esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Stall Mechanisms of Pitching Airfoil: IDDES Study Across Different Mach Numbers
## 4 Aerodynamics at a Mach Number of 0.5  
### Aerodynamic Load Response at Ma = 0.5  
The aerodynamic characteristics of the pitching NACA 0012 airfoil at a higher Mach number of 0.5 are computed and analyzed. The kinematic parameters, including mean AoA, oscillation amplitude, and pitching frequency, are identical to those in Section 3.1. Correspondingly, the reduced frequency and Reynolds number at the high Mach number are 0.085 and 6.10 \(\times\) 10\({}^{6}\), respectively. Figure 19 compares the dynamic airloads at both Mach numbers, along with the static force at \(Ma\) = 0.5. As shown in Figure 19a, at \(Ma\) = 0.5, the airfoil experiences lift stall at a significantly lower AoA of 15.4\({}^{\circ}\) compared to the lift stall angle of 23.7\({}^{\circ}\) at \(Ma\) = 0.283. By comparing Figures 6 and 19, a similar but less pronounced delay (6.4\({}^{\circ}\)) between dynamic and static stall is observed at \(Ma\) = 0.5. The peak values of lift, drag, and pitching moment all show a notable decrease as the Mach number increase from 0.283 to 0.5. Additionally, post-stall aerodynamic loads exhibit more moderate variations. These differences indicate changes in dynamic stall behavior under the compressibility effects of high Mach numbers.  
Figure 19: Comparisons of calculated airload of the pitching airfoil at \(Ma\) = 0.5 and 0.283: (**a**) _Cl_; (**b**) _Cd_; (**c**) _Cm_.  
Figure 18: Aerodynamic damping of airfoil oscillating motion with different reduced frequencies at \(Ma\) = 0.283: (**a**) cycle-averaged damping; (**b**) time-resolved damping.  
Figure 20 depicts the temporal evolution of aerodynamic force coefficients, alongside their corresponding frequency spectra. Similarly to the case at \(Ma\) = 0.283, the fluctuations of force coefficients at \(Ma\) = 0.5 are primarily governed by the airfoil pitching frequency (\(k\) = 0.085). Additionally, the frequency spectra reveal notable harmonic components. Specifically, the third harmonic frequency (\(k\) = 0.256) is clearly discernible, with amplitudes amounting to approximately 36%, 16%, and 32% of the fundamental frequency for the lift, drag, and pitching moment coefficients, respectively.
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 4 Modelling
### Model Evaluation  
For model comparison, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) indices are standard for comparing the fit of models with different degrees of complexity. The AIC is an information criterion that originated from information theory and also penalizes the models for their complexity while searching for the best compromise between complexity and accuracy. A lower AIC value indicates a better model. Some current studies have also shown the potential of AIC in different domains and recommended it as a good selection of a model to be fitted when analyzing heavy-tailed data [45]. On the other hand, the Bayesian Information Criterion (BIC) uses a higher penalty on model complexity. It is particularly helpful where sample sizes are limited, as it leans more towards the smaller models. The most famous feature of the BIC is that it gives a fairly accurate indication of which model is selected when the sample size enlarges [46].  
As the number of predictors can be very high in high dimensional space, AIC and BIC are relevant. In these contexts, the performance of these criteria can vary. It can be proved that although both factors intend to balance the ability of model fitting and complexity, their asymptotic behaviours can result in different choices again in the high-dimensional case [45]. This finding implies that the type of model selection, namely AIC or BIC, should be chosen depending on the specifics of the context and the characteristics of the dataset within the framework of regression analyses.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
## 2 Sample Preparation
### Extraction
#### 2.2.4 Supercritical Fluid Extraction (SFE)  
Carbon dioxide (CO\({}_{2}\)) has a low critical temperature (31 \({}^{\circ}\)C) and pressure (7.3 MPa) [150], which makes CO\({}_{2}\)-based supercritical fluid extraction (SFE) an ideal tool for extracting thermally liable, non-polar analytes from foods [151]. Since the 1990s, SFE attracted some attention as an alternative extraction technique for the aflatoxin analysis [152, 153, 154, 155, 156, 157, 158]. Compared to conventional liquid extraction techniques or other energy-assisted extraction techniques, SFE offers unique advantages: it minimizes the use of organic solvents, offers easy separation of extraction solvent (CO\({}_{2}\)) and extractants (aflatoxins) by simply releasing the pressure and has potential to be connected with analytical instruments, forming online analysis and eliminating manual sample handling and transfer steps [159].  
Arguably, as a group of relatively polar compounds, aflat are not perfect analytes for SFE so it was no easy task to develop SFE protocols for the aflatoxin analysis. Researchers had to increase SFE pressure and temperature and add modifiers to increase solubility of aflat in CO\({}_{2}\) but these manipulations also contributed to an increase in co-extracted non-polar and fatty interferences, requiring additional clean-up procedures after SFE. According to a study by [PERSON] et al. [153], the optimized SFE conditions, 5000 psi, 80 \({}^{\circ}\)C, 15% modifier (acetonitrile-methanol, 2:1) and 100 mL of liquid CO\({}_{2}\), gave excellent recoveries (94.6% with RSD 6.2%) for aflat extracted from field-inoculated corn, but extensive clean-up was needed to remove co-extracted interferences in the SFE extracts. Similar issues have been reported in other matrices with relative high fat content such as peanut, liver and animal feeds [152, 154, 156]. It is worth noting that when applying SFE to low-fat matrices, less clean-up is needed. [PERSON] et al. [158] used SFE to extract aflat from _Zizyphi fructus_, a traditional Chinese medicine and the resulting extracts were directly injected into LC-MS. Although SFE has been around for many years, the development of new SFE-based aflat methods has been relatively slow. The expensive capital instruments, complex operation/training and time-consuming method development all contribute to the poor uptake of SFE in sample preparation for the aflatoxin analysis.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 2 Background
### Cold Boot Attacks
Additionally, the authors of [10] mention the probability of a bit flipping in the opposite direction stays constant and is very small (circa 0.01) as time elapses, while the probability of decaying to the \"ground state\" increases over time. These results suggest that the attacker could model the decay in a portion of the memory as a binary asymmetric channel, i.e., we can assume that the probability of a 1 flipping to 0 is a fixed number, and that the probability of a 0 flipping to a 1 is another fixed number in a given time. Note that by reading and counting the number of 0 bits and 1 bits, the attacker can discover the \"ground state\" of a specific memory region. Additionally, the attacker can estimate the bit-flipping probabilities by comparing the bit count of original content in a memory region with its corresponding noisy version. Finding encryption keys after procuring memory content is another challenge that the attacker has to address. Such problem has been extensively discussed in [10] for Advanced Encryption Standard (AES) and RSA (Rivest-Shamir-Adleman) keys in memory images. Even though the algorithms presented are scheme-specific, their algorithmic rationale may be easily adapted to devise key-finding algorithms for other schemes. These algorithms simply search for special secret-key-identifying characteristics in the secret key in-memory formats as identifying labels for sequences of bytes. More precisely, these algorithms search for byte sequences with low Hamming distance to these identifying labels and verify that the remaining bytes in a possible sequence satisfy some conditions. Once the previous problems have been dealt with, the attacker now has access to a version with errors of the original secret key obtained from the memory image. Therefore the attacker's aim is to reconstruct the original secret key from its noisy version, which is a mathematical problem. Moreover, we remark that the attacker may have access to cryptographic data associated with the key to be reconstructed (e.g., ciphertexts for a symmetric key encryption scheme) or the attacker may have access to public parameters of the cryptosystem (e.g., public key for a asymmetric key encryption scheme). The main center of interest of cold boot attacks has been so far to devise, develop and implement key-recovery algorithms to efficiently and effectively reconstruct secret keys from its noisy versions for different cryptographic schemes and testing these algorithms to learn how much noise they can tolerate.
# Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7
## 5 Conclusions  
This study proposes an AFP surface defect detection algorithm based on Improved YOLOv7, leveraging computer vision and deep learning. To address the issues of missed detections and false alarms in detecting small-sized defects and overlapping multiple defects faced by the original YOLOv7 algorithm, targeted optimization work was conducted. Firstly, the attention mechanism BiFormer was introduced to enable the model to capture subtle information in feature maps, enhance the correlation of image context information, and improve the extraction capability of features for small targets. Secondly, the AFPN was employed as a replacement for the original YOLOv7's PAFPN. The AFPN fuses neighboring low-level features and gradually incorporates high-level features, avoiding significant semantic differences between non-adjacent levels and maintaining a balance between detailed and semantic information in the AFP defect detection task. Additionally, WIoU was introduced as a replacement for CloU. The WIoU loss function utilizes a dynamic and non-monotonic focusing mechanism, reducing the gradient gains for simple examples and enhancing the model's generalization performance on difficult samples with target occlusions. Through a series of ablation experiments, the effectiveness and superiority  
Figure 10: Comparison of detection results between original YOLOv7 (**a1–f1**) and Improved YOLOv7 (**a2–f2**) (overlap confidence in **e2** is 0.91, overlap confidence in **f1** is 0.89, and overlap confidence in **f2** is 0.94).  
of the improved YOLOv7 model in AFP defect detection were verified. The improved YOLOv7 model achieved the best performance in terms of mAP and FPS, with values of 95.8% and 62 FPS, respectively. This confirms the effectiveness of the improvement strategy, and its detection performance meets the requirements for online detection, making it suitable for industrial applications.  
Conceptualization, L.W. and S.L.; methodology, L.W.; software, S.L.; validation, L.W., S.L. and Z.D.; formal analysis, H.S.; investigation, E.X.; data curation, Z.D.; writing--original draft preparation, L.W. and S.L.; writing--review and editing, L.W., S.L. and Z.D.; visualization, Z.D.; supervision, Z.D.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
We gratefully acknowledge the support of Hiwing Aerospace Materials Research Institute (Suzhou) Co., Ltd., Suzhou, China.  
The authors declare no conflict of interest.
## 4 Discussion
### The Variation of Voltage and Current Generated by the Specim under the Action of Load
Furthermore, since the loads applied in the experiments did not cause the samples to fail, there are limitations in the simulation of real-world load responses by the experimental load conditions. In practical engineering applications, the samples often involve larger sizes, and the amplitude of the radiation signal strength produced by these samples under compression presents a technical challenge that needs to be addressed. Beyond the challenges faced in the practical application domain, this study actually holds significant practical importance. Theoretically, the established electrophysical model is capable of intuitively and accurately depicting the variations in electromagnetic signals of rocks under different compressive loads. The circuit analysis model, which incorporates inductance, capacitance, and resistance, further facilitates the transformation of electromagnetic signal detection into the detection of voltage or current, offering considerable convenience. Based on these physical parameters, subsequent work can apply the theory of damage mechanics to formulate a mathematical model that characterizes damage using the parameter of electromagnetic signals generated by the reconfiguration of particle organization in compressed rocks. This model has important applications in rock mechanics. On the foundation of understanding the relationship between compressive load and electromagnetic signals, and with the aid of a substantial amount of actual experimental data, including data influenced by size effects, it is possible to acquire precursor information about the deformation and failure of rocks under compressive loads, thereby more accurately monitoring and predicting the deformation and failure processes of rocks. This is of great significance for the early identification of potential rock disasters, such as the instability of tunnel-surrounding rocks during excavation. To realize the translation of its practical application, detection sensors can be developed based on the established electrophysical model to capture information on the electromagnetic signals of rocks. The objective is to establish an early warning system in the fields of rock mechanics, geophysics, and dynamic rock disaster early warning systems, which will predict potential rock disasters by monitoring the electromagnetic signals of rocks in real time.
# Enhancing Data Integrity in Blockchain Oracles Through Multi-Label Analysis
## 3 Methods
### Characteristics and Variables Data of Coffee Futures Contracts  
In the second phase, data understanding, the objective is to understand the data sources, their quality, the variables involved, and how they feed into smart contracts. Our data scenario used coffee futures contracts, leveraging these data's high variability and volatility [12]. Information sources include weather data, financial databases (coffee prices and exchange rates), and platforms that provide economic indicators. For this process, we defined the following steps: searching for information sources encompassing the coffee supply chain process in the practice of futures contracts, from the purchase of the contract to its delivery. After characterizing this scenario, we identified 27 variables that contribute to the design and development of data understanding, as shown in Table 1.  
Once the variables were characterized by a chain phase, their values and types were established, and the next step was to evaluate them according to the data integrity characteristics [31]. This process ensured that each variable met the principles of accuracy, completeness, consistency, and reliability, which are fundamental pillars that guarantee that the data used in smart contracts are robust and suitable for proper application [32]. Accuracy was analyzed to confirm that the values of the variables accurately represented actual conditions, minimizing potential errors in the automation of decisions within the contracts [31]. Completeness was crucial, as it verified that no essential data were missing, thus preventing the absence of information from compromising the execution or analysis of the contract. We also evaluated the consistency of the data, indicating that it was coherent and not contradictory across the different phases of the chain or between information sources. Finally, reliability was reviewed, validating that the data sources were trustworthy and verified, ensuring the information was accurate and authentic [33].
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Related Works
#### 2.1.4 NB-IoT  
NB-IoT is a standard developed by the 3 GPP (Third Generation Partnership Project), which is an international telecommunications standardization body. The operation of NB-IoT is performed by telecom operators and is an extension of the 4G cellular network infrastructure [17] (4G LTE service providers such as Verizon and AT&T in the United States). The data transfer rate can reach 234.7 kbps [17], and it supports up to 50,000 devices per cell [16]. An important feature is that the battery life of an NB-IoT radio can be as long as ten years [16].
## 7 Experimentation and Analysis
### Algorithm Performance Validation
#### 7.1.1 Algorithm Convergence Accuracy Comparison  
Figure 10 shows the experimental results of the algorithm on F6, F8, and F12. From the experimental results, the GLSOAG shows a weak accuracy degradation at the late stage when it is approaching more than 190 iterations on F6. In addition, the GLSOAG is far ahead of the other algorithms on F6, but the accuracy is overtaken by SOA at the end of convergence. From the experimental results, although SOA achieves a slight advantage in the optimal fitness value, the difference between its optimal fitness value and the average fitness value corresponding to the optimal solution is larger than that of the GLSOAG by 0.6158, proving that in a comprehensive view, the GLSOAG is still superior to SOA. The GLSOAG is far ahead of BSA on F8, but its performance is close to that of SOA and PSO, with the front and end of the iteration in the fore, and its convergence performance is in the sub-optimal ranking from the experimental values. In addition, the GLSOAG achieves a clear lead in the test functions F1 to F5, F7, and F9 to F12 because the convergence accuracy of the algorithm needs to be combined with the optimal fitness value (best)  
\begin{table}
## 2 Materials and Methods
### Estimation Based Diffusion Kalman Filtering (EBDKF)  
The general multidimensional state space model given in [48] is employed in this context to compare with the proposed method. The model basically consists of four update equations, which are updated in each node of the network at each iteration step. The first estimate is known as the individual estimate at \(i\)th iteration step \(\hat{x}^{ind}_{k,i|i}\) given by Equations (12) and (13) as,  
\[(\hat{p}^{ind}_{k,i|i})^{-1}=(\hat{p}^{ind}_{k,i|i-1})^{-1}+{H_{k,i}}^{T}{R_{k,i }}^{-1}{H_{k,i}} \tag{12}\]  
\[(\hat{p}^{ind}_{k,i|i})^{-1}\hat{x}^{ind}_{k,i|i}=(\hat{p}^{ind}_{k,i|i-1})^{-1 }\hat{x}^{ind}_{k,i|i-1}+{H_{k,i}}^{T}{R_{k,i}}^{-1}{z_{k,i}} \tag{13}\]  
where \({H_{k,i}}\) is the individual observation matrix, \(\hat{p}^{ind}_{k,i|i}\) is a calculated covariance matrix, and \({R_{k,i}}\) is the sensor noise covariance matrix. These individual estimates are then distributed to neighbors and a local estimate\(\hat{x}^{loc}_{k,i|i}\) is calculated based on individual estimates received from the neighbors as given in Equations (14) and (15).  
\[(\hat{p}^{loc}_{k,i|i})^{-1}=\sum\limits_{leN_{k}}{(\hat{p}^{ind}_{l,i|i-1})^ {-1}} \tag{14}\]  
\[(\hat{x}^{loc}_{k,i|i})=(\hat{p}^{loc}_{k,i|i})\sum\limits_{leN_{k}}{(\hat{p }^{ind}_{l,i|i-1})^{-1}}(\hat{x}^{ind}_{l,i|i}) \tag{15}\]  
where \((\hat{p}^{loc}_{k,i|i})\) is the local covariance matrix calculated based on received individual covariance from neighbors. \((\hat{x}^{loc}_{k,i|i})\) is the local estimate of the neighbors and sensor node itself individual estimates. These local estimates are broadcasted to neighbors and diffusion update occurs at each of the sensor nodes once the local estimate broadcasted from each of the neighbors are receive as shown in Equation (16).  
\[(\hat{x}^{dif}_{k,i|i})=\sum\limits_{l\in N_{k}}{c_{l,k}(\hat{x}^{loc}_{l,i|i -1})} \tag{16}\]  
where \((\hat{x}^{dif}_{k,i|i})\) is the diffusion estimate and \(C_{l,k}\) is the diffusion matrix which is determined based on the relative degree rule in Equation (17).  
\[c_{l,k}=\frac{n_{l}}{\sum\limits_{s\in N_{k}}{n_{s}}} \tag{17}\]  
The degree of a node is total number of neighbors and node itself. Finally, time update is done based on Equations (18) and (19).  
\[(\hat{x}^{ind}_{k,i+1|i})=F_{i}(\hat{x}^{dif}_{k,i|i}) \tag{18}\]  
\[(\hat{p}^{ind}_{k,i+1|i})=F_{i}((b^{-1}_{k,k})(p^{loc}_{k,i|i})){F_{i}}^{T}+G_ {i}((b^{-1}_{k,k})(Q_{i})){G_{i}}^{T} \tag{19}\]  
In this time update step, individual estimates to be used in the individual update step of next iteration are calculated. In Equations (18) and (19), \(F_{i}\) and \(G_{i}\) are coefficient matrices associated with system state and process noise, respectively. \(b^{-1}_{k,k}\) is the degree of the \(k\)th node.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 5 Main Implementation and Results
### Data Analysis
#### 5.3.1 Use of Temperature to Determine the State of the Steam Trap  
Normal Operating State:  
During normal operation, the steam trap continuously discharges condensate produced within the system, which is mixed with flash steam. As a result, a steady flow of condensate with a small amount of steam passes through the steam trap. This leads to a temperature difference between the inlet and outlet of the steam trap, where the inlet temperature is expected to be higher than the outlet temperature.  
Operational Failure: Valve Open (Leakage):  
In the event of a leak, the conditions on both sides of the steam trap become equal, resulting in the inlet and outlet temperatures converging. Specifically, the inlet temperature remains constant, while the outlet temperature rises to match the inlet.  
Operational Failure: Valve Closed (Blockage):  
If the steam trap experiences a blockage that halts condensate discharge, the temperature at the inlet will decrease as the pipe becomes fully flooded with condensate. The outlet temperature will further decrease due to the absence of steam flow.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 3 Methods and Materials
### Mathematical Preliminaries
#### 3.2.2 Piecewise Linear Map  
A piecewise linear map divides an interval into smaller segments with linear transformations applied within each segment [23].  
\[f(x)=\begin{cases}\frac{x}{a}&o\leq x<a\\ (1-r),(x-1)&a\leq x\leq 1\end{cases}\]\(x\) represents the basic variable in the chaotic system (the current state). R represents the control parameter.  
Where \(x=0.55973\), and \(a=0.4\). This map has the following features:  
Ability to generate chaotic sequences based on the initial condition;  
High sensitivity to initial values, ensuring secure transformations;  
Linearity within segments but nonlinearity overall [23].
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Data Fusion and Sharing  
Currently, multiple drones are connected to perform various operations simultaneously. Incorporating data fusion and sharing for IoD allows the processing and merging of several data sources to generate correct information for decision making. Moreover, data fusion based algorithms for multi-UAV further support more efficient performance in the smart environment. In this sub-section, we outline the three different types of data fusion and sharing for the IoD, namely distributed, centralized, and cloud-based.