INSTANCE – the Italian seismic dataset for machine learning

The Italian earthquake waveform data are collected here in a dataset suited for machine learning analysis (ML) applications. The dataset consists of nearly 1.2 million three-component (3C) waveform traces from about 50 000 earthquakes and more than 130 000 noise 3C waveform traces, for a total of about 43 000 h of data and an average of 21 3C traces provided per event. The earthquake list is based on the Italian Seismic Bulletin (http://terremoti.ingv.it/bsi, last access: 15 February 2020​​​​​​​) of the Istituto Nazionale di Geofisica e Vulcanologia between January 2005 and January 2020, and it includes events in the magnitude range between 0.0 and 6.5. The waveform data have been recorded primarily by the Italian National Seismic Network (network code IV) and include both weak- (HH, EH channels) and strong-motion (HN channels) recordings. All the waveform traces have a length of 120 s, are sampled at 100 Hz, and are provided both in counts and ground motion physical units after deconvolution of the instrument transfer functions. The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities. This rich set of metadata allows the users to target the data selection for their own purposes. Much of these metadata can be used as labels in ML analysis or for other studies. The dataset, assembled in HDF5 format, is available at http://doi.org/10.13127/instance (Michelini et al., 2021).

Nigerian Gas Transportation Network Code NGTNC; Emerging Opportunities for Local Gas Transmission Operations

Historically, both regulatory and contractual constraints have inhibited the overall optimization of natural gas transmission systems. The Nigerian National Petroleum Corporation (NNPC) currently supplies gas either as source of fuel or as feedstock to different industries. More local industries are now aware of the advantages and benefits of using gas; hence creating an increase in demand. Recent changes in the regulatory framework and the introduction of the Nigeria Gas Transportation Network Code (NGTNC) to deepen the growth of gas market in the country are however, fostering the pipeline companies into a new competitive position, creating strong incentives as well as opportunities. This work provides a section-by-section summary of the Code for the benefit of those who are passionate about understanding the nuances of the Code and of course makes cogent survey and recommendations, to expedite the success of the Code. In the course of this research, questionnaires were administered and 130 respondents were chosen based on their level of knowledge and experience in the industry ranging from operations, management, regulatory and Gas Associations. Responses were collated and analysed using simple statistical tools, tables, and graphs to identify opportunities. The result of the study illustrates the stakeholder's presumption and commitments in using NGTNC for optimized Gas transmission operations.

INSTANCE – the Italian seismic dataset for machine learning

The Italian earthquake waveform data are here collected in a dataset suited for machine learning analysis (ML) applications. The dataset consists of near 1.2 million three-component (3C) waveform traces from about 50,000 earthquakes and more than 130,000 noise 3C waveform traces, for a total of about 43,000 hours of data and an average of 21 3C traces are provided per event. The earthquake list is based on the Italian seismic bulletin (http://terremoti.ingv.it/bsi) of the ``Istituto Nazionale di Geofisica e Vulcanologia'' between January 2005 and January 2020 and it includes events in the magnitude range between 0.0 and 6.5. The waveform data have been recorded primarily by the Italian National Seismic Network (network code IV) and include both weak (HH, EH channels) and strong motion recordings (HN channels). All the waveform traces have a length of 120 s, are sampled at 100 Hz, and are provided both in counts and ground motion units after deconvolution of the instrument transfer functions. The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities. This rich set of metadata allows the users to target the data selection for their own purposes. Many of these metadata can be used as labels in ML analysis or for other studies. The dataset, assembled in HDF5 format, is available at http://doi.org/10.13127/instance (Michelini et al., 2021).

The geometry of face memory

The ability to recognize familiar visual objects is critical to survival. Neurons in inferotemporal (IT) cortex represent the percept of visual objects using a distributed axis code. However, the network code for the memory of visual objects remains elusive. Here, we measured neuronal responses to familiar and unfamiliar faces in two face patches, AM and PR. In both areas, familiar and unfamiliar faces were represented in distinct subspaces. The familiar face subspace was shifted relative to the unfamiliar face subspace at short latency and then distorted to increase neural distances between familiar faces at long latency. Our results suggest that memories of familiar faces are represented in IT and perirhinal cortex by a distinct long-latency code that is optimized to distinguish familiar identities.One Sentence SummaryFamiliar and unfamiliar faces are represented in distinct neural subspaces, with the former represented by a unique long-latency geometry.

Photovoltaic Inverter Profiles in Relation to the European Network Code NC RfG and the Requirements of Polish Distribution System Operators

The presently observed rapid increase in photovoltaic (PV) micro-installation connections to low-voltage networks, resulting from numerous financial support programmes, European Union (EU) energy policy and growing social awareness of environmental and economic issues, raise the question if PV inverters widely available in EU market fulfil the numerous technical requirements specified in European and Polish regulations. The paper presents the results of an experimental study carried out on three PV Inverters widely available in the EU in accordance with the EU network code NC RfG, standard EN 50549-1:2019 and internal Polish distribution system operators’ (DSOs’) regulations, governing PV inverter cooperation with the low-voltage distribution network. The laboratory test stand scheme and its description are presented. In each test, at least one of the inverters encountered issues, either with the operation in required frequency ranges (one PV inverter), activating reactive power control modes (all three PV inverters), maintaining required power generation gradient after tripping (one PV inverter) or under-voltage ride through immunity (one PV inverter). The obtained results have shown that all tested PV inverters did not meet requirements that are the key to maintaining reliable and safe operation of transmission and distribution electrical networks.

Transient Aero-Thermo-Mechanical Multidimensional Analysis of a High Pressure Turbine Assembly Through a Square Cycle

Better understanding and more accurate prediction of heat transfer and cooling flows in aero engine components in steady and transient operating regimes are essential to modern engine designs aiming at reduced cooling air consumption and improved engine efficiencies. This paper presents a simplified coupled transient analysis methodology that allows assessment of the aerothermal and thermomechanical responses of engine components together with cooling air mass flow, pressure and temperature distributions in an automatic fully integrated way. This is achieved by assembling a fluid network with contribution of components of different geometrical dimensions coupled to each other through dimensionally heterogeneous interfaces. More accurate local flow conditions, heat transfer and structural displacement are resolved on a smaller area of interest with multidimensional surface coupled CFD/FE codes. Contributions of the whole engine air-system are predicted with a faster mono dimensional flow network code. Matching conditions at the common interfaces are enforced at each time step exactly by employing an efficient iterative scheme. The coupled simulation is performed on an industrial high pressure turbine disk component run through a square cycle. Predictions are compared against the available experimental data. The paper proves the reliability and performance of the multidimensional coupling technique in a realistic industrial setting. The results underline the importance of including more physical details into transient thermal modelling of turbine engine components.

Ranking Cloud Computing Criteria in Developing Electronic Communications Services Using MCDM

According to the results and considering fuzzy calculations related to the capabilities of cloud computing in developing electronic communications services, the most important criteria in the "IT management in steel industries" cluster having (A) network code was "communicating with steel industries` costumers" having (AB) network code and fuzzy network weight equal to 0.096; the most important criteria in "cloud computing capabilities" cluster having (B) network code were "reducing steel industries` costs" having (BA) network code and fuzzy network weight equal to 0.191; and "providing rapid services to steel industries` costumers" having (BB) network code and fuzzy network weight equal to 0.120. on the other hand, the most important criteria in "developing electronic communications services" cluster having (C) network code was "storing the data in electronic communications services" having (CD) network code and fuzzy network weight equal to 0.123, since based on fuzzy logic calculation, they had the highest fuzzy rank in Matlab programming environment regarding cloud computing capabilities in developing electronic communications services.