Direct delivery of remote sensing data to user computer systems using satellite-based data broadcast techniques

Taking a byte out of big data

A new numerical procedure for the study of finite temperature quantum dynamics is developed. The method is based on the observation that the real and imaginary time dynamical data contain complementary types of information. Maximum entropy methods, based on a combination of real and imaginary time input data, are used to calculate the spectral densities associated with real time correlation functions. Model studies demonstrate that the inclusion of even modest amounts of short-time real time data significantly improves the quality of the resulting spectral densities over that achievable using either real time data or imaginary time data separately.

The ability to monitor data in real time has become increasingly important as the industry continues digitalization efforts. Over the past several years this contractor has upgraded the capabilities of a real time monitoring system that has been providing value through improved operational efficiency and visibility of operations across its onshore fleet. These updates open a wide set of alerting opportunities that were previously not possible by distributing the alerting system to local, rig-based devices. An overview of the technology and architecture necessary to make this system possible, as well as the impact on performance will be discussed. This contractor's efforts have focused on converting the system from a cloud-based system to an edge-based system, with all computations and alerts issued at the rig location. The architecture comprises migrating cloud applications to run on the edge and leveraging edge hardware and rig historians to enable alerting on all data available at the rig location. Streamlining and synchronizing the rules/alert definitions and notifying users instantly using cloud infrastructure is a novel implementation. Housed within the edge platform, the system can operate on batches of data in real time without being held back by cloud computing related latency. Preexisting infrastructure made it possible to receive exception-based alerting within 15 minutes of an event occurring. With the new implementation running on the edge, it is possible to receive alerts to the user's phone, email and/or web-based dashboard within a minute. Leveraging increased real-time monitoring capabilities, this contractor has been able to reduce the amount of premature equipment failures as well as improve drilling connection operations. These successes have resulted in a reduction in capital spend and improved spud to release times. The above will be reviewed in detail. This change in data architecture from a cloud-based to a distributed rig-based computing system provides a more efficient way to trigger alerts and opens the possibility of generating alerts locally at the driller's console. Previously, the cloud-based alerting system only had access to data streamed off-location, providing limited insight into equipment health and drilling conditions. As a result of this update, we can provide end users with alert notifications containing detailed messages and specific data from a wide range of local data sources, including thousands of equipment-related data feeds, downhole data, complex artificial intelligence(AI)/machine learning(ML) model outputs, and drilling performance metrics which were not previously synchronized into a system that allowed alerts to be generated across this entire dataset. Quicker computing and notification delivery times will pave the way for well control, drilling dysfunction and more sophisticated equipment health alerts on the driller's console in real time.

Local government reform 2001 is an important part of the Government of Pakistan's programme for democratic renewal. The main feature of introducing the Local Government System was to empower the people at the grassroots level and to transfer power from the elite to the masses. This paper uses councillors‘ data from north-western Pakistan to examine the economic and social status of village representatives. Land ownership, high income, education and political connections increase the chances of election to public office at the district and sub-district level, but do not have much impact at the village level. There is evidence of genuine change at union (village) level as the majority of councillors (65 per cent) at this level belong to low-income group and small landowners. So the system has changed the identity of the locally politically dominant group. But, at the same time, results suggest that while local governments units are helpful in creating spaces for a broader range of non-elite community leaders to emerge, elite control of decision making is pervasive. However, it is expected that the presence of non-elite members in the councils may change the allocation of resources in favour of the disadvantaged group with the passage of time, provided elections to councils are held regularly.

It is important to measure and analyze offshore platform motion data for the safety of offshore platforms that are installed in an extreme environmental conditions. Especially, in the Gulf of Mexico, because of the hurricane and loop current that has been strengthened due to changes in the ocean environment, a monitoring system is necessary to measure the offshore platform motion data in real time, as well as simultaneously analyze it for safe operation and accident prevention. In order to develop such a monitoring system, it is extremely critical to obtain accurate data. However, since data measured in real time is not suitable for analysis due to quality problems, the data must be processed. The problem is that the processed data differs depending on the processing methods being employed, and results in entirely different analysis results. Incorrect results can mislead the current state of the offshore platform causing missed maintenance periods or, in the worst case, major accidents. Therefore, verifying both the adequacy of the methods used to process the data and the accuracy of the results is most vital. In this paper, improving reliability of the monitoring system that can measure offshore platform motion data and analyze data in real time, the validity of the method used in measurement data processing and the accuracy of the processed data were verified.

Testing for Contemporary Fiscal Policy Discretion with Real Time Data

In most developing countries, smallholder farms are the ultimate source of income and produce a significant portion of overall crop production for the major crops. Accurate crop distribution mapping and acreage estimation play a major role in optimizing crop production and resource allocation. In this study, we aim to develop a spatio–temporal, multi-spectral, and multi-polarimetric LULC mapping approach to assess crop distribution mapping and acreage estimation for the Oromia Region in Ethiopia. The study was conducted by integrating data from the optical and radar sensors of sentinel products. Supervised machine learning algorithms such as Support Vector Machine, Random Forest, Classification and Regression Trees, and Gradient Boost were used to classify the study area into five first-class common land use types (built-up, agriculture, vegetation, bare land, and water). Training and validation data were collected from ground and high-resolution images and split in a 70:30 ratio. The accuracy of the classification was evaluated using different metrics such as overall accuracy, kappa coefficient, figure of metric, and F-score. The results indicate that the SVM classifier demonstrates higher accuracy compared to other algorithms, with an overall accuracy for Sentinel-2-only data and the integration of optical with microwave data of 90% and 94% and a kappa value of 0.85 and 0.91, respectively. Accordingly, the integration of Sentinel-1 and Sentinel-2 data resulted in higher overall accuracy compared to the use of Sentinel-2 data alone. The findings demonstrate the remarkable potential of multi-source remotely sensed data in agricultural acreage estimation in small farm holdings. These preliminary findings highlight the potential of using multi-source active and passive remote sensing data for agricultural area mapping and acreage estimation.

Drilling programs continue to push into new and more complicated environments. As a result, accurate measurement and analysis of drilling data in real time are becoming more critical by means of minimizing failure costs. The measurement of actual wellbore shape in real-time can be considered as one of the key components to detect problems such as borehole instability. Abnormal wellbore shape will allow drawing conclusions on the stress field. Accurate wellbore caliper measurements in real-time will also allow determining cement volume requirements with less measurement time. Measuring flow rate accurately behind the bit with ultrasonic sensor will also help the driller to detect washouts along drill string in real time. This paper describes experiments related to the accuracy of ultrasonic sensor measurements for estimation of wellbore diameter in drilling fluids with different mud weights and additives in real time. A fully automated test robot has been designed and tests have been performed in different drilling conditions. The test robot allows moving vertical as well as lateral movement of a sensor head in an artificial wellbore which can be run with different fluids. In addition, bubbles were added to the mud while measuring borehole diameter by the sensor. Tests have been performed in relation to the radial position of the caliper tool inside the wellbore, including different objects inside the borehole simulating latches or dog legs. The wellbore profiles have been measured with different axial and rotational surveying speeds. Numerical simulation of ultrasonic measurements and comparison of the results to the recorded data gives an estimate on the measurement accuracy at different RPMs of drill string. It is also shown that gas bubbles can be detected and compensated with an appropriate accuracy if circle fitting methods like the Kasa method in combination with robust error models are applied. Introduction Recognition of variations in borehole shape in real-time drilling allow the drilling engineer to actuate appropriate counteractions to avoid costly failures, or to implement alterations in the drilling practices to optimize the shape of the borehole and thus improve the drilling efficiency. The received data help the driller to make proper decisions such as reaming a critical zone, changing the flow rate to reduce erosion or modifying the string rotation speed to reduce vibrations. Real time application of ultrasonic caliper tools also provides a method for calculating borehole volumes. On a final bit run for instance, the sensor may collect the caliper data during trip-out for the estimation of cement volumes. Other applications of ultrasonic caliper tools might include real time casing wear and borehole stability detection, evaluation of borehole cleaning and determination of tight spots or formation ledges (Maranuk et al., 1997). This paper reviews tests and experiments which have been performed by the ultrasonic sensor in drilling fluids with different mud weight. Effects of air bubbles on the recorded data by ultrasonic caliper were determined. However, even with the modern technology improvements, ultrasonic caliper cannot provide absolute reliable information for all drilling environments and conditions. Several operational factors such as tool centralization, mud density, borehole wall roughness, tool position, gas bubbles and drillstring vibration in the borehole among others need to be considered in order to optimize the accuracy of the borehole caliper data. For this reason analysis of circle fitting algorithms based on the Kasa method have been implemented.

Gender Watch is a strategy to advocate policy that is based on pro-women data. Gender Watch is developed to improve access and participation of poor and marginalized women to government social protection. The improvement of access started with the development of poor women capacity and organizing in grass root level with the establishment of Women School in Gresik Regency. In this school, women collect data, work with many stakeholders, submit the obtained data to the policy maker, and oversee the Regional Development Planning Forum (Musrenbang) in the village up to the regency. The work and the contribution of Women School in development force the Gresik Regional Government to be committed to allocate the budget for Women School and to replicate Women School in several villages. The commitment of the regional government is included in Mid-Term Regional Development Plan (RPJMD), City Work Plan (RKPD), and Regent’s regulation. The paper outlines the process and the experience of organizing in the grass root level and the data-based advocacy effort, so the policy advocacy strategy that stresses on the organizing of the grass root women through women schools, attracts the attention of the regional government to allocate the budget in the village level up to the regency level.

Real time solutions: an innovative approach to making business-time critical driven decisions. It is designed to monitor, support, and optimize operations in real time and remotely to mitigate cost and risk at work sites. The workflows allowed real time monitoring and intervention of the drilling operating parameters from the office which resulted in greater operating efficiency and thus greater financial saving. This technology was instrumental to the Angsi development team's success in drilling Malaysia's longest development well to-date, Angsi-A028ST1 at a total depth of 6,339 meters.Real time well construction workflows begin when real time data on the rig site are gathered and transmitted to PETRONAS Carigali Sdn.Bhd, PCSB office via VSAT link. Data can be accessed via a web-based interface for 24 hours coverage anytime anywhere. The real time data are integrated into a geological and geophysical project database and fed into a 3D visualization tool which amalgamates the planned and current well trajectories within the geological earth model incorporating neighbouring wells with operations data. The simultaneous display of "knowledge-management-cubes" from daily drilling operational reporting software enables the team to review contingency plans and be pre-emptive by the captured lesson learnt.This technology creates multidisciplinary collaborations through the ether space, reducing communication gaps and greatly improving the decision making process. Examples: reducing the number of check trips, shortening the planned TD by comparing the real time data against correlated model, and helping in handling operational problems. The team has greatly benefited from RTO - technologies, people and processes - with a total cost saving of more than USD 1 million.This paper presents the real time system setup, architecture and scope of work in enabling 2D and 3D well visualization and integration. The value-added and the relative cost savings, observations and feedback are also presented

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Filtergraph: An interactive web application for visualization of astronomy datasets

Nowadays, when the market is developing itself dynamically and the customer requirements are higher and higher, traditional way of supply chain management is not enough. Complexity of supply chain is still increasing, because of globalization - companies spread their geographical range in order to find better sources of raw materials, half finished products and finished products. What is more they also want to gain access to new markets and acquire new customers. And last but not least, companies want to strengthen their competitive position and increase income. All these challenges make that companies are forced to change way of functioning and management of supply chain. Supply chain visibility is a key to success, which provides speed, reliability and flexibility. In the response for all these needs more and more often Supply Chain Control Towers are launched. It is kind of center of supply chain management. Thanks to appropriate technology company has an access to all important data in real time - it guarantee visibility, which is such important. What is more, it also help to make all crucial decision, both short and long term. This article present Supply Chain Control Tower conception and show opportunities, how to apply this idea in retail company.

Research interface on a programmable ultrasound scanner

The use of large and diverse data in real time (called Big Data) affects many business processes and models. The tourism industry, characterized by manifold sub-sectors and players, provides a variety of starting points for Big Data usage. Examples are the optimization of transport offers using transaction data or a comprehensive analysis of destination trends based on social media posts. Big Data is a trending topic, however, the general discourse centres around potential ideas but fewer practical solutions. Based on a systematic literature analysis of initially 148 peer-reviewed journal articles, this article evaluates the current state of Big Data research within tourism. For this purpose, research articles centering around tourism-related Big Data were investigated according to the actual state of implementation of an IT solution, whether they truly grasp or represent Big Data in technological terms, and which added value they create for the tourism industry and research community. One key finding is that traditional data analysis is often wrongfully subsumed under the Big Data label. Further, the scientific literature predominantly discusses ideas or theoretical considerations, fewer tangible Big Data implementations, and fails to address and/or meet all requirements to be classified as Big Data. Only a minority of the presented solutions processes data in real time, whereas many rely on only one data source or structured data. Furthermore, most articles revolve around post-trip data analyses and are set to a destination context. In contrast, other tourism sectors as well as data interpretation and usage in pre-trip and on-trip phases are less represented. Lastly, this literature analysis provides an overview of true Big Data solutions already in operation and enables researchers to validly classify their own research activities in order to plan initiatives more specifically.