esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Artificial Intelligence (AI) in Sports Performance Analysis  
### Definition and History  
AI is a branch of computer science that is focused on developing systems that can execute tasks mirroring human intelligence [20]. These tasks include learning from experiences, comprehending natural language, video analysis, recognizing patterns, solving problems, and decision-making. Although the AI boom of today sees a plethora of new models being developed daily for a wide array of tasks, the concept of AI is not new. The theoretical groundwork for a \"thinking\" machine can be traced back to the 1940s and 1950s. Inspired by biological neurons, Pitts and McCulloch [88] proposed the McCulloch-Pitts neuron, a computational model that simplifies the function of a biological neuron into multiple inputs and a single output. In the 1950s, pioneering computer scientist Alan Turing posited the concept of a machine capable of imitating human intelligence [89], marking a significant milestone in the development of AI. This was further advanced by McCarthy et al. [90], who theorized that every aspect of learning or intelligence could be defined so precisely that a machine could simulate it, which is considered the birth of AI as a field. Subsequently, Rosenblatt [91] proposed the Perceptron, which stands as the earliest form of a neural network capable of learning from its inputs.  
Despite these early ground-breaking evolution benchmarks, the progress in developing modern neural networks was slow. This was largely due to computational limitations and data scarcity, leading to periods known as \"AI winter\", characterized by reduced funding and interest in AI research [92; 93]. The tide turned in the 1980s and 1990s with the introduction of backpropagation by Geoffrey Hinton and then the emergence of deep learning in the 2000s and 2010s. These developments have culminated in the creation of the powerful neural networks we see today, bringing an end to the 'AI winter' and propellingAI research to the forefront of technological innovation. Within the context of machine learning, modern AI systems can process vast amounts of data, identify intricate patterns, and enhance system performance without the need for explicit programming [94]. Deep learning, a subset of machine learning, deploys artificial neural networks designed to emulate the function of the human brain. Finally, in the 2020s, the proliferation of high-capacity models, such as the Generative Pretrained Transformer (GPT)--the engine that gives life to ChatGPT--heralded a new era in the AI landscape [95; 96; 97]. These large-scale models demonstrate an unprecedented capacity to generate human-like text, making AI technology more accessible and usable to the public and not just computer scientists. The success of these models has stirred a competitive environment among leading tech companies, each vying to develop and deploy the most advanced AI technologies. This competition not only spurs technological advancement but also contributes to the popularization of AI applications in everyday life.
# Research on the Tooth Surface Integrity of Non-Circular Gear WEDM Based on HPSO Optimization SVR
###### Abstract  
Non-circular gears have the characteristics of gear ratio accuracy, good dynamic performance, and wide application prospects but are difficult to manufacture. Wire electrical discharge machining (WEDM) can process almost all kinds of non-circular gear. In order to solve the problem that the process parameters are mainly adjusted using the operator's experience and to improve the surface quality of non-circular gears machined using WEDM, this research took Pascal gears processed with a fast-walking WEDM machine as the object, conducted orthogonal tests, and used hybrid particle swarm optimization (HPSO) to optimize support vector regression (SVR) with different kernel functions, to predict various surface integrity indicators. The results showed that the rbf kernel function had a better performance in the prediction model of surface integrity indicators, which can provide a reference for the parameter selection of non-circular gear machining using WEDM. The final predicted results were \(R^{2}=0.9978\), \(MAPE=0.4534\) for surface roughness, \(R^{2}=0.9483\), \(MAPE=3.1673\) for surface residual stress, and \(R^{2}=0.9786\), \(MAPE=0.4779\) for surface microhardness.  
non-circular gears; WEDM; surface integrity; SVR; HPSO; predict
## 2 The Design of the Element  
### Element Configuration  
A 3D exploded view of the proposed stacked patch element is shown in Figure 1a. It includes four layers of dielectric substrates, namely one F4 BTMS350 (\(\varepsilon_{\text{r}}\) = 3.50, tan\(\delta\) = 0.0024 at 40 GHz) and three F4 BTMS300 (\(\varepsilon_{\text{r}}\) = 3.00, tan\(\delta\) = 0.0019 at 40 GHz), and five layers of copper cladding, namely the top decoupling surface (Layer 1), the radiating patch (Layer 2), the slot layer (Layer 3), the feeding line (Layer 4), and the bottom ground (Layer 5). Sub 1 is F4 BTMS350, with its upper surface being the decoupling surface layer. Sub 2 is F4 BTMS300, with its upper surface being the radiating patch. Sub 3 is F4 BTMS300, with its upper surface being the slot layer. Sub 4 is F4 BTMS300, with its upper surface being the T-shaped microstrip feeding line and its lower surface being the ground, with a metallized hole as a feeding probe connecting the feeding line. F4 BTMS350 and F4 BTMS300 are high-frequency dielectric substrates from the F4 BTMS series, produced by Wangling Company in Taizhou, China, where '350' and '300' represent the dielectric constants of 3.50 and 3.00, respectively.  
Figure 1b-e provide the top views and specific dimensions of the decoupling surface, radiating patch, slot layer, and feeding line. Figure 1a,d,e show that there are four metallized walls around the I-shaped slot of the slot layer and the feeding line, connecting the slot layer and the ground, similar to an SIW cavity formed by metallized holes, which serves to confine electromagnetic waves and improve radiation efficiency [28; 29]. The element-specific geometrical parameters are demonstrated in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(L_{1}\) & 0.65 & \(W_{1}\) & 0.08 \\ \(L_{2}\) & 0.54 & \(W_{2}\) & 0.54 \\ \(L_{3}\) & 0.88 & \(W_{3}\) & 0.48 \\ \(L_{4}\) & 0.30 & \(W_{4}\) & 0.37 \\ \(L_{s}\) & 0.52 & \(W_{s}\) & 0.52 \\ \(L_{H}\) & 0.55 & \(W_{H}\) & 0.20 \\ \(D_{x}\) & 1.30 & \(D_{y}\) & 1.30 \\ \(D_{in}\) & 0.18 & \(D_{out}\) & 0.29 \\ \(P_{t}\) & 0.72 & \(W_{f}\) & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The element geometrical parameters (unit: mm).  
Figure 1: (**a**) Three-dimensional exploded view of the element. Top view of (**b**) decoupling surface, (**c**) radiating patch, (**d**) slot layer, and (**e**) feeding line.
## 2 Communication System Model and Theory
### Correlation Cost Factor-Based Updates to Matched Filters Reception Method for M-SS (CCF-MSS)
The following sections will discuss the process of coarse Doppler estimation, the calculation methods for correlation cost factors, and the theoretical derivation of the receiver decoding process.
# A Survey of Game Theoretic Approaches to Modelling Decision-Making in Information Warfare Scenarios
## 1 Introduction  
Game-theory is a mathematical language for describing strategic interactions and their likely outcomes [1]. The application of game-theoretic approaches to information- and cyber-security problems has been of recent interest to capture the nature of information warfare between an attacker (or group of attackers) and a defender [2; 3; 4]. Game theoretic techniques are utilised to perform tactical analysis of the options available in response to a cyber-threat. Various games have been developed to illustrate the different requirements for effective strategies in information warfare [2; 3]. These games are analysed to establish their equilibrium points and suggest beneficial strategies for players. However, it is increasingly recognised that it is necessary to model players in greater detail, including their intent, objectives and strategies [5] and their motives [6]. This paper presents a taxonomy of games that map to information warfare and cyber crime problems, as a precursor to future research on decision-making in such scenarios.  
Information warfare is not a new concept [7]. People and organisations (and even animals) have long been gathering information about their environment and transmitting information to others. However, manifestations of information warfare are changing as information and communicationtechnologies (ICT) play a steadily greater role in our lives. Our increasing dependence on these technologies has escalated international concern for information- and cyber-security [8; 9].  
A number of review papers have been written to cover game-theoretic approaches to different categories of information warfare. Table 1 presents a summary of some notable surveys. The columns represent the related areas of information warfare that are included in the current review. The rows highlight the categories of information warfare that each of the ten selected reviews touch upon. As can be seen, most review papers only cover one or two related topics of information warfare. This shows a need for a comprehensive review of game-theoretic approaches across a range of information warfare related topics. This survey aims at addressing this gap and provides a comprehensive coverage of different areas of information warfare where game-theoretic approaches have been proposed. We break these into a number of key sub-categories in Section 2.4, which also cross into the areas of cyber warfare and cyber crime, which have not been a focus in existing surveys. We extend and revise existing taxonomies of information warfare to inform a systematic review of the game theory literature in Section 4.
## 2 Materials and Methods
### Robust and Controllable
\right] \tag{14}\]  
\[a_{0}=(1-c_{10}\cdot\lambda-c_{20})\cdot I\]  
\[a_{1}=-c_{10}\cdot\frac{\lambda}{2}(1-\lambda)\cdot I\]  
\[a_{2}=-c_{10}\cdot\frac{\lambda}{6}(1-\lambda)(2-\lambda)\cdot I\]\[a_{3}=-c_{10}.\frac{\lambda}{24}(1-\lambda)(2-\lambda)(3-\lambda)\cdot I\]  
\[a_{4}=\omega(k)\cdot I\]  
\[a_{5}=(-c_{10}\cdot\lambda-c_{20})\cdot I\]  
\[a_{6}=-c_{10}\cdot\frac{\lambda}{2}(1-\lambda)\cdot I\]  
\[a_{7}=-c_{10}.\frac{\lambda}{6}(1-\lambda)(2-\lambda)\cdot I\]  
\[a_{8}=-c_{10}.\frac{\lambda}{24}(1-\lambda)(2-\lambda)(3-\lambda)\cdot I\]  
\[a_{9}=\omega(k)\cdot I\]  
and \(\Delta A\) and \(\Delta B\) denote the uncertainty matrices of the (system matrix) \(A_{I}\) and (input matrix) \(B_{I}\), respectively, as shown in the following equation:  
\[a_{1}=-c_{10}\cdot\frac{\lambda}{2}(1-\lambda)\cdot I\]0  
In this study, a sufficient condition is proposed to explain that the linear system with unstructured parametric uncertainties is robust and controllable: assume that the linear interval system of Equation (11), \(x(k+1)=A_{I}\cdot x(k)+B_{I}\cdot u(k)\), is controllable. If the following conditions are true, Equations (13) and (15) are robust and controllable. \[a_{1}=-c_{10}\cdot\frac{\lambda}{2}(1-\lambda)\cdot I\]1  
where  
\[\varphi_{j}=\left\{\begin{array}{l}\mu\left(-S^{-1}U^{H}E_{j}V\left[ \begin{array} []{cc}I_{n^{2}}&0_{n^{2}\times n(m-1)}\\ -\mu\left(S^{-1}U^{H}E_{j}V\left[\begin{array}{cc}I_{n^{2}}&0_{n^{2}\times n (m-1)}\\ \end{array}\right]\end{array} \right]^{T}\right),\epsilon_{j}\geq 0\\ I_{n^{2}}\text{ denotes the identity matrix of }n^{2}\times n^{2}\text{, and }\overline{n}\text{ represents the number of uncertain matrices. The matrices }S,E_{j}\) are defined as follows:  
\[E_{j}=\left[ \begin{array}{ccccccccccccc}I_{n}&0&\bullet&\bullet&\bullet&0& 0&\bullet&\bullet&\bullet&0&B_{j}\\ -A_{j}&I_{n}&\bullet&\bullet&\bullet&0&0&\bullet&\bullet&B_{j}&0\\ \bullet&-A_{j}&\bullet&\bullet&\bullet&\bullet&\bullet&\bullet&\bullet& \bullet&\bullet&\bullet\\ \bullet&\bullet&\bullet&\bullet&\bullet&\bullet&\bullet&\bullet&\bullet& \bullet&\bullet&\bullet\\ 0&0&\bullet&\bullet&\bullet&I_{n}&0&B_{j}&\bullet&\bullet&0&0\\ 0&0&\bullet&\bullet&\bullet&-A_{j}&B_{j}&\bullet&\bullet&0&0\end{array} \right] \tag{18}\]  
and \(E_{I}=E_{0}+\sum_{j=1}^{n}\epsilon_{j}E_{j}\) allows singular value decomposition to become  
\[E_{I}=U\left[ \begin{array}{cc}S&0_{n^{2}\times n(m-1)}\\ \end{array} \right]V^{H} \tag{19}\]  
where \(U\in R^{n^{2}\times n^{2}}\) and \(V\in R^{n(2n-1)\times n(2n-1)}\) are unitary matrices, \(S=diag[\sigma_{1},\sigma_{2},\cdots,\sigma_{n^{2}}]\). The singular values of \(E_{I}\) are \(\sigma_{1}\geq\sigma_{2}\geq\cdots\sigma_{n^{2}}\geq 0\). The proof of this sufficient condition is shown in Appendix A.
# An FPGA Implementation of a Convolutional Auto-Encoder
###### Abstract  
In order to simplify the hardware design and reduce the resource requirements, this paper proposes a novel implementation of a convolutional auto-encoder (CAE) in a field programmable gate array (FPGA). Instead of the traditional framework realized in a layer-by-layer way, we designed a new periodic layer-multiplexing framework for CAE. Only one layer is introduced and periodically reused to establish the network, which consumes fewer hardware resources. Moreover, by fixing the number of channels, this framework can be applicable to an image of arbitrary size. Furthermore, to effectively improve the speed of convolution calculation, the parallel convolution method is used based on shift registers. Experimental results show that the proposed CAE framework achieves good performance in image compression. It can be observed that our CAE framework has advantages in resources occupation, operation speed, and power consumption, indicating great potential for application in digital signal processing.  
convolutional auto-encoder; neural network; image compression; FPGA Article
## 5 Performance Measures  
The literature has identified several commonly used metrics such as accuracy (A), recall (R), precision (P), specificity (F), false alarm rate (FAR), F1-score, and throughput. Note that accuracy, recall, precision, specificity, and FAR are based on the four primary-level attributes as given below. F1-score is a derived metric based on recall and precision. The four primary-level attributes are as follows [1; 73]:  
* True positive (\(T_{P}\)): legitimate traffic correctly classified as legitimate.
* True negative (\(T_{N}\)): illegitimate traffic correctly classified as illegitimate.
* False positive (\(F_{P}\)): legitimate traffic incorrectly classified as illegitimate.
* False negative (\(F_{N}\)): illegitimate traffic incorrectly classified as legitimate.  
Based on the above four attributes, _accuracy_, which measures the proportion of the total number of correct classifications, is mathematically represented as follows:  
\[Accuracy=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}} \tag{6}\]  
Similarly, _recall_ measures the number of correct classifications penalized by the number of missed entries and is represented by the following equation:  
\[Recall=\frac{T_{P}}{T_{P}+F_{N}} \tag{7}\]  
Likewise, _precision_ is a measure of the number of correct classifications penalized by the number of incorrect classifications, as given by the following equation:  
\[Precision=\frac{T_{P}}{T_{P}+F_{P}} \tag{8}\]  
_Specificity_ is defined as the measure of the prediction of the negative class in the dataset and is represented by the following equation:  
\[Specificity=\frac{T_{N}}{T_{N}+F_{P}} \tag{9}\]  
_False alarm rate_ is the measure of inaccurate classification when the model classifies normal traffic as malicious. It is calculated using the following equation:  
\[FAR=\frac{F_{P}}{T_{P}+F_{P}} \tag{10}\]  
The \(F1\)_score_ is defined as the measure in which both recall and precision are used. In the case of an unbalanced dataset, we usually calculate the \(F1\)_score_. It is defined by the following equation:  
\[F1\;score=\frac{2\times R\times P}{R+P} \tag{11}\]  
_Throughput_ is also an important measure in analyzing the effectiveness of an ML model [21, 29, 74, 75]. The main argument is that DDoS detection is a time-critical task that is to be performed on high-speed networks. Therefore, high throughput results in low latency [76]. Throughput is measured as follows:  
\[Throughput=\frac{Number\;of\;predictions}{Time} \tag{12}\]
## 5 Reconstruction Methods
### Pros and Cons  
This section describes the advantages and limitations of the aforementioned anomaly detection models for time series data. AE-based methods automatically extract data features but cannot capture all the complexities of the data and have difficulty interpreting the meaning of reconstruction errors. VAE is adept at explicitly modeling probabilities and provide a theoretical foundation for understanding data distributions. However, the models rely on the assumption of Gaussian distributions, which may limit the expressive power of these models. GAN-based models learn complex data distributions through adversarial training, maintaining an impressive fidelity to the original data distributions. Nevertheless, they are very challenging to train due to issues like vanishing gradients [75], which can hinder model stability and convergence. Diffusion models perform well when facing noise and missing data, as they can infer missing parts during the generation process, allowing them to better understand the distribution of normal data when detecting anomalies. However, when handling the boundary between missing and observed parts, the model may exhibit inconsistencies [49]. Additionally, capturing the dynamic characteristics of time series may require further adjustments and improvements.
## Results and Discussion
### Carbon Emissions  
The CO\({}_{2}\) and CH\({}_{4}\) daily fluxes from treatments along the experiment are shown in Figure 3A,B. Higher CO\({}_{2}\) fluxes were observed over the first 20 days of the experiment (630 to 1670 mg CO\({}_{2}\) m\({}^{-2}\) h\({}^{-1}\)), followed by a small reduction (640 to 510 mg CO\({}_{2}\) m\({}^{-2}\) h\({}^{-1}\)) until the end of the experiment (Figure 3A). Compared to treatment Control, the amended treatments reduced significantly (\(p<0.05\)) the daily CO\({}_{2}\) fluxes, in about 19%, during the first 13 days of experiment. After this day until the end of the experiment, no significant differences (\(p>0.05\)) were observed between all treatments including Control (Figure 3A). The cumulative CO\({}_{2}\) emissions, expressed in g m\({}^{-2}\), from amended treatments were significantly lower (\(p<0.05\)) in about 5% when compared with treatment Control. When expressed as % of applied C, the CO\({}_{2}\) emissions from treatments Biochar and Biochar + Clinoptilolite were significantly lower (\(p<0.05\)) in about 50% than from treatments Control or Clinoptilolite (Table 3).  
The CO\({}_{2}\) losses coming from the microbial degradation of the organic matter and the hydrolysis of the urea (S  
Methane is produced mainly by microbial decomposition of organic matter under anaerobic conditions [33; 34]. Biochar reduced the CH\({}_{4}\) emission by 55% while clinoptilolite seems had no effect on such loss (Table 3). Previous studies [32; 35; 36] reported that the emission of CH\({}_{4}\) from animal manures could be reduced by 50-95% by adding biochar and clinoptilolite alone or in combination (5-10% _w/w_ of each additive), this being explained by the adsorption ability of the additives. However, in this study, the addition of clinoptilolite to pig slurry did not cause an increase or a reduction in CH\({}_{4}\) emissions because the experiment has been done on the liquid fraction of the slurry.  
The C (CO\({}_{2}\) + CH\({}_{4}\)) emissions (in g m\({}^{-2}\)) were not significantly different (\(p>0.05\)) among amended treatments (Table 3). However, the cumulative C losses, as % of applied C, from the treatments Biochar and Biochar + Clinoptilolite were significantly lower (\(p<0.05\)) in about 50% relative to treatments Control or Clinoptilolite (Table 3). The cumulative emissions expressed as GWP did not differed significantly (\(p>0.05\)) between treatments (Table 3). Thus, the addition of biochar or clinoptilolite to pig slurry did not cause an increase or a reduction in GWP in this study.  
Figure 3: Carbon dioxide (**A**) and methane (**B**) fluxes from each treatment. Vertical bars represent the standard error of the mean (\(n=3\)).
# Removal of Metal Ions in Spin-on Hardmask Using Functionalized Porous Silica Adsorbents
## 2 Experimental
### Material Characterization  
To investigate the morphology, particle size, and pore structure of synthesized MCM-41 and SBA-15, transmission electron microscopy (TEM, JEM-2100, JEOL, Tokyo, Japan) was employed. Small-angle X-ray diffraction (SAXRD, Ultima3, Rigaku, Tokyo Japan, 0.5-10\({}^{\circ}\), 40 kV/40 mA) was used to confirm the structural ordering of the mesoporous frameworks. Nitrogen adsorption-desorption isotherms were obtained to analyze porous characteristics. Specific surface area, average pore size, and total pore volume of the adsorbents were determined using the Brunauer-Emmett-Teller (BET) method, and the pore diameter distribution was calculated using the Barrett-Joyner-Halenda (BJH) method. Zeta potential measurements were conducted using a zetasizer (ZETASIZER ADVANCE Pro, Malvern Panalytical, Malvern, UK). Metal ion concentrations were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES, Agilent 5900, Santa Clara, CA, USA).