Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible. Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information:Open Data Portal Reuses — UK Power Networks

The analysis of the interplay between structural and functional networks require experiments where both the specific structure of the connections between nodes and the time series of the underlying dynamical units are known at the same time. However, real datasets typically contain only one of the two ways (structural or functional) a network can be observed. Here, we provide experimental recordings of the dynamics of 28 nonlinear electronic circuits coupled in 20 different network configurations. For each network, we modify the coupling strength between circuits, going from an incoherent state of the system to a complete synchronization scenario. Time series containing 30000 points are recorded using a data-acquisition card capturing the analogic output of each circuit. The experiment is repeated three times for each network structure allowing to track the path to the synchronized state both at the level of the nodes (with its direct neighbors) and at the whole network. These datasets can be useful to test new metrics to evaluate the coordination between dynamical systems and to investigate to what extent the coupling strength is related to the correlation between functional and structural networks.

We provide the times series of N=28 Rössler electronic oscillators for 20 different network configurations (compressed file with tag R1 to R20). For each network structure, we recorded the times series for 101 different coupling strengths between oscillators. Each one of the 101 corresponding files is labeled as ST_X_Y.dat where X is a value between X=0 and X=100 that corresponds, respectively, to the minimum and maximum coupling strength. The value of Y corresponds to the repetition number, which can be 1, 2 of 3 (i.e., we repeated the same experiment three times). Data files contain the second variable of the 28 nodes arranged in columns with a length of 30000 points. In a second file named Structure.zip, all the network structures are given, each file having a name Net_R.dat, where R=1, 2… 20. The degree of each node (i.e., number of output connections) is the same for all network configurations, where the specific neighbors of each node are re-arranged randomly.

Introduction UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables .
Care is taken to protect the private affairs of companies connected to the 132 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted. This dataset provides monthly statistics across these named circuits from 2021 through to the previous month across our license areas. The data is aligned with the same naming convention as the LTDS for improved interoperability. To find half-hourly current and power flow for the circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 132kV Circuits Half Hourly Data. If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach

The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.

Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance Statement

Creating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets.
There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.

Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON) We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. For more information click here: Open Data Portal Reuses — UK Power Networks

MPLS Circuit Services Market Outlook

The global MPLS (Multiprotocol Label Switching) Circuit Services market size was valued at approximately USD 35 billion in 2023 and is projected to reach around USD 50 billion by 2032, growing at a CAGR of 4% during the forecast period. This market growth is primarily driven by the increasing demand for reliable and efficient networking solutions across various industries. The need for enhanced security, improved bandwidth management, and reduced latency are some of the key growth factors fueling the MPLS Circuit Services market.

One of the primary growth factors for the MPLS Circuit Services market is the ever-increasing demand for higher bandwidth and better quality of service (QoS) across various sectors. Businesses are continuously expanding their digital footprint, which necessitates robust and scalable networking solutions. MPLS technology provides an efficient way to manage and prioritize data traffic, ensuring that critical applications receive the necessary bandwidth and low-latency connections. This is particularly crucial for industries such as BFSI, healthcare, and IT and telecommunications, where downtime or slow connections can lead to significant operational disruptions and financial losses.

Another significant factor contributing to the market's growth is the rising adoption of cloud-based services and applications. As organizations increasingly migrate their workloads and data to the cloud, there is a growing need for secure and reliable connectivity between data centers and cloud service providers. MPLS Circuit Services offer enhanced security features, such as traffic segregation and encryption, which are essential for maintaining data integrity and confidentiality. Additionally, the ability to establish virtual private networks (VPNs) over MPLS circuits allows businesses to securely connect multiple locations and remote workers, further driving the demand for MPLS services.

The growing trend of digital transformation across industries is also propelling the MPLS Circuit Services market. Organizations are leveraging advanced technologies such as IoT, AI, and big data analytics to gain insights, improve operational efficiency, and enhance customer experiences. These technologies generate massive amounts of data that need to be transmitted and processed in real-time, necessitating a robust and reliable network infrastructure. MPLS Circuit Services provide the necessary bandwidth, low latency, and QoS to support these data-intensive applications, making them an integral part of the digital transformation journey.

Regionally, North America dominates the MPLS Circuit Services market, accounting for the largest market share. The region's well-established IT and telecommunications infrastructure, coupled with the presence of major industry players, drives the demand for MPLS services. Furthermore, the increasing adoption of cloud services and the growing focus on network security contribute to the market's growth in this region. Other regions, such as Asia Pacific and Europe, are also witnessing significant growth due to the rapid digitalization of businesses and the ongoing expansion of data center infrastructures.

Layer 2 VPN Analysis

Layer 2 VPN services are an integral part of the MPLS Circuit Services market, providing point-to-point and point-to-multipoint connectivity over a shared network infrastructure. These services are particularly popular among enterprises that require a secure and dedicated connection for their critical applications. One of the primary advantages of Layer 2 VPN is its ability to support a wide range of protocols, including Ethernet, Frame Relay, and ATM, making it highly versatile and suitable for various use cases. Additionally, Layer 2 VPNs offer low latency and high bandwidth, essential for real-time applications such as video conferencing and VoIP.

The demand for Layer 2 VPN services is driven by the increasing need for secure and reliable connectivity between data centers and branch offices. Organizations with multiple locations require a seamless and efficient way to connect their sites, ensuring uninterrupted access to critical applications and data. Layer 2 VPNs provide the necessary security features, such as traffic segregation and encryption, to protect sensitive information from unauthorized access. Furthermore, the ability to extend Layer 2 networks over an MPLS infrastructure simplifies network management and reduces operational costs, making it an attractive option for businesses of all sizes.

Another significant f

These data represent the training set for a neural network designed to classify normal and faulty electrical circuit operation. They represent four device classes, and two operating states (normal and abnormal). Though recorded as .wav files, these data were from current measurement sensors.

Raw experimental and simulation data used to generate the figures in "Observation of Antichiral Edge States in a Circuit Lattice" by Y. Yang et al. [Paper abstract] We constructed an electrical circuit to realize a modified Haldane lattice exhibiting the phenomenon of antichiral edge states. The circuit consists of a network of inductors and capacitors with interconnections reproducing the effects of a magnetic vector potential. The next nearest neighbor hoppings are configured differently from the standard Haldane model, and as predicted by earlier theoretical studies, this gives rise to antichiral edge states that propagate in the same direction on opposite edges and co-exist with bulk states at the same frequency. Using pickup coils to measure voltage distributions in the circuit, we experimentally verify the key features of the antichiral edge states, including their group velocities and ability to propagate consistently in a Möbius strip configuration.

The largest available cellular level connectivity map, of a 0.1 mm sample of the mouse retina Inner Plexiform Layer, was analysed using network models and visualized using spectral graph layouts and observed cell coordinates. This allows key nodes in the network to be identified with retinal neurons. Their strongest synaptic links can trace pathways in the network, elucidating possible circuits. Modular decomposition of the network, by sampling signal flows over nodes and links using the InfoMap method, shows discrete modules of cone bipolar cells that form a tiled mosaic in the retinal plane. The highest flow nodes, calculated by InfoMap, proved to be the most useful landmarks for elucidating possible circuits. Their dominant links to high flow amacrine cells reveal possible circuits linking bipolar through to ganglion cells and show an Off-On discrimination between the Left-Right sections of the sample. Circuits suggested by this analysis confirm known roles for some cells and point to roles for others.

The "SPD LTDS Appendix 1 Circuit Data (Table 1)" dataset provides data that is derived from power system analysis software and therefore the circuit parameters detailed are based on the equipment between analytical node points. As some circuits may have intermediate node points, or a number of components, this aspect should be taken into consideration when assessing overall (end-to-end) circuit parameters. Those circuits labelled S/C, or short-circuit, represent circuit breakers, switches, or busbar connections of effectively zero impedance.Click here to access our full Long Term Development Statements for both SP Distribution (SPD) & SP Manweb (SPM).For additional information on column definitions, please click on the Dataset schema link below.Note: A fully formatted copy of this appendix can be downloaded from the Export tabunderAlternative exports. DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). If you wish to provide feedback at a dataset or row level, please click on the “Feedback” tab above. Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Clickhere to access the Data Triage documentation for the Long Term Development Statement dataset.To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page.Download dataset metadata (JSON)

The human social cognitive network contains multiple regions within the amygdala

https://doi.org/10.5061/dryad.gtht76hwm

Description of the data and file structure

This dataset contains all the functional connectivity and task activity maps, surface parcellations, and ROIs used in our research paper. All anatomical and surface files needed to open these maps are also included.

Files and variables

File: DBNO.zip

Description: Folder containing all relevant data from the Detailed Brain Network Organization (3T) dataset.

DBNO/parcellations: contains MS-HBM network parcellations on the surface for each subject. Each parcellation is k =14.

DBNO/task_activity: Folder containing task activity (Theory of Mind, Episodic Projection) maps on the surface for each subject. All

• DBNO/task_activity/episodic_projection: run-level and z-scored composite episodic projection activity maps
  • DBNO_0#_episodicprojection_high...

The "SPM LTDS Appendix 1 Circuit Data (Table 1)" dataset provides the data that is derived from power system analysis software, and therefore the circuit parameters detailed in the tables are based on the equipment between analytical node points. As some circuits may have intermediate node points, or a number of components, this aspect should be taken into consideration when assessing overall (end-to-end) circuit parameters. Those circuit sections labelled S/C, or short circuit, represent circuit breakers, switches, or busbar connections of effectively zero impedance.Click here to access our full Long Term Development Statements for both SP Distribution (SPD) & SP Manweb (SPM). For additional information on column definitions, please click on the Dataset schema link below.Note: A fully formatted copy of this appendix can be downloaded from the Export tab under Alternative exports.Download dataset metadata (JSON)If you wish to provide feedback at a dataset or row level, please click on the “Feedback” tab above. Data Triage:As part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Long Term Development Statement dataset.To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page.

Market Size and Growth: The global MPLS Circuit Services market size is valued at USD XXX million in 2025 and is projected to reach USD XXX million by 2033, growing at a CAGR of XX% over the forecast period. The market growth is driven by increasing demand for secure and reliable data transmission, rising cloud computing and data center traffic, and the proliferation of smart devices. Key Trends and Drivers: The MPLS Circuit Services market is witnessing several key trends, including:

Integration with SD-WAN: MPLS is increasingly integrated with SD-WAN, enabling businesses to optimize network performance and reduce costs. Automation and Network Slicing: Advances in automation and network slicing are simplifying network management and enabling granular control over network resources. Increased Adoption in Healthcare and Education: MPLS is gaining popularity in healthcare and education sectors due to its ability to support real-time data transfer and enhance patient care.

A collection of 54 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Reasoning about someone’s thoughts and intentions – i.e., forming a ‘theory of mind’ – is a core aspect of social cognition and relies on association areas of the brain that have expanded disproportionately in the human lineage. We recently showed that these association zones comprise parallel distributed networks that, despite occupying adjacent and interdigitated regions, serve dissociable functions. One network is selectively recruited by social cognitive processes. What circuit properties differentiate these parallel networks? Here, we show that social cognitive association areas are intrinsically and selectively connected to anterior regions of the medial temporal lobe that are implicated in emotional learning and social behaviors, including the amygdala at or near the basolateral complex and medial nucleus. The results suggest that social cognitive functions emerge through coordinated activity between internal circuits of the amygdala and a broader distributed association network, and indicate the medial nucleus may play an important role in social cognition in humans.

Planar Lightwave Circuit Splitter Market Outlook

The global market size for Planar Lightwave Circuit Splitters was valued at approximately USD 800 million in 2023 and is projected to reach USD 1.5 billion by 2032, growing at a CAGR of around 7%. This growth trajectory is driven by the increasing demand for high-speed and reliable data transmission, particularly in telecommunications and data centers. As the world continues to move towards advanced digital communication networks, including 5G and fiber optics, the need for efficient signal distribution solutions, such as planar lightwave circuit splitters, is becoming more critical. These components are pivotal in supporting the backbone of modern communication infrastructures, ensuring seamless and rapid data transfer across networks.

The expansion of fiber optic networks globally is a significant growth factor for the planar lightwave circuit splitter market. With the surge in internet usage and the subsequent demand for higher bandwidth, telecommunication companies are increasingly investing in the extension and enhancement of their fiber optic networks. This requires a robust and efficient mechanism for signal splitting and distribution, which is where planar lightwave circuit splitters play a crucial role. Additionally, advancements in fiber optic technologies have made these splitters more efficient and cost-effective, further fueling their adoption. The transition from copper to fiber optics in several regions is also contributing to the increased demand for these splitters.

Another key driver is the rising investment in data centers, driven by the exponential growth in data generation. As businesses and consumers rely more heavily on cloud services, the infrastructure supporting these services must be scaled and optimized. Data centers, which form the core of cloud computing, require efficient and high-performance networking solutions, including planar lightwave circuit splitters. These components help manage the complex web of fiber optic cables within data centers, ensuring data is transmitted quickly and efficiently. The need for high-capacity and low-latency data transmission is particularly pressing in data-centric industries, such as financial services and media streaming, driving further demand for advanced splitter solutions.

The telecommunications industry's ongoing transformation is another significant factor propelling market growth. With the rollout of 5G networks and beyond, there is an increased need for reliable and high-performance network components to handle the anticipated surge in data traffic. Planar lightwave circuit splitters are integral to these networks, distributing optical signals efficiently and maintaining network integrity. This technological evolution towards more sophisticated and capable communication networks necessitates continuous innovation in optical components, including splitters, which are essential for achieving the desired network performance and reliability.

Planar Lightwave Circuit PLC Splitters are a cornerstone in the telecommunications industry, offering a compact and efficient solution for splitting optical signals. These devices are designed to distribute optical signals from a single input to multiple outputs, which is essential in fiber optic networks. The ability to maintain signal integrity while splitting the signal makes PLC splitters an invaluable component in modern communication systems. As the demand for high-speed internet and data services continues to grow, the role of PLC splitters becomes increasingly critical, ensuring that signals are delivered efficiently and reliably across vast networks. Their robust design and high performance make them suitable for a variety of applications, from residential broadband to large-scale data centers, highlighting their versatility and importance in the industry.

Regionally, the Asia Pacific market is anticipated to experience significant growth, driven by the rapid expansion of telecommunications infrastructure and the growing number of internet users. Countries such as China and India are investing heavily in their digital infrastructure, aiming to provide widespread high-speed internet access. This regional growth is complemented by North AmericaÂ’s established telecommunications sector, which continues to invest in upgrading existing systems to accommodate newer technologies. Europe is also contributing to market growth with its focus on enhancing connectivity and broadband access across the continent. Each of these regions presents uniq

Quantum cellular automata (QCA) evolve qubits in a quantum circuit depending only on the states of their neighborhoods and model how rich physical complexity can emerge from a simple set of underlying dynamical rules. The inability of classical computers to simulate large quantum systems hinders the elucidation of quantum cellular automata, but quantum computers offer an ideal simulation platform. Here, we experimentally realize QCA on a digital quantum processor, simulating a one-dimensional Goldilocks rule on chains of up to 23 superconducting qubits. We calculate calibrated and error-mitigated population dynamics and complex network measures, which indicate the formation of small-world mutual information networks. These networks decohere at fixed circuit depth independent of system size, the largest of which corresponding to 1,056 two-qubit gates. Such computations may enable the employment of QCA in applications like the simulation of strongly-correlated matter or beyond-classical c...

Biodiversity conservation in landscapes undergoing climate and land-use changes requires designing multipurpose habitat networks that connect the movements of organisms at multiple spatial scales. Short-range connectivity within habitat networks provides organisms access to spatially distributed resources, reduces local extinctions and increases recolonization of habitat fragments. Long-range connectivity across habitat networks facilitates annual migrations and climate-driven range shifts. We present a method for identifying a multipurpose network of forest patches that promotes both short- and long-range connectivity. Our method uses both graph-theoretic analyses that quantify network connectedness and circuit-based analyses that quantify network traversability as the basis for identifying spatial conservation priorities on the landscape. We illustrate our approach in the agroecosystem, bordered by the Laurentian and Appalachian mountain ranges, that surrounds the metropolis of Mont...

The dataset is structured to support Exponential Random Graph Model (ERGM) analysis of network dynamics across three periods (2011–2014, 2015–2017, 2018–2020). It comprises network adjacency matrices and proximity measures, derived from patent collaboration data sourced from Incopat.The corresponding analysis code is available on GitHub: https://github.com/Li-ucas-casisd/circuit-ERGM/tree/mainCitationThis paper is undergoing review in technology analysis & strategic management. The specific citation methods will be made public after the peer review is completed.

Planar Waveguide Circuit Splitter Market Outlook

The global market size for Planar Waveguide Circuit Splitters is projected to witness significant growth, expanding from $X billion in 2023 to $Y billion by 2032, at a compound annual growth rate (CAGR) of Z%. This remarkable growth is primarily driven by the increasing demand for high-speed internet and the rapid expansion of telecommunications infrastructure worldwide.

One of the primary growth factors for the planar waveguide circuit splitter market is the ongoing expansion and upgrading of telecommunications networks. With the proliferation of 5G technology, telecom operators are investing heavily in infrastructure that can support massive data flows with low latency. Planar waveguide circuit splitters play a crucial role in splitting optical signals, thereby enhancing the capability and efficiency of fiber optic networks. Additionally, the rising demand for broadband services in both urban and rural areas has further accelerated the deployment of these devices.

Another significant growth driver is the burgeoning data center industry. As cloud computing, big data analytics, and Internet of Things (IoT) continue to gain traction, the need for sophisticated data centers has surged. Data centers require reliable and efficient optical networks to handle large volumes of data traffic. Planar waveguide circuit splitters are essential components that facilitate effective data management and distribution within these centers, thereby boosting their operational efficiency and reliability.

The increasing adoption of fiber-to-the-home (FTTH) services is also fueling market growth. Consumers' growing preference for high-speed internet services has led to a surge in FTTH deployments, particularly in developing regions. Governments and private sector players are investing in fiber optic infrastructure to meet this demand. Planar waveguide circuit splitters are integral to FTTH networks as they enable the efficient distribution of optical signals to multiple end-users, thus helping to bridge the digital divide.

In the context of enhancing network efficiency and capacity, the Band Splitter Module emerges as a pivotal component in modern telecommunications. These modules are designed to divide a single input signal into multiple output signals, each carrying a portion of the original bandwidth. This functionality is crucial in optimizing the use of available bandwidth, particularly in densely populated urban areas where network demand is high. By employing band splitter modules, telecom operators can ensure that their networks are not only more efficient but also more adaptable to the varying demands of different user groups. This adaptability is essential in maintaining service quality and reliability, especially as the number of connected devices continues to grow exponentially.

From a regional perspective, Asia Pacific is expected to dominate the market, driven by significant investments in telecommunications infrastructure by countries like China, Japan, and India. North America and Europe are also poised for substantial growth due to the widespread adoption of advanced telecommunications technologies and the presence of key industry players. Meanwhile, regions like Latin America and the Middle East & Africa are gradually catching up, supported by governmental initiatives to enhance digital connectivity.

Type Analysis

Different types of planar waveguide circuit splitters, such as 1x2, 1x4, 1x8, 1x16, and others, cater to various applications and needs. The 1x2 type splitters are among the most basic and commonly used, primarily in scenarios requiring minimal signal division. They are extensively utilized in smaller networks and specific segments of larger networks where signal integrity is critical. The demand for 1x2 splitters is driven by their simplicity, cost-effectiveness, and reliability. As a result, they are a popular choice for initial deployments and smaller-scale projects.

The 1x4 and 1x8 types find applications in more complex network structures where multiple signal splits are necessary. These splitters are essential in medium-sized networks, providing a balance between performance and cost. Their usage is prevalent in urban FTTH deployments, where multiple households or businesses need to be connected via a single fiber optic line. The growing urbanization and the push for smart city initiatives are expected to drive the demand for these types

one problem that remains unresolved is how to reliably measure the arc duration.

The data that support the findings of this article titled "Simulating Thiele's Equation and Collective Skyrmion Dynamics in Circuit Networks".