esa-sceva/satcom-chunk-collection huggingface dataset:
# Unveiling the Shadows--A Framework for APT's Defense AI and Game Theory Strategy
## 5 Conceptual Framework
### Data Simulation and Preprocessing with Synthetic Data Generation  
Cybersecurity threats have evolved beyond conventional attacks, with APTs leveraging sophisticated attack vectors to maintain long-term, covert presence in targeted systems. Existing defense mechanisms, such as signature-based detection and rule-based intrusion detection systems (IDS), struggle to counter adaptive, evasive threats. To address this, we propose a hybrid APT defense framework that combines unsupervised learning for  
Figure 4: Workflow of the proposed hybrid APT defense framework.  
anomaly detection and game theory for strategic mitigation. Real-world labeled datasets for APT detection are scarce due to confidentiality and data protection concerns. Therefore, we opted to generate a synthetic dataset that simulates realistic APT behaviors within network traffic. The dataset is statistically controlled and allows flexibility for testing anomaly detection algorithms under diverse scenarios. To evaluate the proposed framework, we simulate network traffic (Table 3), incorporating both benign and malicious patterns with normal traffic in Gaussian distribution (\(\mu=50\), \(\sigma=10\)) and attack traffic (APT behavior) in Gaussian distribution (\(\mu=100\), \(\sigma=30\)) and a dataset composition with 90% normal traffic and 10% APT-infected traffic. The Gaussian parameters were selected to simulate a statistically meaningful separation between benign and malicious behavior.  
These values were inspired by empirical observations and modeling of APT behavior patterns described in previous studies, such as that of [PERSON] et al. (2018). Next, we normalized and preprocessed the data using standardization techniques to ensure consistency across features. Categorical variables were encoded numerically, and outliers were filtered using robust statistical methods. Data from logs of network traffic, system events, and user activity are continuously collected and analyzed in real-time. Feature engineering techniques are applied to convert raw inputs into structured data representations. Statistical methods help reduce noise and enhance the clarity of patterns relevant for anomaly detection.  
We use the isolation forest (IF) algorithm, which is an unsupervised machine learning model designed for anomaly detection by recursively partitioning data. This model (Figure 6) isolates anomalous instances with fewer splits, leading to a contamination level of 5% (assumption that APTs form a minor portion of network activity), and a scoring mechanism describing the anomaly score as determined by the depth of partitioning and instances exceeding a predefined threshold are flagged as APT-related activity.
## 3 Results
### Risk of Bias  
Figure 3 shows the risk of bias judgements using the RoBANS 2 tool, with no study being classified as low risk in all domains. Ten studies showed a high risk of bias [3; 16; 19; 21; 23; 24; 25; 26; 27; 28]. For unclear risk, in comparability of the target group, one study was included due to the absence of age-related information [23]. In target group selection, 13 studies included only male or only female participants [3; 4; 5; 11; 13; 14; 17; 20; 23; 24; 26; 27; 29]. With confounders, it was unclear whether training intensity, wind speed and ambient temperature were adequately controlled [3; 12; 13; 14; 15; 18; 19; 20; 21; 23; 24; 26]. In the blinding of assessors, studies did not report whether blinding was implemented [4; 5; 12; 13; 14; 15; 16; 17; 20; 22; 27; 29] and in the incomplete outcome data, the missing data were either not described or insufficiently explained [3; 4; 5; 12; 13; 14; 16; 17; 20; 22; 27; 29]. Finally, in selective outcome reporting, not all prespecified outcomes were reported [5; 27].  
As illustrated in Figure 4, the methodological quality of the included studies exhibited substantial variability across bias domains. The most critical limitations were observed in the reporting of blinding of assessors: 32% of studies were rated as high risk (the highest among all domains), with 64% receiving an unclear judgment. For target group selection, 14% of studies were deemed high risk, and only 27% demonstra  
Figure 3: Low (+), unclear (7) or high (\(-\)) judgments by domain for each study. Colors indicate judgment levels: green = low risk, yellow = unclear risk, red = high risk. D = domain as D1: comparability of the target group; D2: target group selection; D3: confounders; D4: measurement of intervention/exposure; D5: blinding of assessors; D6: outcome assessment; D7: incomplete outcome data; and D8: selective outcome reporting [3; 4; 5; 11; 12; 13; 14; 15; 16; 17; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29].  
for the control of confounders, only 4.6% of studies were rated high risk and 41% as low risk. Notably, the measurement of intervention/exposure domain showed robust methodological rigor (100% low risk). Other domains with favorable ratings included selective outcome reporting (91% low risk). However, incomplete outcome data had a 68% unclear risk due to insufficient reporting. The overall risk distribution across all studies was high risk and unclear (45 and 59%) and no studies were classified as uniformly low risk across all domains.
# An In-Depth Review of the Weibull Model with a Focus on Various Parameterizations
## 5 Monte Carlo Simulation Studies
### MC Simulation Study II: Residuals Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{\(\beta_{1}=0.2\);} & \(\beta_{2}=1.0\); & \(\zeta_{1}=0.2\); & \(\zeta_{2}=1.0\) \\ \cline{2-7}  & \multicolumn{4}{c}{Randomized Quantile} & \multicolumn{4}{c}{Cox–Snell} \\ \hline \(n\) & Mean & SD & CSk & Mean & SD & CSk \\ \hline
30 & \(0.0319\) & \(0.9772\) & \(0.0273\) & \(1.0073\) & \(0.9587\) & \(1.3523\) \\
50 & \(-0.0288\) & \(1.0180\) & \(0.1452\) & \(0.9868\) & \(0.9880\) & \(1.1914\) \\
100 & \(0.0152\) & \(1.0165\) & \(-0.3226\) & \(1.0084\) & \(0.9403\) & \(1.4739\) \\
500 & \(-0.0029\) & \(1.0066\) & \(-0.0848\) & \(0.9978\) & \(0.9802\) & \(1.7448\) \\ \hline  & \multicolumn{4}{c}{\(\beta_{1}=0.2\);} & \(\beta_{2}=0.5\); & \(\zeta_{1}=0.2\); & \(\zeta_{2}=1.0\) \\ \cline{2-7}  & \multicolumn{4}{c}{randomized quantile} & \multicolumn{4}{c}{Cox–Snell} \\ \hline \(n\) & mean & SD & CSk & mean & SD & CSk \\ \hline
30 & \(0.0726\) & \(0.9760\) & \(-0.0228\) & \(1.0433\) & \(0.9477\) & \(1.2336\) \\
50 & \(0.0131\) & \(1.0012\) & \(-0.0942\) & \(1.0043\) & \(0.9477\) & \(1.5201\) \\
100 & \(-0.0237\) & \(0.9867\) & \(0.3180\) & \(0.9791\) & \(1.0466\) & \(2.1566\) \\
500 & \(0.0099\) & \(0.9854\) & \(0.1456\) & \(1.0048\) & \(1.0183\) & \(2.0615\) \\ \hline  & \multicolumn{4}{c}{\(\beta_{1}=0.5\);} & \(\beta_{2}=1.0\); & \(\zeta_{1}=0.2\); & \(\zeta_{2}=1.0\) \\ \cline{2-7}  & \multicolumn{4}{c}{randomized quantile} & \multicolumn{4}{c}{Cox–Snell} \\ \hline \(n\) & mean & SD & CSk & mean & SD & CSk \\ \hline
30 & \(-0.0715\) & \(1.0858\) & \(-0.1631\) & \(0.9710\) & \(0.9773\) & \(1.7365\) \\
50 & \(-0.0193\) & \(1.0235\) & \(-0.0200\) & \(0.9914\) & \(0.9712\) & \(1.6429\) \\
100
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 4 Results
### Semi-Automatic Mapping of the Flooded Area  
The applied semi-automatic approach consists of initially elaborating the co-flood images or the post- and pre-flood images. For each type of elaboration, we defined an empirical threshold that allowed detecting of the flooded area. All of this processing was done in QGIS software, and the automatic flood maps are in raster format.
# A Study on the Feasibility of Natural Frequency-Based Crack Detection
###### Abstract  
Owing to the long-standing statement that natural frequency-based crack detection is not sensitive enough to localised damage to identify small cracks, many natural frequency-based crack detection methods are validated by detecting cracks of moderate size. However, a direct comparison between the crack severity causing a measurable natural frequency change and the crack severity reaching the initiation of crack propagation or leading to brittle fracture is constantly ignored. Without this understanding, it is debatable whether the presented crack detection methods are feasible in practical situations. Through natural frequency calculation and linear elastic fracture mechanics, this study is dedicated to filling the above gap in knowledge. To directly utilize the solution of stress intensity factor, common fracture toughness test specimens featuring a single-edge crack are used. These specimens are essentially cracked rectangular plates under uniform uniaxial tension. Considering the stress resultants obtained via the extended finite element method, the natural frequency of the loaded cracked plates is calculated using the Rayleigh-Ritz method incorporating corner functions. In addition, assuming the specimens as structural components under remote uniform tension, the development of critical load versus crack length is derived based on the solution of the stress intensity factor. Thus, critical crack lengths corresponding to a series of safety factors are obtained by equating service load with critical load. After obtaining natural frequencies of the cracked plates with critical crack lengths, the natural frequency drop caused by a critical crack can be computed. Hence, the critical crack length can be compared with the crack length when the frequency drop is measurable. It is found that the brittleness of the employed metals plays a vital role in the feasibility of natural frequency-based crack detection.  
natural frequency; crack detection; Rayleigh-Ritz method; corner functions; in-plane load; stress intensity factor Article  
A Study on the Feasibility of Natural Frequency-Based Crack Detection  
2024 14 11712[[https://doi.org/10.3390/app142411712](https://doi.org/10.3390/app142411712)]([https://doi.org/10.3390/app142411712](https://doi.org/10.3390/app142411712))
## 2 Materials and Methods
### Feature Extraction
\begin{table}
\begin{tabular}{l c} \hline \hline
**Time-Frequency Feature** & **Mathematical Expression of the Time-Frequency Feature** \\ \hline The mean of the CWD (\(TF_{1}\)) & \(TF_{1}=\frac{1}{T\times F}\sum_{t=1}^{T}\sum_{f=1}^{F}\Gamma_{x}(t,f)\) \\ The variance of the CWD (\(TF_{2}\)) & \(TF_{2}=\frac{1}{T\times F}\sum_{t=1}^{T}\sum_{f=1}^{F}(\Gamma_{x}(t,f)-TF_{1}) ^{2}\) \\ The skewness of the CWD (\(TF_{3}\)) & \(TF_{3}=\frac{1}{T\times F\times(TF_{2})^{3/2}}\sum_{t=1}^{T}\sum_{f=1}^{F}( \Gamma_{x}(t,f)-TF_{1})^{3}\) \\ The kurtosis of the CWD (\(TF_{4}\)) & \(TF_{4}=\frac{1}{T\times F\times(TF_{2})^{2}}\sum_{t=1}^{T}\sum_{f=1}^{F}( \Gamma_{x}(t,f)-TF_{1})^{4}\) \\ Sum of the logarithmic amplitudes & \(TF_{5}=\sum_{t=1}^{T}\sum_{f=1}^{F}\log(|\Gamma_{x}(t,f)|)\) \\ of the CWD (\(TF_{5}\)) & \(TF_{6}=\frac{1}{T\times F}\sum_{t=1}^{T}\sum_{f=1}^{F}|\Gamma_{x}(t,f)-TF_{1}|\) \\ \hline Root mean square value of the CWD (\(TF_{7}\)) & \(TF_{7}=\sqrt{\left(\sum_{t=1}^{T}\sum_{f=1}^{F}\Gamma_{x}(t,f)\right)/(T\times F)}\) \\ interquartile range of the CWD (\(TF_{8}\)) & \(TF_{8}=\frac{1}{F}\sum_{f=1}^{F}\left(\Gamma_{x}\Big{(}\frac{3(T+1)}{4},f\Big{)} -\Gamma_{x}\Big{(}\frac{(T+1)}{4},f\Big{)}\right)\) \\ The flatness of the CWD (\(TF_{9}\)) & \(TF_{9}=T\times F\times\frac{\prod_{t=1}^{T}\prod_{f=1}^{F}(|\Gamma_{x}(t,f)|)^ {1/T\times N}}{\sum_{t=1}^{T}\prod_{f=-1}^{F}(|\Gamma_{x}(t,f)|)}\) \\ The flux of the CWD (\(TF_{10}\)) & \(TF_{10}=\sum_{t=1}^{T-1}\sum_{f=1}^{F-1}(\Gamma_{x}(t+1,f+1)-\Gamma_{x}(t,f))\) \\ The normalized Renyi entropy of the CWD (\(TF_{11}\)) & \(TF_{11}=\frac{1}{1-3}\log_{2}\sum_{t=1}^{T}\sum_{f=1}^{F}\left(\frac{\Gamma_{x }(t,f)}{T\times F\times TF_{1}}\right)^{3}\) \\ The energy concentration of the CWD (\(TF_{12}\)) & \(TF_{12}=\left(\sum_{t=1}^{T}\sum_{f=1}^{F}\sqrt{|\Gamma_{x}(t,f)|}\right)^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The time-frequency features extracted from the CWD-based TFR of each EEG segment [6; 7; 23]. The time-frequency features extracted from the CWD-based TFRs of the EEG segments associated with a given temporal position of the sliding window are grouped to form a feature vector. In particular, the number of features comprised within each feature vector is equal to \(14\times 12\), where 14 represents the number of EEG segments at each window position, which is equal to the number of EEG electrodes, and 12 represents the number of time-frequency features extracted from the CWD-based TFR computed for each EEG segment at a particular window position.
## 2 Study Site and Available Data
### Available Data
#### 2.2.3 Streamflow Data  
Simulated streamflow data from WiMMed (Watershed Integrated Model for Mediterranean Environments) were used [67]. WiMMed distributed a physically-based model that is composed of several modules. Among them, the snow module (SnowMed) uses a punctual mass and energy balance extended to a distributed scale using depletion curves [3]. Actual evapotranspiration is calculated using Penman-Monteith PE formulation [74; 75], whereas infiltration is estimated using the Green and Ampt [76] approach in a two-layer soil discretization. Groundwater modeling is based on a two-stage linear model, one being the slow aquifer response and the other the fast aquifer response which will have a faster impact on flow [77]. The model was designed and developed especially for arid Mediterranean environments. Specifically, it was calibrated and validated in Sierra Nevada. The statistical results obtained after comparison of the model and the measured flow values were a Pearson correlation coefficient of 0.84, a Kling Gupta Efficiency (KGE) of 0.59 for the daily flow values, and an average absolute bias in the daily discharge of the river of 1.9 m\({}^{3}\)/s [78]. WiMMed simulates the different streamflow components. In this study, we used baseflow due to its connection to melting dynamics. In addition, snow simulations were used to characterize SWE values over certain targeted periods.
## 2 Methods
### Apparent Elastic Properties  
We consider the in-plane elastic and plastic properties of the SVEs shown in Figure 2. Each SVE undergoes three load cases corresponding to tension in the \(x\)-direction, tension in the \(y\)-direction, and \(xy\) shear loading conditions. A _Mixed Boundary Condition_ (MBC) similar to [52] is used in the \(x\)-\(y\) plane. That is, in normal directions to the four facets Dirichlet BC (displacement BC) are assigned, whereas for the tangential directions Neumann BC (traction BC) is reinforced. A similar mixed BC is also used on the bottom and top facets with normals parallel to the \(z\)-axis. That is, \(u_{z}=0\) and \(\sigma_{zx}=\sigma_{zy}=0\). As detailed in [53], mixed BCs are an appropriate choice for homogenization as they provide stiffness  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Phase** & **C\({}_{\text{11}}\) (GPa)** & **C\({}_{\text{22}}\) (GPa)** & **C\({}_{\text{33}}\) (GPa)** & \(\dot{\gamma}_{\text{0}}\) & \(n\) & \(h_{0}\) & \(S_{s}\) (MPa) & \(m\) \\ \hline Martensite & 417.40 & 242.40 & 211.10 & 0.001 & 20 & 40,000 & 700 & 2.5 \\ Ferrite & 218.37 & 113.31 & 105.34 & 0.001 & 20 & 4500 & 370 & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The model parameters of ferrite and martensite.  
values that are between the upper limit _Kinematic Uniform Boundary Condition_ (KUBC, all applied displacements) and lower limit _Static Uniform Boundary Condition_ (SUBC, all applied tractions) solutions.  
The homogenized elasticity constitutive equation in Voigt notation is  
\[\tilde{\sigma}=\mathbf{C}\tilde{\gamma},\quad\text{for}\quad\tilde{\sigma}= \begin{bmatrix}\tilde{\sigma}_{xx}\\ \tilde{\sigma}_{yy}\\ \tilde{\sigma}_{xy}\end{bmatrix}\quad\text{and}\quad\tilde{\gamma}=\begin{bmatrix} \tilde{\varepsilon}_{xx}\\ \tilde{\varepsilon}_{yy}\\ 2\tilde{\varepsilon}_{xy},\end{bmatrix} \tag{2}\]  
where \(\tilde{\sigma}\) and \(\tilde{\gamma}\) are the average stresses and strains. The apparent elasticity tensor \(\mathbf{C}\) is obtained by satisfying Equation (2) for all three loading conditions.  
The in-plane bulk modulus is  
\[\kappa=\frac{1}{\mathbf{S}_{11}+\mathbf{S}_{22}+2\mathbf{S}_{12}} \tag{3}\]  
where \(\mathbf{S}=\mathbf{C}^{-1}\) is the compliant matrix. Other elastic properties (Young's modulus, \(E\) and shear modulus, \(G\)) are computed from the isotropic \(\mathbf{C}_{\text{iso}}\) obtained by the angular average of \(\mathbf{C}\) over all in-plane rotation angles [13; 54]. Different anisotropy measures are available to measure the isotropy and directionality of elastic properties [13; 55; 56; 57; 58; 59; 60]. We adopted the Ranganathan-Ostoja anisotropy index [57; 58]\(A_{\text{RO}}\) to describe the anisotropy of elasticity tensor.
## 2 State of the Art
### Technologies
#### 2.3.4 Category 4 (Monitoring/Control as Operations)  
This is an additional straightforward category, with clear repercussions for the process. A clear subcategorization can involve monitoring, control and modelling. Process monitoring and control sustain the process with respect to high quality [6; 84; 85], while offline optimization involves process parameters, key performance indicators (KPIs), process planning, supply chain management, etc., creating a close relationship with the manufacturing processes' operation and design. A concrete example would be online optimization based on machine learning (ML) techniques [86] with concurrent resource management.  
Process control, however, is a complicated subcategory [87]. The concept of statistical process control, for instance, can also be integrated here, even if it is not directly linked to the deterministic control approach as expressed by control theory.  
Modelling techniques are also highly relevant in digital twins' formation and in metamodelling. Thus, the techniques can be documented under this category, as well as the solution techniques. The latter are highly relevant in the workflows of digital twins as they define the use of the models and the computational time is highly relevant. If the models are fast enough, then they can be integrated in real-time decision making. The issue of their adaptivity should also be taken into account; semi-analytical methods and reduced order model (ROM) [88; 89] approaches are highly adaptive and they can be used in uncertainty management schemes (i.e., in cases where the material properties of the workpieces differ from batch to batch).
## 4 Model Construction  
### Modeling Framework  
The model framework, depicted in Figure 3, is segmented into three distinct parts. Moreover, the significance of the symbols in the diagrams is elaborated upon in Section 4 and shall not be reiterated here.  
1. Obtaining criteria data. Firstly, we computed aesthetic assessment values by amalgamating objective formulae with abstract rectangular samples [26]. Secondly, physiological measurements of visual cognition were acquired utilizing eye-tracking technology to collect pertinent eye movement indicator data. Let the set of aesthetic evaluation criteria be \(\{C_{e1},C_{e2},C_{e3},C_{e4},C_{e5}\}\), and the set of eye movement criteria is \(\{C_{e1},C_{e2},C_{e3},C_{e4},C_{e5}\}\).
2. Constructing a weighted indicator sequence matrix based on the ICRITIC. Utilizing the ICRITIC method for weight assignment to criteria, a weighted criteria sequence matrix is constructed, where \(X_{1}\) denotes the data sequence of the first eye movement indicator under \(N\) samples, \(X_{2}\), \(X_{3}\), \(X_{4}\), \(X_{5}\) and so on and \(Y_{1}\) denotes the data sequence of the first aesthetic indicator under \(N\) samples, \(Y_{2}\), \(Y_{3}\), \(Y_{4}\), \(Y_{5}\) and so on.  
Figure 2: Evaluation criteria system of interface layout.  
3. Gray H-convex correlation model. Optimizing the gray H-convex correlation model based on weighted indicator sequence matrices. Using the visual cognitive criteria sequence as the reference sequence, i.e., \(Y=(Y_{1},Y_{2},Y_{3},Y_{4},Y_{5})\), and using the layout aesthetic criteria sequence as the comparison sequence, i.e., \(X=(X_{1},X_{2},X_{3},X_{4},X_{5})\). The mapping relationship between layout aesthetics and visual cognitive features is clarified by calculating the correlation between the indicator data sequence curves, where \(\hat{\gamma}_{H(X_{a},X_{m})}(k)\) is denoted as the gray H-convexity coefficients of \(X_{a}\) and \(X_{m}\) at \(t=k\), an \(\hat{R}_{H}(X_{a},X_{m})\) is denoted as the gray H-convex correlation degree between sequences \(X_{a}\) and \(X_{m}\). The meaning of the symbols in the diagrams is detailed in Section 4.2.3 and will not be repeated here.
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 2 Study Area and Datasets  
### Study Area  
The Batifa area is located in the northern Duhok Governorate in the Iraqi Kurdistan region, between latitudes 37 deg4'58.5'' and 37 deg22'8.460'' N and longitudes 42 deg48'45.212'' to 43 deg11'51.440'' E. The region covers an area of approximately 528 km2, with altitudes ranging from 512 m to 2301 m above sea level (Figure 1). The average temperature in Batifa is approximately 27 degC, whereas the annual average rainfall is approximately 65.7 mm [21]. The landscape of the region includes hilly and mountainous terrain, grasslands, and valleys. The area experiences wet, chilly winters and dry summers. The region is characterized by a wide variety of land uses owing to both environmental and human interference [22]. The soil in the study area is primarily gravel and shallow. However, lithosols cover the study area, particularly along the ridges and summits. The soils were formed from the parent materials obtained from limestone, marl, mudstone, and sandstone. However, limestone is the main bedrock from which the parent material is drawn in this area [23]. In general, these soils are calcareous because they contain high amounts of carbonate [24]. Rendzina soils are typically found on ridge tops, but the soil is not disturbed due to favorable topography [25].
## 4 Results and Analysis  
We first conducted experiments on the Pavia University and CAVE datasets at a downsampling factor of 32. Then, to describe the experimental results objectively and fairly, we show the subjective results of the fused images and the objective results of the six fusion metrics, respectively. Figure 3 shows false-color images from LR-HSI, fusion images, and ground truth images. It can be seen that the fused image is clearer than LR-HSI.