esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
## 4 Research on Machine Learning-Based IDSs
### Session-Based Attack Detection
#### 4.3.2 Sequence Feature-Based Detection  
Different from flow, the packets in sessions have a strict order relationship. The sequence features mainly contain the packet length sequence and the time interval sequence. Analyzing the sequence can obtain detailed session interaction information. Most machine learning algorithms cannot deal with sequences, and related methods are relatively rare. At present, most sequence feature-based detection adopts the RNN algorithm.  
Encoding raw data is a common preprocessing step for RNN methods. The bag of words (BoW) model is a frequently used text processing technology. [PERSON] et al. [56] proposed a DDOS detection method based on the LSTM using UNB ISCX 2012 dataset. They first extracted 20-dimensional features from the packets and encoded them with BoW. Then, they concatenated the packets in sequence, resulting in matrices with a size of m*n, where m was the number of packets in a session and n was the dimension of a packet, and both m and n were variable. Finally, they trained a CNN to extract local features and an LSTM to classify the sessions. They provided comprehensive experimental results, reaching accuracy, precision, recall, and F-measure scores of 97.606%, 97.832%, 97.378%, and 97.601%, respectively.  
One of the drawbacks of the BoW is that it is unable to represent the similarity between words. Word embedding approaches overcome that problem. [PERSON] et al. [57] proposed a session detection method based on a bi-LSTM. Because LSTMs had made great strides in NLP, they expressed the sessions as a specific language. They conducted experiments on the ISCX IDS dataset. First, they grouped packets on the basis of IP addresses to obtain sessions. Then, they encoded the sessions with the word embedding. Finally, they trained an LSTM model to predict abnormal sessions. To utilize the contextual information, they adopted a bi-LSTM model to learn the sequence features in two directions.  
In addition to text processing technology, the character-level CNN is a novel encoding method. [PERSON] et al. [58] proposed a hierarchical deep learning detection method in which a session contains not only packet contents but also the packet time sequence. Then, they designed a hierarchical deep learning method using a CNN to learn the low-level spatial features and an LSTM to learn the high-level time features, where the time features are based on the spatial features. They conductedexperiments on the DARPA 1998 and the ISCX 2012 datasets. They first applied the CNN to extract spatial features from packets. Next, they concatenated the spatial features in sequence and extracted time features using the LSTM model. The resulting model achieved accuracies between 99.92% and 99.96%, and detection rates between 95.76% and 98.99%.
## 4 Proposed Method
### DNC Method
#### 4.2.2 DNC Method  
The DNC method consists of two main steps. First, it calculates the sum of the local clustering coefficients of the first-order neighbors of a node, denoted as \(slc_{i}\). Then, the node's DNC value is obtained by adding its degree to the sum of the first-order neighbor local clustering coefficients. The \(slc_{i}\) is defined as follows:  
\[slc_{i}=\sum_{j\in\Gamma_{i}}c_{j} \tag{10}\]  
where \(c_{i}\) represents the local clustering coefficient of node \(i\).  
Subsequently, the \(DNC\) value of a node is computed as follows:  
\[DNC_{i}=k_{i}+\alpha*slc_{i}\ (\alpha>0) \tag{11}\]  
where \(\alpha\) denotes an adjustable parameter to control the effect of \(slc\) on the calculation results. In this paper, \(\alpha=1\) is used for the calculation.  
DNC considers both the node's own information and the node's neighbors' information, that is, the sum of the local clustering coefficients of the first-order neighbors. It measures the density of connections among its neighbors. By considering the sum of first-order neighbor local clustering coefficients, this method can comprehensively capture the local network structure between the node and its most immediate associates, providing insights beyond the node's individual information. By adding the node's self-information, it can more accurately estimate the node's importance within its neighborhood. This prevents highly connected nodes (those with numerous neighbors) from being overly emphasized in importance assessment while underrating nodes with low degrees (having fewer neighbors). A person's influence in social networks is frequently connected to their position in the social network and their ties with first-order neighbors. The sum of the first-order neighbor local clustering coefficients can better reflect an individual's influence in the social network.  
The pseudocode for DNC is provided in Algorithm 1:
# On the Effect of Filling Rate on Positive Macrosegregation Patterns in Large Size Cast Steel [PERSON]
## 3 Results and Discussions
### Effect of Filling Rate on Segregation Severity  
Frequency distribution of segregation ratios were examined in the hot-top and the upper section of the casting body for the two investigated ingots. The results are plotted in histograms in Figure 3. It can be seen in Figure 3a that in the hot-top of ingot HFR, the intensity of positive segregation of all the analyzed elements is more pronounced (histograms with \(R_{i}>0\)), especially in higher segregation regions (\(R_{i}>0.1\)); solute concentrations close to the nominal composition are less pronounced (histograms in the region \(R_{i}=0\)). In contrast, in the upper section of the casting body of ingot HFR (Figure 3b), the positive segregation is less intense (histograms with \(R_{i}>0\)) and more solutes are present with concentrations near the nominal composition (\(R_{i}=0\)).  
The above-mentioned tendency, _more severe_ segregation in the hot-top of HFR and _less severe_ segregation in the casting body of HFR, was more remarkable when the evolution of carbon and manganese was examined along the central axis, as shown in Figure 4a,b. The axial concentration  
Figure 3: Frequency distribution of segregation ratios: (**a**) In the hot-top; (**b**) In the upper section of ingot body.  
Figure 2: (**a**) Partition coefficients of different alloying elements determined based on linearized binary phase diagrams with respect to iron; (**b**) Evolution of diffusion coefficients of solutes in liquid iron as a function of temperature.  
examination of chromium presented less severe segregation in both the casting body and the hot-top of HFR, as shown in Figure 4c. Moreover, it can be seen that in both ingots, the axial concentrations of carbon present a stepped monotonic increase from the cutting section to the top center of the hot-top. In contrast, positive segregation of manganese and chromium were found to increase progressively in a fluctuating way. The fluctuation was more distinct in HFR. The most intense positive segregation regions of manganese and chromium in HFR were found to lie about 5 cm below the corresponding regions in the low filling rate case, LFR.  
An examination of the concentration distribution along the cutting line of the two ingots (i.e., 30 cm below the hot-top/casting body interface), as shown in Figure 5, revealed that the concentration profiles of all the three solutes in the casting body of HFR are below the LFR case and closer to the nominal composition (\(R_{i}=0\)), presenting less severe segregation.  
Figure 4: Segregation ratio profiles along the ingot central axis of the two ingots: (**a**) carbon; (**b**) manganese; (**c**) chromium.  
Figure 5: \(Cont\).  
The above results indicate that the filling velocity has a clear influence on the resultant macrosegregation, at least in the studied zones, of large size ingots. The obtained results confirm the observations of [PERSON] et al. [13] in small size ingots, despite the fact that in large size ingots the filling time is very short compared to the total solidification time.
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
## 5 Execution of the Proposed Model
### Building Graph Instances  
Most of the public street maps are usually not available in a graph format. To build graphs, street map data and footprints of the buildings are obtained from \"OpenStreetMap\" [65] using \"OSMmx\" [66] and \"NetworkX\" [67] tools. Then, a simplified graph is created, it combines a street map and buildings together with respect to the real position of the street nodes and buildings in \(V\). Each edge between a building and its nearest street node is built using Open Source Routing Machine (OSRM) [54]. Edges are assigned a weight that is proportional to the walking distance extracted from OSRM. An example of a resulting instance is shown in Figure 6. Gray edges represent streets. The nodes on the streets represent street nodes, while the nodes outside the streets represent buildings in the area. Input data and parameters for all instances are summarized in Table 4.
###### Abstract  
Understanding the effects of synthetic parameters in the catalytic activity of heterogeneous catalysts is of utmost importance when aiming for optimal reaction conditions. Hence, we disclose in this work the synthesis and characterization of cerium-modified MCM-41 materials. In addition, it was observed for the first time, differences in catalytic activity when using different cerium synthetic precursors: CeCl\({}_{3}\)7H\({}_{2}\)O and Ce(NO\({}_{3}\))\({}_{3}\): 6H\({}_{2}\)O (Ce-MCM-Cl and Ce-MCM-NO\({}_{3}\), respectively). A mechanism for cerium incorporation in MCM-41 was proposed, where [Ce(OH)\({}_{3}\)] species were hydrogen bonded to silicate anions, forming framework Ce-O-Si bonds during condensation and, consequently, causing distortion of the typical hexagonal mesophase. It was also observed that Ce(OH)\({}_{3}\) formed aggregated layers with template assemblies during synthesis, resulting in non-framework CeO\({}_{2}\) species on the MCM-41 surface after calcination. These CeO\({}_{2}\) species were preferentially formed for Ce-MCM-NO\({}_{3}\) and were attributed to the nitrate ions' strong binding to template molecules. In the solvent free liquid-phase oxidation of benzyl alcohol (BzOH), Ce-MCM-Cl achieved better BzOH conversions and benzaldehyde (BzD) yields, while Ce-MCM-NO\({}_{3}\) offered increased BzD selectivity. The catalysts' reusability was also studied over three catalytic runs, where Ce-MCM-NO\({}_{3}\) was more resistant than Ce-MCM-Cl towards deactivation. The observed catalytic behavior shows the importance of metal precursors in the obtainment of materials with desirable final properties.  
M 41; cerium; benzyl alcohol; oxidation; benzaldehyde +
Footnote †: journal:  
_Catalysts_
# A Gene Expression Programming Model for Predicting Tunnel Convergence
## 4 Prediction of Tunnel Convergence by GEP
### Data Sets
#### 4.1.1. Geotechnical Parameters  
In the KUR project, various geotechnical tests were conducted throughout the tunnel alignment and tunnel convergence was recorded during tunnel construction. The boreholes were drilled at every 100 m throughout the tunnel alignment in the KUR project and some of the convergence stations are not located exactly at the same location point of the boreholes. Therefore, the nearest neighbor was used to determine the soil properties at the location of convergence stations. The nearest neighbor is a simple method for classifying objects based on closest training examples. This method is a type of instance-based learning, in which the function is only approximated and an object is classified by a majority vote of its neighbors.  
Ground water level, as an important influential parameter, may affect the soil movements. High water level over the tunnel invert increases the possibility of the flow of water towards the tunnel face, which may in turn lead to large displacements in the tunnel face and, consequently, large convergences. In line 2 of KUR, the study of boreholes and hand-dug wells illustrated no water table in the containing soil of the tunnel and underground stations. Consequently, ground water level was not investigated in this paper as an influencing parameter.  
Soil cohesion and friction angle, as independent parameters of soil strength, have also been investigated to estimate tunnel convergence using GEP. It is obvious that soil cohesion is a suitable parameter to evaluate only fine grained soils. However, due to the fact that this project includes a wide range of fine grained and coarse grained soils, this parameter was selected to be used in evaluating the surface settlement using AI methods. In addition, the friction angle, which represents the shear strength of granular soils, has also been investigated in simulations.  
Two deformation parameters of soil which include elasticity modulus and Poisson's ratio have also been investigated. Obviously, more plastic points are formed in the area with low elasticity, and as a result, the convergences are expected to be larger in these areas.
## 5 Numerical Examples
### Caradonna-Tung Rotor  
The two-bladed Caradonna-Tung rotor [33] in hover was studied here. The blade was rectangular, untwisted, and untapered and had a radius of 1.143 m and a chord of 0.1905 m. The sectional airfoil was NACA0012. The root cutoff used in simulation was equal to the chord length. The rotation speed was 1250 rpm, and the tip mach was 0.439. A total of 60 panels were arranged along the chordwise direction, with 20 panels in the spanwise direction on each blade. An azimuthal step of 5\({}^{\circ}\) was adopted.  
In Figure 7, the predicted thrust coefficient at a collective of 5\({}^{\circ}\), 8\({}^{\circ}\), and 12\({}^{\circ}\) was compared with the experimental data. Figure 8 shows the predicted tip vortex location in both the radial direction (\(r/R\)) and the axis direction (\(z/R\)) variation as a function of the vortex age at a collective of 8\({}^{\circ}\). Figure 9 shows the pressure coefficient at span locations of 50%\(R\), 68%\(R\), 80%\(R\), 87%\(R\), and 96%\(R\). The results obtained by the present method were compared with both the experimental data and the numerical results that were obtained with a code coupling CFD and free wakes. It can be seen that good correlations exist between the present method and both the hybrid CFD method and the experiment.  
Figure 6: Three-node finite volume beam.  
Figure 8: Tip vortex positions at a collective pitch angle of 8\({}^{\circ}\).  
Figure 7: Thrust coefficient at different collective pitch angles.  
Figure 9: _Cont_.
## 3 Results and Discussion
### FGD Through Zeolite 13X
#### 3.3.2 Application of the Thomas Model  
Figure S13 shows the results of the linearized Thomas model (Equation (6)) as a linear plot of ln(C\({}_{0}\)/C\({}_{1}\)-1) against time under varying operational conditions. The Thomas parameters, obtained from the linear regression, are summarized in Table S11.  
The R\({}^{2}\) values obtained for 13X [75 \({}^{\circ}\)C\(\_\)30 RH%] and 13X [75 \({}^{\circ}\)C\(\_\)45 RH%] were 0.95 and 0.92, respectively, indicating a best fit to the experimental data at lower humidity conditions. Figure S13 shows a clear deviation from linearity for 13X [75 \({}^{\circ}\)C\(\_\)45 RH%] between 5 and 30 min. This is attributed to the low values of C/C\({}_{0}\) observed in the breakthrough curve (Figure 5).  
The \(k_{TH}\) value was approximately twice as high for 13X [75 \({}^{\circ}\)C\(\_\)45 RH%] as for 13X [75 \({}^{\circ}\)C\(\_\)30 RH%], in accordance with the \(\mathfrak{\mu}_{\max}\) values reported in Table 2. Furthermore, the results for the adsorption capacity parameter, \(q_{0}\), were found to be in accordance with the values calculated by the numerical integration of the experimental data.
## 2 Materials and Methods
### Build Layout
#### 2.3.1 Build Space Segmentation  
The build space is first segmented to allow re-orientation of all specimens without violating the required distance between parts. The segmentation serves to define fixed positions in the build space to facilitate comparisons between the different positions and builds in the experiment as this is a known source of variation. This experiment is conducted on an EOSINT P395 with a build volume of 340 \(\times\) 340 \(\times\) 620 mm. Due to temperature gradients along the edges of the build envelope, it is recommended to keep a safe distance of 20 mm to the edge, effectively reducing the available build space to a 300 \(\times\) 300 mm square in the xy-plane. Similarly, it is advisable to keep a certain distance to the bottom of the build space to allow the environment to stabilize (both in terms of temperature and power distribution) before the sintering begins. For this experiment, a safe distance of 6 mm was applied.  
To avoid cross-contamination between specimens in the same build, all specimens should be located at a safe distance from each other. For this experiment, a 10 mm safe zone is considered around all specimens in all directions. The test artifact is designed to fit a grid of three by three specimens in any orientation without violating the safe zone. The first step of build space segmentation is presented as two 2D-projections with a top view in Figure 3a and a front view in Figure 3b. The green discs represent the area potentially occupied by specimens, and red rings encapsulating each disc represent the safe zone. Additionally, the red square frame demarks the safe distance from the edges of the build envelope. The coordinates in the figure are center coordinate components relative to the machine coordinate system [37]. The positions are defined based on the center point at (170, 170), from which the remaining positions are located at the extremes of (170 \(\pm\) 100, 170 \(\pm\) 100), i.e., an orthogonal grid with 100 mm distance from center to center. This grid is repeated five times in the z-direction, as illustrated in Figure 3b, yielding a total of 45 defined positions in the build space. These positions may formally be described by three components {\(i\), \(j\), \(k\)} where \(i\)\(\in\) {1, 2, 3}, \(j\)\(\in\) {1, 2, 3} and \(k\)\(\in\) {1, 2,..., 5}. The five layers in the z-direction are distributed to account for the required safe distance and adjusted to the closest multiple of the layer thickness (120 \(\upmu\)m), resulting in a distance of 99.72 mm from center to center. The lowest center point is determined by finding the lowest viable position that does not violate the 6 mm safe distance from the bottom and then rounding up to the closest multiple of the layer thickness.
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
The results showed that the PGDL approaches did not outperform baseline predictions in temporally and spatially similar holdout experiments. [PERSON] et al. [23] compared three DL models for DO forecasting in a fishery pond: recurrent neural network (RNN), LSTM, and gated recurrent unit (GRU). The RNN performed the most; the GRU and the LSTM showed comparative accuracy, but the latter was more computationally costly. [PERSON] et al. [24] introduced a new DL model by coupling the convolutional neural network (CNN) and the bidirectional long short-term memory (BiLSTM) with an attention mechanism to tackle the discontinuous dynamic change in DO concentrations in long time series. It achieved short-term DO prediction and revealed its response features of timing and magnitude of abrupt DO depletion. [PERSON] et al. [25] presented an improved GRU with an attention mechanism for multi-step and interval DO prediction, achieving an R\({}^{2}\) > 0.98. [PERSON] et al. [26] proposed new graph and transformer-based deep learning models to forecast DO levels in natural streams, achieving an R\({}^{2}\) of 0.97. Despite the advances, ML for DO modeling has its limitations. One drawback is the black-box nature of many ML models (e.g., [10; 27; 28; 29]), which hinders interpreting how diverse environmental factors affect DO predictions. This lack of transparency reduces stakeholders' trust and confidence in model applications for decision-making. Another drawback is the need for hyperparameter tuning [14], which is complex and time-consuming. The performance of ML models heavily depends on the careful selection of hyperparameters, and improper tuning often leads to suboptimal predictions. To address these challenges, this study aims to establish explainable ML models with automated hyperparameter optimization to enhance model interpretability and improve predictive accuracy and computational efficiency. As a result, this study contributes an optimized XAI (explainable AI) model that provides transparent insights and achieves high predictive performance through efficient hyperparameter optimization. Ultimately, this approach enhances the reliability and applicability of DO modeling, making it a valuable tool for informed decision-making in water resource management and environmental conservation.
## 2 Materials and Methods
### Machine Learning and Mapping  
The pixel values corresponding to the ground truth data (geo-location points) were extracted from both the ultra-resolution and very high-resolution suites of images. Three machine learning classifiers, Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and a predicted-probabilities-based fusion (SoftVoting) of the RF and XGBoost classifiers, were employed for the differentiation and mapping of land cover and vegetation types.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indices** & **References** \\ \hline Normalized Difference Vegetation Index & [PERSON] et al. [62] \\ Green–Red Vegetation Index & [PERSON] et al. [63] \\ Soil-Adjusted Vegetation Index & [PERSON] [64] \\ Modified Soil-Adjusted Vegetation Index & [PERSON] et al. [65] \\ Atmospherically Resistant Vegetation Index & Kaufman and Tanre [66] \\ Modified Chlorophyll Absorption Ratio Index & [PERSON] et al. [67] \\ Non-Homogeneous Feature Difference & [PERSON] [68] \\ Structure-Insensitive Pigment Index & [PERSON] et al. [69] \\ Enhanced Vegetation Index & [PERSON] et al. [70] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radiometric indices calculated and used in the research.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Suite** & **Features** & \multicolumn{2}{c}{**Total Features**} \\ \hline \multirow{3}{*}{Very high-resolution suite} & Multi-spectral bands & 8 & \\  & Multi-spectral indices & 9 & \\ (2 m) & Color-transformation & 27 & \\  & (multi-spectral) & \\ \hline \multirow{3}{*}{Ultra-resolution suite} & Panchromatic band & 1 & \\  & Pan-sharpened bands & 8 & \\ \cline{1-1}  & Textural features & 8 & 44 \\ \cline{1-1}  & Color-transformation & 27 & \\ \cline{1-1}  & (pan-sharpened) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the features extracted for very high-resolution and ultra-resolution mapping suites.  
Random Forests (RF) is an ensemble of decision trees, which are built by splitting the attributes of the data and averaging the output value of all trees [72]. The Gradient Boosting (GB) technique was designed to minimize the loss function of the model by adding weak learners [73]. XGBoost is one of the implementations of the Gradient Boosting technique designed for highly efficient and parallel machine learning capabilities [74]. The RF and XGBoost classifiers have shown outstanding performance in classification of the satellite data [75; 76; 77; 78; 79; 80]. Researchers have reported higher performance of the fusion of multiple classification results in land cover and vegetation mapping [81; 82].  
The ten-fold cross-validation method was utilized for quantitatively evaluating the performance of the ultra-resolution suite and the very high-resolution suite based on accuracy metrics, such as overall accuracy, kappa coefficient, F1-score, recall, and precision. After confirming the best-performed classifier and its parameters through the 10-fold cross-validation method, the best-performed model was built by training on 85% data for the prediction (mapping) of new data.