esa-sceva/satcom-chunk-collection huggingface dataset:
# Aircraft Turbine Engine Labyrinth Seal CFD Sensitivity Analysis
## 1 Introduction  
The aircraft turbine engine consists of three fundamental segments - a compressor, a combustion segment, and a turbine [1]. The connection between the compressor disc and the turbine disc is provided by a rotating shaft. Part of the engine performance loss originates between the rotating shaft and the stator case, because the secondary air flows here and does not follow the primary path [2]. A labyrinth seal is a way of suppressing the airflow in cavities between the shaft and the case [3; 4]. Current turbomachinery (not only aircraft engines) is facing problems of reducing the airflows outside the primary air flow path, but simultaneously there are problems with the necessity of turbine blade cooling [4; 5] and bearings control [6] requiring fresh airflow. The labyrinth seal is advantageous due to its relatively simple manufacture procedure and the fact that its use does not disrupt the balancing of the shaft. A relevant geometric parameter of the labyrinth seal is its radial clearance (RC), i.e., the radial length of the gap between the labyrinth seal teeth and the opposite wall. Due to the contactless disposition, the radial clearance cannot be zero and it is evident that it will increase during operation life of the engine. The aim of this article is, based on the computational fluid dynamics (CFD) [4], to clarify and describe the influence of the radial clearance on the thermodynamic parameters of the labyrinth seal.  
The physical principle of the turbomachinery labyrinth seal operation is energy loss in cavities between the teeth in the seal; the loss practically restrains the parasitic flow between the rotating shaft and the fixed case. Because the labyrinth seal is contactless, the air flows and thus a vortex is developed between the teeth. Modern labyrinth seals are characterized by pressure and mass flow drop, named sealing effect. Initial results of research can be found in the steam turbine's design. However, the important difference between a steam turbine and a turbine engine consists of very different angular velocities of the shaft. In steam turbines, the velocity is approximately 500 rad/s and, in turbine engines, it is approximately 4100 rad/s [1; 7; 8]. Literature sources addressing the topic are rather rare [9; 10; 11]. In the presented article, the influence of different settings of the radial clearance and different locations of the teeth are examined, developing the previous research presented in [12]. The number of teeth was out of interest. The typical number of teeth [3; 5; 13] used was equal to four, because it corresponds to the standard conditions in labyrinth seals.
## 3 Results: Combined Hashing Scheme for MVL Linked List
### Formation of Modified MVLL with Internal and External Parameters  
According to Figure 2 the entry for the modified version of MVLL should contain number \(m\), time tag \(t\), parameters \(\mathbf{e}_{m}=\big{\{}\mathbf{e}_{1,m}\,,\ldots,\mathbf{e}_{p,m}\big{\}}\), and externally assigned hash values \(h{=}\{h_{1}\,,\ldots,h_{Q}\}\). It also should include external and internal parameters \(\mathbf{e}_{m}=\big{\{}\mathbf{e}_{m}{}^{(int)}\,,\,\mathbf{e}_{m}{}^{(ext)}\big{\}}\) instead of the set \(\mathbf{e}_{m}=\big{\{}\mathbf{e}_{1,m}\,,\ldots,\mathbf{e}_{p,m}\big{\}}\), i.e., subsets \(\mathbf{e}_{m}^{ext}=\big{\{}\mathbf{e}_{1,m}^{ext},\ldots,\mathbf{e}_{v,m}^{ext}\big{\}}\) and \(\mathbf{e}_{m}^{int}=\big{\{}\mathbf{e}_{1,m}^{int},\ldots,\mathbf{e}_{p,m}^{int}\big{\}}\). For comfort we will use shortened notation \(\mathbf{e}_{m}^{ext}=\mathbf{e}_{m}^{(e)}\) and \(\mathbf{e}_{m}^{int}=\mathbf{e}_{m}\). Besides this, entry should contain internal hash functions \(H^{(int)}(\mathbf{r},\mathbf{p})\) of the last and of the previous entry \(\mathbf{e}_{m-1}\). The identifier of the agent and its license number are also actual parameters. Thus, modified MVLL function containing entries with numbers from \(1\) up to \(m\) has the structure \(h^{(out)}=\mathbf{\ell}_{ll}(m,\,t,\,e_{m},\mathbf{e}_{m}^{(e)},\,\mathbf{H}_{m}^{(int)}h_{ 1,m},\ldots,h_{Q,m},\)\(e_{m-1},\,\mathbf{e}_{m-1}^{(e)},\,\mathbf{H}_{m-1}^{(int)}h_{1,m-1},\ldots,h_{Q,m-1})\). Then final logic expression will respond to Exp. (9):  
Note that data for external ledger should respond to 32-64 bit format of data, as internal microcontrollers or FPGA for IoT devices can mainly use 8-bit or sometimes 16-bit formats [75]. In any way, proposed formal procedures leave enough freedom of choice either to complement data by 0s for the necessary number of elder bits, or to sequentially unite bytes of several variables into one variable, or to combine both variants.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection
### Datasets  
Datasets are needed during the learning process to train and test ML models; therefore, dataset reliability and size are crucial to obtaining accurate results [92]. For instance, datasets that are large enough to have samples of normal traffic allow ML algorithms to learn normal network behavior, enabling the detection of unknown attacks by considering any deviation beyond the known usual behavior as unusual. It is challenging for the system to differentiate between the characteristics or signatures of a specific intrusion and a malfunction, which may cause false positives. Overall, any dataset collection for a specific network scenario should be performed over a sufficient time period to collect a sufficient volume of samples for each data class. Dataset parameters such as whether the dataset is balanced or imbalanced, the number of samples per class, dataset size, and dataset dimensionality can influence the selection of a proper ML classification technique and affect the behavior of the ML classifier.  
Dataset quality affect the performance of ML models. First, ML algorithms used with a certain dataset may not be applicable for other datasets, as they may have differences in the number of classes to be distinguished, number of instances or samples for each class, and number of attributes that differentiate each class. Second, dataset characteristics such as being labeled or not (i.e., balanced or imbalanced), the number of features (i.e., dataset dimensionality), and feature importance can affect model quality. Feature extraction methods are usually used to filter out potential and relevant features. Third, the criticality and real-time nature of the WSN application at the time of data collection may result in noisy samples and irrelevant features, which can affect the final classification results and the ability of the trained model to differentiate between normal and abnormal behavior. For instance, increasing attack timespan traces and capture size can be used to control the imbalance within the dataset, thereby enhancing the learning process and allowing the algorithm to learn more differences between normal and attack samples. In addition, retraining ML models is possible, and can take place periodically during network progress as new or unknown attacks occur, allowing an ML model to modify its behavior and improve its detection accuracy.  
Specialized datasets that consider the long list of cyberattacks targeting WSNs, whether collected using real-time experiments or computer-simulated, are limited. WSN-DS [92], NSL-KDD and its predecessor KDD Cup 1999 [93], CICIDS2017 [94], and UNSW-NB15 [95] are the most commonly used datasets been utilized for training and testing ML-based detection models in the context of securing WSNs. It is worth mentioning that none of these datasets except for WSN-DS are tailored to the need of developing ML models for WSNs, which motivates a need to generate new datasets or collect actual logs of real normal network data and simulated attacks.
## 2 Theoretical Method
### Introduction of Basic Model  
Shale rock containing a large number of pores is used as the research model in this paper. Generally, the porosity of shale rock is between 0.4% and 10%. A square sheet with a side length of 2 \(\mu m\) was taken with minimal thickness, as illustrated in Figure 2. It has an elastic modulus of \(E=22.5\ GPa\) and a Poisson's ratio of \(v=0.2\). The model's boundary conditions were as follows: the model's left side was fixed, while the right side was given a 0.002 \(\mu m\) displacement. The unit of equivalent modulus and stress output is \(MPa\). The goal of this work is to investigate the change law of mechanical characteristics of porous materials by varying parameters such as porosity, pore concentration degree, and pore number. This model is used in all of the investigations in this article, with the exception that the influence factors are adjusted according to the study topic.  
Figure 1: (a) \(S\) domain (b) \(S\) domain of unit circle.  
Figure 2: Pore model with 15 pores and 10% porosity.  
Generate a Random Model by Matlab  
1. Generate a circle of control points at any place within the 4 \(\mu m^{2}\) rectangle, as indicated in the red points in Figure 3a. Many points are formed equally over the rectangle, forming set \(A\), as shown in the blue points in Figure 3b. It should be mentioned that the pores generated in the model are round or elliptical to prevent the influence of pore shape.
2. Delete all of the points in set \(A\) that are surrounded by control points, but just a portion of the points that are not. Set \(B\) is formed by the remaining points in the rectangle area, as represented by the black points in Figure 3c. The distribution of points around the control point can be seen to be dense. The distribution of points at a distance from the control point is sparse. In the area surrounding the control point, there are no points.
3. According to the two-dimensional Delaunay triangulation method, all of the points in set \(B\) form an equilateral triangle grid as much as possible, as shown in Figure 3d.
4. A Voronoi grid is formed, as shown in Figure 3e. The model's main grid is generated after the preceding steps are completed. Adding boundary conditions, applying load, and eliminating rigid body displacement on the corresponding nodes can be used to perform subsequent calculations.
## 2 Materials and Methods
### Methods
#### 2.3.4 PCA-Pix2 Pix and Feature-Pix2 Pix (F-Pix2 Pix)  
The preprocessed images underwent feature extraction using the approach detailed in Section 2.3.2. The resulting images were cropped to a size of 256 \(\times\) 256 with a stride of 128. These cropped images were combined to a dataset, which served as the input for Pix2 Pix, with a 6:22 ratio for training, validation, and testing, respectively. The GF-1 dataset comprised 1128 images, OLI had 1864 images, and GF-2 had 6205 images. However, only images with a proportion of needleleaf and broadleaf forest greater than 20% were retained from the GF-2 dataset for training and validation to ensure a balanced sample. Figure 5 displays the forest identification flow chart.  
The model's initial learning rate was set to 0.0005, and the batch size was 1. We used the Adam optimizer with linear decay [28], which occurred every 50 iterations. The dataset used for input was GF-2, and \(D\) and \(G\) models were trained alternately. The learning rate remained constant for the initial 100 epochs, followed by 300 epochs of decay. A self-adaptive threshold segmentation approach was applied, utilizing the area of needleleaf and broadleaf forests from the current year. Results were produced by applying this method to other images for identification of forest/non-forest and needleleaf/broadleaf forest, which were then arranged in a mosaic format. To mitigate the impact of a single dataset on the experimental results, we employed a five-fold cross-validation method.  
The segmentation process resulted in some small spots that were removed using majority/minority analysis, clump, and sieve techniques. Based on the segmentation results, source-classified image statistics, including pixel count, minimum, maximum, and average values, and the standard deviation of each band, were calculated for each class. In the final stage, vector files were generated from these results.  
The experimental configuration was Intel(R) Core(TM) i9-12900 KF 3.20 GHz, NVIDIA GeForce RTX 3080 Ti.
# Establishment of a Pressure Variation Model for the State Estimation of an Underwater Vehicle
## 2 Methodology and Results
### Flow Simulation
#### 2.3.2 Simulation Set-Up  
The computational domain adheres to the recommended ITTC (International Towing Tank Conference) procedure (2011) [12], ensuring it is sufficiently large to prevent backflow or reflection. Physical conditions apply the boundary domain with a velocity inlet defined at the fluid domain's upstream, top, bottom, and sidewalls, and a pressure outlet imposing the downstream. A no-slip wall surrounds the AUV, while an elliptical cover serves as an overset region to facilitate the AUV's movement in straight, turning, gliding, and spiral motions. Mesh generation was automated using the cut-cell method for volume and surface, employing a trimmed cell mesher for volume grids and a surface remesher for high-quality surface mesh. The AUV encompasses a boundary layer consisting of 10 layers of prismatic  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\alpha\) & \multicolumn{2}{c}{\(-\mathbf{10}\)} & \multicolumn{2}{c}{\(-\mathbf{20}\)} & \multicolumn{2}{c}{\(-\mathbf{30}\)} \\ \hline \multirow{2}{*}{\(r\)} & **Predicted** & **Relative** & **Predicted** & **Relative** & **Predicted** & **Relative** \\  & \(\alpha\) & **Err. (\%)** & \(\alpha\) & **Err. (\%)** & **\(\alpha\)** & **Err. (\%)** \\ \hline \multirow{2}{*}{\(10\)} & \(-10.03\) & \(0.29\) & \(-28.71\) & \(43.57\) & \(-33.94\) & \(13.12\) \\  & \(-9.96\) & \(-0.38\) & \(-20.89\) & \(4.45\) & \(-33.87\) & \(12.92\) \\  & \(-9.89\) & \(-1.14\) & \(-20.55\) & \(2.77\) & \(-26.94\) & \(-10.20\) \\ \hline \(\mathbf{U}\) & \multicolumn{2}{c}{\(-\mathbf{10}\)} & \multicolumn{2}{c}{\(-\mathbf{20}\)} & \multicolumn{2}{c}{\(-\mathbf{30}\)} \\ \hline \(\mathbf{r}\) & \multicolumn{2}{c}{\(\mathbf{Predicted}\)\(\beta\)} & \multicolumn{2}{c}{} \\ \hline \(10\) & \(0.29\) & \(-0.57\) & \(-0.03\) \\  & \(0.42\) & \(0.22\) & \(0.08\) \\  & \(0.69\) & \(0.43\) & \(0.91\) \\ \hline \hline \end{tabular}
\end{table}
Table 18: Predicted drift angle and relative errors for spiral motion (\(U=3.0\) knots, \(\beta=0\)).  
cells. The simulation consistently maintained a \(y+\) value below one, refining the conditions around the AUV's trajectory using volumetric control to enhance calculation precision. Figure 6 illustrates the computational mesh, boundary conditions, and boundary domain for straight, turning, and gliding motions. The AUV simulation was conducted at a water depth of 7 m from the surface for straight and turning motions and at the same depth for gliding motion.
## 3 Results and Discussion
### Effect of the Petrochemicals on the Slurry Atomization  
Comparison of the average values of the sizes and velocities of aerosol particles at different distances from the nozzle can show the general effect of the addition of petrochemicals onto the atomization of the slurry. Figure 4 shows the mean sizes and velocities of aerosol particles at different distances from the nozzle. One can see, that CWS sizes first grow with propagation distance and further go down to certain intermediate level. The changes of sizes of CWSP aerosol particles is much smaller. They grows a bit after 0.6 m propagation in the free space.  
Looking onto the Figure 4b one can see, that changes of the mean velocities of CWS aerosol particles again looks like an oscillation. First they increases from 3.16 m/s to 5 m/s with further decrease to 3.5 m/s. This effect is conjugated with changes of the particle sizes: the drying of small particles decrease amount of the slow fraction of the aerosol. Together with this, some of the big particles become smaller due to the same drying with corresponding increase of the further deceleration due to the interaction with air.  
At the same time, the CWSP aerosol shows domination of trivial deceleration due to the interaction with air on the other effects that occurs during the propagation. The mean velocity decreases from approx. 6 m/s to 4 m/s after the 0.6 m travel in free space. The final values of the mean size of CWSP aerosol particles become 1.37 times bigger than for CWS aerosol. The final values of the mean velocity of CWSP aerosol become 1.13 times higher than for CWS one.  
It worth noting that comparison of the nozzle flow rate for these two fuels show that CWSP allows approximately 10% advantage of the volume rate (11.2 mL/s for CWS and 12.1 mL/s for CWSP). Taking in account that density of the CWSP is just 2-3% smaller, one can see that weight flow rate of CWSP is clearly higher.  
Figure 4: The mean values for sizes (**a**) and velocities (**b**) of the aerosol particles at different distances from the nozzle.
## 5 Future Scope and Applicability of the Proposed Model in Seaports
### Integration with IoT and Sensor Networks  
To augment its capabilities, our model can integrate with the Internet of Things (IoT) devices and sensor networks deployed within seaports. By incorporating the data from various sensors, such as vehicle detectors, environmental sensors, and container tracking devices, the model can gain a more comprehensive understanding of the seaport environment. This integration empowers the model to make well-informed decisions, considering contextual factors such as weather conditions, container availability, and the overall port capacity. The synergy between the deep learning model and IoT/sensor networks enhances the overall efficiency and responsiveness of seaport operations.
# How 5G Wireless (and Concomitant Technologies) Will Revolutionize Healthcare?
## 2 Challenges Posed by the Current Healthcare System
### Challenges with EHRs  
The Electronic Health Record (EHR) is a repository containing patients' digital data that are exchangeable securely with multiple authorized users. It stores retrospective, prospective and concurrent health information with the purpose to support efficient, continuing and quality service in integrated health [4]. In contrast to the virtues and success of EHRs, there are various challenges and limitations. The lack of interoperability is a major problem because hospitals and physicians are mostly not connected. This causes the patients' health information to be constrained within hospitals and laboratories.
## 5 Performance Evaluation
### Comparison with other Platforms  
To better understand the performance characteristics of DDNB, we compared it with other blockchain application platforms that employ the concept of smart contract. [PERSON] et al. measured the performance of two different versions of Hyperledger Fabric: v0.6 and v1.0 [41]. Between the two versions, there was a major re-architecture in the overall platform, including changes in the consensus model and the addition of support for channels. They deployed a simple money transfer application (chaincode) which has both the 'invoke' and 'query' functions. [PERSON] et al. measured the performance of two permissioned blockchain implementations--Ethereum and Hyperledger Fabric v0.6 [42]. While the main Ethereum platform uses a public blockchain network, the software is open-source and allows developers to configure the network to work as a private network, where only granted nodes can participate in the network.  
The two studies evaluated performance using various metrics, and we decided to focus on a common metric for all studies; TPS of executing 1000 transactions. The functions used in the comparison are the two representative functions in all compared schemes: _invoke_ which changes the state of the blockchain, and _query_, for simple queries. As the performance result data for Hyperledger Fabric v0.6 differ in the two studies, we used median values for comparison.  
Table 3 presents the TPS of the transactions executed on the respective platforms. Since the Blockchain Layer of DDNB is based on Hyperledger, the difference in performance between Hyperledger is not remarkable. However, DDNB's query function (_GetBalance_) outperforms Ethereum by 10.5 times, and 6.05 times for the invoke function. The benefits of separating the business logic in DDNB can be maintained while there is no performance penalty.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Status Functions**} & \multicolumn{5}{c}{**GetBalance**} \\ \hline DDNB & 768.8 & 208.1 \\ Ethereum & 73.2 & 34.4 \\ HLF v0.6 & 296.3 & 203.0 \\ HLF v1.0 & 461.0 & 185.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average TPS of various platforms for query and invoke functions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Status Functions**} & \multicolumn{5}{c}{**State Functions**} \\ \hline \hline
**Dataforms** & **Functions** & **Query** & **Invoke** \\ \hline DDNB & 768.8 & 208.1 \\ Ethereum & 73.2 & 34.4 \\ HLF v0.6 & 296.3 & 203.0 \\ HLF v1.0 & 461.0 & 185.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transactions per second (TPS) of _GetBalance_ and _SendCoin_.
## 4 Simulation and Lake Experiment Results
### Results of Simulations
The comparison between the IPNLMS-MuD and IPNLMS-TA curves shows a performance improvement in multidirectional over  
Figure 4: The estimation accuracy versus SNR of different single-direction adaptive algorithms with/without denoising method 1. Figure 3: (**a**) The UWA channel impulse response; (**b**) The UWA channel scattering function. single-direction channel estimation. It should be noted that when the SNRs are higher than 9 dB, denoising method 3 no longer has its advantages. However, considering the characteristic of low SNR in the marine environments, denoising method 2 is still valuable in the UWA field. Earlier in this section, we verified that all three denoising methods can be used to enhance the estimation accuracy of adaptive channel estimation algorithms in UWA channels. To choose the method with the best estimation accuracy, we combine three denoising methods and compare their performance with other methods mentioned above, as shown in Figure 6. The IPNLMS-MA-MuD means, on the basis of IPNLMS-MuD, an MA filter is added in each direction of the adaptive channel estimation. Through the comparison, we can find that IPNLMS-MA-MuD performs best in these four adaptive channel estimation algorithms, especially in low SNR conditions, which conform to the characteristics of the marine environment. In conclusion, the simulation experiments we conducted above preliminarily demonstrate the improved estimation accuracy of the proposed method over previous methods. Figure 5: (**a**) The estimation accuracy versus SNR of different single-direction adaptive algorithms. (**b**) The estimation accuracy versus SNR of single-direction and multidirection adaptive algorithms.