esa-sceva/satcom-chunk-collection huggingface dataset:
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 3 Proposed SIEM Framework
### Investigation Module  
Security appliances often generate multiple alerts over a long period of time to detect attacks. This can result in an excessive number of alerts, for instance, in the case of a bot-infected host that repeatedly probes multiple targets on a network. While most IDSs log related information about a specific rule violation triggered by certain network communication, not all IDSs effectively group alerts related to the same attack campaign. As a consequence, the number of false alerts may increase unnecessarily. The improper configuration of detection rules and the generation of recurring alerts for persistent unsolicited communications can also significantly increase the number of false positives.  
The investigation module incorporates data visualization techniques to simplify the complex process of investigating alerts. By working in tandem with the SOAR tools integrated into the SIEM solution, the security analysis process is streamlined, which empowers analysts to respond to threats with greater speed and precision. The integration of data visualization allows security analysts to identify trends and anomalies in the data  
Figure 2: Performance evaluation scheme. (**a**) Cross validation; (**b**) Rolling origin. The subset with a green background indicate it is the testing set for the CV-round. In (**a**), the subset distinguished by a green background signifies its designation as the testing set, while those identified with a white background indicate their inclusion in the training set for each cross-validation round.  
quickly through graphical formats, e.g., charts and graphs. It also provides a user-friendly interface to interact with the SIEM and helps engage stakeholders in the communication of security risks and priorities.  
A previously proposed visualization tool [61] has undergone enhancements to visualize correlated alerts, and the updated version (Section 4) offers a clear graphical interpretation of the analyzed alert, thereby helping security analysts discern the links and correlations between various security incidents.
## 3 Near-Field Simulation of Metasurfaces with Varying Dimensions
### Near-Field Simulation of the Compact Metasurface  
#### 3.2.1 Normal Incidence of a Linearly Polarized Plane Wave on a Compact Metasurface  
The expression for the change in the electric field of the multi-mode vortex wave with an azimuth angle \(\varphi\) can be represented by Equation (3), where m can be an arbitrary integer or fraction. Based on the size of the metasurface, a design phase can be generated for each coordinate position of the metasurface element. The desired rotation phase can be obtained by dividing the design phase by two, and an \(EE\) unit is used to generate the required rotation phase, as depicted in Figure 7a. Figure 7b,c illustrate, respectively, the vector E field and amplitude field of E obtained through full-wave simulation software.  
\[E=\sum_{m}e^{j_{m}\varphi} \tag{3}\]  
The design simulation of a 20 \(\times\) 20 rectangular metasurface's vortex wave is depicted in Figure 8 using full-wave simulation software. The design mode is \(l=-3\), with the incident wave being a uniform linearly polarized wave. The calculated frequency is 10 GHz. Figure 8a,b illustrate the vector electric field and electric field amplitude observed at \(z\) = 130 mm, where the maximum electric field amplitude measures 1.59 V/m. It can be observed that the electric field amplitude generated by the metasurface takes on an irregular ring shape. Figure 8a showcases the scattering electric field, which has been extracted from the full-wave simulation software. By calculating its OAM spectrum, amplitude, and phase, it becomes evident that a vortex wave with mode \(l=-3\) is generated while maintaining high mode purity. Figure 8b,c demonstrate complex vector noise superimposed on the extracted scattering electric field, with noise amplitudes of 0.2 and 0.3, respectively. Through calculation, one can obtain the OAM spectrum, amplitude, and phase of this scattering electric field affected by noise; it should be noted that there is a reduction in purity within its OAM spectrum due to the noise influence.  
Figure 6: \(EE\) unit and reflection spectrum for the \(EE\) unit with different rotation angles under circular polarization wave excitations. (**a**) Top view and (**b**) side view of the \(EE\) unit, where \(n=1.75\) mm, ellipticity is 2.55, \(rb=1.3\) mm, ellipticity is 0.4, period = 10 mm, \(H=3\) mm. (**c**) The magnitude (\(r_{ll}\)) of the unit; (**d**) the phase of the unit.  
Figure 7: Near-field simulation of the compact metasurface. (**a**) The designed 20 \(\times\) 20 metasurface; (**b**) vector electric field; (**c**) magnitude of the electric field.
## 4 Cumulative Damage Theory and Numerical Model Establishment
### Concrete Model of the Pipeline  
To better reflect the cumulative damage to the concrete pipelines as a result of the explosion, the Riedel-Hiermaier-Thoma (RHT) model, which includes more failure criteria, was used in this study. The p-a equation of state of the RHT model is given by Equation (8) [37].  
\[\alpha(t)=\max\Biggl{(}1,\min\Biggl{\{}\alpha_{0},\min_{\rm s\leq t}\Biggl{[}1+( \alpha_{0}-1)\biggl{(}\frac{p_{\rm comp}-p(s)}{p_{\rm comp}-p_{\rm crush}}\biggr{)} ^{N}\Biggr{]}\Biggr{\}}\Biggr{)} \tag{8}\]  
\[p_{\rm c}=p_{\rm comp}-\bigl{(}p_{\rm comp}-p_{\rm crush}\bigr{)}\biggl{(}\frac{ \alpha-1}{\alpha_{0}-1}\biggr{)}^{1/N} \tag{9}\]  
where \(\alpha_{0}\) is the initial porosity of the material, p and e, respectively, represent pressure and internal energy, \(p_{\rm comp}\) and \(\rho_{\rm s}\) are the pressure and density values of the material when it is compacted, \(p_{\rm crush}\) is the critical pressure at which the material begins to crush, N is an index related to the porosity, and \(p_{\rm c}\) is the capping pressure.  
The material model introduces three limit surface equations for the elastic limit, residual strength, and failure surfaces to describe the relationship between the elastic limit, residual strength, failure strength, and hydrostatic pressure when the material is subjected to an impact load, as shown in Figure 6. Change stage of mechanical properties of materials under impact load of RHT model [38].  
The cumulative damage process of the RHT material model occurred only in the softening stage after the damage. The damage variable \(D\) in the process can be calculated by an approximate interpolation between the failure surface and participating strength surface, as shown in Equations (10) and (11).  
\[0\leq D=\sum\frac{\Delta\varepsilon_{\rm p}}{\varepsilon_{\rm p}^{\rm failure }}\leq 1 \tag{10}\]  
\[\varepsilon_{\rm p}^{\rm failure}=D_{1}\Bigl{(}p^{*}-HTL^{*}\Bigr{)}>\varepsilon _{\rm p}^{\rm m} \tag{11}\]  
where \(D_{1}\) is a parameter related to the damage variable, \(p^{*}\) is the standardized equivalent pressure, \(\varepsilon_{\rm p}^{\rm m}\) is the minimum equivalent plastic strain parameter of the material, and \(D\) varies from 0 to 1, which is consistent with the damage variable of the JH-2 material model; both indicate the extent of damage to the material from the time damage sets in, to the point of complete failure.  
Figure 6: Change stage of mechanical properties of materials under impact load of RHT model.
## 5 Result and Discussion
### Evaluation of the Discriminator Models Performance  
We reported quantitative performance of the extracted trained discriminator models to evaluate authentication on the three unseen paintings, A, B, and C, composed of image patches with a resolution of 256 \(\times\) 256 pixels. The results illustrated the superior capability of the PatchGAN model in distinguishing the artist's unique brushstroke style from the forgeries, with an average precision of 0.72 and accuracy of 0.82. We compared the performance of the PatchGAN model with other models proposed in [13], namely PixelGAN and ImageGAN, for brushstroke counterfeit identification. The statistical results of the discriminator models are summarized in Table 2. Additionally, Figure 6 illustrates the corresponding ROC curves of the discriminator models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unseen Paintings** &
\begin{tabular}{c} **Mean of Forgery Score (Mean of FFT** \\ **Dissimilarity Value \(\pm\) Standard Error of Mean)** \\ \end{tabular} & **Painting’s Picture** \\ \hline Original Painting-A & 1769.24 \(\pm\) 24.48 & \\ \hline Fake Painting-B & 2336.24 \(\pm\) 29.11 & \\ \hline Fake Painting-C & 2101.94 \(\pm\) 37.16 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mean of forgery scores of each tested painting using FFT.  
Figure 5: The data distribution of patch-level forgery scores using FFT frequency analysis.
# _Ku_-Band CMOS Power Amplifier with Three-Stack Power Stage to Enhance Output Power and Efficiency
## 2 Typical CMOS Power Amplifiers  
In general, the power amplifier is designed to have the same structure as the driver and power stages constituting the power amplifier for the use of a single supply voltage. For example, both the driver and power stages are designed in a common-source structure or a cascode structure [11]. This design method has the advantage of using a single supply voltage in the power amplifier and suggests that each of the driver and power stages has room for additional structural optimization. In this study, the structures most commonly used in the typical power amplifiers were investigated. Based on this, an optimum structure of a power amplifier to obtain high output power and high efficiency is proposed.
# Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
## 3 Separation Calculation Model Based on Track Prediction
### Separation Calculation Model
#### 3.2.1 Conflict Frequency  
A model for calculating conflict frequency is constructed by designing a gas model. The advantage of a gas model is to quantify collision risk between uniformly distributed aircraft in an area and reflect free flight mode. According to the basic concept of the gas model [14], the conflict frequency between the host and the intruding aircraft is related to  
Figure 3: LSTM’s individual element structure. The two input variables in the asterisk are dotted.  
Figure 2: LSTM’s overall structure.  
the instantaneous aircraft density, the frontal area of their encounter direction, and their respective speeds. To calculate the conflict frequency, some variables are preset.  
To average the risk of conflicts occurring in all directions, the area where the host and intruding aircraft might conflict is assumed to be a circle with a radius equal to the separation \(D\). If the distance between the centers of the two aircrafts is within \(D\), a conflict occurs, as shown in Figure 4.  
Therefore, the conflict area is as shown in Equation (1):  
\[\sigma_{\text{cft}}=\pi\cdot D^{2} \tag{1}\]  
In the formula, \(v_{\text{rel}}\) represents a conflict zone and \(D\) is the separation. The volume of the conflict airspace swept by the UAV in unit time is as shown in Equation (2):  
\[V_{\text{cft}}=\sigma_{\text{cft}}\cdot v_{\text{rel}} \tag{2}\]  
In the formula, \(V_{\text{cft}}\) represents the volume of the conflict airspace swept by the UAV in unit time and \(v_{\text{rel}}\) is the relative speed, determined as a compromise value, as shown in Equation (3):  
\[v_{\text{rel}}=\sqrt{v_{\text{host}}^{2}+v_{\text{i}}^{2}} \tag{3}\]  
Within the aforementioned airspace area, conflict frequency equals the product of the unit volume and the aircraft density, as shown in Equation (4):  
\[Freq=\rho_{0}\cdot\sigma_{\text{cft}}\cdot v_{\text{rel}} \tag{4}\]  
In the formula, \(Freq\) is conflict frequency and \(\rho_{0}\) is the number of aircrafts per unit volume, assuming that conflicts occur independently among aircrafts. The total conflict frequency within the airspace range can be obtained by multiplying the conflict frequency of a single aircraft by the number of aircrafts and divided by 2, as shown in Equation (5):  
\[Freq=\frac{N}{2}\cdot\rho_{0}\cdot\sigma_{\text{cft}}\cdot v_{\text{rel}} \tag{5}\]  
In the formula, \(N\) is the number of aircrafts.
## 1 Introduction
### Contributions  
Interest in maritime activities has been increasing in recent years, and the currently deployed communications infrastructure (CI) cannot meet the needs of emerging use cases. In this case, A2S integrated maritime communication technology complements the ground and satellite parts, bringing unique advantages in terms of coverage, transmission efficiency, and so on. Considering the integration of A2S integrated technology into MIoT, this survey provides relevant supplements to the A2S integrated MIoT technology, introduces the key technologies applied therein, and analyzes their algorithms. In more detail, our contributions are as follows:  
* This paper introduces the MIoT and A2S integrated technologies in detail, and discusses the types and functions of sensor technologies applied to various applications, emphasizing the wide application of air base stations (ABSs).
* We summarize the technology and propose the cloud/edge computing algorithm, analyze and discuss the algorithm, further introduce the routing protocol (RP), and optimize the spatial position and trajectory (SPAT).
* Then, in order to illustrate the wide application of A2S integrated MIoT in the world, some specific cases are provided to demonstrate the role of MIoT in different fields.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Short Summary** & **Introduction** \\ \hline
[39] & A survey on the demand for maritime communications enabled by state-of-the-art hybrid satellite-terrestrial MIoT. & The external auxiliary information can used to build up an environment-aware, service-driven, and integrated satellite-terrestrial MIoT. & The external auxiliary information can used to build up an environment-aware, service-driven, and integrated satellite–air–ground MIoT. \\ \hline
[40] & A survey on UAV-aided maritime communications. & The state of the art in UAV-aided maritime communications is presented. \\ \hline
[41] & A survey on communication and networks for autonomous marine systems. & The major advancements on state-of-the-art autonomous maritime vehicles and systems are reviewed. \\ \hline
[42] & A holistic overview of the different forms of maritime communications is presented. & The latest advances in various marine technologies and some emerging use cases of the MIoT are provided. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._We also analyze the challenges faced by the current technology, discuss the development prospects of the future MIoT, and finally put forward solutions.
###### Abstract  
Hydrodynamic noise is induced by hydrodynamic phenomena, such as pressure fluctuations, shear layers, and eddy currents, which have a significant impact on ship performance, pumping equipment efficiency, detection accuracy, and the living environment of marine organisms. Specifically, hydrodynamic noise increases fluid resistance around the hull, reduces speed and fuel efficiency, and affects the stealthiness of military vessels; whereas, in pumping equipment, noise generation is usually accompanied by energy loss and mechanical vibration, resulting in reduced efficiency and accelerated wear and tear of the equipment. Traditional physical experiments, theoretical modeling, and numerical simulation methods occupy a key position in hydrodynamic noise research, but each have their own limitations: physical experiments are limited by experimental conditions, which make it difficult to comprehensively reproduce the characteristics of the complex flow field; theoretical modeling appears to be simplified and idealized to cope with the multiscale noise mechanism; and numerical simulation methods, although accurate, are deficient in the sense that they are computationally expensive and difficult to adapt to complex boundary conditions. In recent years, intelligent algorithms represented by data-driven algorithms and heuristic algorithms have gradually emerged, showing great potential for development in hydrodynamic noise optimization applications. To this end, this paper systematically reviews progress in the application of intelligent algorithms in hydrodynamic noise research, focusing on their advantages in the optimal design of noise sources, noise prediction, and control strategy optimization. Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future research directions. It is hoped that this review can provide useful references for theoretical research and practical engineering applications involving hydrodynamic noise, and point the way toward further exploration in related fields.  
hydrodynamic noise; numerical simulation; intelligent algorithm; energy efficiency; optimization and control +
Footnote †: journal: Applied sciences
# A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting
## 2 Related Work
### Deep Learning Method
In [23], two deep learning methods, time-dependency convolutional neural network (TD-CNN) and cycle-based long short-term memory (C-LSTM) network, were proposed to improve the forecasting performance of electricity load forecasting. The similarity between previous work and this paper is that all methods are based on neural networks because neural networks have strong nonlinear fitting ability and are suitable for complex electricity forecasting, and the main difference is that this paper adopts a new NODE method and combines it with an LSTM-based model to improve forecasting accuracy. Previous works such as [23] do not address problems caused by discrete observation interval. In this work, we discuss how NODE solves these problems and how NODE improves the forecasting accuracy. Overall, a traditional ARIMA model requires a highly stable and small dataset. Machine learning models such as SVM lack the ability to process large-scale data due to the memory problems and they cannot extract the internal relation of time series. LSTM requires discretize observation and emission intervals and ignores the continuity of time series. Compared with the previous three types of method in STLF, the NODE-based model has the following advantages:  
* Saves computing consumption. NODE does not store any intermediate quantities of the forward pass. Therefore, NODE has constant memory cost. * Highly dynamic and flexible. Due to continuous observation interval, NODE removes hysteresis of LSTM-based models. Thus, the NODE-based model dynamically adapts to drastic change of series to reduce the forecasting error. * Captures complex nonperiodic patterns. Through learning the law of internal evolution equation, NODE can extrapolate input series to approach expected target series in latent space. In this way, NODE builds continuous temporal connection of time series to capture complex nonperiodic patterns. These advantages address all the previous problems we mentioned. Therefore, NODE is a potential method for electricity load forecasting. More detailed advantages of NODE are discussed in Section 5.3.
# A Brief Overview of Energy Efficiency Resources in Emerging Wireless Communication Systems
## 3 Energy Efficiency Techniques in Wireless Communication Systems
### Renewable Energy Resources  
In Benin city, Nigeria, an on-grid and a standalone PV system for a telecommunication base station were analyzed and compared [62]. The operational expenditure for an on-grid system was lower than the standalone PV system. It was suggested that an on-grid system is a very cost-efficient scheme in areas with an adequate electricity supply. Later in 2016, a standalone PV system was also analyzed in South Korea. The authors in [63] highlighted that it would be impossible to adopt this system in areas with extremely low solar irradiation. This opinion agrees with the work done in [64] earlier in 2015 in Malaysia, which was compared with another site in Germany that solar irradiation is an important factor to be considered if a PV system is to be utilized because it affects the overall effect  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Feature**} & \multicolumn{1}{c}{**RF Communication**} & \multicolumn{1}{c}{**Optical Wireless Communication**} \\ \hline Power consumption & Medium & Relatively low \\ \hline Cost of technology & High & Low \\ \hline Noise Sources & Similar frequency & Near external light sources \\ \hline Multipath signal diminishing & Yes & No \\ \hline Radio frequency & Yes & No \\ electromagnetic interference & & \\ \hline Data speed & Mbps & Gbps \\ \hline Bandwidth & Regulated & Not regulated \\ \hline Safety & Poor & Good \\ \hline Passes through walls & Good & Cannot \\ \hline Direction of Beam & Fair & Average \\ \hline Usable bandwidth & Small & Quite large \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between RF and optical wireless systems.  
cost and gas emissions. A PV/DG system was considered, unlike the work done in [55, 56] that thought of just standalone PV systems. In 2019, another PV/DG system [65] proved to be a more considerable system that should be adopted in Nigeria as opposed to an on-grid system suggested in [62] because most base stations in Nigeria run almost totally on diesel generators because of the power supply problem in Nigeria. So, integrating a PV with the conventional DG system would reduce the overall expenditure and environmental emissions caused by several base stations in Nigeria. A similar analysis in [66] had earlier been done in 2018 in Nigeria to show that PV systems should be added to the conventional DG system. The work added an analysis indicating that it is better to have an optimized generator scheme for maximum utilization of the PV resource. All the authors used HOMER as the simulation software, except those that used PVSYST6.0.7 [62]. Further improvements in the proposed models are achievable by incorporating the right software metrics into the simulation software [67]. However, the additional software metrics will be at the expense of the desired efficiency level within the governing ethics and standards [68, 69].
# Air-Based Contactless Wafer Precision Positioning System
## 2 State of the Art  
A survey of recent developments in the field of contactless manipulation is presented in [9]. The use of airflow for manipulation and control of the localised deformation of wafers has also been studied in [10], while a pick-up head for wafers has been designed using similar concepts in [11]. Ultra-sound air film technology has also been studied for this application in [12]. However, while this technology provides a viable alternative for contactless pick-up of wafers, it is mainly foreseen to be used in a pick-up head and not for complete manipulation and/or transportation of wafers using airflow.  
The start of the development of the new contactless actuation concept at TU Delft was in 2007 [3]. The concepts use the same physics to control the position of the substrate. First, the substrate is levitated on a layer of air. Subsequently, the substrate is preloaded in the out of plane direction with a controlled vacuum source. This increases the out of plane stiffness at the fly height. This height can be adjusted by changing the force the vacuum and pressure source are applying.  
The in-plane actuation is achieved by creating an airflow tangential to the substrate surface. The air is flowing from the high-pressure area to the lower pressure following the path of the least resistance. This flow of air exerts a viscous traction force on the substrate. This innovative idea has lead to multiple concepts that will be discussed in this section.