esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design of a Self-Adaptive Evolutionary Algorithm
### Representation of Solutions  
Chromosomes (or individuals) are used in EAs to represent a solution to a given problem [20; 21]. A hybrid chromosome representation will be adopted in this study, where one part of the chromosome will be represented using integers, while the other part will be represented using real-valued numbers. The first part of each chromosome, which contains integer numbers, will be used to denote the vessel to berthing position to service order assignment (i.e., the **BSPSR** solution). The second part of each chromosome, which contains real-valued numbers, will be used to denote the crossover and mutation  
Figure 3: The main SAEA steps.  
probabilities (that will be referred to as \(p^{c}\) and \(p^{m}\), respectively). The chromosome components (e.g., berthing position identifiers, vessel identifiers, crossover and mutation probabilities) will be referred to as \"genes\" [20]. Location of a gene along the chromosome will be referred to as \"locus\" [20]. Value of a gene will be referred to as \"allele\" [20]. The adopted chromosome representation is not common among canonical EAs, which have been used in the BSP literature, as the proposed SAEA applies the self-adaptive parameter control strategy. An example of the SAEA chromosome representation is illustrated in Figure 4.  
In the considered example, 8 vessels are scheduled for service at the MCT. Berth \"1\" is assigned to serve vessels \"3\", \"2\", and \"7\" (in that specific service order); on the other hand, berth \"2\" is assigned to serve vessels \"5\", \"8\", \"1\", \"6\", and \"4\" (in that specific service order). Moreover, the probability of the presented chromosome to undergo a crossover operation (if selected as a parent) is \(p^{c}=0.50\), while the probability of each gene of the presented chromosome to undergo a mutation operation is \(p^{m}=0.05\). The length of each chromosome in the population can be determined as \(|I|+2\), i.e., summation of the number of vessels, calling for service at the MCT, and \"2\", where additional 2 genes are required to store the information regarding the crossover and mutation probabilities.
## 3 Results and Discussion
### Effects of Corrosion  
When rust forms on connection parts, it becomes impossible to attach and detach porcelain insulators during maintenance. If a special load--such as typhoon, ice, snow, or earthquake--is applied, or flexibility is required for repair work, the connection part may break. As the service life increases, the cement inside the cap deteriorates, and the inside of the cap is exposed such that corrosion may occur. To prevent this, a zinc ring is sometimes installed between the porcelain and cap [11].  
Figure 5: Pin position where crevice corrosion occurs.  
For the case of the pin, a rusted pin may expand and cause breakage of the porcelain. When crevice corrosion occurs at the interface between the cement and pin, the diameter of the pin decreases and, in the worst case, the connection is broken. Figure 5 shows the corrosion at the pin of a porcelain insulator and Figure 6b shows a broken pin due to the severe crevice corrosion.
# Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy
## 3 Proposed Methods
### Quantization Scheme for Rhythmic Attention
#### 3.1.3. Algorithm for Detecting \(k\) Value  
We regard note onsets as pulses of the s point process, and our purpose is to find a value of \(k\) such that the maximum number of points within a given interval fall on either the equally spaced points of the interval or on its boundaries. Given a series of event times within a specified interval, we aim to calculate the \(k\) value based on the following criteria:  
1. For a sequence of events with a total duration, let \(t_{i}\) represent the normalized time of each event, where the total duration is normalized to 1.
2. For each event time \(t_{i}\), check if \(t_{i}\) can be expressed in the form \(\frac{a}{bk}\), where \(a\) and \(b\) are integers and \(a\perp b\).
3. If \(t_{i}=\frac{a}{bk}\), then increment the corresponding \(k\) value by \(\frac{1}{bk}\).
4. The final \(k\) value is the maximum sum obtained through the increments. Formally, let \(T=\{t_{1},t_{2},\ldots,t_{n}\}\) be the set of event times. Define \(k(t)\) as follows:  
\[k(t)=\sum_{\begin{subarray}{c}a,b\in\mathbb{Z}^{+}\
eq d\\ t=\frac{a}{bk}\end{subarray}}\frac{1}{bk}. \tag{5}\]  
We aim to determine \(k\) such that  
\[k=\max_{k}\sum_{t\in\mathbb{T}}k(t). \tag{6}\]  
For a musical piece, based on the theoretical length of each note and the relative starting positions, we can calculate each note's quantified attention value.  
This quantization scheme, based on fractal and rhythmic structure theories, provides a foundational metric for evaluating note attention within a musical piece. It sets the stage for subsequent analyses involving the identification of fundamental rhythmic patterns and hierarchical structuring.  
Using this foundational metric, we can now proceed to develop basic rhythmic grouping operators, which further enhance our ability to discern and organize musical hierarchies.
## 4 Experimental Results and Analysis
### Impacts of the Orbital Parameters on LEO-LEO Occultation Events
#### 4.1.1 Orbital Parameter Setup for the Two-Satellite Experimental Demonstration Mission
It should be noted that the inclination of the Rx and Tx orbits was set to 90\({}^{\circ}\) for the 'raan' and 'h' groups to avoid the RAAN precession having an influence on the simulation results; however, except for the 90\({}^{\circ}\) case, the simulation results of the 'i' group were affected by both the inclination and the RAAN precession. Since the RAAN precession is slow and its impact on the short-term occultation event number and spatial-temporal distribution is not obvious, a long-term series of three months (90 days) of occultation events were simulated for each experimental group.
## 3 Methodology
### Model Architecture
#### 3.3.2 Bidirectional Long Short-Term Memory
BiLSTM consists of two LSTM units, where one operates in the forward direction to process the input sequence, whereas the other operates in the reverse direction. As depicted in Figure 6, the forward LSTM analyzes the input sequence from the beginning to the end, whereas the backward LSTM operates in the reverse order. At each time step, the hidden states of the two LSTMs are concatenated to form the final hidden state sequence, which is then passed to the next layer. This process is mathematically described by Equations (8)-(10). \[\overrightarrow{h}_{t}=\overrightarrow{LSTM}\Big{(}x_{t},\overrightarrow{h}_ {t-1},\overrightarrow{C}_{t-1}\Big{)} \tag{8}\]  
\[\overleftarrow{h}_{t}=\overleftarrow{LSTM}\Big{(}x_{t},\overleftarrow{h}_ {t+1},\overleftarrow{C}_{t+1}\Big{)} \tag{9}\]  
\[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{3}\]0  
where \(\overrightarrow{h}_{t}\) represents the hidden state of the forward LSTM at time \(t\), \(\overrightarrow{LSTM}\) is the LSTM function for processing the input sequence in the forward direction, and \(\overrightarrow{C}_{t-1}\) is the cell state of the forward LSTM at time \(t-1\). Similarly, \(\overleftarrow{h}_{t}\) represents the hidden state of the backward LSTM at time \(t\), \(\overleftarrow{LSTM}\) is the LSTM function for processing the input sequence in the backward direction, and \(\overleftarrow{C}_{t+1}\) is the cell state of the backward LSTM at time \(t+1\). \(x_{t}\) represents the input sequence at time \(t\), and \",\" denotes vector concatenation. In order to effectively capture bidirectional temporal dependencies within network traffic sequences, we introduced a BiLSTM layer comprising 64 units. In order to enhance the model's generalization performance, we incorporated a dropout layer after the BiLSTM layer, with a dropout rate set to 0.5. The dropout technique randomly selects and discards a portion of neuron outputs with a specified probability, setting the discarded outputs to zero [50]. This dropout operation mitigates the risk of overfitting by reducing interdependencies between neurons in the neural network. The output from the dropout layer is then fed into a fully connected layer, where each neuron in the preceding layer is linked to each neuron in the current layer. This fully connected layer further enhances the model's feature representation [51]. Finally, by employing the softmax function, the model generates probability distributions for each category, thereby facilitating the accurate classification of different traffic types. The CNN-BiLSTM architecture described above, along with its key parameters, is summarized in \[f_{t}=\sigma\Big{(}W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{3}\]1
## 6 Specific Application of Topic Prediction Models and Algorithms
### Deep Learning or Machine Learning Analysis Models and Algorithms Based on Time Sequence
#### 6.1.2 The Markov Model  
Depending on whether the system's state is fully observable, and whether the system is automatic or controlled, the most common Markov models can be divided into four categories: Markov Chain, the Hidden Markov Model (HMM), Markov Decision Process (MDP), and the partially observable Markov Decision Process (POMDP). As a statistical model, the Markov model is also applied in topic prediction.  
Based on the LDA, [PERSON] proposed a method to predict subject evolution trends combined with the Markov Chain [44]. [PERSON] et al. used the Hidden Markov Model (HMM) including double stochastic processes to quantitatively predict the technical trends of marine diesel engines [39]. [PERSON] combined the LDA theme model with the HMM model to predict research themes and development trends related to 3D printing technology [77]. [PERSON] used Markov and Hidden Markov to build an evolution prediction model of each theme's persistence and the degree of interest, and to predict topics in the field of cloud computing [1].
## 6 Limitations and Future Perspectives  
In this work, we analyzed a dataset of limited size, therefore we avoided more sophisticated strategies such as those based on deep neural networks that require large sample sizes, preferring two simpler methods such as RF and Lasso. Such methods are rather well established in the literature for classification tasks in various clinical contexts. Moreover, these methods offer the advantage of providing the most important features for the algorithm's decisions proving a higher explainability of the impact of the clinical predictors on the overall performance. This issue is particularly relevant as it enables the integration of the perspectives of both clinical specialists and algorithm developers, allowing synergy between the different entities and improving the machine learning paradigms in each specific clinical context.  
Figure 5: Correlation between the probability scores of the samples in the hold-out test set resulting from each classification scheme.  
Although our methodological choices were conditioned by the dimensionality of the dataset, it should be emphasised that for high-dimensional datasets, other approaches should be evaluated in conjunction with Lasso. As an example, [PERSON] et al. [53] proposed a network constrained regularization approach for classification exploiting networks to model interconnections among features, overcoming the existing shortcomings in addressing the correlations among features. In addition, when the number of features increases, robust feature selection techniques are critical for successful performance since these methods usually have better stability and reproducibility [54]. Accordingly, with higher dimensionality of the features and complex data structure, more feature selection algorithms should be evaluated in order to avoid the oversimplification of the predictive models. On the other hand, although the proposed framework is fully scalable and would allow a larger number of machine learning algorithms combined with different sampling strategies to be compared for future developments, it should be noted that the computational complexity and the execution time increase with the number of algorithms to be compared. Further analyses will be carried out to identify the best combinations of ML algorithms and sampling strategies that ensure the best balance among classification accuracy, computational complexity and interpretability of the models.
## 4 Measurement Results
### Preliminary Study Model  
The model uses K Nearest Neighbor classification, which can only achieve an average accuracy of 57.23% for the forward-reverse pair and an average accuracy of 50.87% for the grasp-release pair using the time and frequency features as shown in Table 3. The classification is performed with the three nearest neighbors, as mentioned before, and with a 10-fold cross validation scheme. Either increasing or decreasing the number of neighbors results in a significant drop in accuracy.  
Figure 21 shows that the performance of the classifier on the dataset is below 60%, which is expected due to the nature of the dataset. The result could be improved if the features are extracted from the spatially and temporally filtered signals rather than using only the temporally filtered signal. However, these results are left unimproved since the objective of the preliminary study is to gain an overall picture of what is involved in neural signal processing and classification.
## 6 Comparison to Other Designs
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**PUF Type** & & **Reliability** & & **Uniqueness** & **Uniformity** & **Description** \\ \hline  & **Mean** & **Temp.** & **VDD** & **Mean** & **Mean** & **Data Basis** & **Ref.** \\ \hline TV-PUF & 96\% & \(-\)25 \({}^{\circ}\)C...125 \({}^{\circ}\)C & \(\pm\)20\% & 50.02\% & 49.70\% & Simulation only & [23] \\ TG-PUF & \(\approx\)100\% & \(-\)40 \({}^{\circ}\)C...85 \({}^{\circ}\)C & \(\pm\)10\% & \(\approx\)50\% & - & Prototype IC & [24] \\ VTC-PUF & 97.9\% & 0 \({}^{\circ}\)C...85 \({}^{\circ}\)C & \(\pm\)10\% & 49.8\% & 50.1\% & Simulation only & [25] \\
**This work** & 99.20\% & \(-\)20 \({}^{\circ}\)C...80 \({}^{\circ}\)C & \(\pm\)10\% & 48.84\% & 45.60\% & Prototype PCB & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Security metrics comparison with related PUFs. in addition to its unique benefits of accessibility and bitwise understanding on analog and digital signal levels. Because of the low design complexity of the herein proposed analog-based PUF platform, the PUF design is a strong candidate as a lightweight security primitive.
## 2 Analysis of Rockburst Manifestation Characteristics
### Manifestation Law of Rockburst on Both Sides of the Roadway  
#### 4.3.1 Analysis of Vertical Stress Distribution on Both Sidewalls Under Different Schemes  
Vertical stress distribution along the roadway's strike direction was simulated for each scheme. The vertical stress curves for the coal pillar side and working face side under different schemes are shown in Figure 11, and the peak vertical stresses are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Scheme** & **Peak Stress on Coal Pillar Side (MPa)** & **Peak Stress on Working Face Side (MPa)** \\ \hline No Relief & 24.7 & 20 \\ Scheme 1 & 17.7 & 16 \\ Scheme 2 & 18 & 12.09 \\ Scheme 3 & 18.4 & 7.87 \\ Scheme 4 & 13.2 & 16.5 \\ Scheme 5 & 13.6 & 12.5 \\ Scheme 6 & 13.9 & 8.07 \\ Scheme 7 & 9.38 & 16.8 \\ Scheme 8 & 9.23 & 12.8 \\ Scheme 9 & 9.78 & 8.07 \\ \hline \end{tabular}
\end{table}
Table 3: Peak vertical stress on the roadway’s two sides following hydraulic fracturing pressure relief.  
As illustrated in Figure 11 and Table 3, the peak vertical stress on the coal pillar and working face sides is 24.7 MPa and 20 MPa, respectively, under the no-relief condition. After applying hydraulic fracturing, the peak stresses on both sides are significantly reduced, indicating the efficacy of hydraulic pressure relief in undermining the concentrated roof load on the surrounding rock.  
Among all schemes, Scheme 9 demonstrates the most substantial reduction in vertical stress, decreasing to 9.78 MPa on the coal pillar and 8.07 MPa on the operating face--reductions of 60.4% and 59.7%, respectively, compared to the no-relief state. Overall, a borehole inclination of 45\({}^{\circ}\) results in the best fracturing effect, suggesting that this angle is favorable for crack propagation along preferential planes, significantly alleviating stress concentration among surrounding rocks.
# An Improved Approach to DNS Covert Channel Detection Based on DBM-ENSec
## 4 Experiments and Analysis of Results
### Parameterization  
This experiment seeks to improve the efficacy of the Deep Boltzmann Machine with Enhanced Security (DBM-ENSec) in feature extraction and optimization during the deep learning phase by fine-tuning the architecture of the Deep Boltzmann Machine. In particular, the following parameters were optimized:  
The number of neurons in the hidden layers of the DBM.  
The number and size of filters in the convolutional layers.  
The comparison of two pooling strategies: maximum pooling and average pooling.  
After conducting a comprehensive evaluation of multiple configurations, the following architectural framework has been selected:  
DBM layer: 8 \(\times\) 8  
CNN_1 layer: 64 \(\times\) 3  
CNN_2 layer: 128 \(\times\) 3  
Pooling method: Maximum pooling  
The detailed tuning process is shown in Table 3.  
In the section on integrated classifiers, particular attention was given to tuning the parameters of each ensemble learning model to ensure optimal performance in leveraging the refined feature set. Following this, a systematic threshold adjustment was performed to determine the optimal decision boundary.  
To objectively evaluate threshold selection, we conducted a series of comparative experiments across a range of threshold values from 0 to 1 in steps of 0.1. For each setting, we recorded the corresponding accuracy of the integrated model, as shown in Figure 10. The results demonstrate that the accuracy remains relatively stable between thresholds of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Num** & **Dbm Layer** & **CNN\_1** & **CNN\_2** & **Pooling Layer** & **Accuracy** \\ \hline
1 & 8 \(\times\) 8 & 8 \(\times\) 3 & 16 \(\times\) 3 & 2 \(\times\) 1 & 99.86\% \\
3 & 8 \(\times\) 8  
0.1 and 0.6 but degrades beyond 0.7. Notably, the threshold value of 0.1 yielded the highest accuracy, indicating a strong discriminative capability at this point.
## 2 Experimental Setup, Methods, and Procedures
### Chemical and Metallographic Analysis  
The samples were analyzed for chemical composition using a spark emission spectrometer SPECTROMAXx (SPECTRO Analytical Instruments GmbH, Kleve, Germany). The measured values, shown in Table 1, are compared with the material standard STN 17 240 in the Slovak Republic to ensure adherence to the required standards for maintaining material properties such as hardness and corrosion resistance.  
Metallographic analysis requires properly prepared samples to observe the microstructure of materials. Initially, a long rod of the material was cut into smaller samples, rinsed with water and alcohol, and dried with warm air. Dentacryl (methyl methacrylate casting compound) and bakelite (phenol-formaldehyde polycondensate) were used to press the samples in a Struers CitoPress-1 (Struers S.A.S., Champigny-sur-Marne, France) machine for proper attachment. The next step involved grinding and polishing the samples with a Struers TegraPol-15 automatic polisher. After grinding, the samples were washed and dried, and then an etchant solution (G + HF + HNO\({}_{3}\); G: glycerine; HF: hydrofluoric acid; HNO\({}_{3}\): nitric acid) was applied to reveal the material's structure. The microstructure of the etched stainless steel samples was examined using a NEOPHOT 32 (Zeiss AG, Oberkochen, Germany) light microscope and documented using the NIS Elements 4 (Nikon Corporation, Minato, Tokyo, Japan) program at varying magnifications. The article discusses the investigation of selected mechanical and magnetic properties.
## 3 Lattice Construction Algorithm
### Input and Output  
The input to the proposed construction method consists of the lattice frame and of strut radii. For each node, the associated coordinates are required and, for each edge, two radii are to be defined, one per end. Thus, lattice structures with thickness gradients can be constructed. The coordinates as well as the linking and radii information are stored in matrices, which could be generated by any lattice frame design algorithm. In this work, they are assumed to be given. Figure 3 gives an overview of the lattice parameters.  
The developed algorithm solidifies the lattice frame by generating a set of surface patches that describes the lattice structures' surface without gaps.  
Figure 2: Flowchart of the automated lattice construction algorithm.
## 3 Anaerobic Digestion of Food Waste
### Key Parameters
#### 3.2.4 Ammonia Content and Formation  
Ammonia is formed during the biodegradation of proteins or other nitrogen-rich substrates [127; 128]. Ammonia and the C/N ratio are directly related as high nitrogen contents produce ammonia formation, whilst C content has a positive effect on avoiding ammonia inhibition [125; 129; 130]. Ammonia can act in two different ways, on one side enhancing the buffer capacity of AD neutralizing VFA [131; 132], and, consequently, enhancing biogas production. On the other side, ammonia can act as an inhibitor for bacteria growth, especially acetoclastic methanogens [133; 134; 135], being responsible for methane production via acetate transformation. For example, an ammonia concentration between 400 and 5700 mg/L produces a 56% loss of methanogens activity, while acidogens were not affected [133; 136]. Ammonia content increases with temperature and pH, so it is related with other basic parameters [137]. Ammonia as an inhibitor has been widely studied in order to set or establish the threshold between its positive effect (exploiting its buffer capacity) and its negative effect (as inhibitor). As an example, in semicontinuous AD of FW, the inhibition effects were observed when the ammonium concentration in the AD reactor exceeded 2 g/L [138]. Below this threshold, ammonium acted as a buffer neutralizing the acidification effect from VFA accumulation. Over the limit, ammonia strongly inhibited methanogenesis shifting methane formation from acetate by acetoclastic methanogens to a syntrophic acetate oxidation, leaving hydrogenotrophic methanogenesis as the only methane formation way. However, it did not affect the hydrolysis and acidification stages, causing an acetate and propionate accumulation with its consequent pH decrease.  
High concentrations of ammonia can lead to lower biogas production, and also to ammonia emission from the effluent, making the digestate less suitable as fertilizer [139; 140; 141; 142]. Many techniques for ammonia removal had been studied and approached, combining physical, chemical, and biological techniques [143; 144; 145; 146; 147].