esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Training
### k-Fold Cross-Validation Setup  
k-fold cross-validation maximizes data utilization for model evaluation [21]. This method reserves a portion of samples as the test set, while randomly partitioning the remainder into k mutually exclusive subsets of equal size. For each iteration, k-1 subsets serve as the training set, with the remaining subset used for validation--rotating this process k times. Considering the dataset size and computational costs, 5-fold cross-validation (k = 5) was adopted. The dataset was divided into 5 subsets via stratified sampling, with 4 subsets constituting the training set and 1 subset as the test set. Each subset maintained identical leakage type distributions as the original dataset without sample duplication: spot-type leakage (21.45%), linear leakage (22.70%), area-type leakage (19.47%), and mixed-type leakage (36.38%). This prevented data distribution bias from influencing the results. Each subset contained 304 images, yielding 1216 training images (4 folds) and 304 validation images (1 fold). The 5-fold cross-validation scheme is illustrated in Figure 6.  
During validation, model weights were reinitialized before each experimental run while maintaining identical hyperparameters, ensuring data partitioning remained the sole variable. To guarantee sufficient model convergence, the training epoch count was set to 100. Within memory constraints, the batch size was fixed at 8. The Adam optimizer with cosine annealing decay was employed during training: The initial learning rate was set to \(1\times 10^{-4}\), and weight decay (L2 regularization coefficient) to \(1\times 10^{-3}\) to constrain the parameter scale, preventing overfitting to tunnel background noise while preserving  
\begin{table}
\begin{tabular}{c c c} \hline
**Leakage Type** & **Number of Images** & **Proportion (\%)** \\ \hline Spot-Type Leakage & 326 & 21.45 \\ Linear Leakage & 345 & 22.70 \\ Area-Type Leakage & 296 & 19.47 \\ Mixed-type Leakage & 553 & 36.38 \\ Total & 1520 & 100 \\ \hline \end{tabular}
\end{table}
Table 4: Summary table of various types of water seepage in the dataset.  
Figure 6: Five-fold cross-validation schematic diagram.  
sensitivity to faint leakage features. The learning rate decayed following a cosine curve, decreasing to a minimum of \(1\times 10^{-5}\). Additionally, an adaptive learning rate adjustment strategy was implemented: If validation loss failed to decrease for three consecutive epochs, the learning rate was halved.
# The Influence of Feature Representation of Text on the Performance of Document Classification
## 2 Background
### Performance of Document Classification  
The commonly used measure of classification performance is a classification accuracy, i.e., the ratio of data correctly classified by a given model. Note however, that in document classification, we often encounter tasks where the distribution of class labels is highly imbalanced. In such cases, accuracy does not provide sufficient insight into classification performance and therefore, alternative measures of classification performance are being employed [60]. In our comparative study, we employ four performance measures, introduced below.  
The alternative measures observe the number of correctly classified and misclassified examples for each class label \(c\). There are two types of correctly classified examples: true positives (TP), i.e., correctly classified examples from class \(c\), and true negatives (TN), i.e., correctly classified examples that are labeled with a class label other than \(c\). Similarly, we observe two types of misclassified examples: false positives (FP), i.e., examples labeled with a class label other than \(c\) and misclassified into class \(c\), and false negatives (FN), i.e., examples from class \(c\) and misclassified into a class other than \(c\). Given these four numbers, we can calculate precision, recall and F1-score for a given class label \(c\) as [60]:  
\[\textit{recall}_{c}=\frac{TP_{c}}{(TP_{c}+FN_{c})} \tag{1}\]  
\[\textit{precision}_{c}=\frac{TP_{c}}{(TP_{c}+FP_{c})} \tag{2}\]  
\[\textit{F1-score}_{c}=2\frac{\textit{precision}_{c}\times\textit{recall}_{c}} {(\textit{precision}_{c}+\textit{recall}_{c})} \tag{3}\]  
Furthermore, the area under the receiver-operating curve (AUROC) [61] is another commonly used measure of the performance of document classification models. The receiver-operating curve explores the trade-off between the true-positive rate, i.e., \(TP_{c}/(TP_{c}+FN_{c})\), and false-positive rate, i.e., \(FP_{c}/(TN_{c}+FP_{c})\), when changing the value of the decision boundary. The latter is the probability threshold that decides what what the minimal value of the estimated probability is for an example to belong to class \(c\) for which the classifier decides to predict class \(c\). In turn, the area under the curve measures the potential of a given classification model to discriminate class \(c\) from the other classes.  
Finally, in cases where the classification task involves more than two class labels, we aggregate the per-class measures into a single performance measure using the macro-average scheme [62]:  
\[\textit{macro-average}=\frac{1}{|C|}\sum_{c\in C}\textit{measure}_{c} \tag{4}\]  
where \(C\) is the set of class labels in the data set of interest, while _measure_ is any of _precision_, _recall_, _F1-score_ or AUROC.
# Modeling of Distorted Degradation Data Based on Oil Analysis
###### Abstract  
Degradation data are important in judging a machine's health condition and providing early warning of machine failure. However, interference factors (e.g., oil top-ups) may distort degradation observations, causing the observed data to deviate from the actual physical degradation curve that engineers rely on. To address this distortion problem, this paper proposes a statistical correction framework to recover the actual degradation curve. The main contributions are as follows: First, we developed a degradation correction model that automatically identifies oil top-up events while globally rectifying distorted data, achieving an accurate reconstruction of physical degradation curves. Second, we developed a three-step explosion search algorithm for robust parameter estimation. Notably, though our methodology was initially developed for wear degradation analysis, this data-driven framework demonstrates adaptability to broader degradation scenarios. Finally, numerical simulations and case studies confirmed the practical effectiveness of the proposed method.  
P o rgnostics and Health Management (PHM); condition monitoring; oil top-up; wear 2020
## 1 Introduction
### Related Work
Due to the unique advantages of covert communication, it has been applied in many real-life scenarios, such as covert military communications and the interconnection as well as intercommunication of the Internet of Things (IoT) [21] or UAV networks [22]. The authors of [23; 24; 25] investigated the information-theoretic limits of covert communication. In [26; 27], a positive covert rate was realized. Moreover, interference and channel uncertainty of covert communication were discussed in [28]. Besides, in [29], the authors investigated the performance of covert communication in terrestrial relay networks. Moreover, the authors of [30] studied the covert performance under a scenario consisting of a source-destination pair, a passive warden, and multiple relays.
## 6 Comparison Results
### Energy Management System Performance Comparison
In this regard, Table 8 presents the results of evaluating the power profile that the MG exchanges with the utility network achieved by the EMSs under analysis. In addition, Figure 9 shows the grid power profile for each EMS design, where it can be seen that all the designs meet the objective of smoothing the power profile exchanged with the main grid (i.e., minimizing power peaks and fluctuations). Finally, Figure 10 shows the grid power profile ramp-rates, along one year, achieved by the analyzed EMSs. As can be seen, the fuzzy logic-based EMS obtained through nature-inspired algorithms present a considerable reduction in the power ramp-rates of the grid power profile, which facilitates the control of grid operators and the penetration of RES into the mains. \begin{table}
\begin{tabular}{c c} \hline \hline
**EMS Strategy** & **Fitness** \\ \hline EMS [43] & 9 \\ CS optimized EMS [40] & 7.76 \\ PSO optimized EMS & 7.88 \\ DE optimized EMS & 7.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fitness comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \
\begin{tabular}{c} **Population** \\ **Length** \\ \end{tabular} &
\begin{tabular}{c} **Time per** \\ **Iteration (min)** \\ \end{tabular} & **Iterations** & **Conv. Time (h)** \\ \hline CS & 25 & 16.1 & 243 & 65.21 \\ PSO & 25 & 16.1 & 107 & 28.71 \\ DE & 25 & 16.1 & 295 & 79.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Convergence time analysis for CS, PSO, and DE algorithms. Figure 8: Convergence chart. Figure 9: Grid power profile comparison for different EMS designs (**a**) heuristic, (**b**) CS algorithm, (c) PSO, and (**d**) DE algorithm. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**EMS Strategy** & \(P_{G,MAX}\) & \(P_{G,MIN}\) & \(MPD\) & \(APD\) & \(PPV\) & \(PVR\) \\  & (**kW**) & (**kW**) & (**W/h**) & (**W/h**) & (**W/h**) & \\ \hline
**EMS-FC**[43] & \(2.56\) & \(-1.89\) & \(846\) & \(75\) & \(1.26\) & \(0.34\) \\
**EMS-CS**[40] & \(2.51\) & \(-1.66\) & \(469\) & \(75\) & \(1.25\) & \(0.32\) \\
**EMS-PSO** & \(2.51\) & \(-1.71\) & \(489\) & \(76\) & \(1.26\) & \(0.32\) \\
**EMS-DE** & \(2.50\) & \(-1.60\) & \(487\) & \(74\) & \(1.29\) & \(0.32\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Grid power profile quality criteria comparison.
## 2 Multimodal Learning Techniques for Fault Diagnosis
### Hybrid Models and Ensemble Learning  
Hybrid models provide a compelling strategy for fault diagnosis by combining various machine learning techniques to capitalize on their individual strengths. For example, integrating decision trees with neural networks or support vector machines (SVMs) can lead to more accurate and robust fault detection systems [23,24]. The core idea behind a hybrid model can be mathematically expressed as follows:  
\[\hat{y}=\text{Model}_{1}(X)\oplus\text{Model}_{2}(X) \tag{4}\]  
Here, \(\oplus\) denotes the method for integrating the outputs of the models this could involve simple averaging, majority voting, or weighted summation, depending on the task requirements.  
A specific and widely used type of hybrid approach is **ensemble learning**. This methodology focuses on combining multiple base models to create a stronger, more gen eralizable predictive system [25]. Common ensemble methods include techniques like bagging, boosting, and stacking. A typical ensemble model can be represented as follows:  
\[\hat{y}_{ensemble}=\frac{1}{M}\sum_{m=1}^{M}\hat{y}_{m} \tag{5}\]  
In this equation, \(M\) denotes the number of base models, and \(\hat{y}_{m}\) is the prediction output of the \(m\)-th model.  
The primary strength of ensemble learning is its capacity to minimize the prediction errors that individual models may generate, thereby improving the reliability of fault diagnosis systems [26]. For instance, in applications related to fault diagnosis for rotating machinery, ensemble methods have shown superior classification performance by merging predictions from models like decision trees, random forests, and neural networks [27].  
**Case studies**. Ensemble learning in industrial applications highlights its effectiveness. In power grid systems, for example, multiple machine learning models are used collectively to assess the health status of transmission lines [28]. In manufacturing environments, hybrid models are similarly utilized to predict equipment failures, facilitating preemptive maintenance actions, and minimizing operational downtime [29].  
As illustrated in Figure 1, the ensemble learning model combines predictions from different classifiers, improving fault detection accuracy, particularly in noisy environments.  
Hybrid models and ensemble learning techniques offer a robust and flexible framework for enhancing fault diagnosis accuracy. These approaches can effectively manage the complex, noisy, and variable data typically encountered in industrial systems by strategically combining various algorithms [26; 30].
# Load Frequency Control for Multi-Area Power Plants with Integrated Wind Resources
###### Abstract  
To provide a more practical method of controlling the frequency and tie-line power flow of a multi-area interconnected power system (MAIPS), a state observer based on sliding mode control (SOboSMC) acting under a second-order time derivative is proposed. The proposed design is used to study load frequency control against load disturbance, matched and mismatched uncertainty and parameter measurement difficulties of power systems that exist in the modern power plant, such as multi-area systems integrated with wind plants. Firstly, the state observer is designed to optimally estimate system state variables. The estimated states are applied to construct the model of the MAIPS. Secondly, a SOboSMC is designed with an integral switching surface acting on the second-order time derivative to forcefully drive the dynamic errors to zero and eliminate chattering, which can occur in the first-order approach to sliding mode control. In addition, the stability of the total power system is demonstrated with the Lyapunov stability theory based on a new linear matrix inequality (LMI) technique. To extend the validation of the proposed design control for practical purposes, it was tested in a New England system with 39 bus power against random load disturbances. The simulation results confirm the superiority of the proposed SOboSMC over other recent controllers with respect to overshoot and settling time.  
renewables plants; state observer; sliding mode control; load frequency control 26 March 2021
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 3 Background
### IoT Security Issue  
To recap, the IoT ecosystem includes physical or virtual entities that connect to the Internet. These entities have IP addresses and the ability to interact with other objects or human users. The exponential growth of IoT devices and the advances in technology have resulted in the adoption of these devices in diverse types of applications in our lives. In today's world, the IoT is an integral part of most fields. For IoT devices with limited computational ability, memory, radio bandwidth and power resources, it is generally unaffordable or not even possible to have them perform security tasks, particularly under heavy data streams because of the requirements for intensive computing and no latency.  
Figure 2: The protocols and standards of IoT.  
In the heterogenous IoT environment, IoT attacks come in different forms. There are physical attacks, such as side channel attacks and sleep denial attacks. There are network attacks, such as routing attacks, sybil attacks and man in the middle attacks. There are software attacks, such as viruses, trojans and malware insertion [12]. The IoT attacks have increased with the number of vulnerable devices linearly, since 70% of IoT devices are vulnerable devices [19]. Consequently, many incidents have occurred, and they affect society and economies. Therefore, following a high number of incidents involving IoT products, IoT security became a major concern. It has been said that most attacks are botnet-based attacks in IoT environments. Again, on IoT devices, many security vulnerabilities still exist because most of them do not have adequate memory and computing resources for robust security mechanisms [19].  
The impacts of IoT botnets could be great, and most IoT malware attempts to create a botnet [19; 20]. For example, in October 2016, Mirai Botnet target the Domain Name Server (DNS) provider company (Dyn) through exploiting different kinds of vulnerable IoT devices, including closed circuit television cameras, routers and digital video recorders (DVRs). Using those devices, requests were sent from ten million IP addresses. This attack generated traffic exceeding 1 Tbps and brought down major Internet platforms, including Twitter, the Guardian, Netflix and CNN. Consequently, the attack caused disruptions in those services and disrupted the Internet overall.
## 2 Materials and Methods
### Method
#### 2.3.3 SMA  
Soil moisture refers to the water content contained within unsaturated soil layers and serves as a crucial resource for both agriculture and natural vegetation [64]. Drought conditions occur when soil moisture is insufficient to satisfy the transpiration demands of crops and vegetation, leading to disruptions in normal physiological processes and potentially impacting crop yields [65]. Additionally, soil moisture is closely linked to variations in surface temperature and precipitation [66]. Consequently, soil moisture serves as a vital component for monitoring agricultural drought. Since the water content in the root zone directly influences the growth and physiological activities of plants, changes in soil moisture within this zone serve as a crucial indicator of agricultural drought [67; 68; 69]. Consequently, the soil moisture data utilized in this study represent the weighted average of the moisture levels in three layers: the first layer (0-7 cm), the second layer (7-28 cm), and the third layer (28-100 cm). The formula for calculating the weighted average is as follows [70]:  
\[SMRZ=\frac{SM_{1}\times d_{1}+SM_{2}\times d_{2}+SM_{3}\times d_{3}}{d_{1}+d_{2 }+d_{3}} \tag{9}\]  
where \(SMRZ\) is the root zone soil moisture; \(d_{1}\), \(d_{2}\) and \(d_{3}\) are the depth of layer 1, layer 2 and layer 3; and \(SM_{1}\), \(SM_{2}\) and \(SM_{3}\) are the soil moisture of layer 1, layer 2 and layer 3.  
However, the natural conditions in different regions lead to variations in the actual values of soil moisture. SMA normalizes these regional differences, highlighting deviations more clearly and making SMA a key element in many practical drought monitoring systems [71; 72]. The formula for calculating SMA is [72]:  
\[SMA=\frac{x_{i}-\overline{x}}{\sigma} \tag{10}\]  
where \(x_{i}\) is the soil moisture value of each pixel, \(\overline{x}\) is the average soil moisture value of all pixels during 2001-2021, and \(\sigma\) is the standard deviation of soil moisture of all pixels during 2001-2021.
# A Registration Method for Historical Maps Based on Self-Supervised Feature Matching
## 4 Results
### MapReduceistration  
We applied the RANSAC algorithm to the match pairs obtained earlier to estimate the transformation model between the images. We selected SIFT2, SuperGlue, DeDoDe, and RoMa, which have better performance in Section 3.4, to be compared with our method. The results show in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Extractor** & **Matcher** & \(N_{all}\) & **ROCC** \\ \hline \multirow{2}{*}{SIFT2} & NN + ratio & 1532 & 0.103 \\  & MapReduce & 1231 & 0.132 \\ \hline \multirow{3}{*}{SuperPoint} & NN + mutual & 2431 & 0.079 \\  & SuperGlue & **3101** & 0.197 \\  & MapReduce & 2873 & 0.213 \\ \hline \multirow{2}{*}{MapExtraction} & SuperGlue & 2731 & 0.208 \\  & MapReduce & 2313 & **0.310** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different feature extraction and match algorithms.  
Relative to SIFT2, MapReduce Registration demonstrates a 10%+ speedup, 15-200% higher mutual information (MI), and several-fold improvements in ROCC and RMSE. Compared to SuperGlue, MapReduce Registration achieves >50% faster runtime, mostly 20-35% higher MI, and 10% lower RMSE. Although DeDoDe occasionally outperforms in ROCC for specific cases, MapReduce Registration dominates across the majority of test scenarios. RoMa's dense matching strategy yields an excessive number of correspondences, often exceeding practical utility. However, this dense correspondence set introduces noise, increasing computational overhead and degrading ROCC compared to other methods.  
In summary, MapReduce registration emerges as the most balanced and effective solution, delivering state-of-the-art registration accuracy while maintaining competitive runtime efficiency. It outperforms competing methods across nearly all metrics, solidifying its position as the preferred choice for historical map registration tasks.
###### Abstract  
Orbit uncertainty propagation plays an important role in the analysis of a space mission. The accuracy and computation expense are two critical essences of uncertainty propagation. Repeated evaluations of the objective model are required to improve the preciseness of prediction, especially for long-term propagation. To balance the computational complexity and accuracy, an adaptive Gaussian mixture model using virtual sample generation (AGMM-VSG) is proposed. First, an unscented transformation and Cubature rule (UT-CR) based splitting method is employed to adaptive update the weights of Gaussian components for nonlinear dynamics. The Gaussian mixture model (GMM) approximation is applied to better approximate the original probability density function. Second, instead of the pure expensive evaluations by conventional GMM methods, virtual samples are generated using a new active-sampling-based Kriging (AS-KRG) method to improve the propagation efficiency. Three cases of uncertain orbital dynamical systems are used to verify the accuracy and efficiency of the proposed manuscript. The likelihood agreement measure (LAM) criterion and the number of expense evaluations prove the performance.  
uncertainty propagation; Gaussian mixture model; adaptive splitting strategy; active sampling; virtual sample generation
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
## 3 Control Methods
### Linear Quadratic Regulator  
In vehicle suspension control studies, the LQR control scheme is significant. The effectiveness of this technique is that the performance indicator elements are measurable as per the requirements of user or other restraints. The state-variable form of the system is written as follows:  
\[\dot{X}(t)=Cx(t)+Du(t) \tag{9}\]  
State and control vector are \(x(t)\in R^{n}\) and \(u(t)\in R^{m}\), when the initial condition is zero. Here, we suppose that our states are measurable. As state-variable feedback control (SVFB) provides essential closed-loop properties. So, state-variable feedback regulator is written as:  
\[u(t)=-Gx(t) \tag{10}\]  
where \(G\) is the matrix of feedback gain and \(u(t)\) is the control input. Substituting (10) in (9) yields:  
\[\dot{X}(t)=(C-DG)x(t) \tag{11}\]  
The general form of the cost function of the \(n\)-_th_ order system is written as follows [30]:  
\[M=\int_{0}^{\infty}[X^{T}(t)Q(t)X(t)+u^{T}(t)R(t)u(t)]dt \tag{12}\]  
Figure 3: Block diagram of PID controller interface.  
To build an optimal SVFB, first we need to define performance index.  
\[M=\frac{1}{2}\int_{0}^{\infty}(x(t)^{T}Qx(t)+u(t)^{T}Ru(t))dt \tag{13}\]  
where matrix \(Q\) and \(R\) are representing diagonal matrices of admissible order. Now, replacing the SVFB control in (13), we get:  
\[M=\frac{1}{2}\int_{0}^{\infty}x(t)^{T}(Q+G^{T}RG)xdt \tag{14}\]  
Therefore, feedback gain G is represented as follows:  
\[G=R^{-1}D^{T}P \tag{15}\]  
where \(P\) is the auxiliary matrix which must satisfy the Riccati equation as given below:  
\[PC+C^{T}P-PDR^{-1}D^{T}P+Q=0 \tag{16}\]  
In control theory, Riccati equation has great significance. By using the quadratic equation, we can solve the auxiliary matrix \(P\) if the entries of matrices \(Q\), \(R\), \(C\) and \(D\) are known.