esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate RANS Simulation of Wind Turbine Stall by Turbulence Coefficient Calibration
## 3 Investigation of the RANS Simulation Error
### The Similarity between Rotor Simulation and Airfoil Simulation  
Figure 10 shows the C\({}_{t}\)\({}^{\prime}\) versus AoA at the three sections of r/R = 0.3, r/R = 0.47, and r/R = 0.95. For comparison, it also shows the corresponding curves of the S809 airfoil, which are obtained by replacing the aerodynamic force of each blade section with that of the airfoil. The shadow regions with sloping lines are determined by the positive simulation error (computational data subtracts experimental data). The points S (S\({}^{\prime}\)), E (E\({}^{\prime}\)), and P (P\({}^{\prime}\)) represent the starting, the ending, and the peak of the positive error, respectively. The shadow regions for both the airfoil and the blade sections are \(\Lambda\)-shaped, which is evidence of the similarity of simulation error between them. The shadow region for the root section (r/R = 0.3) is located at the upper right of the shadow for the airfoil, due to the rotational effect, which makes the stall delayed and thus makes the simulation error delayed as well. The two shadow regions at the mid-span section (r/R = 0.47) have large overlapping area, which is further evidence of the similarity of error between the airfoil and rotor simulations. The shadow region  
Figure 8: The simulated limiting streamlines on the suction side of the NREL Phase VI blade at various wind speeds.  
Figure 9: The predicted C\({}_{t}\)\({}^{\prime}\) versus wind speed at three typical blade sections, with a comparison to the measured data of the NREL Phase VI experiment.  
for the tip section (r/R = 0.95) becomes smaller than that for the airfoil, suggesting that the 2-D airfoil simulation may not be more accurate than the 3-D rotor simulation. Figure 11 shows the magnitude of the simulation error. The peak error of the blade sections is found lower than that of the airfoil. One reasonable explanation may be that the rotational effect and the 3-D effect produce a gentler slope in the process from light stall to deep stall.  
The above results suggest that there is a high degree of similarity of error between the airfoil and the rotor simulations, and the rotor simulation error is approximately at the same level of the airfoil simulation error. That leads to a conclusion that the main source of the rotor simulation error already exists in airfoil simulation. Therefore, it is feasible to achieve better rotor simulation by calibrating the RANS method in airfoil simulation.
# A Study on the Amplitude Comparison Monopulse Algorithm
###### Abstract  
This paper presents two amplitude comparison monopulse algorithms and their covariance prediction equation. The proposed algorithms are based on the iterated least-squares estimation method and include the conventional monopulse algorithm as a special case. The proposed covariance equation is simple but predicts RMS errors very accurately. This equation quantitatively states estimation accuracy in terms of major parameters of amplitude comparison monopulse radar, and is also applicable to the conventional monopulse algorithm. The proposed algorithms and covariance prediction equations are validated by the numerical simulations with 100,000 Monte Carlo runs.  
monopulse radar; amplitude comparison; covariance prediction; least-squares estimation; performance analysis +
Footnote †: journal: Applied Sciences
# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering
## 2 A Ranging Error Mitigation Algorithm for CIR Signal Characteristics Based on Fuzzy Inference  
### Theory of the Algorithm  
The ranging errors mitigation algorithm for CIR signal characteristics based on fuzzy inference estimates the ranging errors in accordance with the changes in CIR signal characteristics. The commonly used standard CIR signal characteristics of UWB include total energy, maximum amplitude, normalized strongest path, signal-to-noise ratio, RT, average excess delay, RMS delay and kurtosis [25]. However, the level of standard implementation varies with UWB hardware manufacturers, so the actual UWB hardware should be considered together during the selection of CIR signal characteristics. In this paper, three CIR signal characteristics (RSSI, FPPL and RT) provided by the DWM1000 hardware were adopted in the ranging errors mitigation algorithm for CIR signal characteristics based on fuzzy inference.  
RSSI is a signal characteristic possessed by almost all radio equipment, including DWM1000. The energy of the received signal reflects the average level of received power, which is closely correlated with the total energy characteristics in standard CIR signals. Therefore, RSSI is usually replaced by the received energy. The calculation formula of RSSI officially provided by DWM1000 is as follows:  
\[RSSI=10\times\log_{10}(\frac{C\times 2^{17}}{N^{2}})-A \tag{1}\]  
where \(C\) is the power value of CIR, \(A\) represents the constant of the pulse repetition frequency, and \(N\) stands for the cumulative count value of the synchronization code.  
The FPPL, officially given by DWM1000, was utilized to calculate the power estimation of the earliest first path signal arriving at the receiver. Since the discrete value of the CIR signals was obtained by sampling the wireless point signals at the time of reception, the FPPL was estimated by means of three sampling amplitudes of CIR signals that were first collected by DWM1000, which were calculated using the formula below:  
\[FPPL=10\times\log_{10}(\frac{F_{1}^{2}+F_{2}^{2}+F_{3}^{2}}{N^{2}})-A \tag{2}\]where \(F_{1}\), \(F_{2}\) and \(F_{3}\) mean the first three sampling amplitudes of CIR signals, respectively.  
_RT_ referred to the difference between the time of the first sampling point on the first path and the time of the sampling point with the maximum amplitude, which was calculated according to the following formula:  
\[RT=t_{F_{\text{max}}}-t_{F_{1}} \tag{3}\]  
where \(F_{\text{max}}\) is the maximum amplitude among all the sampling points of the CIR signals, and \(t_{F_{1}}\) represents the sampling time of the first sampling amplitude \(F_{1}\) on the first path, which can be read through the officially designated register.  
The RSSI, FPPL and RT characteristics of the CIR signals reflected the severity of NLOS conditions at different levels. Specifically, the RSSI indicates the severity of NLOS in general and can be effectively classified, while FPPL and RT embody small changes in NLOS. Hence, the CIR signal characteristics can be used to mitigate the ranging errors.
## Results  
### Patient Characteristics  
The average age across the STEMI group was 60.1 \(\pm\) 12.9 years and 61.6 \(\pm\) 21.3 years in the control group (\(p\) = 0.7002). The majority of the STEMI patients were male (76.3%), compared to 46% in the control cohort, however, the difference was not statistically significant (\(p\) = 0.818). Serum creatinine, urea (BUN), or CRP did not differ between both groups (Table 1).  
The STEMI patients mostly featured a pronounced cardiovascular risk profile. Of them, 60.5% suffered from arterial hypertension, 50% from hypercholesterolemia, and 26.3% from diabetes mellitus. Furthermore, 39.5% were current smokers and 15.8% had quit smoking in recent years. The culprit lesion in most of our STEMI patients was either RCA (44.7%) or LAD (47.4%). The median left ventricular ejection fraction (LVEF) after myocardial infarction was 45.0% (Table 2).  
\begin{table}
\begin{tabular}{l c c c} \hline  & \begin{tabular}{c} **STEMI** \\ **(n = 38)** \\ \end{tabular} & \
\begin{tabular}{c} **Control** \\ **(n = 50)** \\ \end{tabular} &
\begin{tabular}{c} **\(p\)-Value** \\ \end{tabular} \\ \hline \multicolumn{4}{l}{**Overall Characteristics**} \\ \hline
**Age [Years]** & 60.1 \(\pm\) 12.9 \({}^{\text{b}}\) & 61.6 \(\pm\) 21.3 \({}^{\text{b}}\) & 0.7002 \({}^{\text{d}}\) \\
**Sex [Male]** & 29 (76.3%) \({}^{\text{c}}\) & 23 (46\%) \({}^{\text{c}}\) & 0.818 \({}^{\text{f}}\) \\ \hline \multicolumn{4}{l}{**Laboratory Findings**} \\ \hline
**Serum Creatinine [mg/dL,** \\ reference range 0.51–0.95 mg/dL** \\
**Urea (BUN) [mg/dL, reference** \\ range 16.6–48.5 mg/dL]** \\
**C-reactive protein (CRP) [mg/L,** \\ reference range < 5 mg/L]** \\ \hline \end{tabular} &
\begin{tabular}{c} 1.0 \(\pm\) 0.2 \({}^{\text{a}}\) \\ 0.9 \(\pm\) 0.5 \({}^{\text{a}}\) \\ 0.3431 \({}^{\text{e}}\) \\
**32.0 \(\pm\) 13.0 \({}^{\text{a}}\)** \\
**32.5 \(\pm\) 36.8 \({}^{\text{a}}\)** \\
**C-reactive protein (CRP) [mg/L,** \\ reference range < 5 mg/L]** \\ \hline \end{tabular} &
\begin{tabular}{c} 32.5 \(\pm\) 36.8 \({}^{\text{a}}\) \\ 0.9074 \({}^{\text{e}}\) \\
**9.0 \(\pm\) 42.0 \({}^{\text{a}}\)** \\ \end{tabular} &
\begin{tabular}{c} 0.90 \(\pm\) 0.7 \({}^{\text{a}}\) \\ 0.9074 \({}^{\text{e}}\) \\
**24.1 \(\pm\) 72.4 \({}^{\text{a}}\)** \\ \end{tabular} &
\begin{tabular}{c} 0.9074 \({}^{\text{e}}\) \\ 0.9981 \({}^{\text{e}}\) \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Patient characteristics. \({}^{\text{a}}\) Presented as median \(\pm\) interquartile range, \({}^{\text{b}}\) presented as mean \(\pm\) standard deviation, \({}^{\text{c}}\) number of patients (with percentage based on the number of patients with a non-missing value for that characteristic), \({}^{\text{d}}\) based on Student’s \(t\)-test for variables following a Gaussian distribution, \({}^{\text{e}}\) based on Mann–Whitney U test for non-parametric variables, \({}^{\text{f}}\) based on chi-squared test/[PERSON]’s exact test as appropriate for categorical variables.
# SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs
## 4 Architecture and Ontology Models
### High-Level Architecture  
As mentioned before, our customers require different deployment options, and so we divided them into three categories and developed three architectures. In the first category, customers prefer to not connect their machines to the cloud and some even do not want to connect to the Internet due to the sensitive nature of their business, and to protect their data. The architecture proposed for this category is shown in Figure 1a. The second category of customers opted for an entirely connected architecture, in which the machines/gateways in their factories are directly connected to the cloud. For this category, we proposed the architecture shown in Figure 1b. The third architecture targets the customers who refuse to connect their machines to the cloud, but are ready to deploy a local on-premise server between the cloud and their machines. For this use case, we proposed a hybrid architecture Figure 1c, where most of the collected data stays in the local server, and only an anonymized part of the data are transmitted to the cloud.  
In all these cases, each machine is connected to an industrial IoT gateway, e.g., Modicon M262 ([[https://www.se.com/www/en/product-range/65771-modicon-m262/](https://www.se.com/www/en/product-range/65771-modicon-m262/)]([https://www.se.com/www/en/product-range/65771-modicon-m262/](https://www.se.com/www/en/product-range/65771-modicon-m262/))) to collect the run-time data of the machine and the information provided by maintenance personnel and/or operators. Each gateway is connected to a central entity (either a Local SemKoRe or a SemKoRe Server) which collects the data  
Figure 1: SemKoRe architectures; IoT gateway connected to a real machine: Local server.  
from all the gateways and then aggregates and shares with the gateways connected to the same type of machine.  
Though, the Achilles heel of our proposal remains the case of the first category of customers, i.e., who do not want to connect their factories to the cloud. The unique technical solution to share the maintenance knowledge consists of using physical supports (e.g., USB keys, CDs,...) with human intervention. However, the internal business case analysis is still in progress. We believe that once the business case is finalized, the technical adaptation of the SemKoRe services will be trivial and will not require lot of efforts.  
In this paper, we focus on the cloud-based architecture because it covers all the constraints and features of the other architectures. This architecture is also implemented in a proof-of-concept to demonstrate the feasibility (see Section 6).
## 2 Materials and Methods
### Cellular Planning  
Cellular planning is important in order to increase network capacity and avoid interference between the increasing number of active devices in an area. A good plan not only reduces network costs, but also increases the quality of service. Ideally, the coverage area should be uniform, without overlaps or empty spaces. One can theoretically solve this problem using regular polygons (triangles, squares, and hexagons). The hexagon is most-often used because it is the polygon closer to the real coverage of an omnidirectional antenna. Problems begin to appear when one understands that the real coverage might not be close to a hexagon at all, as illustrated in Figure 1. Aspects like terrain topography and urban density change the way power decays over time. Another problem is that traffic distribution is not uniform. Different cell sizes must be used--smaller ones for areas with more traffic and vice-versa. In consequence, the network becomes less and less uniform.  
Knowing this, there are three types of interference [6] which we need to account for:  
* **Co-channel interference**: The interference created from other cells with the same frequency. This is solved by spacing cells with the same frequency and splitting the cell in sectors (sectorization).
* **Adjacent Interference**: The interference coming from adjacent channels in adjacent cells. This is easily solved with quality filters and making sure there are sufficient differences between the frequencies used in those cells.
* **Self interference**: A consequence from a cell's signal reflecting from obstacles (buildings, mountains, etc.). Solved by correctly splitting downlink and uplink frequencies.  
There are multiple ways to allocate frequencies. The most used are either fixed frequency allocation, where frequency allocation respects a set of constraints as well as possible, and minimum span allocation, where allocation is done using the minimum amount of frequencies still respecting those constraints. But there are other \"out of the box\" solutions. In 2010, [PERSON] et al. [7] proposed a new method of frequency planning using a cell's latitude, longitude, and azimuth, and comparing them with that cell's neighbors, thereby predicting which cells will interfere with each other. However, this process is still manual and requires human intervention.
## 2 Materials and Methods
### Bias Risk Assessment  
Each study's potential bias was evaluated following the PRISMA guidelines [25; 26; 27]. Case series were evaluated according to the criteria stablished for assessing the risk of bias in non-randomized studies of interventions [29], based on seven domains: (1) bias due to confounding; (2) bias in the selection of participants into the study; (3) bias in the classification of interventions; (4) bias due to deviations from the intended interventions; (5) bias due to missing data; (6) bias in the measurement of outcomes; and (7) bias in the selection of the reported result. The documents that met the inclusion criteria were judged for each of the domains as having a low, moderate, serious, or critical risk of bias [29]. On the other hand, either randomized clinical or experimental studies were evaluated according to the criteria stablished for assessing risk of bias in randomized trials [30], according to five domains: (1) bias arising from the randomization process; (2) bias due to deviations from the intended interventions; (3) bias due to missing outcome data; (4) bias in the measurement of the outcome; and (5) bias in the selection of the reported result. The studies that met the inclusion criteria were judged for each of the domains as having a low, some concerns, or high risk of bias [30].  
Disagreements between reviewers during the bias assessment process were resolved through consensus discussions to ensure consistency and reliability in the classification of bias levels. Examples of potential bias in the included studies were documented, such as inadequate randomization procedures, lack of blinding of outcome assessments, and incomplete reporting of clinical outcomes. In addition, the limitations of this review were acknowledged, including the heterogeneity among the included studies in terms of PRP preparation methods and outcome measures. These factors were taken into account when interpreting the overall results of this review. The bias assessment results were visualized using the ROVIS tool and the ROBINS-I and RoB2.0 datasets ([[https://mcguinlu.shinyapps.io/robvis/](https://mcguinlu.shinyapps.io/robvis/)]([https://mcguinlu.shinyapps.io/robvis/](https://mcguinlu.shinyapps.io/robvis/)), accessed on 14 February 2025) [31].
## 2 Methodology
### Fuzzy Control System
#### 2.2.2 Inference  
The inference process is applied when indicators need to be aggregated, based on the hierarchical diagram previously described. This process relies on if-then rules to evaluate and combine the fuzzy sets of two or more indicators. In this case, aggregation is carried out between two indicators, as the hierarchical structure was designed to simplify calculations, although the method can be extended to more indicators if necessary.  
The if-then rules used in the inference process are derived from semantic aggregation matrix based on linguistic combinations of input variables. They reflect expert knowledge since both the linguistic input values and the aggregation logic were developed in consultation with domain experts. This ensures that the inference process aligns with practical and contextual understanding.  
The inference is based on a fuzzy rule matrix, such as the one shown in Table 2, where the fuzzy linguistic variables (e.g., Very low, Low, Average, High, Very high) are combined according to pre-defined rules. Each combination of input values from the two indicators results in an aggregated fuzzy value, which corresponds to the output linguistic value.  
In this matrix, the rows and columns represent the linguistic values of the two indicators being aggregated, and the intersection of each row and column gives the resulting output linguistic value.  
In certain cases, it is appropriate to introduce a middle or neutral judgment (labeled as \"Average\") during the aggregation process. This judgment is particularly useful in situations where the performance of the company with respect to two indicators is conflicting or shows intermediate levels of performance. The \"Average\" judgment helps better represent situations in which neither indicator significantly dominates the other, allowing for a more balanced assessment. This neutral value will be especially important for subsequent aggregations, where the results from multiple indicators are combined and evaluated.  
The final output of the inference is the \"truth value\" of each indicator (Equation (3)).  
\[\text{Truth value}=\prod_{j=1}^{i}\mu_{j}(\chi_{j}) \tag{3}\]  
In the formula above:  
i: total number of KPIs considered  
\(\mu_{j}(\chi_{j})\): membership degree of the j-th KPI
# Discrete Time Model for Process Meta Language with Fictitious-Clock
## 1 Introduction
Automated extraction is based on the formal description and is methodologically sound [14]. For that, we have prepared a Promela framework for the abstract SDL machine. It supports predefined and user-defined sorts, a communication subsystem, multiple process instances, many of the SDL constructs, including SAVE construct, priority input, and others. Additionally, it includes unique variables--probes. During the simulation and formal verification, probes monitor if rules of the abstract SDL machine are broken. To ease editing, examination, syntax and redundancy check, simulation, verification, and to transform a Spin simulation trail into standard Message Sequence Chart (MSC), our research group is developing the Eclipse Rich Client Platform Integrated Development Environment for the Spin Model Checker (SpinRCP) [15; 16; 17]. In Promela, we do not know the exact time interval that elapses between two events. Therefore, we can not model systems where correct execution depends on the timing parameters. This motivated us to check available solutions and try to apply them to our framework for modeling SDL Systems in Promela, e.g., DT Promela and DT Spin [11; 18]. Based on previous experiences with Spin model checker extensions that were not able to keep up with the mainstream version of Spin, we have formed the following research question: Can a high-level Discrete Time Model be used with standard Promela to model a real-life SDL system with timers? Due to various shortcomings of existing solutions, we have decided to propose a new Discrete Time Model for Promela (DTMP) that could be added seamlessly to our framework. We have demonstrated use of the DTMP with the 6.4.6 version of the Spin tool. The research results that are presented in this paper are implemented in the _sdl2 pml_ tool and were verified with an industry-size SDL description of the complex system [13]. An additional overview of our framework is available in [14; 19]. This paper is organized as follows. Section 2 presents an overview of related work which includes various extensions of the Spin tool to support modeling of discrete and dense time. Section 3 gives a detailed description of the proposed DTMP that can be used with the mainstream version of the Spin tool. Experimental results are presented and discussed in Section 4. We use models of Fischer's Mutual Exclusion Protocol and Parallel Acknowledgment with Retransmission protocol to demonstrate the use of DTMP and compare the verification results with the DT Promela models from [20]. We conclude with discussion and directions for further research work.