Coherent Data Research Articles

Protocol for the creation of a core outcome set for clinical trials of interventions to improve sleep in people with cognitive impairment: The Sleep in Cognitive Impairment Core Outcome Set (SCICOS) study.

Background While sleep disturbance is common in dementia, leading to negative outcomes, there is growing evidence that sleep disturbance begins early in prodromal dementia and may contribute to cognitive decline. Sleep is therefore an important treatment target throughout the natural history of dementia and there is increasing interest in sleep as a modifiable risk factor. Clinical trials of interventions to improve sleep in people with cognitive impairment are beset by wide heterogeneity in the outcome measures reported. A core outcome set (COS) is urgently required to improve the coherence and comparability of data. Aim To produce a COS for clinical trials of interventions to improve sleep in people with cognitive impairment: the Sleep in Cognitive Impairment Core Outcome Set (SCICOS). Methods The Core Outcome Set-STAndards for Development: the COS-STAD recommendations will be followed. A systematic review (Registration: CRD42024556750) will identify outcome measures used in relevant clinical trials. Qualitative interviews involving people living with cognitive impairment and their caregivers will ascertain the outcome measures important to them. Finally, a modified Delphi process, involving people living with cognitive impairment and their caregivers as well as expert clinicians and researchers, will be conducted to reach consensus regarding the final composition of the SCICOS. Conclusion Interventions to improve sleep in people with cognitive impairment may reduce distress associated with sleep disturbance and, potentially, slow progression of cognitive decline. Creating the SCICOS will facilitate development of more meaningful and coherent data to drive progress in this emerging field.

Multiple-Input Multiple-Output Microwave Tomographic Imaging for Distributed Photonic Radar Network

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach.

Testing for coherence and nonstandard neutrino interactions in COHERENT data

We analyze data from the CsI, liquid Ar and Ge detectors of the COHERENT experiment and confirm within 1.5σ that the measured elastic neutrino-nucleus scattering cross section is proportional to the square of the number of neutrons in the nucleus, as expected for coherent scattering in the standard model. We also show how various degeneracies involving nonstandard neutrino interaction parameters are broken in a combined analysis of the three datasets. Published by the American Physical Society 2024

A bibliometric analysis for remote sensing applications in bush encroachment mapping of grassland and savanna ecosystems

AbstractGrasslands and savannas are experiencing transformation and degradation due to bush encroachment (BE). BE has been monitored using restrictive traditional techniques that include field surveys and manual long-term observations. Owing to the limitations of traditional techniques, remote sensing (RS) is an attractive alternative to assess BE because of its generally high precision and return interval, cost-effectiveness, and availability of historical data archives. Furthermore, RS has an added advantage in its ability of acquiring global coherent data in near-real time compared to the snapshot acquisition mode with traditional surveying techniques. Despite its extensive application and vast possibilities, a critical synthesis for RS successes, shortcomings, and best practices in mapping BE in savannas and grasslands is lacking. Thus, broadly, the direction, which this type of investigation has taken over the years is largely unknown. This study sought to connect and measure the progress RS has made in mapping BE in grassland and savanna ecosystems through bibliometric analysis. One hundred and twenty-three peer-reviewed English written documents from the Web of Science and Scopus databases were evaluated. The study revealed 13.05% average annual publication growth, indicating that RS and BE mapping research in grasslands and savannas has been increasing over the survey period. Most published studies came from the USA, while the rest came from South Africa, China, and Australia. The results indicate that BE has been extensively mapped in grasslands and savannas using coarse to medium resolution data. As a result, there is a weak relationship (r² = 0.324) between the dependent variable (aerial images) and the independent variable (percentage of woody cover). This connotes the need to improve BE assessments in grasslands and savannas by integrating recent high-resolution data, machine learning algorithms and artificial intelligence.

Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph Autoencoders

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person’s long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning.

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.

UWLPIPE: Ultra-wide Bandwidth Low-frequency Pulsar Data Processing Pipeline

For real-time processing of ultra-wide bandwidth low-frequency pulsar baseband data, we designed and implemented an ultra-wide bandwidth low-frequency pulsar data processing pipeline (UWLPIPE) based on the shared ringbuffer and GPU parallel technology. UWLPIPE runs on the GPU cluster and can simultaneously receive multiple 128 MHz dual-polarization VDIF data packets preprocessed by the front-end FPGA. After aligning the dual-polarization data, multiple 128M subband data are packaged into PSRDADA baseband data or multi-channel coherent dispersion filterbank data, and multiple subband filterbank data can be spliced into wideband data after time alignment. We used the Nanshan 26 m radio telescope with the L-band receiver at 964 ∼ 1732 MHz to observe multiple pulsars. Finally, we processed the data using DSPSR software, and the results showed that each subband could correctly fold out the pulse profile, and the wideband pulse profile accumulated by multiple subbands could be correctly aligned.

Paediatric patient referrals from Greenland to the National University Hospital, Rigshospitalet, Denmark.

Greenlandic patients may be referred to Denmark for specialised diagnostics and treatment. The main collaborator for these activities is the National University Hospital, Rigshospitalet, Copenhagen. We aimed to investigate the referral pattern of Greenlandic paediatric patients to Rigshospitalet. This was an observational quality assurance project comprising all Greenlandic patients below 18 years who received healthcare services at Rigshospitalet in the 2017-2021 period. This period was chosen to obtain the most updated, available and coherent data possible. Unique patients and disease courses were stratified by paediatric subspecialities and procedures. During the five-year period, a total of 310 unique patients were referred to Rigshospitalet, resulting in a total of 676 disease courses and yielding an average 62 annual referrals of paediatric Greenlandic patients. This represents around 0.5% of all Greenlandic children. Age groups were distributed as 28% aged 0-1 years, 23% 2-4 years, 13% 5-9 years, 21% 10-14 years and 16% 15-17 years. During the study period, the number of disease courses increased by 89% with most patients being managed as outpatients. The subspecialities with most referrals were ophthalmology (17%), oto-rhino-laryngology (16%) and cardiovascular diseases (10%). Approximately 0.5% of Greenlandic children were referred annually to Rigshospitalet with a marked increase being observed during the five-year study period. We observed a shift towards an increasing proportion of outpatient treatments at Rigshospitalet. None. Not relevant.

GEUKE: A geographic entities uniformly explicit knowledge embedding model

AbstractKnowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.

Adiabatic weak coherent MHz linewidth O-band single-photon carrier for low erroneous phase decoding

For weak coherent single-photon secure data communication among short-reach metropolitan intra-/inter-city networks at the O-band (1250-1350 nm), the commercially available semiconductor laser sources are emerging but still suffering from high single-mode-fiber (SMF) loss, broad linewidth, and unstable wavelength. To overcome such disadvantages for enabling the efficient phase-coding link with sufficient secure key rate, a specifically designed adiabatic package with active temperature-/current-feedback control is proposed for the paired O-band MHz-linewidth master-to-slave injection-locked DFBLDs and a polarization-maintaining 1-bit-delay interferometer is stabilized with using a passively adiabatic cell to achieve accurate differential phase decoding. Even though, the phonon-induced phase fluctuation still occurs at rising and falling edges of the decoded long-pattern secure data bits delivered from the slave DFBLD, which is mainly attributed to the intra-cavity heating under excessive free-carrier generation via the master DFBLD injection. To stabilize the differential-phase-shift (DPS) keying protocol, the phase-code distortion caused by over-injection-induced Auger heating is effectively suppressed by reducing the overly biased injection with precise master-injection-level control. The rising-/falling-edge damping distortion of the phase-shift-encoded secure bit-stream envelope is suppressed by appropriately decreasing the DC bias current and adjusting the AC encoding amplitude of the master DFBLD. Such operation reduces the incorrect π phase shift in the injection-locked slave DFBLD biased at optimized below-threshold DC offset, thus allowing single-photon DPS-keying data transmission over 15-km SMF with slightly increasing the single-photon bit-error ratio from <3% (0-km) to 6.2% (15-km).

Differential associations of five riverine organism groups with multiple stressors

The decline of river and stream biodiversity results from multiple simultaneous occuring stressors, yet few studies explore responses explore responses across various taxonomic groups at the same locations. In this study, we address this shortcoming by using a coherent data set to study the association of nine commonly occurring stressors (five chemical, one morphological and three hydraulic) with five taxonomic groups (bacteria, fungi, diatoms, macro-invertebrates and fish). According to studies on single taxonomic groups, we hypothesise that gradients of chemical stressors structure community composition of all taxonomic groups, while gradients of hydraulic and morphological stressors are mainly related to larger organisms such as benthic macro-invertebrates and fish.Organisms were sampled over two years at 20 sites in two catchments: a recently restored urban lowland catchment (Boye) and a moderately disturbed rural mountainous catchment (Kinzig). Dissimilarity matrices were computed for each taxonomic group within a catchment. Taxonomic dissimilarities between sites were linked to stressor dissimilarities using multivariable Generalized Linear Mixed Models.Stressor gradients were longer in the Boye, but did in contrast to the Kinzig not cover low stress intensities. Accordingly, responses of the taxonomic groups were stronger in the Kinzig catchment than in the recently restored Boye catchment. The discrepancy between catchments underlines that associations to stressors strongly depend on which part of the stressor gradient is covered in a catchment. All taxonomic groups were related to conductivity. Bacteria, fungi and macro-invertebrates change with dissolved oxygen, and bacteria and fungi with total nitrogen. Morphological and hydraulic stressors had minor correlations with bacteria, fungi and diatoms, while macro-invertebrates were strongly related to fine sediment and discharge, and fish to high flow peaks. The results partly support our hypotheses about the differential associations of the different taxonomic groups with the stressors.

Creating synthetic energy meter data using conditional diffusion and building metadata

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Fréchet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future.

A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

AbstractMicrophysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

From Bricks to Bytes: Navigating Data Management in Construction Technology

With the advent of technological tools, systems, and mechanisms, the operating system in the construction industry has changed enormously. While technological advancements cover a broad area of software and hardware, those involved in data flow have significant roles in data sustainability. The data flow presupposes various requirements, interactions, consequences, and resource utilization and therefore plays a crucial role in construction organizations’ success. The importance of the data flow subject and the scarcity of pertinent studies provoked the current research, which aimed at the exploration of the data flow in construction companies and the effects of technological advancements on different stages of this flow. The overarching research question was, “what factors or processes generate data that do not add value to the construction organization.” In this context, the added value was considered a tradeoff between all consumed resources, including cost, human resources, hardware and software, and associated challenges on one side and the advantages of data generated on the other side. An extensive literature review was performed to examine the main milestones in the data-generative events in the construction industry, with a special emphasis on recent technological advancements such as BIM, VR, AR, and drones. In addition, a group of construction professionals who were in charge of processes was surveyed to produce empirical data for supplementary analysis. This paper contributes to the body of knowledge by providing a clear analysis of the data flow in construction companies which helps decision-makers to design and implement consistent and coherent data generative systems.

Downlink Learning-aided Precoding for Cell-Free networks Specially D esigned for beyond 5G Communication System

Objectives: With increase in number of access points in Cell-Free Massive Multiple Input Multiple Output (CFMM) system, spatial complexity of calculating precoding vector increases with traditional precoding schemes. So, to reduce computational complexity, network cost, and run time, a new deep learning -aided precoder is specially designed for CFMM system. The performance of downlink (DL) Cell-Free Massive Multiple Input Multiple Output (CFMM) system operating under Rayleigh fading channel model is analyze using new deep learning-based precoding scheme. Methods: We introduce new deep learning-aided precoder and an improved version of basic scalable pilot assignment algorithm which enhances system performance. We derive closed- form expression for average DL spectral efficiency (SE) for the proposed scheme, which is then compared with Minimum Mean Square Error Successive Interference Cancellation (MMSESIC), Regularized Zero Forcing (RZF), Least Square (LS) and Maximum Ratio (MR) combining techniques. We analyze the proposed scheme with perfect channel state information (CSI), instantaneous CSI, coherent transmission, non-coherent transmission, different pilot configuration. Findings: The spectral efficiency obtained with CFMM-MMSESIC and the proposed scheme is 3 bits/s/Hz, and 2.5bits/s/Hz respectively, which clearly indicates the difference of 20 % only. So, the proposed scheme gives near optimal results, and we can use simple linear combining techniques instead of complex non-linear combining techniques. Also, the proposed scheme with coherent data transmission gives performance curve, which is very close to MMSESIC scheme as desired. The performance of the proposed system is improved by reducing run time and computational complexity as compared to conventional linear precoding schemes. Novelty: The proposed precoder improves performance by reducing run time and computational complexity. Advanced pilot assignment algorithm enhances performance by reducing pilot contamination. Keywords: Cell-Free Massive Multiple Input Multiple Output, Pilot Contamination, Precoding, Minimum Mean Square Error Successive Interference Cancellation, Maximum Ratio, Regularized Zero Forcing (RZF), Least Square (LS)

Neutrino window to scalar leptoquarks: From low energy to colliders

Leptoquarks are theorized particles of either scalar or vector nature that couple simultaneously to quarks and leptons. Motivated by recent measurements of coherent elastic neutrino-nucleus scattering, we consider the impact of scalar leptoquarks coupling to neutrinos on a few complementary processes, from low energy to colliders. In particular, we set competitive constraints on the typical mass and coupling of scalar leptoquarks by analyzing recent COHERENT data. We compare these constraints with bounds from atomic parity violation experiments, deep inelastic neutrino-nucleon scattering and collider data. Our results highlight a strong complementarity between different facilities and demonstrate the power of coherent elastic neutrino-nucleus scattering experiments to probe leptoquark masses in the sub-TeV range. Finally, we also present prospects for improving current bounds with future upgrades of the COHERENT detectors and the planned European Spallation Source. Published by the American Physical Society 2024

Grid Turbulence Measurements with an Acoustic Doppler Current Profiler

The motivation for this study is to investigate the abilities and limitations of a Nortek Signature1000 acoustic Doppler current profiler (ADCP) regarding fine-scale turbulence measurements. Current profilers offer the advantage of gaining more coherent measurement data than available with point acoustic measurements, and it is desirable to exploit this property in laboratory and field applications. The ADCP was tested in a towing tank, where turbulence was generated from a grid towed under controlled conditions. Grid-induced turbulence is a well-studied phenomenon and a good approximation for isotropic turbulence. Several previous experiments are available for comparison and there are developed theories within the topic. In the present experiments, a Nortek Vectrino acoustic Doppler velocimeter (ADV), which is an established instrument for turbulence measurements, was applied to validate the ADCP. It was found that the mean flow measured with the ADCP was accurate within 4% of the ADV. The turbulent variance was reasonably well resolved by the ADCP when large grid bars were towed at a high speed, but largely overestimated for lower towing speed and smaller grid bars. The effective cutoff frequency and turbulent eddy size were characterized experimentally, which provides detailed guidelines for when the ADCP data can be trusted and will allow future experimentalists to decide a priori if the Nortek Signature can be used in their setup. We conclude that the ADCP is not suitable for resolving turbulent spectra in a small-scale grid-induced flow due to the intrinsic Doppler noise and the low spatial and temporal sample resolution relative to the turbulent scales.

Free space optical communication system: a review of practical constraints, applications, and challenges

Abstract FSO (free space optical) communication holds emanated position in the role of propitious innovation with exorbitant-speed using the data transmission mechanism without the wire, propounding an accomplishable explication to the surging need for bandwidth in communication networks. This research paper probes into the improvements and obstacles in FSO communication systems, pursuing to anticipate a compendious encapsulation of the state-of-the-art establishment in this discipline. The evaluation incorporates a meticulous inspection of vital constituents such as laser sources, modulators, and receivers, elucidating their contribution to accomplishing coherent data transfer. The scrutinizing also investigates innumerable atmospheric circumstances affecting FSO links, including weather conditions and turbulence, and puts forward ingenious approaches to alleviate their repercussion on communication performance. Furthermore, the paper explores the homogenization of ultra-modern signal processing techniques to amplify the credibility and robustness of FSO systems, corroborating seamless communication even in challenging environments. Moreover, the research inquiries into the neoteric experimental framework and real-world execution of FSO communication, shedding light on heuristic contemplation, and substantiating theoretical unearthing. The conclusion of this research shells out treasures perception of the ongoing attempt to optimize FSO communication for variegated applications, including, inter-satellite communication, high-speed internet access, and last-mile connectivity. As FSO perpetuates progress, this paper suffices as a recent and inclusive resource for researchers, engineers, and policymakers maneuvering the edges of optical communication technologies.

Ultrafast Bragg coherent diffraction imaging of epitaxial thin films using deep complex-valued neural networks

Domain wall structures form spontaneously due to epitaxial misfit during thin film growth. Imaging the dynamics of domains and domain walls at ultrafast timescales can provide fundamental clues to features that impact electrical transport in electronic devices. Recently, deep learning based methods showed promising phase retrieval (PR) performance, allowing intensity-only measurements to be transformed into snapshot real space images. While the Fourier imaging model involves complex-valued quantities, most existing deep learning based methods solve the PR problem with real-valued based models, where the connection between amplitude and phase is ignored. To this end, we involve complex numbers operation in the neural network to preserve the amplitude and phase connection. Therefore, we employ the complex-valued neural network for solving the PR problem and evaluate it on Bragg coherent diffraction data streams collected from an epitaxial La2-xSrxCuO4 (LSCO) thin film using an X-ray Free Electron Laser (XFEL). Our proposed complex-valued neural network based approach outperforms the traditional real-valued neural network methods in both supervised and unsupervised learning manner. Phase domains are also observed from the LSCO thin film at an ultrafast timescale using the complex-valued neural network.

Chaotic phase noise-like encryption based on geometric shaping for coherent data center interconnections.

The network traffic of data centers (DCs) has increased unprecedentedly with the rapid development of digital economy. However, the data transmission faces security threats in the distributed optical interconnection and intensive interaction of DC networks. In this paper, we propose a chaotic phase noise-like encryption algorithm using geometric shaping (GS) for coherent DC interconnections (DCIs). A GS constellation is used to improve transmission performance, and it is combined with coherent equalization algorithms to improve security performance. Then, a chaotic encryption is designed based on phase noise-like transformation (PNLT). The data are effectively scrambled, and the confusion level of phase can be increased. Finally, 216 Gb/s 8-quadrature amplitude modulation (8-QAM) encrypted data are successfully verified on a 240 km transmission link of DCIs. The results show that this scheme can achieve a bit error rate (BER) performance gain of 1.1 dB and provide a highly compatible solution for realizing security enhanced DCIs.