esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principles
In NASA's research [25], a spectrum analyzer was used to measure the spurious radiation power of various 3G mobile phones in the 1565-1585 MHz range, and the results were compared with the noise floor. The results showed that the spurious radiation power did not exceed \(-50\) dBm. However, mobile phones that connect to the TianTong satellite operate in a frequency band closer to the GPS L1 band and are new products introduced in the past three years. No measurements of their spurious radiation power have been reported. Therefore, NASA's measurement data can only be used as a reference for this study. This study does not measure the spurious radiation from this type of mobile phone, as the exact value is not the main focus of this research. Instead, the relevant standards [26] are referenced, which specify a spurious radiation limit of \(-40\) dBm in the GPS antenna's operating frequency band. Therefore, the emission limit specified in the standard is likely based on a worst-case scenario. Using the spurious radiation power and the MEF, the \(IPL_{target}\) can be determined. Additionally, the transmitting antenna was configured to emit a 5G NR TM1.1 standard waveform during the experiment. The 5G NR TM1.1 standard waveform is defined by the Third Generation Partnership Project (3 GPP), ensuring that this study aligns with current standards [27]. To determine whether this device may cause electromagnetic interference with the GPS antenna, the IPL value should be calculated and compared with \(IPL_{target}\).
# Hunting Molecules in Complex Matrices with SPME Arrows: A Review
## 5 SPME Arrow Applications
### Food Analysis  
There are not as many publications on SPME Arrow in food analysis, as compared to traditional SPME fibers [8]. However, in the past couple of years, several applications have spanned the analysis of diverse matrices including fish and rice [52; 53]. For instance, [PERSON] and coworkers compared carboxen/polydimethylsiloxane (CAR/PDMS) SPME sorbents in the fiber format to the arrow format for the HS extraction of volatiles present in salt-fermented sand lance fish sauce [53]. The researchers reported that alcohols, aldehydes and pyrazines, with the exception of 1-pentenol, were more effectively extracted using the SPME Arrow. Some compounds that are believed to be important to the flavor profile were only observed using the arrow device. [PERSON] and coworkers described a modified zeolitic imidizolate framework (ZIF-8) as a solid phase microextraction support on the arrow construct [54]. The group compared the novel adsorbent to a commercially available carboxen/polydimethylsiloxane device for the sampling of volatile amines in wastewater and food samples (salmon and mushrooms). The researchers found that the commercial ZIF-8 material exhibited a small pore size (5.6 A), which likely excluded the model amine compounds, resulting in a low extraction efficiency. The acidification of the material significantly increased the extraction of small volatile amines, presumably due to an increase in pore size. The modified ZIF-8 arrow design provided comparable extraction efficiencies for small, volatile amines from several matrices as compared to commercial carboxen/polydimethylsiloxane SPME Arrow devices. [PERSON] and coworkers investigated metal organic framework (MOF) sorbents applied to arrow SPME devices for the determination of PAH contaminants in fish samples [55]. The authors employed a zirconia based UiO-66-molybdenum disulfide composite and compared the recovery of PAH contaminants from seafood samples to both commercially-available arrow and fibers coated with PDMS/carboxen/DVB coatings. The combination of the MOF composite sorbent and the arrow format resulted in and increased number of PAH species detected. [PERSON] et. al. described the analysis of low molecular weight aliphatic amines from various matrices using several different modifications of silica sorbent applied to the arrow format [56]. The scientists demonstrated that dimethylamine and trimethylamine could be effectively detected and quantitated in mushroom samples using these devices.
# Augmented Reality and the Digital Twin: State-of-the-Art and Perspectives for Cybersecurity
## 3 Literature Review and State-of-the-Art
### Quantitative Results  
This four-stepped selection process to determine relevant academic literature within the area of interest was carried out in June 2021. The search initially resulted in 355 unique publications related to the specified search term. After applying the quality criteria, 32 papers are labeled relevant to the literature analysis. Based on these papers, we perform a forward and a backward search. This additional search unveils one relevant publication. Therefore, a total of 33 relevant publications is identified (cf. Figure 3). The small number of papers identified through the last step of our selection process (i.e., Forward & Backward Search) confirms the comprehensibility of the previous process steps.  
The first publication we determine relevant has been published in 2016. Along the following years, the number of published papers per year is increasing, as displayed in Figure 4. This trend seems to change with a smaller number of papers in 2020 and 2021. However, the number of relevant publications being published in 2021 is not yet representative as we only include the first half of the year into our search. Thus, the final number of publications from 2021 is very likely to be higher than shown in Figure 4. Additionally, the small number of years in which we were able to identify papers would not allow for a resilient interpretation of the seemingly decreasing amount of publications. It could just as well be that an over proportionally high number of papers was published in 2019 and that the approximately linear growth of previous years is continuing again in 2020 and 2021. However, due to the short time span for which data are available, such interpretations must be regarded as currently not verifiable hypotheses.  
Even considering the initial 355 unique results before applying the inclusion criteria, no publications have been published before 2014. This result can be interpreted from two perspectives. First, the research on combining DTs with AR is relatively young and was established around 2016. It is even possible to identify this field's origin, as [PERSON] et al.'s work [5] is the first identified relevant publication cited by many of the other papers. Second, identifying only publications from 2016 onward can be related to the rise of the DT concept dating back into this time.  
Therefore, there may be additional, relevant academic work even before 2016 that tackled DTs' combination with AR without using the term \"Digital Twin\" or any of the often synonymously used terms. This possible bias needs to be addressed in further studies. In this context, identifying significant keywords to perform a comprehensible literature analysis poses a major challenge. Although \"Digital Twin\" has been proven to cover most of the respective research, even covering similar approaches as described in  
Figure 4: Publications per year.  
Section 3.1.1, this does not cover relevant approaches introduced before the term \"Digital Twin\" gained relevance.
## 4 Discussion
### Engineering Applications  
The influence of main parameters on the drag coefficient is considered through the water blocking rate, area ratio, inclination ratio, inflow velocity and water temperature stratification ratio. Among them, the water blocking rate has the greatest influence on the drag coefficient, and it is the most critical indicator of the TCC load. The water temperature stratification ratio has little effect on the drag coefficient. In engineering applications, the influence of the water temperature stratification ratio on  
Figure 13: (**a**) Drag coefficient and (**b**) force at different water layer strength ratios (data sources: G1–G33).  
the TCC load can be neglected, which indicates that isothermal water temperature can be assumed to calculate the TCC load.  
In this paper, the relationship between drag coefficient and main parameters is obtained, and expressed by fitting formulas. On one hand, the reservoir studied is a generalized trapezoidal section. In practical engineering application, the cross-section of most reservoirs is irregular. In the calculation of dimensionless drag coefficient, the water blocking rate should be noted. On the other hand, for area ratio, inclination ratio, and the water temperature stratification ratio, these main parameters are variable in the actual environment, and it is not easy to determine the specific parameters in the design process. According to the fitting formulas, it is recommended to use conservative safety factors to express the influence of these main parameters on dimensionless drag coefficient (Cd), so that the load obtained is partial to safety.  
The research results of this paper can be used to guide the design of TCCs. First, according to the requirements of discharged water temperature, a suitable bottom-TCC or top-TCC is selected. In spring and summer, the main purpose of applying bottom-TCC is to increase the water temperature. To lower the water temperature or improve the water quality, the top TCC is more suitable. Then, the simulation model is established by CFD software and verified by physical model experiment. According to the measured vertical temperature distribution, the actual discharged water temperature and the required discharged temperature, the main parameters of TCCs are simulated. Third, the main parameters and the fitting formula in this paper can estimate the corresponding drag coefficient. Finally, using the flow resistance equation, the TCC loads under different velocities can be determined quickly, which is of great significance in the design and construction of TCC projects.
# A Review on Distribution System State Estimation Algorithms
## 3 DSSE Fundamentals and Main Algorithms
### DSSE Algorithms
#### 3.2.4 Summary and Comparison between DSSE Algorithms  
All the aforementioned DSSE algorithms are presented in Figure 4 and summarized in Table 2. It is highlighted that the WLS constitutes the most widely implemented solution. Yet, its model-based nature is directly linked with the accurate knowledge necessity of the distribution system's parameters, the dependency on the quality of the data, etc. This issue can be solved mostly through modern, data-based algorithms such as ANN, the ascending implementation of which challenges the dominance of their tested-in-time, model-based counterparts.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Description** & **Advantages and Disadvantages** \\ \hline WLS [31, 32, 33, 34, 35, 36] & Minimization of the weighted residuals between the estimated and measured values. Objective function (11). & Common, simple, fast model-based approach, but sensitive to data. \\ \hline LAV [37, 38] & Minimization of the sum of absolute values of measurement residuals. Objective function (12). & Model-based approaches. Mostly variations of WLS, trying to overcome the issue of sensitivity to bad data and model parameters. Enhanced robustness but also limited implementation in literature and higher resources requirements such as computational cost, memory usage, etc. Information regarding the architecture and parameters of the distribution system is a necessity. \\ \hline \hline \end{tabular}
\end{table}
Table 2: DSSE algorithms [31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65].  
Figure 4: Main sorts of DSSE algorithms.
# Numerical Calculation and Experimental Study of the Axial Force of Aero Fuel Centrifugal Pumps
## 2 Numerical Simulation of Axial Force  
### Modeling and Meshing  
The aero fuel centrifugal pump studied in this paper is mainly composed of an induction impeller, centrifugal impeller, volute, drive shaft, and other components. Its three-dimensional model is shown in Figure 1 and its main design parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Rated speed/(r/min) & 9000 \\ Maximum fuel flow/(kg/h) & 50,000 \\ Number of blades & 3 (Inducer), 9 (Impeller) \\ Inlet diameter of impeller/mm & 70.3 \\ Outer diameter of impeller/mm & 115 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of a fuel centrifugal pump.  
The fluid domain was extracted from the existing three-dimensional model of the centrifugal pump. Considering the influence of the leakage of the front and rear mouth rings on pump performance and axial force, the water body between the front and rear mouth rings was included in the calculation domain. In order to obtain a stable inlet and outlet flow field, the inlet section and outlet section of the centrifugal pump are extended appropriately to obtain the inlet and outlet fluid domains. The fluid calculation domain model of the fuel centrifugal pump is shown in Figure 2.  
The fluid domain of centrifugal pumps is divided by hybrid grid technology. The fluid domain of solute adopts unstructured mesh with strong adaptability and the rest of the part adopts structured mesh. In order to improve the calculation accuracy, the mesh of the blade boundary and the sealing ring is locally encrypted. The initial mesh quantity is 2.1 \(\times\) 10\({}^{6}\) and then the mesh density is gradually increased; the centrifugal pump head is monitored at the same time. The calculated relationship between the centrifugal pump head and the number of grids is shown in Figure 3. When the number of grids is greater than 4.0 \(\times\) 10\({}^{6}\), the centrifugal pump head tends to be stable and the number of grids continues to increase while the head changes little. Considering the calculation accuracy and computer performance comprehensively, the total number of all grid cells is finally determined to be 4.1 \(\times\) 10\({}^{6}\) and the computational domain grid is shown in Figure 4. The distribution of Y-plus values on the surface of the inducer and centrifugal impeller is shown in Figure 5, which basically meets the calculation requirements of the turbulence model.  
Figure 1: Three-dimensional model of fuel centrifugal.  
Figure 2: Fluid calculation domain model of the fuel centrifugal pump.
# An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming
## 2 Related Concepts
### Search Space  
The automatic software defect repair method based on genetic programming search can be regarded as a process of finding the correct patch corresponding to a defect from a vast patch search space [3; 4]. Researchers have found that the setting of the search space has a significant impact on the effect of defect repair. Therefore, how to set a reasonable patch search space has become the key content of the research on search methods. The patch search space should not be set too large. An overly large search space would lead to excessive repair time or the search algorithm being unable to efficiently find the correct patch. It would also generate a large number of suspected but incorrect patches, greatly increasing the difficulty of verifying the patches in the later stage. At the same time, the patch search space should not be set too small, as a too small search space may not contain the correct repair patch, making it impossible to find the optimal solution. This paper aims to add constraints to the patch search space to effectively control the scope of the search space, while also expanding the search space according to certain rules to ensure that it can contain more correct patches.
## 3 Results  
The mean and standard deviations of fracture resistance among the investigated groups are presented in Table 1.  
The Kolmogorov-Smirnov test displayed normal distribution of data. Specimens of group 1 (positive control) displayed the highest fracture resistance (946.61 \(\pm\) 166.465 N); however, the lowest fracture strength was demonstrated by the specimens in group 2 (negative control) (433.31 \(\pm\) 129.350 N). ANOVA revealed that fracture strengths among study groups were statistically significant (\(p\)\(\leq\) 0.05).  
Individual comparisons among all the investigated groups established that group 2 displayed significantly lower fracture strength among all experimental groups. Group 1 (positive control) showed higher fracture strength than groups 2 (\(p\)\(<\) 0.01) and 5 (\(p\)\(=\) 0.0); however, it was comparable to groups 3 (\(p\)\(>\) 0.05), 4 (\(p\)\(=\) 0.10) and 6 (\(p\)\(=\) 0.33), respectively. Specimens treated with AH plus using different obturation techniques (group 3 and 4) showed comparable outcomes (\(p\)\(=\) 0.99). Similarly, specimens treated with TotalFill BC sealer with different obturation techniques showed statistically similar outcomes (\(p\)\(=\) 0.77). It was also observed that specimens in groups 3, 4, 5 and 6 demonstrated comparable outcomes of fracture strength (\(p\)\(>\) 0.05) (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Group** & **Mean** & **SD** & **ANOVA** \\  & & & **(\(p\) Value)** \\ \hline
1. Positive control & 946.61 & 166.46 & \\
2. Negative control & 433.31 & 129.35 & \\
3. GP-AH Plus-LC & 733.71 & 232.57 & \\
4. GP-AH Plus-SC & 752.77 & 120.58 & \\
5. Totalfill GP-BC-LC & 701.11 & 65.29 & \\
6. Totalfill GP-BC-SC & 797.46 & 204.55 & \\ \hline \hline \end{tabular}  
* SD. Standard deviation, LC. Cold lateral compaction, SC. Single cone, GP. Gutta Percha.  
\end{table}
Table 1: Means and SD of vertical root fracture resistance among study groups.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 7 Discussion
### On Limitations of the Application of the White Box  
The white box calibration examples demonstrated in this paper feature some limitations--mostly practical--that must be mentioned.  
First, the uncertainty components from the reference instruments prevail, emphasizing the need for improving calibration procedures for cup anemometers. Second, the measurement setup is not ideal as measuring at low height above the ground implies high turbulence intensity, which can affect both the reference and lidar measurements, or potential aerosol gradients. On the other hand, a tall mast would require installing the lidar on an expensive stiff platform to avoid extra measurement uncertainties, or significantly tilting up the lidar beam, which would introduce biases.  
In the white box calibration, having access to reconstruction algorithms is mandatory. For commercial systems, these algorithms would need to be provided by the lidars' manufacturers to accredited calibration laboratories under confidentiality agreements. The inadequacy and/or uncertainties of the wind field reconstruction algorithms will need to be assessed, for example via Computational Fluid Dynamics and lidar simulator(s).  
The presented methodology does not address the volume weighing process inherent to DWL. Further work on this question may thus be required for the white box methodology to achieve an even higher degree of genericity.  
A controversial question remains: should the lidar measurements be corrected using the calibration results? Although the VIM provides a clear definition of the calibration and formally requires to apply the calibration relation (i.e., correct the measurements), lidars are currently calibrated in uncontrolled conditions. Indeed, atmospheric field measurements are the most representative of real-world lidar applications. In specific cases where lidars operate in conditions far from those of the calibration, correcting lidar measurements is not always advisable and artificially enlarging the uncertainties may be preferred. This could for instance require adding a classification uncertainty, if available.
## 1 Introduction
### Contributions  
This paper presents a comprehensive review of state-of-the-art RL-based techniques to address resource management challenges in an IoT networking environment. Additionally, this paper also discusses a theoretical framework for applying RL in solving routing problems in IoT devices. IoT and RL-related concepts and the literature review have led to the development of a theoretical framework that aims to increase researchers' understanding and knowledge for future RL-based studies for IoT devices. The framework also aims to serve as an explanation for RL and IoT device's decision-making scenarios. In the field of the IoT, we are still at an early stage regarding ML-based devices in general and RL-based IoT networking decisions in particular. [PERSON] [41] presented their seminal paper on the nature of theory in information systems, and we are at a stage in which we are describing the attributes of RL-based networking and developing taxonomies, i.e., a theory for analyzing and explanation. Numerous research opportunities exist for future development and utilization of design artifacts rooted in robust theories to provide explanations and predictions across various levels of analysis. To highlight prospective areas for future research in the field of RL and the IoT, both areas could significantly benefit from exploring how RL features can be integrated into digital design elements within IoT interfaces.  
* We highlight key issues regarding IoT network resource management with a focus on network layer improvements.
* We examine the RL concept as a potential solution to enhance IoT network routing performances.
* A detailed overview of how RL is being applied within the IoT network layer environment is provided.
* We discuss the challenges and explore associated open issues when using RL algorithms in the context of IoT networks.  
The remainder of the paper is organized as follows. Section 2 presents the key concepts of the IoT environment, including its system model, network, and MAC layer. Section 3 provides an overview of the fundamentals of RL. Section 4 comprehensively reviews existing works and explains the RL model for IoT networking management. Section 5 discusses the challenges and open issues, followed by Section 6, which concludes the paper.