Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does Radiation Code Accuracy Matter?

The accuracy of radiation calculations matters for climate simulations. Radiation code assessments typically validate such quantities as radiative fluxes and heating rates. However, it is not clear how these quantities or their uncertainties affect climate sensitivity—the extent of warming driven by the CO2 radiative forcing. Here, we assess the temperature response uncertainty by comparing simulations based on parameterized radiation codes to that based on a benchmark line‐by‐line model, in an idealized global warming (quadrupling CO2) experiment. We present an idea to quantitatively relate the radiative quantities to equilibrium temperature response. We find that when the temperature change is solely driven by the radiative process (a pure radiative adjustment), the temperature change and its uncertainty can be well diagnosed by the proposed method. Under this situation, the temperature response uncertainty is found to result from the uncertainties in both CO2 forcing and the radiative Jacobians. This calls into attention the importance of Jacobians in the radiation code intercomparison and validation. When the temperature change is driven by both radiation and convection (a radiative‐convective adjustment), the temperature change can no longer be predicted by a simple diagnostic equation. Nevertheless, the validation against the benchmark simulation provides an estimate of the temperature response errors that may be attributed to radiation code inaccuracy. We find that such errors may reach several 10th° Kelvin for surface temperature and more than 1° Kelvin for atmospheric temperatures.
3D multispectral imaging system for contamination detection

The 3D multispectral imaging system proposed in this contribution realizes simultaneous detection of contaminants and 3D localization. The imaging system is composed of a digital pattern projector, a stereo-vision setup of two multispectral filter wheel cameras, and an external light source. A calibration procedure is developed to estimate simultaneously stereo camera parameters and light source parameters. For an acceleration of image acquisition, the entire spectral range is split into two parts which are captured from two different camera views and merged using structured light. The usefulness of the proposed system is demonstrated with an example of cutting oil detection. For this the fluorescence effect is utilized, and specular reflections are filtered out based on the estimation of illumination geometry. Experimental results show that the surface areas with cutting oil could be reliably distinguished from workpieces using the proposed algorithms.
Assessing the Quality of Southern Ocean Circulation in CMIP5 AOGCM and Earth System Model Simulations

The Southern Ocean (SO) is vital to Earth’s climate system due to its dominant role in exchanging carbon and heat between the ocean and atmosphere and transforming water masses. Evaluating the ability of fully coupled climate models to accurately simulate SO circulation and properties is crucial for building confidence in model projections and advancing model fidelity. By analyzing multiple biases collectively across large model ensembles, physical mechanisms governing the diverse mean-state SO circulation found across models can be identified. This analysis 1) assesses the ability of a large ensemble of models contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5) to simulate observationally based metrics associated with an accurate representation of the Antarctic Circumpolar Current (ACC), and 2) presents a framework by which the quality of the simulation can be categorized and mechanisms governing the resulting circulation can be deduced. Different combinations of biases in critical metrics including the magnitude and position of the zonally averaged westerly wind stress maximum, wind-driven surface divergence, surface buoyancy fluxes, and properties and transport of North Atlantic Deep Water entering the SO produce distinct mean-state ACC transports. Relative to CMIP3, the quality of the CMIP5 SO simulations has improved. Eight of the thirty-one models simulate an ACC within observational uncertainty (2σ) for approximately the right reasons; that is, the models achieve accuracy in the surface wind stress forcing and the representation of the difference in the meridional density across the current. Improved observations allow for a better assessment of the SO circulation and its properties.
The Research on Tracking Filter Algorithm of a Certain Type of Short-range Active Defense Radar System

Aiming at the research of tracking filter algorithm for a certain type of radar of short-range active defense system,motion model. And the model of establishment of interception point prediction error ellipsoid. are given. Based on the collection,the application of Kalman filter,α-β filter and least square filter algorithm is simulated respectively for a certain type of radar sytem. According to the comparison of simulation results and the characteristics analysis of active defense system,the method is adopted to measure data filtering.
A method for estimating ice phenology of Finnish waterbodies using Sentinel-1 IW mode data and in-situ wind observations

The subject of this thesis was to find a method for determining ice phenology, or ice freeze-up and break-up dates, in Finnish waterbodies using Sentinel-1 C-band synthetic aperture radar images. The method should minimize manual steps to make it suitable for automatic processing. Though possibilities for detecting these changes are presented by optical earth observation satellites, optical images are often limited by cloud cover and darkness over autumn-winter months in the northern hemisphere. A great advantage of synthetic aperture radars as active sensors is their capability to penetrate through cloud cover and their independence of sunlight to operate. For specific advantages of the Sentinel-1 constellation, data is gathered with a very high acquisition rate near polar regions and all data is openly accessible since 2014. As this thesis is written, few studies have been conducted on observing inland waterbodies’ ice using Sentinel-1 IW mode radar images. To gather data from local wind conditions, lake water / ice / snow surface and temperatures in preparation for the thesis to determine and develop the final method, an automatic sensor station was built to a lake shoreline in southern Finland. The method developed and presented in this thesis is based on the similarity of Sentinel-1 IW mode radar images produced by water surface waves in similar wind conditions. By classifying radar images using similar wind conditions determined by weather station measurements, then estimating a numerical value for the difference between the radar images, waterbodies with open water will feature higher similarity with each other than frozen waterbodies with open water. The difference in similarity is used to determine the dates when changes in ice phenology, freeze-ups and ice break-ups, occur. Calculated by the method, lake freeze-up and break-up periods were determined to be accurate to within few satellite flyovers for select four lakes of different sizes in southern Finland which included the lake with the sensor station. For river portions few hundred meters wide and long, the method was found to distinguish changes in ice phenology for inland river portions better than portions near the sea discharge location. As the method could be used for estimating ice phenology for a variety of waterbodies in Finland not being routinely observed, it will offer possibilities in expansion of freeze-up and breakup models for such waterbodies. There are also potential applications for other watershed models, as seasonal ice can affect certain types of data used to calibrate these models.
Optical Networks A Practical Perspective

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Imaging the Deep Subsurface Plumbing of Old Faithful Geyser From Low‐Frequency Hydrothermal Tremor Migration

Old Faithful Geyser in Yellowstone is one of the most well‐known hydrothermal features in the world. Despite abundant geophysical studies, the structure of Old Faithful's plumbing system beneath ~20‐m depth remained largely elusive. By deploying a temporary dense three‐component geophone array, we observe 1–5 Hz low‐frequency hydrothermal tremor originating from Old Faithful's deeper conduit. By applying seismic interferometry and polarization analyses, we track seismic tremor source migration throughout the eruption/recharge cycle. The tremor source drops rapidly to ~80‐m depth right after the eruption and gradually ascends vertically back to ~20‐m depth, coinciding with the previously inferred bubble trap location. Likely excited by the liquid/steam phase transition, the observed tremor source migration can provide new constraints on the recharge process and deeper conduit geometry. Combined with the shallow conduit structure from previous studies, these results provide constraints on the major fluid pathway down to 80‐m depth.
The Growth Pattern of Pterosaurs Based on Logistic Growth Model

Fictional dragon becomes a new member of the real world family. As an old member, human need to know its origin and evolution, in order to get along with each other in the earth. Based on incomplete data and reasonable assumptions, we analyze the growth pattern of pterosaurs to obtain the function relation between weight and wingspan. With the estimation of height ratio of Daenerys and Drogon, we estimate the wingspan of a 6 year old Drogon, which is 59 meters. Putting the wingspan data into weight-wingspan function, we obtain the weight of Drogon, which is 34.031 tons. The result is of importance to the research of food intake of dragons. Finally, we build a toy ecosystem for dragon and the principles for dynamics of system are well found.
METODE PENELITIAN KUANTITATIF

Harapan penulis, semoga buku ini dapat menjadi bahan pendamping bagi mahasiswa yang mengambil mata kuliah ini dan sekaligus bagi peminat dalam bidang penelitian. Penulis juga menyadari bahwa buku ini jauh dari sempurna, karena itu penulis sangat berterima kasih bila ada kritik dan saran untuk perbaikan pada edisi berikutnya.
Current Status and Future Trends of the Evaluation of Solar Global Irradiation using Soft-Computing-Based Models

The rising interest of high quality solar irradiation estimates is explained by its straightforward applications in solar energy, agriculture and climate change. Regarding the first, accurate measurements of solar irradiation are always required to accurately estimate electrical energy production and levelized cost of electricity on a particular plant in a certain location. This can be done by installing meteorological stations with pyranometers. However, wide areas of the planet today lack of nearby pyranometers in these stations. As a result, solar irradiation uses to be determined using two ways: by satellite-derived databases whose spatial resolution is almost always insufficient to provide a high level of certainty [1] but also can be derived from other meteorological variables using classical parametric [2–4] or even soft computing methods [5, 15]. Other application of estimated irradiation is the correction of not-available or spurious data in pyranometer registers, which in many cases suppose a big issue to estimate the annual irradiation. Classical and soft computing techniques usually require registers of temperature and rainfall, which are commonly measured in many locations. They also need additional short records of solar irradiation to calibrate or train models. As a result, these techniques can provide nearto-real time irradiation estimates that are really useful to evaluate the performance of solar energy plants of previous days. This is a crucial service that can not be properly provided by the free satellite-derived databases [6]. The authors have previously addressed an exploratory study of 22 classical models, also proposing a new methodology to optimize the classical models to on-site characteristics [7]. This assessment was validated in La Rioja region (Northern Spain). The model defined for this region showed a low mean absolute error (MAE) in the range of 2.195MJ/m2day with 41.65% of the residuals along 5 years (2007-2011) within the uncertainty of pyranometers (10%). However, the estimation of the irradiation is still a science with a high percentage of uncertainties and a large amount of irrelevant variables. Soft computing solutions based on machine learning theories are actually revealing themselves as very useful tools for this task. In fact, different softcomputing techniques have already been considered in solar irradiation estimation, i.e. artificial neural networks (ANN) [8, 16], k-nearest-neighbors (kNN) [9] and support vector machines
Crack Propagation and Branching in Burning Solid Propellants and Ignition of Nitramine-Based Composite Propellants

Abstract : Three major tasks performed during the report period of investigation were: i) crack propagation and branching in burning solid propellants, ii) ignition of nitramine-based composite propellants under rapid pressurization, and iii) ignition of nitramine-based composite propellants by CO2 laser. Propellant samples have been recovered by interrupted burning experiments. Four modes of crack propagation and/or branching were observed. The amount of surface area generated by mixed or branched modes is substantially higher than that of the single crack propagation mode, and thus could cause sufficiently severe burning in the damaged zone to result in rocket motor failure. The crack propagation problem is analyzed through the use of basic physical principles, experimental results,and Schaper's theory of crack propagation. A set of governing dimensionless parameters which control and characterize the degree of damage has been obtained. These parameters can be used to develop guidelines for safe operating conditions of rocket motors. Ignition characteristics of a series of RDX-based composite propellants were studied. The times for the onset of light emission (t sub LE) of different propellants under rapid pressurization were compared. It is also found that propellant samples with shorter t sub LE do not necessarily show higher mass consumption, this indicates that propellant vulnerabilty should be evaluated by both t sub LE and the fraction of mass consumed.
Learning L2 pronunciation with a mobile speech recognizer: French /y/

This study investigates the acquisition of the L2 French vowel /y/ in a mobile-assisted learning environment, via the use of automatic speech recognition (ASR). Particularly, it addresses the question of whether ASR-based pronunciation instruction using a mobile device can improve the production and perception of French /y/. Forty-two elementary French students participated in an experimental study in which they were assigned to one of three groups: (1) the ASR Group, which used an ASR application on their mobile devices to complete weekly pronunciation activities, with immediate written visual (textual) feedback provided by the software and no human interaction; (2) the Non-ASR Group, which completed the same weekly pronunciation activities in individual weekly sessions but with a teacher who provided immediate oral feedback using recasts and repetitions; and finally, (3) the Control Group, which participated in weekly individual meetings ‘to practice their conversation skills’ with a teacher who provided no pronunciation feedback. The study followed a pretest/posttest design. According to the results of the dependent samples t-tests, only the ASR group improved significantly from pretest to posttest (p < 0.001), and none of the groups improved in perception. The overall success of the ASR group on the production measures suggests that this type of learning environment is propitious for the development of segmental features such as /y/ in L2 French.
On predicting the sound from a cavitating marine propeller in a tunnel

Model tests in a cavitation tunnel are commonly used in order to predict the radiated noise from full-scale marine propellers. Correction factors and scaling procedures are commonly used in estimating the effects of background noise and the propeller model scale. Less attention is paid to the influence of the tunnel related factors. Above a certain frequency, i.e. first non-plane wave mode cut-on frequency, the sound pressure varies over the tunnel cross-section and non-plane waves are introduced. Below this frequency, only plane waves propagate in the tunnel and no attenuation due to geometrical spreading occurs in the far field of the propeller. Thus, it is difficult to apply source level scaling procedures based on, e.g., spherical spreading of the sound field. Also, rigid tunnel walls near the propeller load it acoustically and may alter the fundamental sound emission compared to emission of the same source in free field conditions. In this paper, sound emission and immission of acoustic sources in a free space and in a waveguide are discussed and sound from a cavitating model-size marine propeller in a tunnel is investigated with numerical methods.
Snow Properties Retrieval Using Passive Microwave Observations

Seasonal snow cover, the second-largest component of the cryosphere, is crucial in controlling the climate system, through its important role in modifying Earth’s albedo. The temporal variability of snow extent and its physical properties in the seasonal cycle also make up a significant element to the cryospheric energy balance. Thus, seasonal snow cover should be monitored not only for its climatological impacts but also for its role in the surface-water supply, ground-water recharge, and its insolation properties at local scales. Snowpack physical properties strongly influence the emissions from the substratum, making feasible snow property retrieval by means of the surface brightness temperature observed by passive microwave sensors. Depending on the observing spatial resolution, the time series records of daily snow coverage and a snowpacks most-critical properties such as the snow depth and snow water equivalent (SWE) could be helpful in applications ranging from modeling snow variations in a small catchment to global climatologic studies. However, the challenge of including spaceborne snow water equivalent (SWE) products in operational hydrological and hydroclimate modeling applications is very demanding with limited uptake by these systems. Various causes have been attributed to this lack of uptake but most stem from insufficient SWE accuracy. The root causes of this challenge includes the coarse spatial resolution of passive microwave (PM) observations that observe highly aggregated snowpack properties at the spaceborne scale, and inadequacies during the retrieval process that are caused by uncertainties with the forward emission modeling of snow and challenges to find robust parameterizations of the models. While the spatial resolution problem is largely in the realm of engineering design and constrained by physical restrictions, a better understanding of the whole range of retrieval methodologies can provide the clarity needed to move the thinking forward in this important field. Following a review on snow depth and SWE retrieval methods using passive microwave remote sensing observations, this research employs a forward emission model to simulate snowpacks emission and compare the results to the PM airborne observations. Airborne radiometer observations coordinated with ground-based in situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to the volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this
SVM-based Network Intrusion Detection Model

In view of the problems of using traditional machine learning method to detect the network intrusions,this paper proposes a network intrusion detection model based on support vector machine(SVM). Experimental results demonstrate that the proposed model has higher detection accuracy of intrusions and avoids the limitation of the detection methods based on traditional machine learning.In the training,considering the effect of different network data features on the intrusion detection results,a new weighted feature classification method is also brought forward,which improves the accuracy of network intrusion detection.
Justification of Ice Melting Power on 10 kV Overhead Power Lines

The fuzzy multicriteria model of the ice melting system on radial 10 kV overhead power lines was developed. Three criteria were taken into account: the minimum of reduced annual costs for ice melting system, the minimum of amount of electric energy not provided to consumers during melting, and the minimum of the amount of electricity consumed for the ice melting. The model also takes into account the fuzzy limitation of the permissible ice melting power associated with the uncertainty of weather conditions. Based on the model we justified the value of ice melting power, which should be provided by serial ice melting devices of industrial production, intended for use on radial 10 kV overhead power lines, taking into account the uncertainty of initial data.
Parsing Indonesian Syntactic with Recursive Neural Network

Sentence is a form of human communication which is closely related to language system. Sentence is one of the recursive structures that are often found in daily conversation. Learning syntactic structure is useful to explore the meaning of the sentence contained on it or translated it into another language such as machine language. The problem is meaning, ambiguity, and the language that is not according to the rules of syntax, causing the command translation become more complex. This research is about parsing Indonesian syntax based on natural language rules for applications in the field of human-machine interaction. Each word that is a part of the sentence, is mapped into vector-space model. To estimate the potential connection of two words, we use the recursive neural network. The potential connection of two words translated into a higher structure to obtain a complete sentence structure. We obtain 93% accuracy, with 50 data-set are given in the learning process to represent a hundred vocabularies.