a new valuable paradigm, relies on crowd workers to perform a set of spatial-temporal tasks at specific locations. It has garnered attention in collecting and processing social, environmental, and other spatio-temporal data by the contribution of individuals, communities and groups of workers in the physical world. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, which requires the workers to be physically traveling to the tasks’ locations in order to perform them, i.e., taking photos or collecting real time weather information at pre-specified location. Existing solutions require crowd workers to disclose their precise locations to untrustworthy service providers. Location updates and tracking in spatial crowdsourcing raise several privacy concerns in that malicious parties could snoop on crowd workers’ whereabouts. Thus, the crowd workers’ privacy could be compromised by disclosing their locations to untrusted and possibly malicious parties. This paper provides a novel framework called Dummies’ Centroid (DCentroid), which aims at preserving location privacy for crowd workers in SC. The framework adapts an anonymous communication technique using a dummy based approach to generate dummy locations, i.e. decoy locations, and send their centroid points (pseudolocations) to service providers for processing. This paper theoretically analyzes the DCentroid framework and guarantees the crowd workers’ privacy, while preserving the functionality of SC, such as the success rate of task assignments, worker travel distances, and system overhead. Practical experimentation on real-world datasets shows that the DCentroid framework protects the crowd workers’ location privacy without affecting the various performance parameters of task assignment.
OmniCells: Cross-Device Cellular-based Indoor Location Tracking Using Deep Neural Networks

The demand for a ubiquitous and accurate indoor localization service is continuously growing. Cellular-based systems are a good candidate to provide such ubiquitous service due to their wide availability worldwide. One of the main barriers for accuracy is the large number of models of cell phones, which results in variations of the measured received signal strength (RSS), even at the same location and time. In this paper, we propose OmniCells, a deep learning-based system that leverages cellular measurements from one or more training devices to provide a consistent performance across unseen tracking phones. Specifically, OmniCells uses a novel approach to multi-task learning based on autoencoders that allows it to learn a rich and device-invariant RSS representation without any assumptions about the source or target devices. OmniCells also incorporates different modules to boost the system’s accuracy with RSS relative difference-based features and to improve the deep model’s generalization and robustness. Evaluation of OmniCells in two realistic testbeds using different Android phones with different form factors and cellular radio hardware shows that OmniCells can achieve a consistent median localization accuracy when tested on different phones. This is better than the state-of-the-art indoor cellular-based systems by at least 101%.
Analysis of BTI, SHE Induced BTI and HCD Under Full VG/VD Space in GAA Nano-Sheet N and P FETs

An ultrafast (10ps delay) characterization method is used to measure threshold voltage shift (ΔV<inf>T</inf>) owing to Bias Temperature Instability (BTI) and Hot Carrier Degradation (HCD) stress in N and P channel Gate All Around (GAA) NSFETs. ΔV<inf>T</inf> time kinetics at various gate bias (V<inf>G</inf>) and temperature (T) for BTI and at various VG and drain bias (V<inf>D</inf>) for HCD is analyzed. Contribution from Self Heat Effect (SHE) induced BTI to overall HCD is estimated under full V<inf>G</inf>/V<inf>D</inf> space and pure HCD contribution is determined.
An Optimization Method for the Gateway Station Deployment in LEO Satellite Systems

Low Earth Orbit (LEO) satellite networks play a major role to provide communication support for the regions beyond the coverage of terrestrial network systems. The positions of gateway stations have influence on the time delay, power consumption and throughput of LEO satellite networks, making it important to deploy the gateway stations at the best position. Aiming at minimizing the inter-satellite hop count, we are the first to study the deployment problem of gateway stations. The gateway deployment problem is formulated as a non-convex optimization problem. According to the characteristics of the deployment problem, we propose an improved genetic algorithm (GA) and an improved simulated annealing algorithm (SA) not only to get the optimal deployment location, but also to greatly reduce the computational complexity. Simulation results and analyses show that compared with random deployment, the optimal locations of the gateway stations obtained by the proposed algorithms can decrease 42.6% average hop count of inter-satellite links and increase 4.4% average capacity of feeder links.
The Intelligent Offense and Defense Mechanism of Internet of Vehicles Based on the Differential Game-IP Hopping

The Road Side Unit (RSU) is an important part of the Internet of Vehicles (IoV), which collects a large number of user’s privacy information and connects vehicles to the Internet. The current RSU is running with static configurations, and the adversary can get enough time to analyze the vulnerability of the RSU and launch attacks, which poses a huge threat to the privacy security of the IoV. Under this asymmetrical condition, the attackers occupy a predominant position, and it is difficult to fully defend. To solve the above asymmetry offense-defense problem, we adopt the moving target defense (MTD) to dynamically change the IP address of the RSU, which increases the difficulty of the attacker’s attack. However, due to the unreasonable selection of the RSU’s IP hopping frequency, it is difficult for the RSU’s IP hopping to exert its defense effect or it may introduce excessive defense overheads. Given the above issue, we propose an intelligent offense and defense mechanism based on a differential game approach, which can adaptively adjust the RSU’s IP hopping frequency and maximize its defense benefits. First, based on the offense and defense relationship, we model the RSU’s IP hopping frequency intelligent arrangement as a differential game (i.e., IP hopping game) in which the attacker attempts to maximize his potential benefits, and the RSU manages to maximize its defense benefits. On this basis, we calculate a potential Nash equilibrium of the IP hopping game. Finally, simulation results show our proposed mechanism can adaptively adjust the RSU’s IP hopping frequency according to the attack frequency and effectively defend attacks. Compared with the mechanisms with a fixed IP hopping frequency, our proposed mechanism can make up for the ineffectiveness of the fixed low-frequency IP hopping defense and avoid the excessive defense overheads of the fixed high-frequency IP hopping defense.
Signal-to-Noise Ratio Contributors and Effects in Proximal Near-Infrared Spectral Reflectance Measurement on Plant Leaves

An extensive study was performed recently on Jacobaea Vulgaris to characterize the plant leaves using the hyperspectral reflectance. Unfortunately, a poor signal-to-noise ratio (SNR) in the spectral data was observed thus making the results less valuable. This paper examines the consequence of using uncontrolled lighting, i.e., solar illumination in the field applications as well as the effect of scattered light on shaded regions of the image. It is shown that a significant reduction of SNR can be achieved by taking these factors into account prior to the processing of the hyperspectral data. Specifically, the standard uncertainty of the spectra for Jacobaea Vulgaris hyperspectral images can be reduced from an average of 50% to better than 20%.
Monitoring a DC Train Supplied by a Reversible Substation

European energy policy has been supporting a gradual shift towards an efficient energy management in order to reduce the railway transport emissions by 50 % within 2030. One of the most promising solutions to reduce emissions is the recovery of braking energy: it is possible to convert kinetic energy back to the electrical form and reinject it back to the grid. Currently, because of the unidirectional nature of the substation, the receptivity of the supply network is limited and only a small part of the traction energy is sent back to the catenary. The remaining part is usually wasted on the train braking rheostats. In this scenario, new installations of reversible substations in DC railway systems, can improve energy savings. In this paper, in order to evaluate the impact of this innovative technology, a preliminary analysis of the data acquired during a measurement campaign conducted jointly with Metro de Madrid (train and substation owner) and HitachiRail (train manufacturer) is presented. Energy exchanged between train and supply line and real-time power quality events survey can be valuable tools to make an estimation of the impact of the reversible substation.
A Compact Physics Analytical Model for Hot-Carrier Degradation

We develop and validate a fully analytical model for hot-carrier degradation based on a thorough description of the physical picture behind this reliability phenomenon. This approach captures and links carrier transport, modeling of the Si-H bond-breakage mechanisms, and simulations of the degraded devices. All quantities evaluated within the model are described by analytical expressions and time consuming TCAD simulations are therefore avoided. We show that the model can capture measured dependencies of the normalized linear drain current change on stress time with good accuracy.
Socio-Cellular Network: A Novel Social Assisted Cellular Communication Paradigm

To handle unprecedented mobile data demands in the next-generation 5G networks, dense deployments of base stations is the most promising solution. However, dense deployments not only leads to co-channel interference but also the underutilization of wireless resources in most scenarios. One way to maximize the gains of such deployments is to allow inter-operator collaborations where multiple operators can share their base stations and licensed spectrum with each other users. In addition to outdoor base stations, ultra-dense deployment of small cells such as femtocells inside homes/offices/public areas can further improve frequency reuse and system throughput. Femtocells are usually owned by end-users who are often reluctant to share them with other users due to trust and performance concerns. Hence, apart from operator collaboration, it is necessary to have collaborations among end-users to share femtocells. In this direction, we propose a unique cellular communication paradigm called Socio-Cellular Network along with an efficient cell selection scheme to facilitate operator and user collaborations to share base stations. Our simulation results show that collaborations among operators and end-users via social networks help to improve the performance of cellular networks in terms of throughput and energy efficiency.
Changes in the fish species composition of all Austrian lakes >50 ha during the last 150 years

The fish communities of all Austrian natural lakes (n ¼ 43) larger than 50 ha in surface area were assessed and the historical fish communities in c. 1850 were reconstructed. During the last 150 years, the fish communities of Austrian lakes have altered: in 49% of lakes at least one indigenous fish species, usually sensitive or small-bodied taxa, is now missing. Conversely, in all but one of the 43 lakes the number of fish species has increased. In particular, certain fish species of interest to angling and commercial fisheries now occur in more lakes. Generalised linear models were used to identify variables that would explain the loss of fish species using categories of lake use (ranking score). The category human population density around the lakes seemed to be one of the main causes for the loss of fish species.
The Solubility of Intramuscular Collagen in Meat Animals of Various Ages

SUMMARY The percentage of total intramuscular collagen solubilized by heating in 1/4-strength Ringer's solution, muscles from 98 cattle, 15 sheep, and 9 pigs at 77°C for 1 hour decreased as chronological age increased. Total intramuscular collagen was high in very young (8-9 weeks) bo vines and in two of the three very old (7-15 years) bovines studied. In the age range of 16 weeks to d1/2 years, variation in total bovine intramuscular collagen was wide. There was no indication of a systematic increase as age increased. The results provide strong presumptive evidence of an increase with age in the number or strength of the cross links of intramuscular collagen and furnish an acceptable explanation as to why total amounts of intramuscular collagen are inadequate to explain toughness associated with increasing chronological age.
ADMISSIONS, DIAGNOSES, AND OUTCOMES FOR EURASIAN SPARROWHAWKS (ACCIPITER NISUS) BROUGHT TO A WILDLIFE REHABILITATION CENTER IN ENGLAND

ADMISIONES, DIAGNOSTICOS Y RESULTADOS PARA INDIVIDUOS DE ACCIPITER NISUS RECIBIDOS EN UN CENTRO DE REHABILITACION DE VIDA SILVESTRE EN INGLATERRA Examinamos retrospectivamente las razones para la admision, el diagnostico y el resultado de gavilanes de la especie Accipiter nisus llevados a un centro para la rehabilitacion de fauna silvestre situado en el noroeste de Inglaterra durante un periodo de cinco anos (2000–2004). En total, 205 aves fueron admitidas durante este periodo, la mayoria adultas. No se noto una diferencia significativa en el numero entre machos y hembras. Hubo una variacion estacional significativa en el numero de aves recibidas: la mayoria se recibieron entre julio y septiembre, un lapso que comprende las etapas finales de la etapa de anidacion, el periodo posterior al emplumamiento y el periodo en que los volantones muestran las primeras senales de independencia. La mayoria de las admisiones (70%) se debieron a colisiones y a heridas traumaticas de origen desconocido. Solo el 24% de la.
Water Retention Mechanism of HPMC in Cement Mortar

In this paper, the effect of HPMC (hydroxypropyl methyl cellulose ether) on the cement mortar water retention (WR) and composition was studied. The relationship between the plastic viscosity and water retention of cement mortar was revealed. The results showed that HPMC formed a colloidal film with a 3D network structure in water, which changed the ability of water to migrate. The HPMC colloid adsorbed on the surface of cement and sand particles and played a bridging role due to the influence of the spatial network structure of the thin film. Fine particles formed a grid-like distribution, and the hydration products formed a unique fibrous tree-like structure. A positive correlation was observed between the plastic viscosity and the water holding capacity of cement mortar. Finally, the mechanism responsible for the improved water retention of cement mortar by HPMC was analyzed using the changing water migration capacity, migration channels, and mortar cohesion.