esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Examples
### Discussion  
Analysis of the recommended action under each observation model can aid the development of a framework that defines the belief state combination for taking each action. After screening out the belief states for each action, the level and combination of these states can be studied to determine the deterioration state level. With such a kind of demarcation, the decision alternative on the type of maintenance action can better be estimated under different uncertainty levels of deterioration.  
The transitional probability was computed for time (\(\Delta t\)) and inspection interval time (T) values equal to 0.0005 years and 5 years, respectively. However, these values are not  
Figure 3: Recommended action for the MDP model of a two-pipeline system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Single Pipeline** & **Two Pipelines System** \\ \hline \multirow{4}{*}{**CPU**} & **Do-nothing** & 64.2\% & 87.0\% \\  & **Inhibit** & 15.0\% & 1.8\% \\  & **Pigging** & 18.3\% & 10.7\% \\  & **Replace** & 25\% & 0.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Recommended action percentage for single and two-pipeline systems.  
necessarily fixed and should be adjusted to meet the needs of specific pipelines. The transitional probability change significantly impacts each belief state's recommended actions. It determines which actions are allowed to be taken in a particular state. The probability matrix for specific maintenance actions can be computed using historical data, expert-opinion analysis methods, or a combination of both. For the POMDP model considered in this paper, only inline instrumental errors have been considered as the sources of the observational disparity. Nevertheless, other sources of errors can be integrated, including the maintenance technician's skill level, pipeline locations, surroundings, and the type of product transported. The variation in the formulation of the observation for one- and two-pipe systems has a limited impact on the overall recommended action except for the run time. When more ILIs are considered, this variation influences the actions. The cost of maintenance can be computed from the actual operating expenses and service charges. A more detailed assessment of these costs would have to be performed to estimate the true value. It is possible to exclude the cost of failure if the deterioration states considered in the model are located well above the risk limits. This means that pipelines will be replaced before reaching risky deterioration levels. Inspection costs can be added to the reward if they can be related to the action. Otherwise, it will not have any impact on the reward since it is a constant value.
## 5 Results and Discussion
### Computational Time Complexity  
The issue of processing time in the IoV environment is imperative because delay is critical. Therefore, the processing time of the proposed hybrid NSGA3 and compared algorithms were recorded and are presented in Table 3. It is clearly indicated that the proposed hybrid NSGA3 converges slower compared to the compared algorithms within the family of the NSGA. This is expected in view of the fact that the filter approach is always faster than the other feature selection methods, as the filter method mainly focuses on the data characteristics and it is independent of the learning algorithm, as reported by [PERSON] et al. [3]). The time difference between the compared algorithms and the proposed hybrid NSGA3 was very close; the difference is minimal. Time matters significantly in the IoV as a small delay may cause serious vehicle collision within the IoV environment. However, the accuracy achieved by the proposed hybrid NSGA3 shows that it can detect and alert the driver ahead of time to avoid collision.
# Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study
## 1 Introduction
Other capabilities to be considered from the perspective of reliability are self-adaptation and self-organization by embedding artificial neural networks (ANNs) in CPSs [22]. Efficient performance of multiple sensors and their online monitoring and self-correction procedures, through the application of machine learning (ML), such as Support Vector Machines (SVM) and ANNs, are essential for reducing maintenance costs, risk associated with uncalibrated and faulty sensors, increasing sensor reliability and, consequently, extended equipment life [23; 24]. With the aim of guaranteeing certain safety and security conditions in some critical applications, the verification of sensory data and subsequent data evaluation are described in this paper on the basis of simulation of virtual and real scenarios, as well as a framework that properly combines both scenarios. A reliability assessment procedure is therefore described in this paper that is applicable to data captured by IoT LiDAR sensors in automotive applications: LiDAR self-testing methodology. The reliability analysis is based on the paradigm of cyber-physical systems (CPS) by distributing nodes locally and globally, as will be explained later on. Each computing node has data-processing methods and machine-learning models for reliability prediction. In addition, a run-time self-learning and decision-making model runs within a global node, in order to determine the best model and the model updating mechanism on request. This paper will be organized into five sections. Following this introduction, the second section will present a review of the state-of-the-art of the CPS-based reliability concept for sensor system reliability using AI methods. Subsequently, the specifications and the requirements obtained from the review of CPS reliability frameworks will be summarized in Section 3. A particular implementation of a CPS-based co-simulation framework will also be proposed in this section. In addition, a case study for the evaluation of an IoT sensor network using a CPS-based co-simulation framework approach will be described in Section 4. In that section, the experimental results and a discussion relating to a comparative study will also be addressed. Finally, the conclusions and future research steps will be presented in Section 5.
# Machine Learning for Early Outcome Prediction in Septic Patients in the Emergency Department
## 4 Discussion
Patients coming to ED may be subjected to a shorter ongoing inflammatory process than other hospitalized patients, which often present systemic inflammation related to other conditions such as recent surgery or cancer [43]. The prompt resolution of the infective noxa in the ED could allow a more rapid resolution of the inflammatory process and reduce gender imbalances [44]. Serum lactate levels were only marginally elevated in this population, a finding that may be related to the low proportion of septic shock (12%). Lactate is one of the most significant markers in septic patients, particularly in septic shock [45]. An increase in lactate is classically related to a shift toward anaerobic tissue metabolism in various types of shock, but can also reflect other metabolic and hepatic clearance conditions [45]. Most patients in this cohort had at least one comorbidity. Cardiovascular disease, COPD, and active cancer are all markers of increased mortality in septic patients presenting to the ED [37]. Nonetheless, comorbidities alone are not a good proxy for patient frailty. Accordingly, only unbalanced logistic regression retained hypertension as an independent contributor for mortality, while balanced logistic and random forest included laboratory data and vital parameters as independent predictors of mortality [30; 33; 37]. The most represented sites of infection were the urinary tract (UTI, 31%) and the abdomen (33%). These data are relevant to give a practical indication for the ED approach: since UTI in the elderly can be asymptomatic, UTI as a sepsis site must always be suspected and excluded. Similarly, we found no association between the type of micro-organism and mortality [37]. _E. Coli_, a common pathogen in the elderly population, was the most commonly identified bacteria in both urinary and blood samples.
## 2 UAV Network Communication Model and Architecture
### Decentralized Communication Architecture
#### 2.2.1 Single Swarm Ad Hoc Network
Usually, similar UAVs can often fly close to each other. Different types of UAVs are far apart. Therefore, the UAVs in the swarm are divided into different groups. Similar UAVs are within the same distance. These are called \"multi-group swarm ad hoc network\" and \"multi-layer swarm ad hoc network\" architectures.
## 2 Materials and Methods
### Experimental Program  
The experimental program was based on the physical and mechanical characterization of two different SMAs produced with and without CF as stabilizing fibers. Once the mix design and the absorption properties of the shredded CFs were defined, tests were carried out on SGC (Superpave Gyratory Compactor, EN 12697-31 [40]) specimens. The mixing and compaction operations and temperatures (170 \({}^{\circ}\)C) were fixed univocally in order to obtain comparable results.  
The volumetric properties of the two different SMAs were assessed with the air voids (AV) content analysis (EN 12697-8 [41]) on three samples for each mixture according to three different compaction levels (10, 100 and 180 gyrations). The evaluation of the workability and compactability properties of the mixtures was corroborated also by the analysis of the gyratory compaction curves.  
For each mixture, nine SGS samples (100 gyrations [40]) were prepared for the mechanical characterization. The cohesion properties were assessed in terms of indirect tensile strength (ITS) at 25 \({}^{\circ}\)C, according to the EN 12697-23 standard [42]. The mechanical analysis was supported with indirect tensile stiffness modulus (ITSM) tests following the EN 12697-26 standard [43]. The thermal sensitivity of the mixtures was evaluated, repeating the ITSM test at three reference temperatures: 10, 20 and 30 \({}^{\circ}\)C. Furthermore, the water susceptibility of the SMAs was investigated in terms of ITS reduction (ITSR, EN 12697-12 [44]), carrying out ITS tests on specimens after saturation in a water bath at 40 \({}^{\circ}\)C for 72 h.  
The durability of the experimental bituminous mixture was also evaluated in terms of resistance to permanent deformation, in compliance with the EN 12697-25 standard [45], according to a uniaxial test configuration. The repeated load axial test (RLAT) is a laboratory-based, quick method for determining the creep characteristics of bituminous mixtures under cyclic loads at a high temperature (40 \({}^{\circ}\)C).
## 6 Simulation Experiment  
To verify the feasibility of the proposed novel method, simulation experiments were conducted in Additive White Gaussian Noise AWGN and multipath channels of a sea medium. The performance of a covert information word \"COVERTCOMMUNICATION\" is analyzed in this experiment. Information is modulated mimicking humpback whale song using bionic Morse code.  
The performance of the multipath channel is verified by the simulation experiment using a Bellhop ray tracing algorithm in MATLAB. Real data of a sound speed profile is extracted from the World Ocean Atlas [44] which is shown in Figure 14a. The channel impulse response based on the sound velocities is graphed in Figure 14b. The ideal surface and bottom are selected in this simulation which has a total reflection. Long distance covert communication has been performed at a five km distance. The transmitter and receiver depth are selected at 20 m and 25 m respectively. The depth of the sea is 60 m. The beam angle is acoustic rays is 30 degrees.  
To overcome the effects of multipath arrivals and reduce BER, the MP estimation algorithm and VTRM technique are used for channel estimation and equalization, respectively. The estimated channel is shown in Figure 15. From the figure, it can be seen that there is one direct path with a high energy coefficient and others are indirect paths with a lower amplitude and a small time delay. These are multipath arrivals which have been created due to the echoes from the surface and bottom of the sea. The high energy components were accumulated, while the low energy delayed arrivals were ignored for the sake of simplicity. The errors were equalized using the VTRM equalization technique discussed in Section 4.2.  
Figure 14: (**a**) Sound velocity profile of the channel. (**b**) Channel impulse response based on sound speed profile.  
Figure 16 illustrates the BER curve of the proposed bionic method for covert information. First the performance of the AWGN channel is observed. The red dotted line BER curve in Figure 16 depicts the performance of the proposed method in the AWGN channel. It can be evidently seen that it is not much affected by AWGN and gives zero BER with SNR greater than 15 dB. The blue line is the BER with simulated channel whose channel impulse response is shown in Figure 14b. It clearly depicts that \(10^{-3}\) BER is achieved at SNR \(-7\) dB. The green curve shows the BER achieved after estimating the channel by MP algorithm. It further decrease the errors drastically from \(-14\) dB. Zero BER is achieved at SNR greater than \(-11\) dB. The resultsprovesthat the proposed method is perfect for underwater communication due to extremely low BER. Further it gives perfect covert LPR constraint covert communication. The proposed method is feasible for perfect covert operation at extremely low BER.  
Figure 16: BER curve of mimicked signals at a five km distance.  
Figure 15: MP Estimated Channel Impulse Response.
# Large-Eddy Simulation of Droplet Deformation and Fragmentation Under Shock Wave Impact
## 4 Results and Discussion
### Shock-Droplet Interaction  
When the shock front encounters the droplet surface, a range of complex flow structures is produced, including reflected, transmitted, and diffracted waves, profoundly altering the local flow conditions around and inside the droplet. While the complex internal and external flow fields are challenging to capture experimentally, recent advances in numerical methods and availability of suitable computational power allow for their observation in numerical simulations. Indeed, as demonstrated by the following results, the proposed LES model is able to accurately reproduce the shock-droplet interaction mechanism and subsequent breakup.  
The compressible flow dynamics associated with the computed shock-droplet interaction is illustrated in Figure 2 for the configuration at \(M_{s}=1.47\). The type of reflected wave (either shock or expansion) actually depends on the relative acoustic impedance of the media on both sides of the boundary [42]. In this case, since \(Z_{\text{water}}>Z_{\text{air}}\), both reflected and transmitted waves result in being shocks (panel (a)). The transmitted wave creates a high-pressure region behind it, driving fluid motion towards the droplet's downstream side [19]. Given the different impedance values, pressure waves travel faster in water than in air, causing the transmitted wave to detach from the incident wave, and first reach the leeward side of the droplet [16; 20]. The diffraction of the Mach stem occurs after it traverses the droplet's equatorial plane, contributing to the delay of the Mach wave near its contact point with the droplet's surface. This Mach wave forms axisymmetrically around the droplet and converges near the rear stagnation point, intensifying the shock and creating a localized high-pressure region (panel (b)). The internal flow remains directed towards the leeward side of the droplet until the shock converges at the rear stagnation point. The high-pressure forces induce localized deformation of the droplet surface, leading to an upstream flow within the droplet. The two opposing flows interact at a saddle point inside the droplet, redirecting the flows towards the droplet's equator. Additionally, as the external airflow evolves around the droplet's periphery, a toroidal (primary) vortex forms near the flow separation point, similar to the flow around a solid sphere (panel (c)). Over time, this vortex structure migrates downstream, creating a recirculation zone and generating a jet flow at the rear stagnation region (panel (d)). Hence, the interaction between the incident shock wave and the droplet results in a combined effect where the recirculated jet flow, along with the expansion of the high-pressure region, causes the leeward side of the droplet to flatten, while taking a cupcake-like shape. Overall, the observed wave dynamics during the interaction process align with previous experimental studies [16; 43], as well as various numerical simulations of shock-induced vaporization of droplets [19; 20].
# Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
## 1 Introduction
However, the frequency resolution and time resolution of wavelet analysis are mutually contradictory, and it is sensitive to noise and interference, making it vulnerable to the influence of signal noise and nonlinear interference [23]. Compared with traditional wavelet analysis, multiwavelet analysis has better adaptability and resolution, which can effectively capture the nonlinear and non-stationary characteristics of rolling bearings, and thus more accurately identify and locate bearing faults, improving the efficiency and accuracy of bearing fault diagnosis. [PERSON] et al. [24] developed a method for compound bearing fault diagnosis by utilizing customized balanced multiwavelets to extract fault information from signals and incorporating adaptive maximum correlation kurtosis deconvolution. [PERSON] et al. [25] established an intelligent indicator-driven approach to construct suitable multiwavelet basis functions for accurate inner-product matching, resulting in a multiwavelet feature extraction method for mechanical fault diagnosis. Multiwavelets, as a promising basis function, have important signal processing properties such as orthogonality, symmetry, compact support, and high-order vanishing moments [27]. However, the existence of orthogonality and the scarcity of wavelet basis functions of specific expressions will lead to the omission of some useful information when constructing multi-wavelet decomposition signals. Legendre multiwavelets offer numerous advantages, including rich regularities, compact support, orthogonality, and vanishing moments [28; 29]. These properties not only enable the identification of essential features across various fault categories in rolling bearings but also significantly reduce the complexity involved in extracting optimal features [30]. Based on this idea, we propose a new fault detection method for the bearing, LMWT, which can effectively extract the characteristic information of the fault signal and achieve rapid and accurate diagnosis of rolling bearing faults. The method for fault detection involves specific steps: initially, the vibration signal undergoes decomposition into various signal components using Legendre multiwavelets. Subsequently, relative energy ratios are computed for these components, and the most responsive component within the fault frequency band is identified. Experimental findings indicate that, when compared to the EWT method, the proposed approach demonstrates superior diagnostic accuracy in addressing rolling bearing fault diagnosis. Notably, its simpler model structure and reduced training parameters in contrast to deep learning models render it significantly valuable in the realm of fault detection. The upcoming sections of this paper follow this outline. Section 2 provides an in-depth exploration of the implementation of Legendre multiwavelets decomposition. Moving forward, Section 3 introduces three distinct experimental settings, accompanied by their respective experimental results and analyses. Finally, Section 4 encapsulates the conclusions derived from this study.
# A Traceable and Verifiable Tobacco Products Logistics System with GPS and RFID Technologies
## 4 Security Analysis
### Resist Known Attacks  
#### 4.5.1. Replay Attack  
To resist replay attack, every encrypted message is added to a sending timestamp and check to see whether the timespan is valid. The timestamp is validated in every phase to resist replay attack, and the validations are shown in Equations (9), (22), (33), (41), (49), (57), (65), (73), (81), (89), (97), and (105). For example, in the issuing ID and manufacture phase, MF adds a timestamp \(T_{5}\) in the message \(M_{3}\), then encrypts the \(M_{3}\) with a cipher message \(C_{MF_{1}}\). The IDI decrypts the message after receiving the cipher message, then checks for the validation of the timestamp. The related equations are shown as follows:  
\[M_{3}=(ID_{MF}||ID_{IDI}||Num||Info||T_{5}) \tag{108}\]  
\[C_{MF_{1}}=E_{Puk_{IDI}}(M_{3}) \tag{109}\]  
\[M_{3}=D_{Prk_{IDI}}(C_{MF_{1}}) \tag{110}\]  
\[(T_{6}-T_{5})\stackrel{{?}}{{\leq}}\tau \tag{111}\]  
Scenario: The attacker listens to a message that is sent from the sender to the receiver. After that, the attacker re-sends the same message to the receiver to achieve a replay attack.  
Analysis: The receiver decrypts and obtains the timestamp in the message, then subtracts the timestamp with the current time; if the timespan is larger than the threshold, it means that the message is a replay attack, so the attack is foiled.
## 3 Results and Discussion
### Application of the Life Prediction Method  
Transient load fluctuations need to be included in the data collection since engine starting, abrupt load loading, and rapid load lowering all affect blade life. The selected data set was made up of 1,023,782 data points from a unit's operation between 9 July 2019 and 3 March 2021, which comprised five starts and shutdowns. The LSTM model, capable of incorporating the characteristics of time series, was employed for EOH calculation based on the performance comparison of the three prediction models in Section 3.2. The EOH results obtained by the LSTM prediction model are shown in Figure 12. Consistent with the EOH trend of real gas turbine blades, the EOH remained stable during the gas turbine outage, and the growth slope steadily decreased throughout the operation. During the first start-up to shutdown process (9 July to 9 October 2019), the EOH increase was relatively small due to the low load operation condition. In the early stage of the third start-up (early February 2020), the EOH was in the condition of high load and large load change, and the EOH rose into jitter mode.  
Figure 11: The EOH prediction effect of three algorithms.