esa-sceva/satcom-chunk-collection huggingface dataset:
# Anomaly Detection in Microservice-Based Systems
## 2 Data and Materials
### Classifier Performance  
This work used four metrics to evaluate the classifier's performance: Accuracy, Recall, Precision, and F1-score. These statistical metrics can be calculated using the following equations:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F1-score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{5}\]  
where True Positives (TP) represent the number of anomalies that the model detects as anomalous, True Negatives (TN) represent the number of anomalies that the model detects as normal, False Positives (FP) denote the number of normal records that the model detects as normal, and False Negatives (FN) are the number of abnormal records that the model detects as normal.  
This study uses 80% of the data for training, and 20% to test the model.
## 3 Results
### Optical Characterization  
Thanks to the appropriate choice of materials, fabricated OFETs are significantly transparent. In order to quantify the actual transparency, absorbance tests have been carried out in the visible spectra (400-800 nm). Results are summarized in Figure 5, where transmittance is reported for complete devices fabricated both on PET and Parylene ultra-thin films. Devices have four well=defined absorption peaks (490, 600, 650 and 700 nm, see Figure S3 in Supporting Information), which are consistent with those typically reported for TIPS pentacene in different kind of solvents [42; 43; 44]. At these wavelengths, the transmittance is of about 70% for devices fabricated on PET, and of about 75% on Parylene C. It is worth noting that the transmittance of the transistor structure fabricated on PET before the semiconductor deposition exceeds 80%; if this data is compared with the transmittance of bare PET (85%), a relative transmittance of 94% can be derived for devices fabricated on PET without a semiconductor, which is reduced to 82% due to TIPS pentacene absorbance. Therefore, the actual transparency can be further improved by properly choosing a material more transparent than TIPS pentacene as the active layer.
# Numerical Simulation of Flow Field of Submerged Angular Cavitation Nozzle
## 2 Numerical Calculation Method
### Meshing  
The grid partition of the 3D nozzle model was achieved in the special preprocessor software Mesh. The 3D physical model of the nozzle is globally divided into unstructured mesh, and it adopts the Inflation expansion operation near its solid wall surface. In order to ensure the calculation effect and reduce the amount of calculation, the mesh is encrypted for the focused research areas of the nozzle and water jet flow field such as the nozzle inlet, cylindrical section, and outlet expansion section. The grid division model is shown in Figure 4.  
When using Inflation for wall boundary layer grid generation, it is necessary to give priority to the thickness of the first layer of the grid. It is considered that the cavitation jet is a high-Reynolds-number flow, so the high-Reynolds-number scheme is adopted to calculate the boundary layer by using the wall function. According to the selected RNG \(k\)-\(\epsilon\) turbulence model, the scalable wall function is provided in CFX. In this case, the grid nodes of the first layer are required to be in the region of turbulence, that is, the value of \(y^{+}\) is between 30 and 300. Assuming that \(y^{+}\) is 30, the height of the grid in the first layer can be obtained according to the formula [30]:  
\[y^{+}=\frac{\rho_{w}u_{\tau}\Delta y}{\mu_{w}} \tag{28}\]  
where \(\Delta y\) is the distance of the first grid point off the wall and \(\rho_{w}\), \(u_{\tau}\), and \(\mu_{w}\) are the density, friction velocity, and molecular viscosity at the wall, respectively. Hence, the first grid length normal to the wall is 0.01 mm. The CFD postprocessing diagram after convergence is shown in Figure 5. The value of \(y^{+}\) is within a reasonable range, so the height of the grid in the first layer can be considered justified.  
For the abovementioned grid division strategy, grids with different refinements were used to verify the grid independence. The highest vapor fraction and the highest jet velocity in the nozzle are shown in Figure 6 for different mesh numbers. It can be seen that the vapor fraction and jet velocity tend to a stable value when the mesh number exceeds 600,000. Considering the computational and calculation accuracy, a scheme with a total mesh number of 674,905 is determined for the computational model. The overall mesh size is 1 mm, and the first grid length normal to the wall is 0.01 mm. The grid along the z-x plane is presented in Figure 7.  
Figure 4: Mesh of calculation domain.
## 2 Unit Cell Design Configurations
### Co-Planner Design Configuration  
Figure 1c signifies the co-planner topology of the FSS shield, where the MJCL and CMSL elements are arranged on the top surface of the dielectric. The MJCL is placed inside the CMSL element. It is noticed that in this arrangement, the FSS structures exhibit improved dual-band reject characteristics compared to their singular performances in terms of attenuation, bandwidth, and notch selectivity. However, the first resonance frequency is shifted downwards due to the electromagnetic coupling effect between the resonating elements. As a result, the attenuation, notch selectivity, and stop bandwidth of band II are significantly improved. In this configuration, the CMSL and MJCL resonate at 7.9 GHz, X-band uplink, and 11.9 GHz, Ku-band downlink, frequencies. Thus, the FSS effectively alleviates the unwanted EMI occurring at the X- and Ku-bands. Moreover, Figure 1c shows  
Figure 1: FSS unit cell: (**a**) MJCL-FSS element; (**b**) CMSL-FSS unit element; (**c**) perspective view of the EM shield in co-planner design configuration.  
Figure 2: Stepwise design procedure of the elements of the FSS unit cell.  
a 3D view of the EM shield. Table 1 shows optimized values of the design variables of the FSS unit elements.
## 2 Spectral Clustering Algorithm
### Spectral Clustering Algorithm Description  
Let \(S=\{s_{1},...,s_{n}\}\) in \(\mathbb{R}^{l}\) be a data set that will be partitioned into \(k\) groups, applying a radial basis function (RBF) kernel to build a symmetric matrix known as the affinity matrix \(A\in\mathbb{R}^{n\times n}\), as defined in Equation (1). The RBF kernel includes a parameter \(\sigma\)--referred to as the kernel bandwidth--that must be adjusted to achieve appropriate data clustering.  
\[A_{ij}=exp\left(-\frac{\left\|s_{i}-s_{j}\right\|^{2}}{2\sigma^{2}}\right)\text{, \ if }i\
eq j\text{ and }A_{ii}=0. \tag{1}\]  
Given the computed affinity matrix \(A\), a diagonal matrix \(D\) is defined, where its \((i,i)-elements\) represent the sum of the elements of the \(i\)-th row of matrix \(A\), as delineated in Equation (2). Subsequently, the diagonal elements of matrix \(D\) are normalized, as specified in Equation (3):  
\[D_{ii}=\sum_{j}A_{ij} \tag{2}\]  
\[D^{-1/2}=\frac{1}{\sqrt{D_{ii}}}. \tag{3}\]  
The Laplacian matrix \(L\) is constructed from the matrices \(A\) and \(D^{-1/2}\), and is denoted by \(L=D^{-1/2}AD^{-1/2}\). The first \(k\) eigenvectors \(x_{1},x_{2},\ldots,x_{k}\) of the matrix \(L\) are used to partition the data set \(S\) into \(k\) mutually exclusive clusters. This process results in the building of the matrix \(X=[x_{1}\ x_{2}\ \ldots\ x_{k}]\in\mathbb{R}^{n\times k}\). Subsequently, the matrix \(X\) is renormalized following Equation (4):  
\[Y_{ij}=\frac{X_{ij}}{\left(\sum_{j}X_{ij}^{2}\right)^{1/2}}. \tag{4}\]  
Figure 1b shows the effect of constructing the \(Y\) matrix from the synthetic data set shown in Figure 1a, whose data are in \(\mathbb{R}^{2}\) space. Although the data in Figure 1b are still in \(\mathbb{R}^{2}\) space (this will not always be the case as the Y matrix will be in \(\mathbb{R}^{k}\)), the distribution has transformed, thus allowing us to proceed with the final step of the algorithm.  
The rows of matrix \(Y\) are considered points in \(\mathbb{R}^{k}\), and the data set is partitioned into \(k\) clusters via the k-means algorithm. Accordingly, the initial point designated as \(s_{i}\) is assigned to cluster \(j\) strictly when the row \(i\) of matrix \(Y\) has been allocated to cluster \(j\).  
Figure 2a shows the effect when we apply k-means directly on the synthetic data set, obtaining an unsuitable clustering of the synthetic data. However, if we follow the spectral clustering algorithm and apply k-means in the space described by the \(Y\) matrix shown in Figure 1b, we obtain two clusters, as shown in Figure 2b. Subsequently, by assigning the memberships of these clusters to the synthetic data set in Figure 1a, we achieve a proper clustering of the synthetic data set, as illustrated in Figure 2c.  
Figure 1: Effect of spectral clustering algorithm on a synthetic data set.
## 2 Materials and Methods
### Leaf Phenology Analyses  
Growing season onset, dormancy and length were calculated based on the seasonal amplitude. LAI has been shown to have a normal distribution over the year in northern latitudes [37], so we consider the start of the growing season to be 20% of the maximum amplitude. This processes has been proven to be more stable for monthly data, compared to an approach based on sudden LAI changes.  
In order to analyze changes in the growing season, we mask regions where there are minimal changes in LAI over the year (e.g., evergreen forests and mixed forest with a small deciduous component). These regions were defined as those where the difference between the maximum and minimum LAI amplitude is less than 0.5. We also masked regions where the LAI decreased in the middle of the summer (drought deciduousness), assuming those months to have constant LAI.  
For the gridcells with enough variation, we calculated a critical threshold value (CT) above which we assume the plants to be photosynthetically active (Equation (1))  
\[CT^{x,y}=LAI_{min}^{x,y}+0.2\times(LAI_{max}^{x,y}-LAI_{min}^{x,y}) \tag{1}\]  
where LAI Min and Max represent minimum and maximum gridcell LAI over one year. The length of the growing season for each year was calculated as the number of months with an LAI value above this threshold; the onset is the first of these months and the dormancy is the last. Since part of the growing season occurs after the end of the year [38], we included the first three months of the following year in the calculations. Hence, the growing season offset can occur on the following year, having DOY higher than 365. Even when calculated monthly all results are presented in days (number of days passed until the end of the calculated month). The procedure was repeated for each gridcell, year and dataset. Mean length, onset and dormancy represent the average over the whole time period (Figure 1).  
In order to quantify the differences between the models and data we calculate the root mean square errors (Equation (2)) between each model and the satellite observations for each grid cell and all growing season variables, and the seasonal amplitude.  
\[RSME=\sqrt{\sum_{x=1,y=1}^{xy=n}\frac{(Sat^{xy}-Model^{xy})^{2}}{n}} \tag{2}\]  
Figure 1: Growing season onset, dormancy (offset) and length calculation based on the seasonal amplitude. A critical threshold value is calculated for each gridcell and each year based on the maximum and minimum Leaf Area Index (LAI).
## 4 Discussion
For instance, the widely used Kaplan-Meier analysis method involves ordering the dataset based on a feature and dichotomizing it into two sets for prediction. It is vital for the rank ordering to be consistent, as changes in the rank order may change the dichotomization and hence results in Kaplan-Meier studies, leading to low reproducibility and low predictive power. In our previous work [9], we reported a methodology to evaluate the rank order of the features and have shown that some radiomics features are reproducible across different scanner models, acquisition parameters, reconstruction methods, and modest variations in slice thickness, provided pixel sizes are resampled to a fixed standard. It was identified that feature reproducibility was highly sensitive to the choice of quantization parameters. This study has successfully validated our previous results [9] and reproduced the changes in radiomics features using different quantization parameters, suggesting the methodology used for the study is robust, even when using a different radiomics feature extraction implementation [3]. These results highlight the importance of reporting the detailed methodology used. Based on studies in the literature [11; 14; 25] and our own results, we recommend excluding unreproducible features from analysis to reduce dimensionality and computational burden. To improve further studies have suggested that deep learning could be considered to improve the image quality of the CT images [26], which may lead to reproducible radiomics features [27]. This will need to be explored further in future studies. In this era of deep learning, [PERSON] et al. have raised a question for further thought for the radiology and quantitative imaging communities: have we already lost a lot of information available when we choose to reconstruct images for visual interpretation? [28]. There are some limitations to this study. Pyradiomics does not comply completely with all the recommendations of the Imaging Biomarker Standardization Initiative (IBSI) [27], for example, the quantization parameters. Although care was taken to keep the suggested stability parameters as close as possible, their implementation would have affected the present study. Hence, IBSI compliance is strongly recommended to allow better reproduction and validation of the treatment results externally [29]. The goal of all radiomics studies is to predict clinically relevant properties and/or disease outcomes, such as disease recurrence or survival. The study has only focused on the reproducibility of the features; however, the reproducibility of a feature does not automatically imply that it is clinically informative. The next stage in evaluating this methodology will be to apply it to the modeling of outcomes.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 2 Preliminaries  
The general discrete-time non-linear system is as follows:  
\[\mathbf{x}_{k}=f(\mathbf{x}_{k-1})+\xi_{k-1} \tag{1}\]\[z_{k}=h(x_{k})+w_{k} \tag{2}\]  
where \(x_{k}\) is the \(n\)-dimensional state vector at time \(t_{k}\). The variable \(z_{k}\) generated from the sensor is the ideal measurement vector without delay; \(\xi_{k-1}\) is the process noise, which satisfies zero-mean Gaussian white noises (\(E\big{[}\xi_{k}\xi_{l}^{T}\big{]}=Q_{k}\delta_{kl}\)); and \(\delta\) indicates the Kronecker delta function. The variable \(w_{k}\) is the measurement noise, and \(f(\cdot)\) and \(h(\cdot)\) denote the state function and measurement function, respectively.
# Dynamic Heat Transfer Calculation for Ground-Coupled Floor in Emergency Temporary Housing
## 2 Solving the Heat Transfer Equation
### Solution of Mixed Boundary Problem  
For the convenience of solving governing Equation (9), \(t^{*}=t/\alpha\) is defined. Then applying the Fourier transform in space \(x\) and the Laplace transform in time \(t^{*}\) to temperature \(\theta_{2}(x,z,t^{*})\), respectively. It is obtained that [29],  
\[\Theta_{2}(p,z,s)=\int_{0}^{\infty}\left(\int_{-\infty}^{\infty}\theta_{2}(x, z,t^{*})e^{-\text{j}px}dx\right)e^{-st^{*}}dt^{*}, \tag{15}\]  
where \(\text{j}^{2}=-1\). The boundary condition could be written as  
\[\theta_{\text{BC}}(x,0,t^{*})=f(t^{*})\cdot H(-x). \tag{16}\]  
\(H(-x)\) is the Heaviside step function. With the application of transforms, it becomes  
\[\Theta_{\text{BC}}(p,0,s)=\left[\pi\delta(p)-\frac{1}{\text{j}p}\right]\cdot F (s). \tag{17}\]  
\(\delta(p)\) is the Impulse function. \(F(s)\) is the Laplace transform result of \(f(t^{*})\).  
According to the differentiation properties of integral transforms, governing Equation (9) could be reduced to a linear 2 nd-order differential equation with constant coefficients. Supposing that \(\theta_{2}(x,z,t)\to 0\) at \(z\to\infty\), the form of general solution is  
\[\Theta_{2}(p,z,s)=\Gamma_{2}(p,s)\cdot e^{-z\sqrt{p^{2}+s}}. \tag{18}\]  
Figure 2: Linear decomposition of ground-coupled floor heat transfer equation.  
By applying the Wiener-Hopf technique [32], it could be calculated that  
\[\Gamma_{2}(p,s)=\frac{\Theta_{\text{BC}}(p,0,s)}{1+h^{*}\sqrt{s}}\cdot\sqrt{- \text{j}\big{(}1+h^{*}\sqrt{s}\big{)}}\cdot\frac{K^{+}(p,s)}{K(p,s)}. \tag{19}\]  
\[K(p,s)=1+h^{*}\cdot\sqrt{p^{2}+s} \tag{20}\]  
is the kernel function. In addition,  
\[K^{+}(p,s)=k^{+}(p,s)\cdot K_{0}^{+}(p,s).k^{+}(p,s)=\sqrt{h^{*}p+\text{j}\big{(} 1+h^{*}\sqrt{s}\big{)}}. \tag{21}\]  
\[K_{0}^{+}(p,s)=\sqrt{K_{0}(p,s)}\cdot\exp\left[-\frac{1}{2\pi\text{j}}\text{P. V.}\int_{-\infty}^{\infty}\frac{\ln K_{0}(\xi,s)}{(p-\xi)}d\xi\right], \tag{22}\]  
where P.V. denotes the Cauchy principal value integral.  
\[K_{0}(p,s)=K(p,s)/k(p,s),k(p,s)=\sqrt{\left(h^{*}p\right)^{2}+\big{(}1+h^{*} \sqrt{s}\big{)}^{2}}. \tag{23}\]
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
## 4 Numerical Results
To further elaborate the significance of the proposed solution against different values of \(K\), Figure 6 plots the BER when \(K=5\) and \(K=10\). It can be seen that different values of \(K\) and \(a\) have different impacts on the performance of the network. Note that at very small values of signal power, the BER for \(K=10\) is better than \(K=5\). However, it changes at very large values of signal power when the BER of \(K=5\) is less than the BER of \(K=10\). It is also worth pointing out that the difference between curves increases for larger signal power. Figure 4: Monte-Carlo simulated coded BER with (**a**) 16-QAM, (**b**) 32-QAM. Figure 5: BER comparison of different schemes.
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 4 Performance Analysis
### Simulation Conditions
#### 4.1.2 Throughput  
Figure 9 shows the network throughput, which is defined as the aggregate number of bits successfully delivered to all destinations in the network, per second. It can be seen that the proposed scheme has the highest network throughput owing to the reduction of inefficient NAV time. In association with Figure 8, we can see that the lower network throughput is caused by a longer NAV time, which implies that an unnecessarily long NAV creates lost opportunities to access the channel even when it is available, degrading the throughput.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 4 Performance Evaluation  
This section presents the simulation setup and performance evaluation of the proposed SRDT-FRR scheme for fault recovery in satellite networks. The experiment is conducted with a constellation of 100 satellites, distributed across 10 orbital planes, with 10 satellites per plane, and an orbital altitude of 1000 km. The inter-satellite link delay is set to range from 10 ms to 20 ms to simulate dynamic network conditions. To evaluate the effectiveness of the SRDT-FRR scheme, it is compared with several baseline FRR methods, including SS-FRR, BP-LSRFD, BP-BFD, and LSRFD. These schemes differ in their fault detection, backup path generation, and recovery strategies, with routing interruption time and primary-backup path switching overhead serving as the key performance metrics. Three fault scenarios--stable, transient, and mixed faults--are designed for the simulations, which run for 600 s. The results show that the SRDT-FRR scheme significantly outperforms the baseline methods in terms of fault detection speed, recovery time, and minimizing path switching overhead, thereby enhancing routing reliability and fault recovery efficiency in satellite networks.
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 3 Proposed Research Methodology
### Random Forest  
The Random Forest approach reduces overfitting and increases prediction accuracy by combining numerous decision trees [27]. Random Forest is frequently applied to classification and regression issues. The basic idea is to build a forest of decision trees, allow each tree to grow and produce a forecast on its own, and then aggregate all of the trees' predictions to produce the final prediction. With this being said, the key features of Random Forest are:  
* Random sampling of the training data: a random subset of the training data is used to train each tree in the forest.
* Random feature selection: a random collection of features is taken into consideration for splitting at each decision tree split.
* Prediction aggregation: the ultimate forecast is derived from the sum of the forecasts made by each tree within the forest.  
The main advantage of Random Forest is that, in comparison to a single decision tree, it is less prone to overfitting, as indicated by the previously mentioned points [28]. This is due to the fact that every tree in the forest is trained using a distinct subset of the data and only takes a portion of the features into account when it splits. Therefore, the final prediction is less likely to be biased towards the idiosyncrasies of the training data. Additionally, Random Forest can handle a large number of input variables and can provide a measure of the importance of each input variable in the prediction.  
Random Forest is an ensemble of decision trees that combines multiple models for robust performance. The collective strength of multiple models achieves robust and accurate performance in identifying cyber-attacks within the network traffic dataset. Whilst considering evaluation of decision trees, it is intuitive to compare the performance with Random Forest.