Compression algorithms for high-data-volume instruments on planetary missions: a case study for the Cassini mission

2021 ◽  
Vol 7 (02) ◽  
Author(s):  
Hua Xie ◽  
Robert A West ◽  
Benoît Seignovert ◽  
Jeffrey Jewell ◽  
William Kurth ◽  
...  
Keyword(s):  
Compression Algorithms ◽  
High Data ◽  
Cassini Mission ◽  
Data Volume ◽  
Planetary Missions

scholarly journals Mathematical Literacy: A Case Study on Padang Students' Mathematical Ability

2021 ◽  
Vol 12 (1) ◽  
pp. 109-120
Author(s):  
Yulia Yulia ◽  
Martin Kustati ◽  
Juli Afriadi
Keyword(s):  
Mathematics Learning ◽  
Descriptive Study ◽  
Qualitative Approach ◽  
Literacy Skills ◽  
Mathematical Ability ◽  
Research Subjects ◽  
Mathematical Literacy ◽  
High Data ◽  
Mathematical Abilities

This study aims to analyze student mathematical literacy ability from the perspective of students' Mathematical Ability. This research is a descriptive study with a qualitative approach. The research subjects were three students of XI IPA 1 MAN 1 Padang with different mathematical abilities: low, medium, and high. Data were collected through documentation, tests, and interviews. The results of the analysis show that students with high abilities can solve routine problems, interpret problems and solve them with formulas, carry out procedures well, can deal with complex situations, use their reasoning in solving problems, can work effectively and interpret different representations and then relate them to the real world. Students with moderate abilities can solve routine problems, interpret problems and solve them with formulas, and carry out procedures properly. Meanwhile, students with low abilities are only able to solve routine questions. Based on these results, it is necessary to look for strategies in the mathematics learning process, which enable the improvement of students' mathematical literacy skills.

scholarly journals The Use of Laser Precipitation Monitor (LPM) of Disdrometer and Weather Radar to Determine the Microphysics Characteristics of Extreme Rainfall in Jakarta. (Jakarta Flood Case Study February 25, 2020)

2021 ◽  
Vol 893 (1) ◽  
pp. 012017
Author(s):  
I D G A Putra ◽  
A Sopaheluwakan ◽  
B P Adi ◽  
K A Sudama ◽  
J Rizal ◽  
...  
Keyword(s):  
Extreme Rainfall ◽  
Weather Radar ◽  
Heavy Rain ◽  
Maximum Diameter ◽  
Convective Clouds ◽  
Data Volume ◽  
Accumulated Rainfall ◽  
The One ◽  
The Relationship

Abstract Heavy rains on February 24, 2020, caused flooding in most parts of Jakarta and its surroundings. The one-day observation of accumulated rainfall from the Laser Precipitation Monitor (LPM) was recorded at 358.6 mm/day at the Kemayoran station on February 25, 2020, at 00.00 UTC (07.00 Jakarta Time). In this study, analysis of the microphysical characteristics of extreme rainfall using LPM installed at Kemayoran meteorology station and weather radar at Cengkareng meteorology station with a spatial radius of 250 km. LPM is used to measure the diameter of the raindrops, the velocity of falling raindrops, LPM reflectivity, and the amount of accumulated rainfall with time resolution per minute and stored in excel data format. While the weather radar is used to measure the reflectivity spatially and temporally in the data volume format (.vol). The method used is, first, to find the relationship between LPM reflectivity and the amount of LPM rainfall with regression analysis. Second, the radar reflectivity is converted into estimated rainfall intensity for the Jakarta area and its surroundings. The results of this study found a relationship between LPM reflectivity (X) and rainfall accumulation LPM (Y) to form a regression relationship with the formula Y = 0.013X with R2 = 0.3777. Based on the record of the LPM time series, the peak of rainfall occurred at 18.17 UTC with 1000 raindrops, the maximum fall speed was 10 m/s, and the maximum diameter is 8.5 millimeters. Based on the results of microphysical measurements of LPM, spatial plots, and vertical cross-section radar, it can be concluded that flooding in Jakarta is due to heavy rain from convective clouds.

scholarly journals Large Dataset Classification Using Parallel Processing Concept

2020 ◽  
Vol 4 (4) ◽  
pp. 191
Author(s):  
Mohammad Aljanabi ◽  
Hind Ra'ad Ebraheem ◽  
Zahraa Faiz Hussain ◽  
Mohd Farhan Md Fudzee ◽  
Shahreen Kasim ◽  
...  
Keyword(s):  
Big Data Analytics ◽  
Large Data ◽  
Business Analytics ◽  
Healthcare Applications ◽  
Current Increase ◽  
High Data ◽  
Huge Data ◽  
Data Volume ◽  
Effective Decision Making ◽  
Experimental Findings

Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.

Including Vibration Characteristics Within Compressive Sensing Formulations for Structural Monitoring of Beams

2017 ◽  
Author(s):  
Vaahini Ganesan ◽  
Tuhin K. Das ◽  
Jeffrey L. Kauffman ◽  
Nazanin Rahnavard
Keyword(s):  
Boundary Conditions ◽  
Compressive Sensing ◽  
Continuous Monitoring ◽  
Structural Changes ◽  
Vibration Monitoring ◽  
High Data ◽  
Data Volume ◽  
Spatial Domains ◽  
Temporal And Spatial ◽  
Detection And Localization

Vibration-based monitoring of mechanical structures often involves continuous monitoring that result in high data volume and instrumentation with a large array of sensors. Previously, we have shown that Compressive Sensing (CS)-based vibration monitoring can significantly reduce both volume of data and number of sensors in temporal and spatial domains respectively. In this work, further analysis of CS-based detection and localization of structural changes is presented. Incorporating damping and noise handling in the CS algorithm improved its performance for frequency recovery. CS-based reconstruction of deflection shape of beams with fixed boundary conditions is addressed. Formulation of suitable bases with improved conditioning is explored. Restricting hyperbolic terms to lower frequencies in the basis functions improves reconstruction. An alternative is to generate an augmented basis that combines harmonic and hyperbolic terms. Incorporating known boundary conditions into the CS problem is studied.

scholarly journals Subjective evaluation of next-generation video compression algorithms: a case study

2010 ◽  
Author(s):  
Francesca De Simone ◽  
Lutz Goldmann ◽  
Jong-Seok Lee ◽  
Touradj Ebrahimi ◽  
Vittorio Baroncini
Keyword(s):  
Video Compression ◽  
Subjective Evaluation ◽  
Next Generation ◽  
Compression Algorithms

A FRAMEWORK FOR COLLABORATIVE REVIEW OF CANDIDATE EVENTS IN HIGH DATA RATE STREAMS: THE V-FASTR EXPERIMENT AS A CASE STUDY

2014 ◽  
Vol 149 (1) ◽  
pp. 23 ◽  
Author(s):  
Andrew F. Hart ◽  
Luca Cinquini ◽  
Shakeh E. Khudikyan ◽  
David R. Thompson ◽  
Chris A. Mattmann ◽  
...  
Keyword(s):  
Data Rate ◽  
High Data Rate ◽  
High Data

scholarly journals Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey

2015 ◽  
Vol 31 (4) ◽  
pp. 737-761 ◽  
Author(s):  
Matthias Templ
Keyword(s):  
Original Data ◽  
Data Sets ◽  
Data Utility ◽  
High Data ◽  
Disclosure Risk ◽  
Statistical Disclosure ◽  
Context Data ◽  
Statistical Agencies ◽  
Utility Measures

Abstract Scientific- or public-use files are typically produced by applying anonymisation methods to the original data. Anonymised data should have both low disclosure risk and high data utility. Data utility is often measured by comparing well-known estimates from original data and anonymised data, such as comparing their means, covariances or eigenvalues. However, it is a fact that not every estimate can be preserved. Therefore the aim is to preserve the most important estimates, that is, instead of calculating generally defined utility measures, evaluation on context/data dependent indicators is proposed. In this article we define such indicators and utility measures for the Structure of Earnings Survey (SES) microdata and proper guidelines for selecting indicators and models, and for evaluating the resulting estimates are given. For this purpose, hundreds of publications in journals and from national statistical agencies were reviewed to gain insight into how the SES data are used for research and which indicators are relevant for policy making. Besides the mathematical description of the indicators and a brief description of the most common models applied to SES, four different anonymisation procedures are applied and the resulting indicators and models are compared to those obtained from the unmodified data. The disclosure risk is reported and the data utility is evaluated for each of the anonymised data sets based on the most important indicators and a model which is often used in practice.

scholarly journals A Predictive Analytics Model for E-commerce Sales Transactions to Support Decision Making: A Case Study

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Shereen Morsi
Keyword(s):  
Decision Making ◽  
Predictive Analytics ◽  
Strategic Decisions ◽  
Business Decision ◽  
Innovative Capabilities ◽  
Data Volume ◽  
Precautionary Measures ◽  
Support Decision Making

Given the significant growth in electronic commerce, firms are seeking technological innovations and innovative capabilities to deal concurrently with the data’ volume generated and gaining insights from it for better decisions. Although recent studies identify predictive analytics as becoming the keystone of all business decision making and a crucial aspect in firms by it is a possible means for driving strategic decisions. Significant inroads into the interrelationships between capabilities and the execution of a pathway to an analytical capability to many Egyptian e-commerce businesses have yet to be made. Therefore, this paper aims to shed light on the importance and the role of using predictive analytics models in the Egyptian e-commerce firms where these tools became dominant resources for gaining valuable knowledge for better decision making by precautionary measures from prediction rates and different applications that have been applied by global e-commerce firms. The aim of the paper was achieved by building a predictive analytics model for sales forecasting by tackling to one of the e-commerce company in Egypt, and the online transaction dataset has been analyzed. The result obtained from the model has been displayed, and some insights extracted from the prediction model have been explained.

scholarly journals Using Python® language for the validation of the CCI soil moisture products via SM2RAIN

2016 ◽  
Author(s):  
Luca Ciabatta ◽  
Christian Massari ◽  
Luca Brocca ◽  
Christoph Reimer ◽  
Sebastian Hann ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Open Source ◽  
Observation Data ◽  
Spatial Coverage ◽  
Data Volume ◽  
Earth Observation Data ◽  
Remote Sensing Techniques ◽  
Open Source Systems ◽  
Validation Tool

Remote sensing techniques provide a new way to obtain hydrological variables (i.e. rainfall and soil moisture), mainly in poorly instrumented areas that are fundamental for natural hazard assessment and mitigation. The ever increasing availability of satellite derived products characterized by high temporal and spatial coverage requires the development of techniques and instruments for big data volume managing. Moreover, the use of open source systems is highly encouraged in order to increase their use by the scientific community. In this study, the application of the SM2RAIN algorithm to the CCI soil moisture product is proposed as a case study. A number of Python® classes and methods have been developed for this purpose, with the aim of creating an open-source web validation tool for SM dataset, within the Earth Observation Data Centre for Water Resources Monitoring (EODC).

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