Campaign Data Research Articles

Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory

This paper introduces an innovative social process model addressing population-wide measures of voter preferences that was tested on data from the 2016 US presidential election. Population-wide, “macroscopic” parameters are needed when privacy, ethics or regulatory constraints block “big data” techniques (e.g., in political contexts to counter “micro-targeting”). Confidence will be eroded if existing trend models and other macroscopic approaches frequently fail to predict outcomes, however campaign data reveal mathematical features that suggest a different possible approach. Given that the populations modelled exhibit self-organisation and memory when transmitting viewpoints, our model is based on mathematical representations of such processes. Its validation indicates the applicability and potential generalisability of this theoretical approach. In order to design a stochastic dynamics model of changing voter preferences, we evaluated probability models for transitions between possible system states (magnitudes of voter preferences), formulated the boundary task for probability density functions and derived a second-order non-linear differential equation incorporating self-organisation and memory. We find consistent dependencies between influences on the system and its reaction, and it is congruent with empirical data. The ability to use researchable global parameters indicates the potential for modelling electoral processes and wider applicability for complex social processes, avoiding dependence on “internal” variables.

Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

Study of Slowly Changing Dimension to Dependent Data Mart to Manage Finance Data

Financial institutions face many challenges of managing and marketing campaigns which leads in its data warehouse. The management of marketing campaign leads in dependent data mart with real time updating and recording difficulties especially when many campaigns are running parallel ways. To securing the customers from being contacted too often for sales-based marketing contacts, the concept of novelty skeleton are introduced to clamp the customers who have been targeted in Sales based campaign for a specified time period. During the novelty Frame, the customer cannot be targeted by other Sales based campaign categorized under the same channel. The introduction of novelty skeleton has increased the difficulties of campaign management and data management. The difficulties of data management include timely update and robust storage system of campaign leads. In this paper, we explained represent the concept of slowly changing dimension on dependent data mart and also studied how it can be used in the data mart of financial institutions to update and maintain marketing campaign records of customers.

Displacement Velocity and Strain Analysis of Opak Fault Monitoring Stations

The Opak Fault is an active fault that can potentially cause earthquakes in Yogyakarta. Periodic monitoring of the Opak Fault activity was previously used more GNSS observation data from the measurement campaign by the Geodesi Geometri dan Geodesi Fisis (GGGF) Laboratory Team, Geodetic Engineering Department, Faculty of Engineering, Universitas Gadjah Mada. However, there are several CORS BIG stations located in Yogyakarta. The CORS BIG data is used to increase the precision of the Opak Fault monitoring station. Therefore, the addition of the CORS is evaluated to obtain a displacement in the monitoring station. The computation of the displacement velocity value of the Opak Fault monitoring station has been done before using the Linear Least Square Collocation and grid search methods. The other method, namely the kriging method, needs to be evaluated for producing a more precise displacement velocity value. The research data includes GNSS campaign and CORS BIG data for six years, 2013 to 2020. The CORS stations around DIY are JOGS and CBTL. The GNNS data were processed to determine the solution for the daily coordinate, displacement, and standard deviation values for each Opak Fault monitoring station. The displacement velocity value is generated by the Linear Least Square method then reduced from the influence of the Sunda Block. The velocity value is used in the strain value estimation around the Opak Fault area at each station using the kriging method combined with the gaussian sequential simulation technique. The estimated displacement velocities are examined for statistical significance compared to the research of Adam (2019) and Pinasti (2019). This research generates the value of the displacement velocity in the east and north components of 12.39 to 30.99 mm/year and 1.96 to -14.11 mm/year, respectively. The displacement direction of all monitoring stations is dominant to the southeast. The Sunda Block reduced the displacement velocity. The east and north components are -2.32 to 2.28 mm/year and -0.52 to 4.2 mm/year, respectively. The displacement direction is towards the northwest. The strain estimation using the kriging method combined with the gaussian sequential simulation technique obtained an average strain value of 0.05 microstrain/year. The result of the data processing at each station has different arrow lengths, meaning that each location has a different strain value.

Overview of the Lake Michigan Ozone Study 2017.

The Lake Michigan Ozone Study 2017 (LMOS 2017) was a collaborative multiagency field study targeting ozone chemistry, meteorology, and air quality observations in the southern Lake Michigan area. The primary objective of LMOS 2017 was to provide measurements to improve air quality modeling of the complex meteorological and chemical environment in the region. LMOS 2017 science questions included spatiotemporal assessment of nitrogen oxides (NO x = NO + NO2) and volatile organic compounds (VOC) emission sources and their influence on ozone episodes; the role of lake breezes; contribution of new remote sensing tools such as GeoTASO, Pandora, and TEMPO to air quality management; and evaluation of photochemical grid models. The observing strategy included GeoTASO on board the NASA UC-12 aircraft capturing NO2 and formaldehyde columns, an in situ profiling aircraft, two ground-based coastal enhanced monitoring locations, continuous NO2 columns from coastal Pandora instruments, and an instrumented research vessel. Local photochemical ozone production was observed on 2 June, 9-12 June, and 14-16 June, providing insights on the processes relevant to state and federal air quality management. The LMOS 2017 aircraft mapped significant spatial and temporal variation of NO2 emissions as well as polluted layers with rapid ozone formation occurring in a shallow layer near the Lake Michigan surface. Meteorological characteristics of the lake breeze were observed in detail and measurements of ozone, NOx, nitric acid, hydrogen peroxide, VOC, oxygenated VOC (OVOC), and fine particulate matter (PM2.5) composition were conducted. This article summarizes the study design, directs readers to the campaign data repository, and presents a summary of findings.

Weather Variability Induced Uncertainty of Contrail Radiative Forcing

Persistent contrails and contrail cirrus are estimated to have a larger impact on climate than all CO2 emissions from global aviation since the introduction of jet engines. However, the measure for this impact, the effective radiative forcing (ERF) or radiative forcing (RF), suffers from uncertainties that are much larger than those for CO2. Despite ongoing research, the so called level of scientific understanding has not improved since the 1999 IPCC Special Report on Aviation and the Global Atmosphere. In this paper, the role of weather variability as a major component of the uncertainty range of contrail cirrus RF is examined. Using 10 years of MOZAIC flights and ERA-5 reanalysis data, we show that natural weather variability causes large variations in the instantaneous radiative forcing (iRF) of persistent contrails, which is a major source for uncertainty. Most contrails (about 80%) have a small positive iRF of up to 20 W m−2. IRF exceeds 20 W m−2 in about 10% of all cases but these have a disproportionally large climate impact, the remaining 10% have a negative iRF. The distribution of iRF values is heavily skewed towards large positive values that show an exponential decay. Monte Carlo experiments reveal the difficulty of determining a precise long-term mean from measurement or campaign data alone. Depending on the chosen sample size, calculated means scatter considerably, which is caused exclusively by weather variability. Considering that many additional natural sources of variation have been deliberately neglected in the present examination, the results suggest that there is a fundamental limit to the precision with which the RF and ERF of contrail cirrus can be determined. In our opinion, this does not imply a low level of scientific understanding; rather the scientific understanding of contrails and contrail cirrus has grown considerably over recent decades. Only the determination of global and annual mean RF and ERF values is still difficult and will probably be so for the coming decades, if not forever. The little precise knowledge of the RF and ERF values is, therefore, no argument to postpone actions to mitigate contrail’s warming impact.

Examining the Auroral Ionosphere in Three Dimensions Using Reconstructed 2D Maps of Auroral Data to Drive the 3D GEMINI Model

AbstractWe use the Geospace Environment Model of Ion‐Neutral Interactions (GEMINI) to create three‐dimensional, time‐dependent simulations of auroral ionospheric parameters in the localized, several 100 km region surrounding auroral arcs observed during a winter 2017 sounding rocket campaign, resolving three‐dimensional features of fine‐scale (km) flow structures in the vicinity of an auroral arc. The three‐dimensional calculations of GEMINI allow (with sufficient driving data) auroral current closure to be investigated without idealizing assumptions of sheet‐like structures or height integrated ionospheres. Datamaps for two nearly sheet‐like arcs are reconstructed from replications of the Isinglass sounding rocket campaign data, and combined with camera‐based particle inversions into a set of driving inputs to run the 3D time‐dependent model. Comparisons of model results to radar density profiles and to in situ magnetometry observations are presented. Slices of volumetric current, flow, and conductance structures from model outputs are used to interpret closure currents in an auroral arc region, and are compared to original in situ measurements for verification. The predominant source of return current region field aligned current closure for these slow time variation events is seen to be from the conductance gradients, including the Hall. The importance of the versus terms in the determination of the current structure provides a more complicated picture than a previous GEMINI study, which relied predominantly on the divergence of the electric field to determine current structure. Sensitivity of data‐driven model results to details of replication and reconstruction processes are discussed, with improvements outlined for future work.

Simultaneous calibration of instrument scale factor and drift rate in network adjustment for continental-scale gravity survey campaign

SUMMARY The scale factor of each relative gravimeter must be calibrated both before and after the fieldwork of a terrestrial gravity survey, to reduce uncertainties and ensure high precision. Conventionally, such calibration is a time-consuming process performed following well-established baselines. We propose a new Bayesian method to estimate the scale factor in a hybrid gravity network that includes several absolute gravity observation stations. In this approach, the scale factor is estimated as a hyperparameter using the Akaike Bayesian information criterion and using known absolute gravity stations in the network or/and calibrated instruments as constraints. Testing the sensitivity of the gravity values and the residuals of the gravity difference between two successive stations to the change of the scale factor demonstrates the robustness of this method. We also test the sensitivity of the estimated scale factor in the presence of Gaussian noise and the non-linear instrumental drift rate. Moreover, if the maximum absolute gravity interval is greater than 60 per cent of the range of gravity values in the network, or if the known scale factors of calibrated gravimeters are well calibrated, this approach can provide reasonable estimates of the daily drift rate and the unknown scale factors, where the latter has an error of <3 × 10−5. We apply this approach to real gravity campaign data from Yunnan in China and use a cross-validation method to compare estimated gravity values and corresponding gravity values obtained from absolute gravity observations at the same stations, to validate how the proposed method improves estimation accuracy of the gravity value at each station.

Factors Influencing Engagement Rate Of A Daily Twitter Campaign To Disseminate Exercise Oncology Literature

Twitter is a potential medium for health researchers and practitioners to disseminate research findings to colleagues and the general public. The Clinical Exercise Physiology Lab (CEPL; Vancouver, Canada) led a scientific Twitter campaign (#365Papers) in 2018, involving tweeting daily about one peer-reviewed publication related to cancer/exercise. Features of this campaign included Throwback Thursdays (selected article published prior to 2018) and guest tweeters (selected article chosen by other exercise oncology researchers). PURPOSE: To report on the impact to-date of the #365Papers campaign and explore how engagement rate differed based on publication features (type of research (i.e., systematic review, randomized controlled trial), journal impact factor, Altmetric Attention Score) and campaign features (i.e., Throwback Thursdays, guest tweeters). METHODS: Campaign data were obtained from Twitter Analytics (Twitter, 2020: San Francisco, USA) and summarized using descriptive statistics. Publication information was extracted by screening titles and abstracts. Each publication’s Altmetric Attention Score was obtained using the Altmetric Bookmarklet (Digital Science, Holtzbrinck Publishing Group, 2020: Stuttgart, Germany). RESULTS: As of October 2020, the #365Papers Twitter campaign received a total of 688,117 impressions and 22,124 engagements (median engagement rate = 3.2%), with the majority of engagement from URL clicks (n = 8279; 37%). Engagement rate did not differ based on type of research (p = 0.53), journal impact factor (r = -0.06; p = 0.27), Altmetric Attention Score (r = 0.01; p = 0.80), nor if the tweet was part of a Throwback Thursday (p = 0.97). However, guest tweets had significantly higher engagement rates versus non-guest tweets (median: 3.6% vs. 3.1%; p = 0.01). CONCLUSIONS: A daily scientific Twitter campaign can promote engagement around new scientific publications, especially when prominent figures in the research field are incorporated in the campaign process.

Owned and Free-Roaming Dogs in the North West of Tunisia: Estimation, Characteristics and Application for the Control of Dog Rabies

Understanding the structure of dog population and the evaluation of accessibility of dogs to vaccination is essential to succeed the fight against dog rabies and to adapt the strategy of its control. We studied the characteristics of the unowned and owned dogs using the marking method during a rabies vaccination campaign in a randomly selected sectors (urban and rural sites) in the north west of Tunisia. During a door-to-door vaccination campaign data on owned dogs were collected to describe the owned population dog. A photographic-recapture method was used to characterize and to estimate the size of the unowned dogs. A total of 1432 households accounting 5403 inhabitants was interviewed during the survey (1298 (90.6%) in the urban site and 134 (9.3%) in the rural site). The dog-owning households were significantly higher in the rural site (76.1% (102/134)) compared to the urban site (17.7% (231/1298)) (P<0.000000). Of the 17.7% dog-owning households in urban site, 58.4% owned one dog and 9% between 4 and 8 dogs. While, of the 76.1% dog-owning households in rural site, 24.5% owned one dogs and 32.3% owned between 4 and 10 dogs. The dog: human ratio was 1:11 in the urban site and 1:1.6 in the rural site. The dog population density was high in the urban site (16 dogs/km2) compared to the rural site (4 dogs/km2). The confinement practices varied significantly among the urban and the rural sites (P<0.000000). The percentage of free-roaming owned dogs was higher in the rural site (51.1%) compared to the urban site (31.4%). The majority of owned dogs in the urban site were confined (66.6%). The majority of dogs in the rural sites were born in the house, although, a high percentage (56.7%) of owned dogs in the urban site were adopted from neighbours, others sectors or countries. The vaccination coverage findings indicated that 77.8% and 84.2% of the owned dog were vaccinated in the urban and rural sites, respectively. The estimated size of the free-roaming dogs was 72 dogs in the urban site (Kalaat Senan) and 16 dogs in the rural site (Sod el Khir).

Data Shepherding in Nanotechnology. The Exposure Field Campaign Template.

In this paper, we demonstrate the realization process of a pragmatic approach on developing a template for capturing field monitoring data in nanomanufacturing processes. The template serves the fundamental principles which make data scientifically Findable, Accessible, Interoperable and Reusable (FAIR principles), as well as encouraging individuals to reuse it. In our case, the data shepherds’ (the guider of data) template creation workflow consists of the following steps: (1) Identify relevant stakeholders, (2) Distribute questionnaires to capture a general description of the data to be generated, (3) Understand the needs and requirements of each stakeholder, (4) Interactive simple communication with the stakeholders for variables/descriptors selection, and (5) Design of the template and annotation of descriptors. We provide an annotated template for capturing exposure field campaign monitoring data, and increase their interoperability, while comparing it with existing templates. This paper enables the data creators of exposure field campaign data to store data in a FAIR way and helps the scientific community, such as data shepherds, by avoiding extensive steps for template creation and by utilizing the pragmatic structure and/or the template proposed herein, in the case of a nanotechnology project (Anticipating Safety Issues at the Design of Nano Product Development, ASINA).

Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey's test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

The NASA AfriSAR campaign: Airborne SAR and lidar measurements of tropical forest structure and biomass in support of current and future space missions

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies.During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30,000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1-hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

Using Campaign Communications to Analyze Civility in Ranked Choice Voting Elections

Theory suggests that ranked choice voting (RCV) may create a more civil campaign environment. As voters must rank candidates, the candidates have an incentive to work with each other more collaboratively. This study uses text analysis software (LIWC) to examine candidate tweets and newspaper articles in RCV versus specifically-chosen plurality cities for evidence of positivity or negativity. In quantitatively comparing the tweets, the results are mixed among the cities. Qualitatively, candidates seem to be more likely to engage each other in RCV cities than in plurality cities. Using LIWC to analyze newspaper articles for campaign tone, one can see that RCV city articles have significantly more positive than negative words. This is the first published study to use direct campaign communication data to study RCV elections and campaign civility. This research validates survey research indicating that citizens perceive RCV campaigns are more civil.

Much Ado About Facebook? Evidence from 80 Congressional Campaigns in Chile

ABSTRACT How do political candidates combine social media campaign tools with on-the-ground political campaigns to pursue segmented electoral strategies? We argue that online campaigns can reproduce and reinforce segmented electoral appeals. Furthermore, our study suggests that electoral segmentation remains a broader phenomenon that includes social media as but one of many instruments by which to appeal to voters. To test our argument, we analyze the case of the 2017 legislative elections in Chile. We combine an analysis of Facebook and online electoral campaign data from 80 congressional campaigns that competed in three districts with ethnographic sources (i.e., campaigns observed on the ground and in-depth interviews with candidates). The results of this novel study suggest that intensive online campaigning mirrors offline segmentation.

Neural network-based optimization of fibres for seismic retrofitting applications of UHPFRC

Ultra-high-performance fibre reinforced concrete (UHPFRC) is an advanced construction material that provides new opportunities in the future of the construction industry around the world. Among those new applications, rehabilitation, and seismic retrofitting of existing damaged or non-ductile concrete structures can be highlighted. The main objective of this paper is to optimise the hybrid blend of fibres that allows a previously optimised eco-friendly ultra-high-performance cementitious paste to achieve the ductility requirements for seismic retrofitting applications at lower costs. To meet this goal, two artificial neural network models (ANNs) were created to predict the energy absorption capacity (g) and maximum post-cracking strain (εpc). A total of 50 own experimental campaign data added to 550 published works throughout the world data were used for training purposes by using the R-code language. Once the models were trained and validated, a multi-objective optimisation was used to select the combination of fibres that achieved the limit values of g ≥ 50 kJ/m3 and εpc ≥ 0.3% considering cost constraints. The experimentally validated results indicated that the adequate blend of high strength steel micro-fibres and hooked end normal strength steel fibres fulfil the threshold values at a lower cost.

Decreasing Operating Room (OR) Traffic to Improve Patient Safety in an Academic Medical Center

Background : OR traffic can contribute to the risk of developing a surgical site infection (SSI) as there is a link between elevated airborne particle counts and door openings. There is growing evidence that traffic patterns, including the number and movement of surgical team members should be carefully monitored during surgery especially for longer duration procedures. In this project, we propose to conduct a needs assessment and develop interventions based on findings to reduce door openings. Methods : The team utilized the Institute for Healthcare Improvement: Plan-Do-Study-ACT (PDSA) approach. The project involved a needs assessment, direct observations during cases, analysis and presentation of data, literature review, and leadership education. A needs assessment consisting of twenty-six surgery observations across five different service lines in seven different OR departments was completed with results on over 1666 door openings. Observations for the entire surgery were obtained and categorized as pre-incision versus post-incision. Reasons for door openings along with healthcare worker title and entry door access was collected. Data trends for each OR department were summarized separately. Results : Overall, across all the OR's, data revealed a high number of door openings. Most staff door openings involved non-scrubbed personnel movement and the most frequent reason was retrieval of supplies. All but one of the OR's had most door openings in the post-incision phase. Primary implementation strategies included 1) educating peri-operative leadership on the implications of OR traffic 2) updating surgeon preference cards and evaluating impact 3) restricting vendor traffic and 4) involving front line staff to launch an improvement campaign. Conclusions : The needs assessment demonstrates that a traffic campaign and further education is needed across all peri-operative areas. Peri-operative areas are sustaining the project with a campaign, staff surveys, education and further data collection.

Rough Surface Scattering Model Comparisons for Radar Altimetry of Sea Ice

Currently, sea-ice thickness is often estimated using microwave radar altimetry. It is well known that radar measurements are highly dependent on the roughness characteristics of the observed scene and can have a profound effect on altimetry-based thickness estimates. This simulation-based study highlights the importance of sea-ice roughness parameterization in the context of radar altimetry. This work provides a new sea-ice roughness parameterization in the form of an elevation spectrum developed from field campaign data. The formulation of this elevation spectrum is partly based on the generalized power law. Simulations have been developed to predict electromagnetic scattering from surfaces described by the new spectrum, with a focus on the X-band (10 GHz) scattering with nadir incidence. In this work, both the analytical small slope approximation and numerically exact Method of Moments are applied to various sea-ice surface spectra, representing a wide range of surface roughness conditions. The results show that the normalized radar cross section of the sea-ice interface varies significantly depending on the surface elevation spectrum and the statistics of the rough interface. Comparisons with numerically exact methods show that analytical approximations to rough surface scattering work reasonably well for the cases considered here, except at large scattering angles.

A novel approach for APT attack detection based on combined deep learning model

Advanced persistent threat (APT) attack is a malicious attack type which has intentional and clear targets. This attack technique has become a challenge for information security systems of organizations, governments, and businesses. The approaches of using machine learning or deep learning algorithms to analyze signs and abnormal behaviors of network traffic for detecting and preventing APT attacks have become popular in recent years. However, the APT attack detection approach that uses behavior analysis and evaluation techniques is facing many difficulties due to the lack of typical data of attack campaigns. To handle this situation, recent studies have selected and extracted the APT attack behaviors which based on datasets are built from experimental tools. Consequently, these properties are few and difficult to obtain in practical monitoring systems. Therefore, although the experimental results show good detection, it does not bring high efficiency in practice. For above reasons, in this paper, a new method based on network traffic analysis using a combined deep learning model to detect APT attacks will be proposed. Specifically, individual deep learning networks such as multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM) will also be sought, built and linked into combined deep learning networks to analyze and detect signs of APT attacks in network traffic. To detect APT attack signals, the combined deep learning models are performed in two main stages including (i) extracting IP features based on flow: In this phase, we will analyze network traffic into networking flows by IP address and then use the combined deep learning models to extract IP features by network flow; (ii) classifying APT attack IPs: Based on IP features extracted in a task (i), the APT attack IPs and normal IPs will be identified and classified. The proposal of a combined deep learning model to detect APT attacks based on network traffic is a new approach, and there is no research proposed and applied yet. In the experimental section, combined deep learning models proved their superior abilities to ensure accuracy on all measurements from 93 to 98%. This is a very good result for APT attack detection based on network traffic.

Bayesian estimation of scale factor for CG-5 relative gravimeter

The terrestrial time-variable microgravity survey is an important means to monitor geodynamic processes underground. The Scintrex CG-5 relative gravimeter, which is the mainstream instrument for terrestrial relative gravity measurement at present, its scale factor (SF) is an important factor affecting the quality of the observation data. Unlike the conventional gravity adjustment procedure, this study takes account of the nonlinear drift rate of the CG-5 gravimeter, depending on the known absolute gravity (AG) in the network, and the scale factor is considered as one of the hyper-parameters to be estimated by means of Akaike’s Bayesian information Criterion (ABIC). In order to quantify the errors caused by the bias of scale factor, we applied this new approach to process actual gravity survey campaign data in Yunnan gravity network from 2018 to 2020, then analyzed the residuals of gravity differences (GD) between station pairs and the difference of GD between two relative gravimeters (CG5 #1169 and CG5 #1170). The cross-validation of the absolute gravity (AG) from the quasi-synchronous observation in the network is also used in the paper. It has shown that this new approach is effective to improve the accuracy of scale factor and adjustment results.