esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Examples  
In this study, five examples were considered: flats with 7, 8, 14, and 31 walls, and a structural component with 17 walls. The first two examples (with seven and eight walls) were chosen because they are comparable to solutions found intuitively. The example of 14 walls can be considered as a medium-sized flat, whereas the fourth can be classified as a large flat for AMC applications. These four examples have openings corresponding to doors and corridors. The fifth example was chosen to be a structural component with no openings so that it can be manufactured in considerable numbers and used in assembling a larger structure.  
In all these examples, to increase comparability, the starting point was chosen as Joint 1, and the first wall to be concreted was chosen as Wall 1, going from joints 1 to 2. With this choice, the number of path possibilities is \(2^{\text{nw-1}}(\text{nw-1})!\), where \(\text{nw}\) is the number of walls. This means that the possibilities for the seven-wall example is 46,080. This number is 645,120, \(5.10118\times 10^{+13}\), and \(2.84813\times 10^{+41}\) for the 8, 14, and 31-wall examples, respectively, and \(1.3712\times 10^{+18}\) for the structural component with 17 members (see Figure 6). It is obvious that having so many path possibilities, it is not possible to choose the best path without recourse to a very elaborate method.  
Figure 4: Determination of the parameter PARrange, where the accepted value is 0.60.  
Figure 5: Determination of the parameter PARsign, where the accepted value is 0.16.  
In the figures with examples, the number 1 vertex is marked with a star and is thus the starting joint. If the ending vertex is different than the starting one, it is marked with a large round point. The coordinate axes are like those shown in Figure 1a (i.e., x is from left to right; y is from top to bottom).
## 2 Literature Review
### Evaluation of PQC Algorithms  
Before evaluating the three algorithms, it is essential to explain lattice-based cryptography, as they all operate using this method. Lattice-based cryptography relies on the hardness of lattice problems which are believed to be resistant to quantum attacks. Although some researchers have suggested that lattice-based cryptography might be vulnerable because of potential algorithmic weaknesses, further studies have identified errors in these assessments which confirmed the security of lattice-based cryptography methods again [47]. CRYSTALS-Kyber is efficient and secure due to the hardness of module lattice problems, with relatively small key sizes. However, it has potential side-channel vulnerabilities and larger ciphertext sizes compared to traditional algorithms. CRYSTALS-Dilithium offers fast signing and verification with robust security, but has larger public keys and signatures, and its implementation can be complex for some applications. Falcon features compact signatures and high verification speed, supported by a strong theoretical foundation. However, it can be complex to implement, may face numerical stability issues, and has larger key sizes. While previous research on using PQC schemes for QKD introduced valuable insights [48; 49; 50], there is still a research gap regarding the optimal solution for addressing both encryption and authentication in QKD. In the next section, we introduce our approach to answer this question.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 3 Background
### Hash Functions
#### 3.3.15 Overview  
While existing lightweight hash functions like Scrypt, Groestl, Myr-Groestl, Keccak, BLAKE, Skein, Quark, X11 to X17, and Lyra family have shown notable successes in specific applications, they each present limitations when applied to IoT-blockchain integration, particularly in terms of computational complexity, energy consumption, and scalability. Scrypt, Groestl, and Myr-Groestl are known for their high security, but they require substantial computational resources, making them less suitable for resource-constrained IoT devices. Although they support parallel processing, their energy consumption remains high, posing challenges for IoT systems with limited battery life.  
Keccak, used in SHA-3, offers a higher level of security than SHA-2, making it a robust choice for blockchain systems. However, its processing time is longer, which can impact performance in environments like IoT, where processing power is limited. Despite its superior security features, Keccak's high computational demands make it less ideal for IoT applications, where performance and energy efficiency are paramount. Quark, BLAKE,and Skein stand out for their lower energy consumption, making them favorable choices for IoT applications. However, this comes at the cost of reduced security compared with more robust algorithms like SHA-2 or Keccak. While X11 to X17 and Lyra family offer energy-efficient solutions, they may not meet the highest security requirements in some blockchain applications.  
While these algorithms have their strengths, HashLEA has been designed to address the specific challenges of IoT-blockchain integration. By focusing on optimizing speed, performance, and energy efficiency without compromising security, HashLEA provides a competitive solution that is faster and more efficient than the reviewed algorithms while maintaining a comparable level of security.  
HashLEA leverages the lightweight and efficient LEA cipher, utilizing an ARX structure to optimize performance. Additionally, the specially developed, novel hash structure introduces a block-level mini-hash mechanism that creates a cascading dependency between input blocks, ensuring that any alteration in the data affects all subsequent computations. Thanks to this novel hash structure, the desired performance and security levels are achieved, making HashLEA suitable for low-resource environments while maintaining robust cryptographic properties. The following sections provide a detailed explanation of the HashLEA algorithm and its contributions to improving IoT-blockchain systems.
### Theoretical Background
#### 1.1.1 Measurement of the Vertical Parachute Speed Using the Classic Method  
The principle of this method (Figure 1) has been reposed on the measurement of the time interval between the impact of the load and the impact of a parachutist (ballast), whereby a long string of a given length (generally 35 m) holds the load and the whole formation is fixed to the parachutist (ballast). In this case, the vertical speed is defined as a moderate parachute speed during the last 35 m above the earth [27].  
Figure 1: Measurement of the parachute vertical speed using the classic method (source: The authors of this article).  
Determining the actual value of the vertical speed is a challenge due to the measuring doubts caused by the deviation between the existing height and length of the rope as a result of:  
* The impact of the characteristics of the fiber that the rope is made of and current meteorological conditions (objective atmospheric temperature and moisture) on the objective string length.
* The impact of several of windings on the rope at the moment of load fall on the rope length.
* The impact of load diversion from the vertical axis due to the horizontal direction of the parachute movement.  
A significant benefit of this method is that it can be used without the exercise of a parachutist jump. The method is dedicated to the parachutes only. The utilization of the method to determine the parachute glider's vertical speed is not possible. The uncertainty of the vertical speed measurement results of a parachute using the classic method is 15-20% [27]. The Technical Metrological Regulation of TPM 0051-93--Designating the measurement of indeterminateness [28] was applied to determine the extended uncertainty of the measuring results (this is applicable for all of the measuring methods mentioned in this study Appendix A).
## 8 Reliability Analysis
### Error Detection and Correction  
The error detection function of the MHC method mainly uses the chain signature and chain synchronization mechanism to verify the message's integrity, authenticity, and non-repudiation in real-time by comparing the node values of the message hash chain in real-time and signing and authenticating the chain at intervals. If the attacker tampers or forges any message content, the verification of the node value and signature of the message chain will fail. Both communicating parties should re-request the message with verification error within a limited time to ensure that the data flow can achieve higher reliability or disable the illegal message sender to reduce network security risk.
## 3 Initial Data Processing: Avoiding Model Bias
This method was chosen to ensure the independence of traffic flows and to separate the data into training and testing sets, avoiding data leakage. This approach leverages the similarity among flows in the same application (see, for instance, Figure 1). Thus, the objective is to train and test with packets in different flows in order to avoid overfitting and data leakage. Next, these individual packets are converted into images so that they can be used to train the neural network. Each byte of payload information is converted to a pixel, and then the pixels are arranged according to the desired dimension. The final step is to generate a convolutional neural network that will process these images, classify them, and extract the most probable application value. With this label, we can use different QoS policies to improve the experience in the network. With this process, we achieved the fact that, from each packet, an application label was extracted individually, without the need to load memories of the rest of the flow data. It is important to work with individual packets and not with network flows to gain speed and reduce latency because, in a high-speed system, grouping data extracted from a traffic flow can be time-consuming, and it can affect the performance of a latency-critical network application such as VoIP, Video calls, or live gaming.
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 3 Implementation of Gimbal Control through PV Image Analysis
### Implementation of GCS  
Figure 9 shows the implemented GCS in our study. The components of the CGS consist of the object with the gimbal, the embedded GCS with dual cameras, and the gimbal control/driving module. The GCS is implemented based on the hardware of Raspberry Pi 3 b+.  
Figure 8: Example of pulse width modulation.  
Figure 9: Implementation of GCS; PWM1 to control roll, PWM0 to control tilt.  
The thermal camera and visual camera are attached to the drone's GCS. The image taken from the visual camera is processed and used to find physical areas of the modules. Through image processing, the GCS calculates control values through module position and angle, and converts them into PWM signals which will be transmitted to the GCB. The motor control to adjust the gimbal tilt and roll is performed by sending out independent PWM signals (PWM1 to control roll, PWM0 to control tilt).
## 4 Discussion
Figure 8: Transmission accuracy curve at torque 5 N.m.  
Figure 7: Efficiency test curve at torque 5 N.m.  
Figure 9: Backlash curve at torque 5 N.m. Figures 7 and 8 show that the transmission efficiency of the three harmonic reducer samples in the initial stage (the experimental stage from 0 r to 2.7 \(\times\) 10\({}^{6}\) r) is relatively stable, while the transmission accuracy is between 0.02\({}^{\circ}\) and 0.022\({}^{\circ}\), and it also remains relatively stable. However, it will have a slight increase, which is negligible for space applications. Figure 9 shows that the hysteresis of the harmonic reducer increases from 0.017 to 0.027 at the initial stage of life, which is almost the same as the increase in transmission accuracy. The good performance of the transmission performance data in the initial stage is mainly due to the rigid wheel-flexible friction pair of the harmonic reducer at this stage, the inner wall of the flexible wheel-wave generator friction pair is in good lubrication state, the surface condition of the friction pair is measured well. There are also no abrasion excess particles between the friction pairs. As the test progresses, in the middle of the experiment (the experimental stage from 2.7 \(\times\) 10\({}^{6}\) r to 5.4 \(\times\) 10\({}^{6}\) r), the transmission efficiency, transmission accuracy, and hysteresis of the tested harmonic reducer are in a certain downward trend in the middle of the test, but the decline is within an acceptable range. This change does not affect the application of the space drive mechanism. As the test continues, in the later stage of the test, the transmission efficiency is in a rapid downward trend, which is mainly due to the wear of the teeth of the rigid wheel and the flexible wheel, the wear of the inner wall of the flexible wheel and the existence of excess wear particles between the friction pairs. These reasons lead to increased drive torque loss, which leads to a decrease in transmission efficiency. The experiment proves that the automatic performance testing system of the space harmonic reducer developed in this paper can fully simulate the high and low temperature environment of vacuum for relevant testing, and accurately acquire the relevant performance parameters of the space harmonic reducer working in space. It provides a relevant test platform for further research and description of the transmission performance of harmonic drive system in space, and provides a test carrier for subsequent research of space lubrication coating materials.
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 2 Related Works
### Principal Component Analysis (PCA)  
Principal component analysis (PCA) is a technique employed to decrease the number of dimensions in extensive datasets, hence enhancing comprehensibility while minimising the loss of information [31]. It converts the initial variables into a different set of variables, known as principal components, which are linear combinations of the original variables. This strategy is very advantageous for improving the effectiveness and productivity of machine learning models by concentrating on the most important features. The utilization of data analysis techniques is advantageous in discerning the fundamental framework of the data and determining the factors that have the utmost significance [32].  
A recent study introduces the PCA test R package (version 0.02) to statistically assess the significance of PCA results, enabling robust applications in ecological and evolutionary datasets [33]. Similarly, PCA's importance in chemometrics was highlighted for analyzing trends in large datasets by reducing complexity in variables and objects [34]. A different study utilized spatial-temporal PCA to address dependencies in multivariate datasets, optimizing variance and integrating spatial indices for social sciences [35]. A recent review from [36] provides a comprehensive comparison of machine learning and statistical techniques such as regression, ensemble, PCA, and hybrid models for water quality forecasting and classification, highlighting their predictive performance of surface water particularly for rivers and lakes in Malaysia.  
PCA proves essential in integrating with other methodologies. The combination of PCA with Data Envelopment Analysis (DEA) aimed to enhance efficiency measurement indices in state financial management [37]. Additionally, PCA was utilized alongside Fuzzy Subtractive Clustering for enhanced clustering outcomes in high-dimensional datasets [38]. These examples illustrated PCA's versatility, spanning ecological studies and chemometrics to financial management and machine learning applications, establishing it as an indispensable tool for extracting actionable insights from complex datasets.  
A key mathematical component of PCA is the eigen-decomposition of the covariance matrix of the standardized data. Specifically, Equation (3) is shown below [39]:  
\[Cv_{i}=\lambda_{i}v_{i} \tag{3}\]  
where \(C\) is the covariance matrix, \(v_{i}\) is the \(i\)th eigenvector (i.e., a principal component direction), and \(\lambda_{i}\) is the corresponding eigenvalue indicating the variance explained by that component. Since \(C\) is real and symmetric, eigenvector \(v_{i}\) is orthogonal, and sorting by descending \(\lambda_{i}\) facilitates dimensionality reduction by retaining components that capture most of the data variance.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Fault-Tolerance and Robustness  
CR-WSNs are prone to failure for various reasons, such as energy depletion, hardware malfunctions, and malicious attacks. Clustering helps to apply a fault-tolerant approach locally, without affecting the complete network. Hence, the network becomes more robust, and easy to maintain and manage. Clustering provides more convenience for network topology control, and responds to channel availability changes, PU arrival, and unpredicted node failures.
## 3 Methodology of Sweeping Eddy Current Pulse Thermal-Tomography
### Thermal Signal Feature Extraction  
The multi-frequency thermal images provide detailed information on defect structures. However, it is difficult to analyze defects through the thermal images directly; some features are needed to quantitatively analyze and distinguish the defects. To facilitate a comprehensive assessment of the heating efficacy resulting from different frequency excitations on the rail, the thermographic signal reconstruction (TSR) value and skewness are used.  
1. TSR The TSR value is calculated using the formula  
\[\text{TSR}=\frac{\log(T_{\text{max}}-T_{\text{init}})}{\log(T_{\text{init}})} \tag{4}\]  
where \(T_{max}\) denotes the maximum temperature attained during heating and \(T_{init}\) represents the initial temperature prior to heating.  
Figure 2: Schematic diagram of skinning depth versus frequency.  
When a uniform thermal excitation is applied to a thicker specimen, the logarithm of the surface temperature is linearly related to the logarithm of the response time [23]. Through Equation (4), each pixel point of the thermal image is quantified as a TSR value, which reinforces the linear relationship between each datum and facilitates the assessment of the heating effect. A higher TSR value represents a greater temperature rise.  
(2) Skewness  
The skewness is calculated using the following formula:  
\[\text{skewess }=\frac{n}{(n-1)(n-2)}\sum_{i=1}^{n}\biggl{(}\frac{x_{i}-\bar{x} }{s}\biggr{)}^{3} \tag{5}\]  
where \(n\) is the sample size, \(x_{i}\) is the \(i\)-th sample value, \(\bar{x}\) is the sample mean, and \(s\) is the sample standard deviation.  
Skewness provides useful information on the asymmetry of thermal distribution. During the heating and cooling phases, due to the rapid heating and cooling phases of the defects, there is a significant asymmetry in the temperature distribution, leading to a great negative skewness.
## 4 Discussion
### Modeling Considerations  
The ACoAA is a vital collateral pathway of the circle of Willis as well as a preferential site of the IA [42]. Inter-hemispheric blood flow through the ACoAA and the reversed blood supply in the ACA-A1 provided critical collateral blood supply to the anterior cerebral circulation when unilateral ICA stenosis occurred [24; 43]. Our previous studies have shown that in the situation of PCoA absence, the collateral flow in ACoAA changes non-linearly with the unilateral ICA stenosis degree due to the collateral mechanism [24; 25]. Therefore, instead of considering the region of UIAs alone in the computational domain [44; 45; 46], modeling the whole anterior circulation would be more appropriate to provide better insight into the hemodynamics change in ACoAA during ICA revascularization.  
The proper selection of BCs in hemodynamics simulations is essential to obtain physiologically significance results. In literature, both steady and pulsatile volumetric flow rates were usually defined as inlet BC [47]. Although the steady-state simulations were regarded as a quick alternative to transient simulations in the previous study [24; 48], their incapability of providing temporal hemodynamic parameters limited their applications. Hence, transient simulations were adopted in this study, and the physiological volumetric ICA flow profiles from a 4D-MRI of a healthy patient were imposed at the afferents of the non-stenoic post-operative computational model. At the outlets of the model,  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**RRT/Pa-1**} \\ \cline{2-5}  & **Severe Stenosis** & **Moderate Stenosis** & **Mild stenosis** & **Non-Stenoic** \\ \hline Maximum & 28.63 & 58.94 & 70.52 & 184.82 \\ Space-averaged & 0.39 & 0.67 & 0.83 & 1.14 \\ \hline \end{tabular}
\end{table}
Table 6: Maximum and space-averaged RRT on the ACoAA.  
Figure 9: The distribution of relative residence time (RRT) on the ACoAA under different degrees of stenosis: (**a**) severe stenosis, (**b**) moderate stenosis, (**c**) mild stenosis, (**d**) non-stenotic.  
0D lumped parameter models were coupled with the efferent arteries of a 3D computational domain in the current study, in which, the compliances of large arteries and resistance of distal capillaries were simulated by using the lumped parameter method. Compared with the widely adopted free outflow conditions, the 0D/3D coupled outlet boundaries are capable of reflecting the influence of the downstream vasculature outside the 3D computational region and provide an effective prediction of velocity fields and pressure-flow relationship [29; 44; 49; 50].
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 3 Study Area and Datasets
### Image Analysis Charts  
Image analysis charts provided by the Canadian Ice Services (CIS) were used to train the model and validate the lake ice product. Image analysis charts are manually generated analyses of SAR imagery in which homogeneous regions are outlined and assigned a label corresponding to the overall fraction of ice concentration in delineated polygons and partial fractions of up to three different ice types. Unlike daily ice charts, image analysis charts are based on the interpretation of SAR images only and have a valid time that corresponds to that of the image acquisition. For labeling water and ice, all pixels with ice concentration less than 10% were labelled as ice. This labeling criterion was done to ensure that labeled  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Acquisition Date**} & \multirow{2}{*}{**Acquisition Time**} & \multicolumn{3}{c}{**Lake Erie**} & \multicolumn{3}{c}{**Lake Ontario**} \\ \cline{3-8}  & & **Train** & **Test** & **Evaluation** & **Train** & **Test** & **Evaluation** \\ \hline  
pril 2014 & 11:22 & & & & \\ \hline
4 April 2014 & 11:34 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of RADARSAT–2 image data used to train, test and evaluate the model.  
pixels were representative of ice and water and other pixels that do not meet the criteria were not included in the training and testing of the CNN. Two examples of image analysis ice charts total ice concentration values (the variable used for training the model) on 19 January and 1 April 2014, are shown in Figure 3. These sample images are taken from the freeze-up and the breakup periods. Freeze-up corresponds to the period of time between the beginning of ice formation and the formation of a complete ice cover (or maximum ice extent in the case when a lake does not form a complete cover) while break-up refers to the period of time between the onset of spring melt and disappearance of ice from the lake surface.