ATLAS Grid Data Processing: system evolution and scalability

The ASTRI Mini-Array is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4-m class Imaging Atmospheric Cherenkov Telescopes (IACTs) at the Observatorio del Teide (Tenerife, Spain). The system is designed to perform deep observations of the galactic and extragalactic gamma-ray sky in the TeV and multi-TeV energy band, with important synergies with other ground-based gamma-ray facilities in the Northern Hemisphere and space-borne telescopes. As part of the overall software system, the ASTRI (Astrofisica con Specchi a Tecnologia Replicante Italiana) Team is developing dedicated systems for Data Processing, Simulation, and Archive to achieve effective handling, dissemination, and scientific exploitation of the ASTRI Mini-Array data. Thanks to the high-speed network connection available between Canary Islands and Italy, data acquired on-site will be delivered to the ASTRI Data Center in Rome immediately after acquisition. The raw data will be then reduced and analyzed by the Data Processing System up to the generation of the final scientific products. Detailed Monte Carlo simulated data will be produced by the Simulation System and exploited in several data processing steps in order to achieve precise reconstruction of the physical characteristics of the detected gamma rays and to reject the overwhelming background due to charged cosmic rays. The data access at different user levels and for different use cases, each one with a customized data organization, will be provided by the Archive System. In this contribution we present these three ASTRI Mini-Array software systems, focusing on their main functionalities, components, and interfaces.

Loss of control accounts for over 25% of aircraft accidents worldwide. This study presents a fault tolerant flight control strategy for increasing aircraft safety. The work has been undertaken within the Action Group on fault tolerant control (FTC) of the European GARTEUR programme, which develops collaborative efforts in Europe to create new FTC technologies that could significantly advance the goals of the aviation safety. After the fault detection and confirmation by the in-flight dedicated systems, a compensation loop is activated to ensure safe recovery. The design of the FTC loop is achieved without any changes in the nominal (and certificated) flight control system (FCS). The FTC scheme design is formulated as an H∞ strong stabilisation problem. Fault compensability is subsequently discussed and formulated as a trim-deficiency analysis problem. The proposed technique is implemented on the SIMONA flight simulator and evaluated through a pilot experiment. The tested scenario corresponds to the landing approach of a large transport aircraft (B747-100/200). The faulty situation is related to trimmable horizontal stabiliser (THS) failures. Piloted flight simulator experiments show that fault tolerance can be achieved under the condition that there exists sufficient remaining control authority.

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

ABSTRACTVertical jump height estimates the ability to oppose gravity and lower body neuromuscular performance in athletes and various clinical populations. The use of force platforms for measuring jump height is increasingly popular due to technological advancements and the equipment's relative ease of use in various settings. However, when utilizing the force platform, ground reaction force (GRF) data must be processed to calculate jump height. While processing the GRF‐time data, several factors could alter the data, leading to inaccurate jump height estimates. These factors include sampling frequency, filtering, cut‐off frequencies of the filter, averaging periods of body weight, integration procedures, selection of take‐off/landing thresholds, and selection of the gravity constant. These data processing steps can alter jump height estimates, with effects ranging from minor (< 0.5%) to major (> 25%). Despite some guidelines on data processing, there is no consistency in the literature or in practice regarding how the GRF‐time data should be processed. Consequently, jump height without specifying the data processing steps may be of limited use to others. The aim of this review was to assist researchers and practitioners in navigating the complexities of data processing to better understand how it influences jump height.

This experience report describes the successful application of statistical software testing to a large-scale realworld equipment replacement project. The Data Processing System at Dungeness B nuclear power station is obsolescent and needs to be replaced by a new system involving different technology. The new Data Processing and Control System (DPCS) involves a large amount of software code, with the potential for software failures, and therefore confidence needs to be established in the dependability of the new software-based system. Statistical software testing (ST) was used as one technique to do this. The aim was to support the claim that with high confidence the higher integrity parts of the replacement software have a failure probability of better than 10-2 failures per demand (fpd). This paper describes how this has been achieved by careful design of the statistical tests and by the development of a large number of supporting tools and procedures. A total of 395 statistical tests with no failure was achieved providing at least 98% confidence that the safety claim has been met.

Structural intensity is a powerful tool which give transfer path of the vibration energy within a structure. Additional dataprocessing allows then to localise sources and sinks ofthat energy.The measurement of these quantities is possible using classical means (accelerometers, stress gauges, . . .) but the data processing is complex and require a lot of accurate sensors because it is based on spatial derivatives of high order. Theoptical techniques (laser vibrometry, holography, . . .) are more suited for that purpose because of the high density ofmeasuring points and because of the well-known advantages of these methods : reduced measurement time and no modification ofthe mass parameters ofthe structure as it is the case when using contact sensors.Within the frames of a BRITE-EURAM program called VIP (Vibration Intensity Processing using full-field multi-pulselaser technique), a TV-holography equipment has been developed, working with a 25 Hz pulsed laser and allowing easy onsite measurements. A measurement procedure has been defmed and a specific data processing has been developed for thedetermination of structural intensity fields.Instead of recording one interferogram only as it is usually done, the equipment allows to record complete sequences ofinterferograms at 25 Hz. This large amount of data which include several (2 at least) mterferograms with some additionalinformation (temporal and frequency domain) is processed in order to reconstruct accurately the complete vibration signal.Different kinds of output data are then given : operational deflection shape, amplitude and phase, Structural Intensity fieldand its divergence through a further step of data processing. The complete procedure with the associated data processing hasbeen tested (for various configurations of excitation and damping) first on a clamped plate, then on a cylinder and at the endon several industrial components.This paper describe the general measurement procedure and the equipment used. The data processing is also presented andvarious measurement results are shown. The conclusion gives the main advantages and limitations of the method andevaluate the application possibilities.Keywords : Vibration, Structural Intensity, TV-Holography, Speckle Interferometry, Modal Analysis, Cadenced PulsedLaser.

Data processing and visualization are crucial steps in data processing that greatly affect the quality of future analyses and machine learning algorithms application. Still, such data processing steps may be tedious and labour-intensive, and often require human effort which is associated with a lot of mistakes when users do not have sufficient computer science knowledge. The current research presents the development of a web based system called Dataverse. The purpose of developing this system is to automate and facilitate the process of data preparation and provide an opportunity for the user to explore the prepared data through interactive visualization. Dataverse is developed using Python programming language in Flask framework. The libraries employed during Dataverse development are mainly Pandas and Plotly. The system provides opportunities for importing datasets from files and online resources. According to experiments conducted, Dataverse facilitates the process of preparing data and makes it less labour-intensive and more efficient

This paper presents an integrated structure for a passive and active fault tolerant control (FTC) design approach in the framework of a robust nonlinear control technique called Dynamic Surface Control (DSC). As motivated by the automated vehicle application, we consider two categories of possible faults: pre-specified (a priori) and non-specified faults. It is first shown that DSC can be considered as a passive FTC approach in the sense that it gives simultaneous robust stability to a set of nonlinear systems even in the presence of model uncertainties and the pre-specified faults. Then, the non-specified fault is classified depending on the fault’s impact on the closed-loop system and isolatability from a fault detection and diagnosis (FDD) system. If a fault is both intolerable and isolatable, an active FTC approach is taken which includes FDD and controller reconfiguration. More specifically, trajectory reconfiguration is considered to accommodate the actuator fault, i.e., to compensate for the performance loss due to the fault within the framework of a switched hierarchical structure. Finally, the integrated structure for the longitudinal control of an automated transit bus is designed through the proposed method. Simulation results of the fault tolerant controller are shown for both single and multiple multiplicative faults. This controller was implemented on the California PATH transit buses in a demonstration of automated public transportation technology in San Diego, California in August of 2003.

The functioning of modern information and communication networks is impossible without data processing. With the emergence of new network services, the amount of information that needs to be processed increases, while the requirements to the data processing quality become more and more stringent. Therefore, the problem of designing and maintaining a scalable data processing system with a flexible quality of service management is becoming more and more important for a network operator. Such data processing systems have a complex internal structure with many interrelated parameters, which makes them difficult to analyze, manage, and expand. This study proposes to use an ontological model to store, represent, and manipulate the information in the operator’s data processing system. The ontological model allows to structure and systematize the data of an information processing system, and transparently reflects the relationships between the parameters of the system to simplify its analysis and scaling. The proposed ontology of a data processing system consists of three related subsystems. The paper describes the proposed ontological model and additionally analyzes the sources of information that needs to be processed in the information and communication network.

Rapid economic development causes increasingly fierce business competition. Moreover, the development of increasingly sophisticated technology, companies are competing to use technology to increase competitive advantage and efficiency in the company. One of the most popular information technology investments today is the Application of Systems and Products in Data Processing (SAP), systems that do business using a single database that can be accessed by all divisions within the company. The purpose of this research is to study and discuss the application of Systems and Products in Data Processing (SAP) conducted at PT. Haier Sales Indonesia. This study uses qualitative methods through in-depth interviews with IT Managers and Logistics Managers and warehouse administration staff using Pareto diagrams and fishbone diagrams. The results of the evaluation of the application of Application Systems and Products in Data Processing (SAP) in Inventory Logistics that occurred at PT. Haier Sales Indonesia is getting better, all data from every activity has been documented and integrated into the system much faster than the manual process that has been done before and can produce accurate reports. Suggestions are given in order to continue training in SAP Systems and Applications for users in developing SAP Systems and Applications.

Introducción: el artículo de revisión es producto de la investigación “Implicaciones de la estructura organizacional: organizaciones como sistema de procesamiento de información”, desarrollada durante el 2017 en la Facultad de Ingeniería Industrial de la Universidad Distrital Francisco José de Caldas. Problema: en las organizaciones se evidencia que las estructuras jerárquicas son el paradigma predominante, a pesar de que investigaciones han mostrado la precariedad de este tipo de estructuras para el procesamiento de información. Objetivo: identificar los rasgos que permitan comprender la empresa como un sistema de procesamiento de información ágil. Metodología: se revisan los rasgos estructurales de los sistemas de procesamiento de información desde diferentes disciplinas. A partir de esta revisión se plantean las implicaciones de comprender a las organizaciones empresariales y en particular el diseño de la estructura como sistemas de procesamiento de información. Resultado: se desarrolla una apreciación sobre las implicaciones de diseñar estructuras con mayor agilidad en el procesamiento de información para la ingeniería organizacional. Conclusión: la comprensión de la organización empresarial, y en particular de la estructura como sistemas de procesamiento de información, ofrece una perspectiva ampliamente diferente del paradigma tradicional. Originalidad: la investigación se encuentra en etapas tempranas y no existen modelos o evidencias empíricas frente a los planteamientos realizados. Los altos niveles de agilidad en la toma de decisiones que exhiben las disciplinas estudiadas, permiten proyectar importantes líneas de investigación al respecto. Limitaciones: la propuesta se encuentra en la fase conceptual y requieren comprobación experimental.

The article describes the development of an architecture for cloud storage of melons based on a Residue Number System (RNS). The developed architecture is evaluated in terms of the reliability of the stored data, and its comparative analysis is carried out with existing data storage methods. The architecture of a multi-cloud big data storage and processing system based on the principles of modular arithmetic is proposed. The proposed modification of the cloud storage and data processing system allows increasing the reliability and fault tolerance of the stored and processed data. Single-level and two-level models for reliable storage of big data were developed based on the simulation results. The comparative analysis showed the advantage of models based on modular arithmetic over models based on traditional arithmetic.

Modern deepwater marine 3D surveys require advanced infield systems in both data collection and data processing, if imaging and turnaround targets are to be met. In this paper we present the case history of a marine 3D reconnaissance survey, which presented challenges for both data collection and data processing. The challenges stemmed from the deep and complex waterbottom which caused issues with streamer geometry and also major multiple contamination within the seismic data.

The relevance of environmental monitoring and environmental pollution control is justified in the introduction of the article in order to monitor the state of the environment and its protection, on the basis of existing regulatory legal acts. Fundamental principles of these types of environmental activities are given. In order to justify the task, a brief description of chemical analytical laboratories (hereinafter referred to as HAL) activities and the tasks of their information systems is provided. Having analyzed a number of HAL information systems operating in the Russian Federation the authorscame to the conclusion that they mainly perform the functions of automated collecting, storing and processing information. The availability of decision-making support systems (DSS) function in the systems has not been established, for example, none of the software systems issues recommendations on changing the measurement regulations in cases of exceeding permissible values. An information system for data collection, processing and analysis from a chemical analytical laboratory with the DSS functions (monitoring changes in measurement regulations and monitoring the timing of metrologicaldevice verification) is proposed. The article describes the main functions of the proposed system available to the user and the administratorin detail. An example and a detailed description of the regulation changing process based on the MPC excess in samples are given. Regulations, the main blocks of the system, their functions and capabilities are described. The program structure and the system algorithm are presented. All system functions have been successfully tested. The figures show the algorithms of user’s and system administratoractivity when testing the database function, the function of reporting on specified parameters to obtain information about the discrepancy between samplenumbers and devices to the specified parameters, the function of automatic sampling and adding samples from the selected device and the function 'Changing the rules'. In conclusion, it is emphasized that this information system allows not only data processing from research results of chemical analytical laboratories in an automated mode, but also to perform certain functions of decision-making support systems.

Data preparation is crucial for achieving good data management following the four foundational FAIR principles—Findability, Accessibility, Interoperability, and Reusability. Processing datasets to achieve high data (and metadata) quality is mandatory in modern applications. However, the data preparation activities that are needed to reach such levels may easily become unsustainable due to, for example, resource intensity or scalability challenges. Moreover, some preparation efforts may become unnecessary if they result in negligible improvements or duplicate actions. This article examines the sustainability aspects of data preparation through the lens of a circular economy. Within the data landscape, this perspective encourages practices that minimize waste, extend the data life cycle, and maximize reuse in alignment with the FAIR principles. We explore these practices and their impact on selecting and configuring effective data preparation strategies to design sustainable, high-quality pipelines. To this end, we propose an evaluation model that integrates data quality metrics with sustainability parameters for human and computational tasks. Finally, we apply the model in a comparative analysis of key data preparation methods, demonstrating its effectiveness in assessing sustainability and quality tradeoffs.