esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks
## 3 ANN Architectures and Training Algorithms  
The topology of the ANN can be described in terms of a directed graph of nodes with a transfer function \(\delta(\sum w_{ji}x_{i}+b_{i})\), where \(x_{i}\) is called a state variable for each node \(i\), \(w_{ji}\) is called a weight carrying some real value between two nodes \(i\) and \(j\), \(b_{i}\) is a real-valued bias term, and \(\delta\) is typically chosen to be a step or a linear function, and is called an _activation_ function. This transfer function also represents the expected output of a system comprising just the input and output layers. It is well known that such a system is not capable of implementing many functions; it is usually necessary to incorporate a few hidden layers. [PERSON] and [PERSON] (2006) showed that two hidden layers are usually an optimum choice capable of representing most of the desired functions. In this work, we also fix the number of hidden layers to two; however, we will vary the number of neurons in the hidden layers to have comparable prediction efficiencies.  
The \(l^{th}\) output, \(y_{l}\), of a network having two hidden layers is given as in Equation (1):  
Figure 1: Scanning electron micrograph (SEM) of a superconducting Nb film with a diluted square array of holes.  
Figure 2: The current–voltage (IV) characteristic of a diluted square array of holes at different temperatures.  
\[y_{l}=\delta_{o}(\sum_{k=0}^{P}w_{lk}^{y}(\delta_{H2}(\sum_{j=0}^{M}w_{kj}^{H2}( \delta_{H1}(\sum_{i=0}^{N}w_{ji}^{H1}x_{i}+b_{i}))+b_{j}))+b_{k}), \tag{1}\]  
where:  
* \(w_{lk}^{y}\) represents the weights from neuron \(k\) in the second hidden layer to the \(l^{th}\) output neurons.
* \(w_{kj}^{y}\) represents the weights from neuron \(j\) in the first hidden layer to neuron \(k\) in the second layer.
* \(w_{ji}^{H}\) represents the weights from neuron \(i\) in the input layer to the neuron \(j\) in the first hidden layer.
* \(x_{i}\) represents the \(i^{th}\) element in the input layer.
* \(b_{i}\), \(b_{j}\), and \(b_{k}\) represent the bias values for the hidden and output layers.
* \(\delta_{H1}\), \(\delta_{H2}\) and \(\delta_{o}\) are the activation functions: \(H1\), \(H2\), and \(o\) stand for the first and second hidden and the output layers, respectively.  
The primary objective of this analysis is to minimize a cost function given by Equation (2):  
\[\Phi=\frac{1}{2}\sum_{l=1}^{M}(y_{l}-d_{l})=\frac{1}{2}\sum_{l=1}^{M}e_{l}^{2} \tag{2}\]  
The layers in the directed graphs are usually arranged in one of the three possible options, giving rise to three different ANN architectures. More specifically, the manner in which the layers are interconnected describes a particular architecture. In what follows, we briefly describe each architecture; comparing them in terms of prediction accuracy, epochs, and training time is the contribution of this work--this is covered in Section 4.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 3 Methodology
### RSQM Model Design and Implementation
The integration of input and output membership functions enables the fuzzy inference system to handle various signal quality conditions ranging from good to worse. When a specific set of input values (CNR, RR, and CCD) is received, their corresponding membership degrees in each fuzzy set (low, medium, high) are determined using the input  
Figure 6: Flow chart of the RSQM. Figure 7: Fuzzy inference system. membership functions. These degrees are then used by the fuzzy rules to compute an overall fuzzy output representing the GNSS signal quality. The output membership functions with overlapping regions allow for gradual transitions in the output quality assessment, reflecting the inherent uncertainties in real-world GNSS signal reception. The membership functions act as a key parameter for classification. They convert raw GNSS signal measurements into fuzzy categories. This fuzzy logic approach allows for a more refined assessment of signal quality by acknowledging the inherent variability of real-world GNSS reception. By incorporating these fuzzy categories into the RSQM algorithm, the system has the potential to achieve improved positioning accuracy across various environments.
###### Abstract  
Increased ammonia (NH\({}_{3}\)) emissions from intensive agriculture negatively affect environmental and ecosystem health, contributing to formation of particulate matter (PM) and the potent greenhouse gas, N\({}_{2}\)O. Better understanding NH\({}_{3}\) emissions from the manure composite process and their behavior as a constituent of the atmospheric aerosol load is a crucial element in creating better farm management systems, improving public health outcomes, and mitigating the broader environmental and climatic impacts of agriculture. Retarded generation of PM with a major constituent source of NH\({}_{3}\) is a primary mechanism for evaluating the effects of agricultural contribution to PM. This study aimed to quantify NH\({}_{3}\) emissions, examine the influence of environmental factors, and investigate the relationship between precursor gases (SO\({}_{2}\), NO\({}_{x}\), NH\({}_{3}\)) and PM\({}_{2.5}\) at a modern manure composite facility in Paju, South Korea. Over 35 days, average internal concentrations of NH\({}_{3}\), SO\({}_{2}\), and NO\({}_{x}\) were significantly higher than external levels. NH\({}_{3}\) concentrations reached 3.64 \(\pm\) 0.06 mg m\({}^{-3}\) at 3 m height and 2.43 \(\pm\) 0.16 mg m\({}^{-3}\) at ground level, while the total NH\({}_{3}\) flux from the facility was 24.47 \(\pm\) 1.39 NH\({}_{3}\)-N kg d\({}^{-1}\). Internal PM\({}_{2.5}\) concentrations (36.9 \(\pm\) 2.6 \(\mu\)g m\({}^{-3}\)) were about 50% higher than external levels (23.7 \(\pm\) 2 \(\mu\)g m\({}^{-3}\)), with a moderate correlation (r = 0.341) suggesting some contribution of external PM\({}_{2.5}\) to internal levels. Despite large quantities of internal emissions, the facility's sealed design with a negative pressure ventilation system effectively minimized external emissions. These results suggest that while manure composting facilities are significant sources of NH\({}_{3}\) and PM\({}_{2.5}\), advanced systems like high-volume ventilation and scrubbing technologies can effectively reduce their impact on regional air pollution, contributing to better environmental management in agriculture.  
particle matter; greenhouse gas; precursor gases; manure composite facility +
Footnote †: journal: applied sciences
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline
**Technique** & \begin{tabular}{c} **Security** \\ **Mechanism** \\ \end{tabular} & \
\begin{tabular}{c} **Energy** \\ **Efficiency** \\ \end{tabular} & **Strengths** & **Weaknesses** \\ \hline \multirow{4}{*}{EEMSR [29]} & \multirow{4}{*}{\(\checkmark\)} & \multirow{4}{*}{\(\checkmark\)} & Presenting a robust and efficient trust \\  & & & technique, balancing energy consumption, \\  & & & improving network lifetime, increasing \\  & & & reliability, enhancing security, high scalability, \\  & & & reducing latency, high data transmission rate \\  & & & when there are malicious nodes \\ \hline \multirow{4}{*}{E-BEENISH [30]} & \multirow{4}{*}{\(\times\)} & \multirow{4}{*}{\(\checkmark\)} & \multirow{4}{*}{\(\checkmark\)} & Reducing energy consumption, improving \\  & & & network lifetime, high scalability, improving \\  & & & the data transmission rate \\ \hline \multirow{4}{*}{
\begin{tabular}{c} MCEAAACO-QSRP \\ [31] \\ \end{tabular} } & \multirow{4}{*}{\(\checkmark\)} & \multirow{4}{*}{\(\checkmark\)} & Lowering energy consumption, providing \\  & & & secure services in the network, reducing \\  & & & end-to-end delay, improving the trust value, \\  & & & and increasing the data transmission rate \\  & & & under black hole attacks \\ \hline \multirow{4}{*}{CBBMOR-TSM [32]} & \multirow{4}{*}{\(\checkmark\)} & \multirow{4}{*}{\(\times\)} & Using a robust trust mechanism, increasing \\  & & & the data transmission rate, lowering energy \\  & & & consumption and latency in the network \\  & & & despite black hole and DDoS attacks, suitable \\  & & & throughput \\ \hline \multirow{4}{*}{ETERS [33]} & \multirow{4}{*}{\(\checkmark\)} & \multirow{4}{*}{\(\checkmark\)} & Designing a strong trust technique, high \\  & & & scalability, suitable throughput, acceptable \\ \cline{1-1}  & & & packet delivery rate, optimal energy \\ \cline{1-1}  & & & consumption, acceptable latency, resistance \\ \cline{1-1}  & & & against some attacks, easy implementation \\ \hline SPRL [34] & \multirow{4}{*}{\(\checkmark\)} & \multirow{4}{*}{\(\times\)} & Protecting against some attacks, low latency \\ \cline{1-1} \cline{3-4}  & & & Lowering communication overhead, \\ \cline{1-1} \cline{3-4}  & & & protecting against rank and Sybil attacks, \\ \cline{1-1} \cline{3-4}  & & & performing both attack detection and \\ \cline{1-1} \cline{3-4}  & & & malicious node isolation \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of different techniques and examination of their strengths and weaknesses.
## 2 Materials and Methods
### Development of the Deep Learning-Based Classification System  
#### 2.1.1 U-Net  
Deep learning is a powerful machine learning technique that can approximate complicated mapping of the input/output space without special preprocessing. A convolutional neural network (CNN) is a deep learning model that is commonly used in the field of image recognition. A CNN determines the best performance in various tasks, such as object detection and semantic segmentation [25]. U-Net [26] is a typical semantic segmentation method based on a CNN that consists of an encoder that extracts the image feature and a decoder that estimates the label map from the extracted feature. In DTPI evaluation, we used a U-Net to segment areas of US images. Considering the data shortage, we used a CNN-based architecture of ResNeXt-50 pretrained by general images as the encoder [27], while the decoder consisted of U-Net, as introduced in the study. ResNeXt-50 was selected because of its relatively small model size and accuracy. Additionally, we selected it because it has already been used in other segmentation tasks and is easy to use with distributed pretrained models.
# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 3 Background and Related Work
### Prior Systematic Literature Reviews and Research Gaps  
Existing systematic literature reviews (SLRs) and comprehensive surveys have significantly contributed to understanding various aspects of AI in cybersecurity. Some reviews  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Theme** & **Subcategory** & **Key References** & **Focus/Findings** & **Limitations/Gaps** \\ \hline \multirow{9}{*}{Hybrid AI Models} & Multi-Modal Threat Detection & [69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91;  
focus broadly on machine learning applications in different cybersecurity domains, detailing algorithms, datasets, and general challenges [8; 10; 25; 26; 27; 28; 30; 31]. Others concentrate on federated learning, exploring its fundamental principles, architectures, and inherent privacy and security challenges within the FL framework itself [37; 39; 40; 43; 54]. While valuable, these reviews often analyze ML and FL either in isolation or from a perspective that does not deeply explore their synergistic combination as a distinct class of \"Hybrid AI Models\" (specifically FL+ML/DL) [61; 64; 75; 117; 118; 119; 120; 121; 122; 119; 120; 123].  
Specifically, there is a gap in systematic reviews that thoroughly  
1. Precisely define and categorize the diverse architectural configurations and techniques of federated machine learning (FML) models in cybersecurity;
2. Provide a structured comparative analysis of FML models across critical performance dimensions like accuracy, privacy, scalability, explainability, and robustness;
3. Synthesize primary research gaps and emerging trends that are unique to the FML paradigm in cybersecurity, including the integration of advanced technologies like quantum computing, neuro-symbolic AI, and agentic AI, and novel privacy-enhancing techniques beyond basic differential privacy or blockchain.  
This systematic review aims to fill this identified gap by offering a comprehensive and structured analysis of FML models in cybersecurity, providing a clearer taxonomy and a detailed synthesis of their current state, performance trade-offs, and crucial future directions.
# QRLIT: Quantum Reinforcement Learning for Database Index Tuning
## 2 Background
### Quantum Computing
#### 2.2.2 Quantum Logic Gates  
The ability to manipulate and control the amplitudes of the states of qubits is a fundamental prerequisite for the implementation of a quantum computing process. This manipulation is performed through quantum logic gates, or simply quantum gates, which allow for the creation of quantum algorithms [22].  
An operation is defined as a matrix that through matrix multiplication transforms one quantum state into another. Equation (11) provides a mathematical demonstration of this process, where \(\mathrm{U}\) represents the operation in question, \(|\psi 1\rangle\) the initial state, and \(|\psi 2\rangle\) the resulting state [22].  
\[\mathrm{U}|\psi 1\rangle= |\psi 2\rangle \tag{11}\]  
A quantum gate that acts on several qubits is described by a matrix of dimensions \(2^{n}\times 2^{n}\), where \(\mathrm{n}\) represents the number of qubits. The most common quantum gates are Pauli-X, Pauli-Z, Hadamard, Controlled NOT (CNOT or CX), and Controlled-Z, which are represented in matrices correspondingly in Equation (13). The Pauli-X gate performs state negation, which is equivalent to a NOT gate in classical computers, and the Pauli-Z gate, also known as a phase-flip gate, transforms the \(|1\rangle\) state into \(-|1\rangle\). The Hadamard gate sets the qubit in superposition, mapping the base state as presented in Equation (12). The Controlled NOT is controlled by the state of a control qubit to perform the negation. In other words, the gate is activated only if the qubit is in state \(|1\rangle\). In conclusion, the Controlled-Z behaves in the same way as Controlled NOT, but in this case, a phase-flip operation is performed.  
\[\mathrm{H}|0\rangle=\frac{|0\rangle+|1\rangle}{\sqrt{2}},\ \ \mathrm{H}|1 \rangle=\frac{|0\rangle-|1\rangle}{\sqrt{2}} \tag{12}\]\[\begin{bmatrix}0&1\\ 1&0\end{bmatrix},\ \ \begin{bmatrix}1&0\\ 0&-1\end{bmatrix},\ \ \begin{bmatrix}1&0\\ 0&-1\end{bmatrix},\ \ \begin{bmatrix}1&1\\ 1&-1\end{bmatrix},\ \ \begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&0&1\\ 0&0&1&0\end{bmatrix}, \tag{13}\] \[\begin{bmatrix}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&-1\end{bmatrix}\]
## 4 Discussion
Furthermore, controlled experimental settings may not fully capture the variability and unpredictability of real-world environments, particularly in high-stakes fields like construction and industrial settings [32; 35]. Moreover, the instruments that allow this are not without their own limitations. For example, the efficiency of measuring brain data with tools such as the EEG is influenced by various factors, like intrinsic (e.g., facial movements and body motion) and extrinsic artifacts (e.g., background noise or electrical currents). Another critical limitation is the variability in physiological signal interpretation among different subjects. Variations in EEG signal patterns, HRV, and other physiological responses can lead to inconsistent workload assessments. This situation presents challenges to the development of universal methods or machine learning-based models, as noted in [42; 46]. Technical challenges also present significant limitations. For example, the accuracy of EEG signal recognition is crucial for the system's effectiveness but can be hampered by noise and artifacts in the data [25; 77]. Similarly, the integration of multimodal data (e.g., EEG, fNIRS, EDA) requires sophisticated processing techniques and reliable synchronization, which can be technically demanding and prone to errors [25; 42]. Moreover, the reliance on specific machine learning models, while beneficial for analyzing physiological data, also has limitations. Models like SVM, KNN, and gradient boost (GB) require careful tuning and validation to avoid overfitting, particularly with small datasets. Therefore, further validation across different scenarios, including laboratory and real-world settings, is essential to guarantee the wider applicability of these models [37]. Future research should address these limitations by incorporating larger, more diverse sample sizes and validating findings in real-world environments, especially in scenarios that might not be well-addressed by current tools. There is also a need to refine machine learning algorithms to improve their generalizability across different tasks and settings. Exploring additional physiological indicators and integrating them with existing measures could provide a more comprehensive understanding of mental workload. Advancements in wearable technology and real-time data processing will further enhance the practical application of these tools, paving the way for more effective human-robot interaction systems aiming to improve productivity and safety in various industrial and collaborative settings. Given the above information, readers should nevertheless take notice that the features and metrics mentioned and explained throughout this review are in no way meant to be taken as a fully exhaustive list of all the possible features and metrics of all sensors. Providing such an exhaustive list is beyond the scope of this review. For more information regarding features and metrics both mentioned and not mentioned in this review, please consult works such as [27; 29].
## 3 Results
### Force Characteristics Results of Segment
#### 3.3.3 Influence of Soil Poisson's Ratio on Segment Forces  
To investigate the impact of the soil's Poisson's ratio (\(\mu\)) on the force characteristics of the segments, we selected soil Poisson's ratios of 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, and 0.45. We then studied the effect of different Poisson ratios on the stress of the segment. The variation in the maximum bending moment and axial force in the section with changes in the soil's Poisson's ratio is illustrated in Table 9 and Figure 7c. It can be observed that the maximum bending moment is negatively linearly correlated with the soil's Poisson's ratio, with its value decreasing as the Poisson's ratio increases. In comparison, the variation in the maximum axial force value is relatively stable. It first decreases and then increases with an increase in Poisson's ratio, reaching a minimum value at a Poisson's ratio of 0.3. This pattern is consistent with the variation observed in the initial support. In terms of soil poison, according to the analysis of this special trend in change, when the Poisson's ratio is less than 0.3, because the vertical strain is greater than the horizontal strain, the soil has less constraint on the horizontal displacement of the excavated structure. The excavation structure is an arch structure, and the members should be mainly compressed. The vertical displacement and horizontal displacement of the structure increase with the increase in depth, and the axial force tends to decrease. When the Poisson's ratio reaches 0.3, the vertical deformation growth rate of the soil around the excavation structure decreases, while the lateral deformation gradually converges and becomes smaller when the lateral earth pressure gradually increases, so the axial force increases. But in general, the change in Poisson's ratio has little effect.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**H (m)** & **Mmax (kN\(\cdot\)m)** & **-Mmax (kN\(\cdot\)m)** & **Nmax (kN)** \\ \hline
24 & 84.74 & 89.51 & 589.05 \\
26 & 91.28 & 96.44 & 634.22 \\
28 & 93.35 & 99.13 & 654.20 \\
30 & 99.98 & 107.05 & 707.77 \\
32 & 104.35 & 110.55 & 735.28 \\
34 & 107.42 & 112.91 & 755.37 \\
36 & 115.63 & 119.88 & 798.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Segment calculation results under different buried depths.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\gamma\) **(kN/m\({}^{3}\))** & **Mmax (kN\(\cdot\)m)** & **-Mmax (kN\(\cdot\)m)** & **Nmax (kN)** \\ \hline
18 & 77.02 & 81.96 & 539.68 \\
19 & 84.13 & 89.46 & 589.53 \\
20 & 87.91 & 93.41 & 616.03 \\
21 & 96.18 & 102.13 & 674.02 \\
22 & 100.79 & 106.97 & 706.36 \\
23 & 104.24 & 110.57 & 727.65 \\
24 & 111.90 & 118.65 & 784.77 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Segment calculation results under different soil bulk density.
## 3 Case Study--Goldeneye CCS Site
### Data
#### 3.1.2 Preprocessing of Data  
There have been five main steps of preprocessing before the data is fed to the model: Removal of time steps, splitting of the time series into two, labeling of data, time lag differencing and standardization of the data. First of all we remove the first 245 time steps of the original data due to a spin-up period of the hydrodynamic simulations, such that the data to be used further have a time dimension of 1100. Secondly, we split the data in two, using the first 550 time steps as training and validation and the last 550 time steps as test data. This is done so the test data becomes less correlated with the training and validation data sets. One of the simulations are without any leakage, and by subtracting the relevant values from the simulation with leak from the one without gives the _true footprint_ of the leakage. Based on the true footprint a threshold can be chosen to label time series as either leak or no-leak. The recommended uncertainty measure from [57] on dissolved inorganic carbon is 1 \(\mu\)mol/kg and corresponds to approximately 1.0236 m mol/m\({}^{3}\) (which is the output of the hydrodynamic simulations) with a density of seawater of 1023.6 kg/m\({}^{3}\). The density will change with depth and location. Here we use a threshold of 0.01 mmol/m \({}^{3}\) such that if the true footprint at some point has been above this value the corresponding time series will be labeled as a leak. The two last steps of the preprocessing is to apply the difference transform and standardize the data. Applying a difference transform can help stabilize the mean of the time series by removing changes in the level  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Leak** & **No-Leak** & **\# Time Series** & **Start/End** \\ \hline Training Data & 75.8\% & 24.2\% & 116,659 & Start \\ Validation Data & 75.8\% & 24.2\% & 49,997 & Start \\ Test Data & 69.8\% & 36.2\% & 6944 & End \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the train, validation and test data.  
Figure 1: (a) FVCOM domain used in which resolution varies from 15 km at the open boundaries to 0.5 km at the release site. The black box indicates the extents of the grid shown in (b). (b) The nested domain with resolution from 0.5 km at the boundary to 3 m at the release site (red star). The black box in (b) indicates the extent of the Goldeneye complex [56].  
of a time series and so eliminating and reducing trend and seasonality. Instead of assessing the time series itself changes in the signal is analyzed (an approximation of the derivative). [PERSON] et al. studied changes in the pH to determine if a leak was present in [39]. It makes sense to apply the difference transform in an anomaly detection perspective, where abnormal changes and patterns in the time series can be a good indicator if a leakage is present or not. Standardization of the data should improve the conditioning of the optimization problem, and speed up the training process.