esa-sceva/satcom-chunk-collection huggingface dataset:
# A Backwards-Tracking Lagrangian-Eulerian Method for Viscoelastic Two-Fluid Flows
## 1 Introduction
The proposed method was shown to produce results in good agreement with available analytic and numerical data from the literature [27]. It has also been shown that the Lagrangian formulation of the constitutive equation allows for the efficient parallelization of the viscoelastic stress calculation, which makes the method highly suitable for GPU-acceleration [28]. Furthermore, the use of an immersed boundary method in combination with the automatically generated grid enables simulation on arbitrary geometry with minimal pre-processing. In the previous studies [27; 28], the proposed Lagrangian-Eulerian method has been validated for single-phase flows with uniform grids. The natural next steps include enabling the simulation of free surface flow as well as simulation on refined grids. Therefore, in the current work, the proposed method is further developed in these two aspects. Firstly, the framework is extended for simulation of viscoelastic free surface flows with the volume of fluid method. Secondly, a backwards-tracking procedure is introduced to the solution of the constitutive equation, partly inspired by the backward-tracking Lagrangian particle method by [PERSON] et al. [10]. The result is a more robust method, due to the structured relation between the Lagrangian nodes and the Eulerian grid. No stabilization method, i.e., neither both-sides diffusion nor reformulation of the constitutive equation, is required for the studied flows. Furthermore, the computational cost as compared to the original Lagrangian-Eulerian method is reduced, since no unstructured interpolation or node redistribution is needed and because the total number of Lagrangian nodes decreased. In the context of multiphase flows, the viscoelastic constitutive equation is only solved inside the viscoelastic phase, which further increases the computational performance. The rest of the paper is structured, as follows. In the next section, the governing equations are presented, followed by a detailed description of the proposed numerical method. In the results section, the new method demonstrated and validated with analytic solutions and numerical data from the literature. Finally, conclusions are drawn and future work is outlined.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Case Study 2: A 4U CubeSat
#### 4.2.2 Ballistic Coefficient and Phase Times  
In both case studies, a single deployable surface was assumed to achieve a ballistic coefficient ratio of 2:1. Increasing the ballistic coefficient, whether through additional deployables or alternative methods, can substantially reduce phase times. For instance, if all four sides of the cube-shaped satellites were deployable, a ballistic coefficient ratio of 5:1 would be achieved, leading to significantly improved phase times, as illustrated in Figure 14. Moreover, this benefit could be further improved, as the analysis does not consider the potential increase in disengagement \(\Delta v\). A higher ballistic coefficient ratio raises the \(\Delta v_{thresh}\) value, thereby enabling a greater separation velocity.  
Furthermore, although both case studies employed a ballistic coefficient ratio of 2:1, the rate of separation differed due to the absolute values of the ballistic coefficients. The case with the lower \(\beta\) values achieved faster phase times than the one with higher \(\beta\) values. This is because differential drag acceleration is not governed by the ratio alone but more so  
Figure 13: Phase times for CS1\({}_{3}\).  
by the difference in inverse ballistic coefficients, as shown in Equation (2). Lower absolute ballistic coefficients yield stronger drag forces and, thus, faster acceleration rates.  
When combined, the effects of differential drag scaling with absolute ballistic coefficients and the cumulative impact of the disengagement \(\Delta v\) lead to significantly reduced phasing times. This suggests that the proposed method is particularly well suited to constellations composed of a larger number of compact, mass-optimized satellites.
# Emerging Industrial Internet of Things Open-Source Platforms and Applications in Diverse Sectors
## 6 Example IIoT Use Cases
### Healthcare  
The Swedish startups Cuvina and Tele2 IoT have collaborated to enable healthcare providers to remotely monitor patients [79]. The solution combines a cellular-connected laptop or tablet with IoT sensors that measure vital data such as heart rate, temperature, weight, blood pressure, and insulin levels. Security is of utmost importance, as medical data are highly sensitive, and many privacy issues are at stake. Cuvina is also planning to integrate third-party products such as connected pill dispensers into its solution, which is intended to act as a centralized hub for various healthcare devices and services [79]. This will enable an interoperable system, allowing devices collecting data to communicate with each other in order to provide better monitoring services to health providers.  
The European Union is looking into e-health matters and potential solutions through a variety of initiatives. The TopCare project, based in Europe, is creating platforms for home care to bring medical care to patients in their own homes and track hospitalization [80]. The TeleCare project concentrates on systems for remote monitoring to help elderly people stay independent [81].
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 5 Conclusions  
Damage detection of catenary insulators is of great significance for the daily maintenance of electrified railway power supply systems. In this paper, the complexity and accuracy of the model are comprehensively considered, and deep learning and Zernike moment are combined to improve the performance of catenary insulator damage detection. According to the analysis and experiments of this paper, the following conclusions can be drawn:  
1. The insulator identification and positioning algorithm based on Mask R-CNN has an accuracy of 96.4% in application, which can meet the accuracy requirements of catenary insulator identification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Total**} & **Number of Correct** & **Number of** \\  & & **Judgments** & **Misjudgments** \\ \hline Undamaged insulator & 483 & 453 & 30 \\ Damaged insulator & 29 & 25 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Damage detection results based on Zernike moment.  
Figure 15: Some test images and contour extraction results. (**a**) Similarity standard deviation and similarity value between pieces of damaged insulator shown in Figure 14; (**b**) Distance between pieces of damaged insulator shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Total**} & **Number of Correct** & **Number of** \\  & & **Judgments** & **Misjudgments** \\ \hline Undamaged insulator & 483 & 397 & 86 \\ Damaged insulator & 29 & 25 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Damage detection results based on Hu moment.  
2. Compared with the Hu moment, the catenary insulator damage detection method based on Zernike moment has better detailed description performance, and the accuracy of insulator damage judgment is increased by 10.94%.
3. Compared with the insulator damage detection method completely based on deep learning, the method based on Zernike moment reduces the dependence on the number of damaged insulator samples; compared with the method completely based on feature matching, the influence of background interference on accuracy can be reduced to a certain extent by using deep learning to locate insulator region.
4. The blurring of the obtained image due to the shaking of the track detection vehicle will affect the accuracy of the method proposed in this paper. In future research, anti-shake algorithms can be considered.  
Writing--review & editing, T.L.; Writing--Original draft, T.H. All authors have read and agreed to the published version of the manuscript.  
The work is fund by the Fundamental Research Funds for the Central Universities of China (2021 YS160).  
Thanks to Zhiyang Innovation Technology Co., Ltd. for providing the experimental platform.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 2 Related Work  
Recently, research has shown intense interest towards analysing the performance of various FEC techniques rather than how to improve these without extra redundancy. Researchers have not taken into consideration the positive effect of the multipath phenomenon on the performance of FEC and how it could be utilised to improve FEC capability. Some authors have compared the performance regarding bit error rate (BER) of different forward error correction codes such as RS, convolutional code (CC), RS-CC, and CC-RS codes [6]. They evaluated the BER of CC at various code rates. Likewise, they evaluated the performance of RS codes for different code rates, as well as block length. Furthermore, they compared the performance of both CC-RS and RS-CC concatenated codes with the individual codes and with uncoded data transmission. Some authors examined the performance of RS codes with binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) modulation over an additive white Gaussian noise (AWGN) channel [7]. Additionally, they compared the performance of RS codes with the BCH codes. After examining the results, they found the RS code performance is better than the BCH code. Some authors implemented RS codes for phase-shift keying (PSK) modulation over the AWGN communication channel. They performed the simulation of RS codes for the same code rates. They showed that the BER performance is poor for lower signal-noise-ratios (SNRs). On the other hand, the BER performance improved for large block lengths [8]. Moreover, other authors have simulated RS and BCH codes in the presence of a Rayleigh fading channel and have shown that the BCH code exceeds the RS code in the binary environment [1].  
This paper investigates the possibility of utilising the multipath phenomenon to improve the performance of FEC over the AWGN and Rayleigh channels. Additionally, analysis of the effectiveness of the utilisation of multipath propagation on the error correction capability of FEC with low redundancy was conducted. Furthermore, a combiner based on Hamming weight to combine the selected paths (redundant copies of the transmitted signal) into one strong signal to decode it is proposed.
###### Abstract  
Ultrasound (US) still represents the mainstay of scrotal imaging. However, contrast-enhanced ultrasound (CEUS) is a relatively novel, but increasingly utilized diagnostic modality. In consequence, we performed a systematic review (SR) and pooled meta-analysis to investigate the diagnostic performance of CEUS in the evaluation of testicular masses (TM). A SR up to June 2021 was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The diagnostic performance of CEUS was evaluated basing on two different endpoints: neoplastic vs. non-neoplastic and malignant vs. benign TM. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) from eligible studies were pooled and summary receiver operating characteristic (SROC) curves were constructed for each endpoint. Overall, six qualified studies were deemed suitable for this meta-analysis. Diagnostic performance of CEUS showed an accuracy of 0.96 in detecting neoplastic masses (sensitivity of 0.89, PPV of 0.85, specificity of 0.62, and NPV of 0.69) and an accuracy of 0.96 in detecting malignant masses (sensitivity of 0.86, PPV of 0.73, specificity of 0.87, and NPV of 0.91). Taken together, CEUS may represent a promising minimally invasive diagnostic tool for characterization of TM, since it allows clinicians to identify neoplastic lesions and exclude malignant tumor.  
Keywords:testing tumor; contrast-enhanced ultrasound; testicular neoplasm; testicular masses +
Footnote †: journal: Applied Sciences
## 3 Modeling Method
### Artificial Neural Networks
#### 3.1.1 Neural Network Architecture  
The multilayer feed-forward back propagation network (MFBPN), first proposed by [PERSON] et al. in 1986 [40], is one of the most commonly used ANN methods due to its simple structure and strong plasticity. The MFBPN is composed of three main parts: an input layer, one or more hidden layers and an output layer. All neurons in each layer are connected to the neurons in the next layer by network weights and biases. The values of the neuron received from the lower layer are multiplied by the specific weights and then summed with the bias. The sum is processed by predefined activation functions and transferred to the next layer, as follows:  
\[y_{j}=f(net_{j})=f(\sum_{i=1}^{n}w_{ij}x_{i}+b_{j}) \tag{2}\]  
where \(net_{j}\) is the weighted sum for the \(j\)th neuron, \(x_{i}\) is the input values of the \(i\)th neuron in the lower layer, \(w_{ij}\) is the weight between the \(i\)th neuron and the \(j\)th neuron, \(b_{j}\) is the bias value of the \(j\)th neuron and \(f\) is the activation function. In the present work, sigmoid activations were used in each layer.  
The MFBPN algorithm consists of two stages: forward propagation and back propagation, as depicted in Figure 7. It can be seen that, in the forward propagation process, each layer is organized in the forward direction and the input values are calculated by Equation (2), transferred from the input layer to the hidden layer and then to the output layer. The predicted values are compared with the experimental values. If the error does not satisfied the predetermined accuracy, the process reverses into the back propagation process. In the back propagation process, the error information is returned from the output layer to the input layer and the connection weights and biases values between the neurons in each layer are modified. This training process is repeated until the error reaches the desired requirement. There are many training algorithms for the back propagation network, including the gradient descent algorithm, Levenberg-Marquardt algorithm, and Bayesian regularization [40]. Among these, the Levenberg-Marquardt algorithm is considered the most efficient in terms of model prediction efficiency and accuracy [41], which was selected for the back propagation training in this investigation.  
Figure 7: Architecture of the multilayer feed-forward back propagation network.
## 3 Experiments and Results
### Stability Experiments  
The stability of the system was tested under laboratory conditions. The range of ambient temperature during the measurement was \(\pm\)0.5 \({}^{\circ}\)C. The experiment lasted 30 min, and data were collected every 10 s. The measurement results were automatically recorded by the software. The experimental results are shown in Figure 10. The standard deviations of the yaw, pitch, and roll are 0.11 arcsec, 0.11 arcsec, and 0.18 arcsec, respectively. The standard deviations of the horizontal straightness and vertical straightness are 0.38 \(\upmu\)m and 0.24 \(\upmu\)m, respectively. The above data prove that the proposed system exhibits good stability.  
Figure 9: Calibration experiments. (**a**) Pitch. (**b**) Yaw. (**c**) Roll. (**d**) Horizontal straightness. (**e**) Vertical straightness.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of the Attacks on CC
#### 5.1.2 Protocol Attacks  
Protocol attacks consume real server resources and equipment used for networking communication, like load balancer (LB) devices or protection devices. The protocol attack is measured in packets per second (Pps). It exploits the weaknesses of network protocols to increase the burden on the victim's resources. Some examples of protocol attacks include a smurf attack, fragmented packet attack, SYN floods, and ping of death [43]. Figure 2 illustrates the topology of a smurf attack.
# Optimized Architecture for Efficient OFDMA Network Design
## 4 Validation and Robustness Study of the Analytical Model  
The objective of this work is to validate and assess the robustness of the analytical model through the development of a specialized simulator. Network dimensioning, for an operator, involves calculating the number of antennas required in a specific region while ensuring key Quality of Service (QoS) parameters such as radio coverage, cellular capacity, and user traffic parameters are maintained. The dimensioning process for OFDMA networks is carried out in two main steps (as shown in Figure 2): evaluating radio coverage and cellular capacity and performing traffic analysis.  
This study focuses on the second step, Traffic Analysis, where MCS probabilities, network configuration, and traffic models serve as inputs. These inputs are essential in determining performance parameters such as average user throughput, average radio resource utilization, and the number of active users.  
To achieve this, a simulator has been developed. The simulator implements an ON/OFF traffic generator, individual wireless channels for each user, and a central scheduler responsible for allocating radio resources (slots) to active users in each frame. In the  
Figure 2: The system studied for the dimensioning study.  
first phase, the simulator validates the analytical model by operating under the same traffic and channel assumptions as the model. In the second phase, the robustness of the model is tested by relaxing these assumptions and introducing more realistic scenarios involving traffic and radio channels.  
The study is conducted across four different scheduling algorithms, each allocating radio resources uniquely. This simulator enables a comparison of variations in performance metrics, such as throughput and resource utilization, between the simulation and the analytical model under various conditions.  
A detailed description of the simulator will follow, explaining its implementation and functionality in evaluating both validation and robustness.  
The parameters to be adjusted for conducting the desired simulator will now be specified. These parameters include traffic parameters, system parameters, and channel parameters.
# Groundwater Prediction Using Machine-Learning Tools
## 3 Techniques Used
### Random Forest  
RF uses an ensemble of classification and regression trees. Each tree is built using a different bootstrap sample (with replacement) from the original data [47]. Compared to traditional trees, RF adds a randomness layer to bagging, since in traditional trees each node is split using the best split among all variables [48]. As for RF, only a random subset of the variables is used when splitting a node during the construction of a tree [47; 48]. As a result of the random constructions, RF provides robustness to overfitting as compared to some other techniques [48; 49].
## 2 Materials and Methods
### Feature Selection  
To select the relevant characteristics for the forecasting model in relation to the target variable (solar irradiance), the Pearson correlation was applied between the meteorological variables recorded in the dataset. Figure 3 presents the correlation matrix showing the relationship between the different variables and Equation (2) presents the Pearson correlation applied.  
\[\rho_{UV}=\frac{\sum{(U_{j}-\mu_{U})(V_{j}-\mu_{V})}}{\sqrt{\sum{(U_{j}-\mu_{U} )^{2}\sum{(V_{j}-\mu_{V})^{2}}}}} \tag{2}\]  
where \(U_{j}\) represents the \(j\)-th observation of variable \(U\), \(V_{j}\) represents the \(j\)-th observation of variable \(V\), and \(\mu_{U}\), \(\mu_{V}\) are the means of the respective variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline
5T vs. 10T & 56.91 & 0.75 \\
5T vs. 20T & 80.48 & 0.65 \\
5T vs. 30T & 90.92 & 0.61 \\
5T vs. 1H & 114.00 & 0.51 \\
10T vs. 30T & 66.72 & 0.77 \\
10T vs. 1H & 93.52 & 0.64 \\
30T vs. 1H & 59.04 & 0.83 \\ \hline \hline \end{tabular} Note: MAE is the mean absolute error and \(R^{2}\) is the coefficient of determination.  
\end{table}
Table 3: Comparison of MAE and \(R^{2}\) between different time series from January to December 2023.  
Figure 2: Temporary resampling for smoothing data with high variability on 16 January 2023. (**a**) Resampling from 5 to 10 min. (**b**) Resampling from 5 to 20 min. (**c**) Resampling from 5 to 30 min. (**d**) Resampling from 5 min to 1 h.  
Figure 3 shows that air temperature (_Air_Temp_C_) and air humidity (_Air_Humidity_Pct_) have strong correlations with solar irradiance, with values of 0.60 and \(-\)0.56, respectively. This may mean that as air temperature increases, solar irradiance also increases, while an increase in air humidity has the contrary effect on solar irradiance.  
To select the characteristics related to the target variable (solar irradiance), those with a correlation higher than 40% were taken. This allows the model to capture temporal patterns in the variables related to the target variable, thus avoiding noise from other variables, which can improve the accuracy and speed of the model.
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 3 Problem Description
### Meshed FWA Deployment
#### 3.2.2 WMN Optimisation Constraints  
Thus, we modelled our experiment using this physical deployment as a starting point, allowing us to measure performance characteristics and then consider optimisations. First, however, we use this section to set out the constraints that our experiment must respect, as far as possible, to maintain the realistic nature of the test. In summary, our constraints can be listed as follows:  
* All nodes (MG and MR) in the deployment are fixed and cannot move.
* Each node has a fixed degree of interconnectedness with the surrounding nodes, which are predetermined in advance based on LOS restrictions.
* However, we assume that nodes may have multiple interfaces that are not always active based on the clustering configuration.
* The deployment uses 802.11 ad 60 GHz links that are optimised using beamforming to provide point-to-point connectivity as provided by the vendor's equipment [44].
* The deployment is configured such that neighbouring links use separate channels so that interference is not a realistic consideration; for example, link 18-19 will not interfere with link 19-20. The considered channels are centred at 60.48 GHz and 62.64 GHz.  
Figure 4: Liverpool 5G Phoenix logical deployment.  
* The links' performance is bounded by the constraints we have modelled through our ongoing network-modelling research, which is reported in [21].
* For the purposes of our experiment, new links may be added between nodes where physical LOS is not currently possible. In each case, we assume the link will exhibit similar properties based on technology and the link distance in a street canyon path loss model [45]. This model allows us to represent the typical urban scenario in Liverpool: a city street with pedestrian sidewalks alongside long tall buildings. In the street canyon channel model, there are two dominant reflected rays, in addition to the direct link, that are considered: the ground-reflected ray and the wall-reflected ray. Furthermore, the random components that represent reflection scattering are considered in the link simulation. The reflection from the distant walls and second-order reflection are taken into account as random components.  
Using this problem description as a basis, in the next section we introduce our experiment with incrementally additional degrees of meshing.