esa-sceva/satcom-chunk-collection huggingface dataset:
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 4 Methodology
### Augmented Tile Graph Visualization
(**a**) Tile graph and (**b**) Color-coded legend illustrating host locations and attack orientations within the tile graph. Hosts are represented by disks, with colors indicating their locations (green for internal, blue for external, orange for DMZ, purple for proxy server, and gray for missing information). Alerts are depicted by lines connecting source and destination hosts in the panels, with colors representing communication orientations (blue for outbound, red for inbound, green for internal, yellow for external, orange for DMZ-related, and gray for unknown). Ellipses A, B, and C in (**a**) highlight distinct host groups. Ellipse A emphasizes a proxy server, exhibiting outbound alerts between source internal hosts and the proxy server as the destination. Ellipse B exemplifies a source host triggering periodic outbound alerts to external destination hosts. Ellipse C demonstrates internal hosts triggering periodic internal alerts between other internal destination hosts. IP addresses in this example were anonymized through random shuffling, ensuring the absence of identifiable information. Overall, the ATG visualizes complex datasets effectively, as demonstrated by the results of the event segmentation algorithm. The power of data visualization is evident in this example because patterns are readily apparent through a visual inspection.
# Performance Evaluation Method for Intelligent Computing Components for Space Applications
## 3 Evaluation Method
### Common Benchmark  
First, this paper selects CoreMark-Pro benchmark tests for the pre-evaluation of processor performance. CoreMark-Pro benchmark tests include five popular integer workloads and four popular floating-point workloads. The integer workloads include JPEG compression, ZIP compression, XML parser, SHA-256 secure hash algorithm, and a memory-intensive version of the original CoreMark. The floating-point workloads include fast Fourier transform (FFT), linear algebra routines derived from LINPACK, a significantly improved version of the Livermore loop benchmark, and a neural network algorithm for pattern evaluation.
## 3 Path-Tracking Control Strategy
### Calculation of Path-Tracking Target  
The CG of the first and the fourth module and all the hinge points are taken as path-tracking control points to ensure the consistent motion of adjacent modules' hinge points and to reduce hinge force. The first module's target path and the lateral tracking error are obtained through the onboard camera or other sensors, and the rotation angle of each hinge plate is obtained through the rotation angle sensor. The target path is represented by a cubic spline curve.  
\[P_{T}(s)=\begin{pmatrix}P_{x}(s)&P_{y}(s)\end{pmatrix}^{T} \tag{29}\]  
As shown in Figure 5, first, the point on the target path closest to the CG of the first module is found as the target position for the first tracking control point \(T_{p}^{1}\). The target positions of subsequent tracking control points are determined by combining the vehicle dimension. The path-tracking target of the vehicle consists of the lateral tracking error of the first module and the heading error of each module:  
\[Y_{d}=\begin{bmatrix}y_{d}^{1}&\psi_{d}^{1}&\psi_{d}^{2}&\psi_{d}^{3}&\psi_{d }^{4}\end{bmatrix}^{T}, \tag{30}\]  
where the heading error of each module can be calculated by the following:  
\[\psi_{d}^{i}=\arctan\Biggl{(}\frac{T_{p}^{i}(y)-T_{p}^{i+1}(y)}{T_{p}^{i}(x)- T_{p}^{i+1}(x)}\Biggr{)}-\psi^{i},\;(i=1,2,3,4), \tag{31}\]  
Figure 5: Schematic diagram of the path-tracking target calculation.
## 3 A Proposed Approach
### Path Planning Approach
#### 3.3.2 Preliminary Planning  
The path planning approach can be divided into three steps. The first step is to compute the planning space representation \(M_{\rm e}\). Based on the \(M_{\rm e}\), in step two, the FM method is executed from the start point to generate a joint potential field, namely, the travel time map [8]. The last step is to produce the path based on the joint potential field by applying a gradient descent algorithm.  
Based upon the representation of static and dynamic obstacles mentioned above, the preliminary planning space representation \(M_{\rm e}(t_{0})\) at time \(t_{0}\) can be calculated as follows:  
\[M_{\rm e}(t_{0})=\min(M_{s}(t_{0}),M_{d}(t_{0}))=\min(M_{s},M_{d}(t_{0})). \tag{6}\]  
Then, the preliminary path \(P_{t_{0}}\) can be produced according to \(M_{\rm e}(t_{0})\). As shown in Figure 5a, the solid red line is the preliminary path planned in a crossing scenario.  
Figure 5: The preliminary path and the path optimization process of the proposed approach. (**a**) The preliminary path, (**b**) The path optimization process.
# Fully Attentional Network for Low-Resource Academic Machine Translation and Post Editing
## 1 Introduction
### Problem Definitions and Motivation  
Especially in recent years, translation services (Google, Bing, Yahoo) developed by large corporations have made considerable progress [3]. For daily spoken language translations, when the source sentence entered into the system is a smooth and grammatically correct sequential sentence, the target sentence structure will be clear and comprehensible in parallel. The abundance and variation of scientific terms in academic studies may decrease the effectiveness of these translation systems. While there are examples of academic translation systems for other languages such as Aspec [4] and Scielo Corpus [5], there is still no research on the Turkish language. The need for an academic translation system and advances in NLP and MT are factors that motivate this study in its emergence. In this study, both a corpus and an NMT model are proposed for Turkish-English academic translations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Turkish in the Literature** & **Google Translate** & **In the Literature** \\ \hline evrissimsel sinir agi girisleri & convolutional neural network entries & Convolutional neural network inputs \\ sozel olmayan yakmik becerileri & non-verbal intimacy skills & Non-verbal immediacy skills \\ nitel arastrrmalarda cesitleme & diversification in qualitative research & Triangulation in qualitative research \\ smuf iç gögttemen davranslänn & classroom teacher behavior & Teacher behavior within classroom \\ beliriszlik hospórüsü seviyesi & level of uncertainty tolerance & Ambiguity tolerance level \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some terms that the Google Translate (Version: 6.36, Date: 11 May 2022) system translates incorrectly or incompletely.  
* This study offers a translation system for researchers and graduate students to get better translations while writing academic papers in English. In this way, spelling and grammar errors can be minimized.
* This study introduces a parallel corpus that has been created comprehensively for the Turkish-English language pair.
* The use of the shallow fusion method added to the base of the translation architectures represented in this study may inspire further studies on different language pairs.
* The parallel corpus created and the proposed model are presented as open-source for researchers working in the field of machine translation. [[https://github.com/ilhamisel/tr-en_translate](https://github.com/ilhamisel/tr-en_translate)]([https://github.com/ilhamisel/tr-en_translate](https://github.com/ilhamisel/tr-en_translate)) (Accessed on 3 November 2022).
## 4 Results and Discussion  
In order to verify the effectiveness of MTL-AMWO, a large number of experiments are conducted in this section. The convergence of MTL-AMWO is compared with MTL-WO and M-FNAS [21]. The accuracy of MTL-AMWO is compared with MTL-WO and M-FNAS. In this paper, the hyperparameter'maximum depth' of the Cart tree is set to 25, and the hyperparameter 'number of hidden layers' of the \(BP\) neural network is set to 1. The hyperparameter '\(ft\)' of AMWO is set to 10, and the hyperparameter '\(T\)' is 100, the hyperparameter '\(Num\)' is set to 40, the hyperparameter '\(\epsilon\)' is 5, and the hyperparameter '\(\eta\)' is 1.01. The hyperparameter '\(T\)' of WO is set to 100, and the hyperparameter '\(Num\)' of WO is set to 40. The hyperparameter '\(T\)' of DE in M-FNAS is set to 100, and the hyperparameter '\(Num\)' of DE is set to 40. The other hyperparameters of the above algorithms are set to default values.  
The experiments are carried out by the sklearn library of Python (version 3.9.13).
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 2 Methodology
### Feature Pyramid Pooling Module  
In 2017, PSPNet [21] proposed the Pyramid Pooling Module (PPM) for the first time. The pyramid structure can not only extract the feature information of different scales in the image but also increase the receptive field area appropriately, but this method is not able to extract the channel feature of the global context. In addition, the calculation in the channel direction cannot effectively obtain the multi-scale features of the image and lacks the information on the pixel direction. Due to the high resolution of remote sensing images of buildings, different sizes of buildings, and chaotic distribution, the global context information is also particularly important in the extraction process.  
Based on this, this paper improves on the spatial pyramid pooling module and proposes FPP. As shown in Figure 3, in order to better extract context from different pyramid scales, the 3 \(\times\) 3 convolution is used after the output of the last 3 scales in the pyramid structure. Extracting different scales of information can integrate context features more accurately. Afterward, the most original information in the first scale is multiplied by the information in the last three scales. Compared with the stacking operation in the spatial pyramid pooling module, the multiplication operation does not generate a lot of parameters. It is worth noting that the 4 scales passed through the down-sampled output are 3 \(\times\) 3 dilated convolutions with dilated rates of 1, 6, 12, and 18, respectively. In addition, average pooling with output characteristics is also introduced, followed by the use of the 1 \(\times\) 1 convolution to increase the linearity of the output characteristics, and then the introduction of the 2 \(\times\) 2 deconvolution and multi-scale feature information for addition and fusion.  
Figure 2: Ordinary residual block and separable residual block. (**a**) Ordinary residual block; (**b**) separable residual block.
## 4 Model Testing of Actual Signal Denoising
### Comparison of Optimization Algorithms  
With the advancement in mathematical theories, various optimization algorithms have emerged. To highlight the advantages of the MVO optimization algorithm, we compare it with the NGO, AOA, and RIME [24; 25] optimization methods. The calculation results are shown in Table 3.  
According to the table, when optimizing the VMD hyperparameters with the four optimization methods, the penalty factor \(\alpha\) converges at 2000 for all methods, and the optimal number of modes for each method is 12. All four methods can be used for optimizing VMD decomposition hyperparameters. However, in terms of computation time comparison, the MVO optimization method presented in this paper has the shortest computation time and the highest efficiency for gate vibration signals.
# Experimental Study on the Heat Transfer Performance of Pump-Assisted Capillary Phase-Change Loop
###### Abstract
The surface micro/nanostructure can be used in the micro-channel to increase the nucleate site density, promote the nucleation of bubbles at low wall superheat, and restrain boiling delay, thus not only eliminating the two-phase instability but also enhancing boiling heat transfer [22; 23; 24]. Some scholars have put forward methods for active suppression of micro-channel flow boiling instability, such as increasing the inlet liquid pressure to prevent backflow [25], using a subcooled liquid jet to prevent bubbles from growing [26; 27], etc. However, the micro/nanostructure is complicated to prepare and cannot be used for active control of instability and the heat transfer process. Active regulating methods may cause a large flow pressure drop and extra pump work. The root cause of two-phase flow instability in the phase-change cooling system is the various liquid-vapor two-phase flow phenomena in the phase-change process. In this paper, the idea of isolating the vapor phase from the liquid phase based on the above analysis is proposed. The specific method is as follows: a porous capillary wick is set between the evaporation surface and the liquid channel so that liquid can be supplied to the evaporation surface through the liquid permeability of the capillary wick. The gas produced by phase-change can be isolated through the vapor isolation characteristics of the capillary wick, which prevents the formation of liquid-vapor two-phase flow, thus solving the two-phase instability of a phase-change cooling system. By establishing a pump-assisted capillary phase-change loop system with methanol as the working medium, this paper studies the effect of liquid-vapor pressure difference on both sides of the capillary wick on the start-up characteristics and steady-state heat transfer characteristics. The proposed system was found to be good for overcoming two-phase flow instability. The heat transfer mode of the evaporator can be effectively controlled by controlling the liquid-vapor pressure difference to enable the evaporator to form efficient thin-film evaporation heat transfer and significantly enhance heat transfer.
# Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement
## 3 Uncertainty Analysis of Spherical Joint Space Rotation Measurement System
### Source Analysis of Uncertainty under Specific Angle Measurement  
In the measurement process, the ball head is made of aluminum. At 20 \({}^{\circ}\)C the temperature coefficient of aluminum is 2.3 \(\times\) 10\({}^{-5}\)/\({}^{\circ}\)C [23], and the experiment is carried out in a constant temperature laboratory, so the influence of temperature on the deformation of the ball head can be ignored. In the design scheme, the eddy current sensor is used to sweep through different contours and grooves. Neural network modeling was used to fit the spherical surface morphology and the corresponding rule of the sensor array output value. The influence of the surface machining error of the ball head has good fault tolerance, and the uncertainty caused by the surface machining error of the ball head can be ignored.  
Based on the uncertainty analysis of the measurement system mentioned above, the uncertainty of the spherical joint space rotation measurement system mainly comes from the errors of the three-dimensional rotary calibration table, the ball head eccentricity, the sensor errors, and the neural network model errors. The influence of sensor errors and model errors on the sampling points can be analyzed in combination.  
According to the concept of MCM simulation, the general form of the uncertainty model of the spherical joint rotation measurement system can be summarized as follows:  
\[\mathrm{y}=\mathrm{Y}+\mathrm{u}_{1}+\mathrm{u}_{2}+\mathrm{u}_{3} \tag{5}\]  
where:  
y--measurement results synthesized according to the uncertainty model;  
Y--results obtained through measurement model calculation;  
\(\mathrm{u}_{1}\)--the uncertainty component of the calibration platform rotation;  
\(\mathrm{u}_{2}\)--the uncertainty component of ball head eccentricity;  
\(\mathrm{u}_{3}\)--the uncertainty component of the sensor and neural network measurement model.
# Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking
###### Abstract  
Pubbing, or using a phone to snub another person, has been investigated through social and personality frameworks. Phubbing involves attending to and performing competing tasks, implying the involvement of attentional abilities. Yet, past research has not yet used a cognitive framework to establish a link between phubbing and attention. Using self-report data from a large online sample, we explored the associations between phubbing and everyday attentional failures. Phubbing was associated with difficulties in attentional shifting and distractibility, frequent attentional lapses, spontaneous and deliberate mind wandering, and attention-related cognitive errors. When examining these attention variables alongside several psychosocial and personality variables, attention-related cognitive errors acted as the biggest predictor of phubbing behavior. Phubbing was also positively correlated with media multitasking, which is a conceptually similar yet distinct technology use behavior. The results suggest that perceived everyday attentional failures are strongly associated with, and to an extent can predict, phubbing behavior, even more so than some social and personality variables. Technology has incorporated itself as a necessity, or at the very least a favored convenience, in most people's lives. Characterizing technology multitasking behaviors from a variety of frameworks can help us better understand who is engaging in these behaviors and why.  
phubbing; technoference; multitasking; attention; media multitasking +
Footnote †: journal: _Future Internet_
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 4 Related Research: Modified Watermarking Approaches in the IoT Applications
### Reversible  
Another technology that is related to authentication in the watermark is called the reversible approach. In this approach, the WSN data are grouped where watermarked bits are counted for each group. After that, the data are embedded with watermark bits before being sent to the upper layer. In the upper layer, the watermarked packets are extracted, and watermark bits are checked in order to restore the original data [108]. The researchers in [109] propose a reversible security framework, which is able to embed the Electronic Patient Record (EPR) securely throughout the medical images. These images are afterwards stored to a mobile cloud-based e-healthcare system where cloud administrators do not have privileges to gain access through the data and the client privacy can be preserved. This approach implements an integral method as the Optimal Pixel Repetition (OPR) where a pixel's permutation embeds the data in a reversible manner. On the other hand, the original image is not required to extract the EPR from the stego image. The proposed approach is resilient to statistical attacks where its histogram invariances between the cover and stego images. Moreover, the researchers in [110] propose a reversible algorithm, which is based on a prediction-error that can reduce the watermark load. Further, the WSN node attempts to group the streaming data into two different sets. In the first set, the watermarked bits are accounted and embedded into the other set. After that, the node sends the watermarkedpackets through to the edge-router and then through to the edge-router directly that checks the packets and restores the original data. Hence, this research focuses on removing the watermarks' complications during the verification process where this removal reduces the delay in removing any additional unwanted information.
## 3 Acoustic Emission Technique
### Global Parameters Computed from the AE Signal  
This work uses two global indexes as failure precursors during a damage process:  
1. Relation between the number of events and the signal amplitude (\(b\)-value): This relation is widely used in seismological applications, with the classic law by [PERSON] and [PERSON] [50] being the primary example of its usefulness. It is mathematically expressed as \[N(\geq A)\propto A^{-b},\] (10) where \(N\) is the cumulative number of signals and \(A\) is the signal amplitude. The physical meaning of \(b\) is discussed in refs. [21; 56]; it is hypothetically related to the fractal dimension \(D\) of the domain from which the cracks emanate, according to the expression \(D=2b\). At the beginning of a damage process within the structure, events originate from a micro-fissure of volumetric distribution, i.e., \(D=3\) and \(b=1.5\), leading to small-amplitude signals. As damage progresses and localization effects occur, events emanate preferentially from the micro-fissure cloud, resulting in macro-crack nucleation.  
The procedure for computing \(b\) is explained in detail [57] and summarized schematically in Figure 2. The amplitudes due to each event are collected and organized in a histogram. Then, a bi-log diagram relating the cumulative number \(N\) of events with amplitudes \(\geq A\) is drawn. Finally, \(b\) is determined as the angular coefficient of the fitting line.  
* Temporal derivative of the system's elastic energy: This criterion takes the local maxima of the system's elastic energy variation rate (i.e., its time derivative) as a  
Figure 2: Precursors from AE tests, obtaining \(b\)-value from Equation (10).  
precursor. Given its original proposition by [38], it is referred to here as the Debski-Pradhan-Hansen (DPH) parameter.
## 1 Introduction
* For an increased revisit of maritime surveillance, a novel DSS Archetype, \"_Constellation of Formations_\", is proposed, with an associated autonomous control law evaluated by simulations. The objective of this work is not to define the constellation parameters but to propose a concept for its implementation adding autonomy by means of this two-level (constellation/formation) autonomous orbit control. Single pass interferometry is selected to maximize the coherence, which can be achieved by satellites flying very closely using a Formation Flying (FF) approach, as was implemented on TanDEM-X [28]. This pioneering mission generated new SAR products by defining the acquisition modes as a function of the relative orbits between both satellites, each of them having a complete SAR instrument. Examples of these relative orbits are given in Figure 3. Satellite Formation Flying (SFF) is the coordination of multiple neighboring satellites to accomplish an objective/goal stated in terms of the relative orbits between them. There are various configurations of Formation Flying missions in order to satisfy the user requirements. Each configuration can be obtained by small changes in the orbital parameters of each deputy satellite with respect to the nominal parameters of the chief satellite. In order to meet the needs of the users, different configurations of Formation Flying missions have been proposed. SFF can be classified depending on the configuration, mode of operation, and other factors.