Average Probability Research Articles

Data Monetization Service Development Using Iterative Lifecycle Framework, Quality Assurance, and Open Web Application Security Project: A Case Study of a Utility Company in Indonesia

The research aims to provide Data Monetization (DM) services for an Indonesian utility company as a pilot to generate additional revenue beyond the primary operation. The service is built using an iterative development lifecycle framework and evaluated based on five Quality Goals (QGs), including application and security testing activities. The framework includes methods for processing and modeling electricity usage data, testing application quality, checking infrastructure quality, and ensuring access security for front-end and back-end applications using the Open Web Application Security Project (OWASP). For data modeling, Support Vector Regression (SVR) is used, and it outperforms Polynomial Regression (PR) and Multi-Layer Perception (MLP) Neural Networks. Furthermore, QG shows strong performance with an Relative Root Mean Squared Error (RRMSE) value < 10%, high forecasting ability with Mean Average Probability Error (MAPE) < 10%, and a near-zero average error rate (Mean Squared Error (MSE)) square using minimal data from four months. The services go through functional and integration test to ensure product quality and application performance, which results in a minimum of 95% service response in throughput, 0.128 seconds for processing 2,000 requests, stability at 300–500 in one second per hour, and 7–21 seconds during peak hours. Additionally, the service passes nine penetration tests and ten vulnerability assessments using the OWASP top 10:2021 category. Based on the comprehensive testing and evaluation results, both the application and the service are considered ready and secured for deployment.

Proposition of Optimization of the Meetinthemiddle Protocol for Quantum Repeaters

Quantum computing relies on concepts derived from classical information theory and quantum physics. In order for quantum communications to be a reality, it is essential to have the most advanced infrastructures in place. In this paper, we explore the significance of quantum communication and the utilization of quantum repeaters, which rely on the phenomenon of quantum entanglement. We present the MeetInTheMiddle protocol for quantum repeaters and its total transmission probability, and then propose an improvement of this protocol based on the comparison of the total transmission probability.

Noninvasive Method Using Mechanical Extensometer for the Estimation of Intracranial Compliance by Repeated Measures Agreement Analysis.

Intracranial compliance refers to the relationship between changes in volume and the resultant changes in intracranial pressure (ICP). This study aimed to assess the agreement of a noninvasive ICP waveform device for the estimation of compliance compared with invasive ICP monitoring employing three distinct methods. We conducted a retrospective analysis of ICP waveform morphology recorded through both invasive (external ventricular drain) and noninvasive (mechanical extensometer) methods in adult patients with acute brain injury admitted to a neurointensive care unit between August 2021 and August 2022. Compliance was calculated as the amplitude of the fundamental component of cerebral arterial blood volume (estimated with concurrent Transcranial Doppler [TCD] recordings), divided by the amplitude of the fundamental component of the invasive and noninvasive ICP waveforms. Subsequently, we assessed the agreement between invasive and noninvasive intracranial compliance by repeated measures correlation coefficient analysis using three methods: TCD-derived, P2/P1 ratio, and time-to-peak (TTP). A linear mixed-effects model was used to compute the concordance correlation coefficient, total deviation index, and coefficient of individual agreement. Coverage probability plot was calculated to estimate the percent of observations within different cut points for each of the three methods. A total of 21 patients were identified for this study. Repeated measures correlation analysis showed a strong correlation (R = 0.982, 95% confidence interval [0.980-0.984], p < 0.0001) between log-transformed noninvasive and invasive compliance. Agreement statistics for TCD, P2/P1 ratio, and TTP indicated that although the concordance correlation coefficient was highest for log(TCD) values, TTP and P2:P1 ratio measures had better agreement with total deviation index and coverage probability plot analyses. Repeated measures correlations suggest that ICP waveform analyses may offer a more accurate estimate of compliance than TCD-derived methods for noninvasive ICP monitoring. Further validations studies are warranted to confidently establish this method as an indicator of intracranial compliance.

Efficient spectrum allocation by heterogeneous automated frequency coordination network within 6 GHz band

This work proposes a heterogeneous automated frequency coordination network (HAFCN) to enhance reliability and enable dynamic spectrum allocation for unlicensed Wi-Fi devices (UWDs) within the 6 GHz band. This process relies heavily on spectrum sensing within the HAFCN. However, the widespread implementation of spectrum sensing by multiple UWDs in an automated frequency coordination (AFC) network using conventional fusion schemes poses computational challenges at the AFC provider. In response to this challenge, we present a selective soft-information (SSI) fusion scheme for the proposed HAFCN. First, we present generic mathematical expressions of false-alarm and missed detection probabilities for the HAFCN using an SSI fusion scheme. Second, a generalized AFC SSI fusion problem (GASFP) is formulated to minimize the system’s total error probability. Further, to mitigate the AFC provider’s overhead in solving the GASFP, this work presents the swift-sensing problem, determining the minimum antennas at UWD required to achieve a desired total error probability. Finally, comparative numerical results demonstrate that the HAFCN with the SSI fusion scheme shows a significant performance improvement over conventional fusion schemes in terms of the total error probability.

Association Model-Based Intermittent Connection Fault Diagnosis for Controller Area Networks

Controller Area Networks (CANs) play an important role in many safety-critical industrial systems, which places high demands on their reliability performance. However, the intermittent connection (IC) of network cables, a random and transient connectivity problem, is a common but hard troubleshooting fault that can cause network performance degradation, system-level failures, and even safety issues. Therefore, to ensure the reliability of CANs, a fault symptom association model-based IC fault diagnosis method is proposed. Firstly, the symptoms are defined by examining the error records, and the domains of the symptoms are derived to represent the causal relationship between the fault locations and the symptoms. Secondly, the fault probability for each location is calculated by minimizing the difference between the symptom probabilities calculated from the count information and those fitted by the total probability formula. Then, the fault symptom association model is designed to synthesize the causal and the probabilistic diagnostic information. Finally, a model-based maximal contribution diagnosis algorithm is developed to locate the IC faults. Experimental results of three case studies show that the proposed method can accurately and efficiently identify various IC fault location scenarios in networks.

Application of wavelet and seasonal-based emotional ANN (EANN) models to predict solar irradiance

This study models solar irradiance at six stations in Iran and the USA on an hourly scale. We explored two seasonal emotional artificial neural networks (EANN): sequence-EANN (SEANN) and wavelet EANN (WEANN). Analyzing ten years of climatic and solar data, we evaluated uncertainty using prediction intervals (PIs) computed via the bootstrap method based on artificial neural networks (ANNs). Unlike standalone EANNs, the proposed seasonal models effectively captured seasonal information and leveraged time series processing advantages. Utilizing Wavelet and Fourier transforms, these models captured long-short autoregressive dependencies in solar irradiance, addressing extended seasonal dependencies. Results showed that the seasonal EANN models outperformed the classic EANN model by approximately 15 % and the classic feed-forward neural network (FFNN) by about 25 % in both training and testing. The WEANN model demonstrated the highest performance in PIs, with an average normalized mean PI width (NMPIW) of 0.8 and an average PI coverage probability (PICP) of 0.96.

Pricing of sustainability-linked bonds

We examine the pricing of sustainability-linked bonds (SLBs), where the cash flows depend on the bond issuer achieving one or more Environmental, Social and Governance (ESG) goals. Investors are willing to accept a 1–2bps lower yield due to the bond’s ESG label, providing evidence of investors caring about environmental impact. Furthermore, we find the average probability of missing the target is 14%–39% so firms set ESG targets that are easy to reach. We find that the SLB market is efficient: the prices of SLBs depend strongly on the size of the potential penalty and there is no evidence of mispricing. Finally, our results suggest that SLBs serve as financial hedges against ESG risk.

CFD modeling of reactive species air cleaner applications in a classroom

Due to increasing concerns related to airborne virus spread indoors, more reactive species air cleaners are being utilized in classrooms. Reactive species generated by air cleaners decompose airborne pathogens chemically, decreasing the risk of infection. Due to the high reactivity of these oxidants, reactive species may be distributed nonuniformly in indoor environments, as are viral aerosols emitted by infectors. Heterogeneous distributions of reactive species may cause spatially non-uniform removal rates of viral aerosols. However, there is little information regarding spatial distributions of either reactive species or viral aerosols in ventilated classrooms. Thus, the objective of this study was to investigate spatial distributions of reactive species and infectious aerosols and to examine how operating conditions of air cleaners affect viral aerosol removal rates. A CFD model simulated the operation of a reactive species air cleaner generating hydrogen peroxide (H2O2) in a mechanically ventilated 237 m3 classroom with nine occupants. The reactive species air cleaner showed a 3–20 times higher equivalent air change rate to a HEPA filter air cleaner with the same inlet and outlet flows. During the operation of reactive species air cleaners, elevated viral aerosol concentration was confined to regions near infectors. This was due to the high reactivity of reactive species, decreasing the infection probability of receptors from 3.2 % to 0.1 % with a 1-hour exposure time. As the room average concentration of reactive species increased from 15.6 to 50.4 ppb, both below the US Occupational Safety and Health Administration (OSHA) Permissible Exposure Limit (PEL) of 1000 ppb, the room average infection probability decreased from 0.3 % to 0.1 %. Due to the residence times of reactive species, the location of reactive species air cleaners affected the inactivation rate of viral aerosol, resulting in a 24 % variation of concentration difference of infectious aerosol with air cleaner locations.

Solving Boolean Satisfiability Problems With The Quantum Approximate Optimization Algorithm

One of the most prominent application areas for quantum computers is solving hard constraint satisfaction and optimization problems. However, detailed analyses of the complexity of standard quantum algorithms have suggested that outperforming classical methods for these problems would require extremely large and powerful quantum computers. The quantum approximate optimization algorithm (QAOA) is designed for near-term quantum computers, yet previous work has shown strong limitations on the ability of QAOA to outperform classical algorithms for optimization problems. Here we instead apply QAOA to hard constraint satisfaction problems, where both classical and quantum algorithms are expected to require exponential time. We analytically characterize the average success probability of QAOA on a constraint satisfaction problem commonly studied using statistical physics methods: random k-SAT at the threshold for satisfiability, as the number of variables n goes to infinity. We complement these theoretical results with numerical experiments on the performance of QAOA for small n, which match the limiting theoretical bounds closely. We then compare QAOA with leading classical solvers. For random 8-SAT, we find that for more than 14 quantum circuit layers, QAOA achieves more efficient scaling than the highest-performance classical solver we tested, WalkSATlm. Our results suggest that near-term quantum algorithms for solving constraint satisfaction problems may outperform their classical counterparts. Published by the American Physical Society 2024

Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data

This paper focuses on predicting road passenger probability and optimizing taxi driving routes based on trajectory big data. By utilizing clustering algorithms to identify key passenger points, a method for calculating and predicting road passenger probability is proposed. This method calculates the passenger probability for each road segment during different time periods and uses a BiLSTM neural network for prediction. A passenger-seeking recommendation model is then constructed with the goal of maximizing passenger probability, and it is solved using the NSGA-II algorithm. Experiments are conducted on the Chengdu taxi trajectory dataset, using MSE as the metric for model prediction accuracy. The results show that the BiLSTM prediction model improves prediction accuracy by 9.67% compared to the BP neural network and by 6.45% compared to the LSTM neural network. The proposed taxi driver passenger-seeking route selection method increases the average passenger probability by 18.95% compared to common methods. The proposed passenger-seeking recommendation framework, which includes passenger probability prediction and route optimization, maximizes road passenger efficiency and holds significant academic and practical value.

Average Probability of Error for Single Uniprior Index Coding Over Binary-Input Continuous-Output Channels

Average Probability of Error for Single Uniprior Index Coding Over Binary-Input Continuous-Output Channels

Alpha Angle Values Predict the Severity of Hip Chondral Damage in Patients With Cam-Type Femoroacetabular Impingement Syndrome: A Systematic Review and Meta-analysis

To assess the role of alpha angle (AA) in predicting the severity of hip chondral damage in patients with cam-type femoroacetabular impingement (FAI) syndrome. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols guidelines, a systematic review was performed to summarize and critically appraise studies analyzing the prognostic capability of AA values in predicting the severity of intraoperatively evaluated hip chondral damage in patients with cam-type FAI syndrome. The risk of bias was assessed through the Quality In Prognosis Studies tool. Meta-analyses based on groups and individual data from studies with a low risk of bias were conducted. We employed a cumulative link mixed model to analyze the relationship between AA and the ordinal outcome (chondral damage). The model was adjusted for sex, age, and the lateral center-edge angle (LCEA). Interactions between sex and AA were also investigated, and probabilities were calculated. Twelve studies with 4,564 patients were included in a qualitative review (aged 30-39 years, 1,822 women [40%]). Studies with a low risk of bias (n= 4; 2,945 patients) indicated that AA values are significantly smaller (mean difference [95% confidence interval] of 10.5° [6.5°-15°]) in the low-grade chondral damage group (grades 0/I/II) compared with the high-grade chondral damage group (III/IV). The cumulative link mixed model with individual patient data from studies with low risk of bias (n= 3; 1,460 patients) indicated that for each 1° increase in AA, the odds of being in a greater category of chondral damage increased by a factor of 1.04 (odds ratio 1.04, P < .0001). Men were at significantly greater risk (odds ratio 2.11, P < .0001) than women, but no significant interaction between sex and AA was observed (P= .054). We estimate the average probability of having high-grade chondral damage to be greater than 33% when AA values surpass 71° for men and 90° for women, and greater than 50% when 89° for men and 108° for women. However, the heterogeneity observed across studies should be considered. The AA is a significant predictor of hip chondral damage severity in patients with cam-type FAI syndrome, regardless of sex and independently of age and LCEA. In addition, men are at a greater risk of chondral damage than women, and this risk increases with aging but decreases with the magnitude of the LCEA. Level III, systematic review of Level II and III studies.

Avoidable Hospitalizations in the Military Health System, Fiscal Years 2018-2019.

Ongoing health reforms in the Military Health System (MHS) are expected to shift locations of ambulatory care for up to 1.9 million beneficiaries. We sought to model the impact of this policy by determining potentially avoidable hospitalizations in the MHS based on different primary care settings. We used the MHS Data Repository to conduct a retrospective cross-sectional study of TRICARE Prime and Prime Plus beneficiaries aged 18 to 64 years during fiscal years 2018-2019. Crude and adjusted risk ratios for each Agency for Healthcare Research and Quality prevention quality indicator based on primary care setting were calcualated to determine the total probability of admission for any of the Agency for Healthcare Research and Quality prevention indicators. We identified a total of 260,690 hospital admissions by patients in the MHS with a designated primary care manager (PCM) from fiscal year 2018 to 2019. Of the total admissions, 11,067 (4.25%) were for Agency for Healthcare Research and Quality prevention quality indicators, 3.63% by direct care PCM at a military treatment facility, and 0.61% by a civilian private sector PCM. Risk of admission was lower for private sector PCMs for urinary tract infection, hypertension, perforated appendix, and angina without the procedure. We did not observe a statistically significant adjusted odds ratio of admission in patients managed by private sector PCMs (1.04 adjusted odds ratio; 95% CI, 0.97-1.11). Our findings indicate no difference in the likelihood of avoidable hospitalizations for beneficiaries with a private sector PCM when looking at all conditions together. Patients with a private sector PCM are protected against hospitalization for several conditions. Our findings indicate no adverse impact on avoidable hospitalizations for beneficiaries transitioned to private sector care from direct care.

Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach

BackgroundAssessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combining deep learning (DL) and machine learning (ML). MethodsThe model features multi-modality input and multi-task output, measuring LV ejection fraction (LVEF), left atrial end-systolic volume (LAESV), and Doppler parameters: mitral E wave velocity (E), A wave velocity (A), mitral annulus e’ velocity (e’), and tricuspid regurgitation velocity (TRmax). The algorithm was trained and tested on two internal datasets (862 and 239 echocardiograms) and validated using three external datasets, including EchoNet-Dynamic and CAMUS. The ASE diastolic function decision tree and total probability theory were used to provide diastolic grading probabilities. ResultsThe algorithm, named MMnet, demonstrated high accuracy in both test and validation datasets, with Dice coefficients for segmentation between 0.922 and 0.932 and classification accuracies between 0.9977 and 1.0. The mean absolute errors (MAEs) for LVEF and LAESV were 3.7 % and 5.8 ml, respectively, and for LVEF in external validation, MAEs ranged from 4.9 % to 5.6 %. The diastolic function grading accuracy was 0.88 with hard criteria and up to 0.98 with soft criteria which account for the top two probability in total probability theory. ConclusionsMMnet can automatically grade ASE diastolic function with high accuracy and efficiency by annotating 2D videos and Doppler images.

Spatial Mapping and Prediction of Groundwater Quality Using Ensemble Learning Models and SHapley Additive exPlanations with Spatial Uncertainty Analysis

The spatial mapping and prediction of groundwater quality (GWQ) is important for sustainable groundwater management, but several research gaps remain unexplored, including the inaccuracy of spatial interpolation, limited consideration of the geological environment and human activity effects, limitation to specific pollutants, and unsystematic indicator selection. This study utilized the entropy-weighted water quality index (EWQI), the LightGBM model, the pressure-state-response (PSR) framework and SHapley Additive exPlanations (SHAP) analysis to address the above research gaps. The normalized importance (NI) shows that NO3− (0.208), Mg2+ (0.143), SO42− (0.110), Cr6+ (0.109) and Na+ (0.095) should be prioritized as parameters for remediation, and the skewness EWQI distribution indicates that although most sampled locations have acceptable GWQ, a few areas suffer from severely poor GWQ. The PSR framework identifies 13 indicators from geological environments and human activities for the SMP of GWQ. Despite high AUROCs (0.9074, 0.8981, 0.8885, 0.9043) across four random training and testing sets, it was surprising that significant spatial uncertainty was observed, with Pearson correlation coefficients (PCCs) from 0.5365 to 0.8066. We addressed this issue by using the spatial-grid average probabilities of four maps. Additionally, population and nighttime light are key indicators, while net recharge, land use and cover (LULC), and the degree of urbanization have the lowest importance. SHAP analysis highlights both positive and negative impacts of human activities on GWQ, identifying point-source pollution as the main cause of the poor GWQ in the study area. Due to the limited research on this field, future studies should focus on six key aspects: multi-method GWQ assessment, quantitative relationships between indicators and GWQ, comparisons of various spatial mapping and prediction models, the application of the PSR framework for indicator selection, the development of methods to reduce spatial uncertainty, and the use of explainable machine learning techniques in groundwater management.

High Campylobacter diversity in retail chicken: epidemiologically important strains may be missed with current sampling methods.

Campylobacter spp. are leading bacterial gastroenteritis pathogens. Infections are largely underreported, and the burden of outbreaks may be underestimated. Current strategies of testing as few as one isolate per sample can affect attribution of cases to epidemiologically important sources with high Campylobacter diversity, such as chicken meat. Multiple culture method combinations were utilized to recover and sequence Campylobacter from 45 retail chicken samples purchased across Norwich, UK, selecting up to 48 isolates per sample. Simulations based on resampling were used to assess the impact of Campylobacter sequence type (ST) diversity on outbreak detection. Campylobacter was recovered from 39 samples (87%), although only one sample was positive through all broth, temperature, and plate combinations. Three species were identified (Campylobacter jejuni, Campylobacter coli, and Campylobacter lari), and 33% of samples contained two species. Positive samples contained 1-8 STs. Simulation revealed that up to 87 isolates per sample would be required to detect 95% of the observed ST diversity, and 26 isolates would be required for the average probability of detecting a random theoretical outbreak ST to reach 95%. An optimized culture approach and selecting multiple isolates per sample are essential for more complete Campylobacter recovery to support outbreak investigation and source attribution.

Analyzing ASEP with imperfect‐phase error and various modulation schemes on IRS‐aided wireless network over κ–μ fading channel

AbstractNext‐generation wireless systems constitute a promising technology, and intelligent reflecting surface (IRS)‐aided mobile communications can be used to design and simulate a smart and flexible radio environment. We examine the average symbol error probability (ASEP) on an IRS‐aided wireless network over a κ–μ fading channel with imperfect phase error. In addition, the ASEP for non‐coherent M‐ary frequency shift keying, M‐ary phase shift keying, and M‐ary differential phase shift keying on an IRS‐aided wireless network over a κ–μ fading channel are analyzed. Furthermore, exact closed‐form mathematical expressions for ASEP across different modulation schemes including ASEP with imperfect phase error are derived. The accuracy of the derived expressions is validated through simulations. The system effectiveness is comprehensively examined, uncovering valuable insights into channel fading values and the number of reflecting components.

A prognostic model for multi-stage degraded equipment under zero life label combining CDBN and Bayesian bidirectional GRU

Predicting remaining useful life (RUL) of multi-stage degraded equipment under the big data era is generally implemented by the following two steps: degradation stage division and RUL prediction by stage. However, three aspects of problems may be encountered, such as zero life label resulting from censored data, degradation stage number dependent on empirical knowledge, and prediction results ignoring the uncertainty of stage results, which leads to poor prediction effect inevitably. Towards this end, this paper proposes a prognostic model for multi-stage degraded equipment under zero life label. Firstly, a degradation stage division model for multi-stage degraded equipment is constructed and trained based on continuous deep belief network (CDBN). Next, we employ the sliding time window to generate the samples with the degradation label, and utilize Bayesian bidirectional gated recurrent unit (GRU) to describe the performance evolution model for each stage. For the concerned equipment in service, the current multiple domain features are input into the stage division model to compute the probability of various stages. Furthermore, the degradation feature can be extrapolated by the prediction model of each stage respectively. As a result, we can obtain the predicted degradation distribution according to the law of total probability. Similarly, the degradation level at any time can be calculated by treating the last prediction as the monitoring information in the iterative manner, and the RUL distribution can be determined combining with the failure threshold. Finally, we resort to a case study to verify the effectiveness of our prognostic model.

Temperature history reconstruction in steel box girders using limited data and proper orthogonal decomposition-based dimension reduction representation

The monitoring temperature history of steel box girders inevitably contains gaps or anomalies due to the malfunctions of the structural health monitoring system. Traditional methods for reconstructing temperature histories face the challenge in accounting for the randomness of temperature variations caused by the uncertainty of complex environmental factors. This research proposes a framework for reconstructing the long-term temperature of steel box girders by combining the limited measurements with a proper orthogonal decomposition (POD) based dimension reduction representation approach, to account for the stochastic nature of daily temperature variations. Unlike the conventional Monte Carlo-based POD method, the developed POD-based dimension reduction representation approach effectively reduces the number of elementary random variables from thousands to two by introducing random functions serving as constraints, overcoming the challenge of high-dimensional random variables inherent in the Monte Carlo methods. The proposed approach divides the measured temperature histories into random high-frequency (HF) and deterministic low-frequency (LF) components and establishes the theoretical models of power spectral density and coherence functions of HF temperature components to accommodate the generation of HF temperature component samples, and finally reconstructs temperature samples by superimposing the LF components and generated HF component samples. The results from a practical example demonstrate that the statistical characteristics of representative HF temperature component samples generated by the POD-based dimension reduction representation align well with the corresponding targeted values, and the proposed method outperforms the traditional POD method, yielding a 60% efficiency enhancement without compromising computational accuracy. The developed framework owns apparent superiority in accuracy compared to the traditional POD and the long short-term memory methods, particularly in continuous and extensive missing data. Moreover, the reconstructed temperature samples with assigned probabilities present complete probability information from the level of total probability. These results advance the probabilistic methods in tackling long-term temperature history reconstruction.

Closed-form energy-based signal detection analysis in presence of a Lomax fading channel in full hyper-Rayleigh regime

The presented research examines the energy-based spectrum sensing problem of a signal transmitted over a wireless communication channel with severe fading. To account for the most challenging propagation conditions, a recently developed Lomax wireless channel model was utilized, which exhibits hyper-Rayleigh characteristics over the entire range of possible parameter values. For the channel model under consideration, the existence of the specific hyper-Rayleigh regimes (i.e., weak, strong, and full) are identified asymptotically and studied numerically for a finite signal-to-noise ratio. For the most severe fading conditions, the system’s performance was assessed in terms of the average probability of detection, receiver operating characteristics, and area under the curve. These metrics were derived in closed-form for the cases of Maximum Ratio Combining at the receiver and without diversity reception. The obtained expressions were analyzed via numerical and statistical simulation as functions of system and channel parameters, including the sensing base, average signal-to-noise ratio, and scale parameter for the signal-to-noise probability distribution. The results from numerical simulations were compared with existing regulations specified in the 5G standard for energy-based detection in medium access procedures.