esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Machine Learning-Based Detection for Encrypted Malicious Traffic  
Machine learning (ML) and deep learning-based intrusion detection methods automatically learn latent patterns in encrypted traffic to identify anomalies [11]. Compared to signature-based and statistical methods, ML not only offers better adaptability, improved detection of unknown threats, and stronger handling of complex data but also introduces new challenges [29].For instance, [PERSON] et al. applied CNN to classify encrypted TLS traffic, achieving over 99% accuracy by incorporating early stopping to prevent overfitting [30]. CNN reduces model parameters through convolution and pooling, facilitating high-dimensional input processing, but requires the manual extraction of flow size and direction features, increasing labor cost [10]. The Whisper approach extracts flow sequence features via frequency domain analysis, improving detection throughput while maintaining accuracy for low-information flows. This addresses limitations in detecting zero-day attacks and mitigates the high processing overhead commonly associated with ML methods, though real-time detection remains challenging [31]. Despite the increasing adoption of text-based, temporal, and graph-structured models for encoding event dependencies, their complexity and optimization requirements still exceed those of traditional detection techniques [23]. Both supervised and unsupervised learning can achieve high accuracy in specific contexts. Deep learning methods that mine complex temporal correlations in traffic enable adaptive classification strategies to reduce false positives [25]. The integration of traditional and emerging methods, alongside optimized feature selection, supports maintaining IDS accuracy while minimizing false alarms, forming effective multi-modal fusion systems [17; 29]. The use of generative adversarial networks (GANs) to synthesize diverse attack samples enhances model robustness, while federated learning and edge computing facilitate scalable distributed monitoring to counter large-scale attacks [3]. Adaptive ML models dynamically update features and rules to respond to evolving network conditions.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 4 Experiment on a Lab-Scale Caisson-Foundation System
### Foundation-Damage Scenarios  
The foundation damage was simulated to mimic a variety of damage scenarios caused by scouring during extreme storm surge events. Figure 9 shows eight levels of foundation damage simulated by sequentially removing various amounts of gravel armor and sand from the foundation mound.  
As shown in Figure 9a, the cut-off width was set to 60 cm (i.e., half of the caisson width). Damage 1 to Damage 4 were introduced by removing the depth of the mound up to 10 cm, 20 cm, 30 cm, and 35 cm, respectively. Damage 1 removed 0.37 kN of gravel, which was approximately 2.3% of the armor gravel. Damage 2 removed a total of 0.90 kN material from foundation. It involved 0.46 kN of gravel (about 2.8% of the armor gravel) and 0.44 kN of sand (about 0.7% of the sand mound). Damage 3 resulted in the removal of 0.56 kN gravel (about 3.4% of the armor gravel) and 0.91 kN sand (about 1.5% of the sand  
Figure 7: Parameters of the caisson breakwater system: (**a**) Oryuk-do caisson breakwater system [19]; (**b**) lab-scale caisson-foundation model [13].  
As shown in Figure 9b, Damage 5 to Damage 7 expanded the damage to foundation-caisson interface with dug hole depths ranging from 100 mm to 300 mm. This resulted in total extracted sand weights of 1.35 kN, 1.56 kN, and 1.77 kN (about 2.2%, 2.5%, and 2.9% of the sand mound), while the removed gravel remained constant at 0.58 kN. The configuration of the foundation damage scenarios is summarized in Table 2.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 4 UWSNs: Communication Models
### Noise Model  
Underwater noise is due to three main factors: scattering, absorption and spreading loss. These losses are mainly caused by unsteady movement of water, ships motion for the sake of tourism or trade, military operations, etc.  
Total noise power spectral density is calculated in the following set of equations:  
\[NL(f)=N_{t}(f)\ +\ N_{\rm s}(f)\ +\ N_{\omega}(f)\ +\ N_{th}(f) \tag{5}\]  
whereas;  
\[10 logN_{\rm s}(f)\ =\ 40\ +\ 20(s\ -\ 0.5)\ +\ 26 log(f)\ -60 log(f\ +\ 0.03) \tag{6}\]  
\[10 logN_{\omega}(f)\ =\ 50\ +\ 7.5\ \omega^{1/2}\ +\ 20 log(f)\ -\ 40 log(f\ +\ 0.4) \tag{7}\]  
\[10 logN_{th}(f)\ =\ -\ 15+\ 20 log(f) \tag{8}\]  
All the four noise factors directly depend on frequency, that's why fluctuation in frequency effects the communication undergoing the environment.
# A Literature Survey on Offline Automatic Algorithm Configuration
## 2 Literature Review
### Model-Free Algorithm Configuration Methods
#### 2.1.1 Meta-EAs  
We previously mentioned the difficulties of finding good parameter values (including conditional and categorical parameters) for heuristics and metaheuristics. This is a complex stochastic black-box mixed-variables optimization problem due to interacting variables, nonlinear objective function, multiple local optima, lack of analytic solvers, and noise [4]. Ironically, in this type of problem, heuristic optimization algorithms are effective solvers and can find good parameter settings [13].  
Because of this reason, using any type of evolutionary method to configure the parameters of another evolutionary algorithm is a natural opinion. They are called Meta EAs because their activity can be considered meta optimization. [PERSON] and [PERSON] [17], and [PERSON] [18] are the first study that aimed to use an Evolutionary Algorithm for optimizing metaheuristics. After these studies, research continued and several studies were conducted about meta-GAs. For details of studies on Meta-GA and critiques about the use of meta algorithms for investigating the best parameter vectors, readers are suggested to see [19].  
One advantage of meta-EAs is that they can be terminated at any point of the tuning process and give a solution at that point. However, they are not suitable for large parameter spaces, because they can provide sustainable solutions only after a large number of evaluations of candidate configurations [13]. Additionally, most of the meta-EAs (i.e., CMA-ES and REVAC) cannot deal with the categorical parameters [13].
# Power System State Estimation Approach Considering Transmission Line Temperature
## 4 Improved Two-Step Etc-Se Approach
### Procedure of Improved Two-Step ETC-SE  
Similar to the proposed AJ-ETC-SE approach, the estimation of voltage phasors can be solved by the Newton method, and represented as,  
\[\Delta\pi_{k}=[\mathbf{H}^{T}(\pi_{k})\mathbf{R}^{-1}\mathbf{H}(\pi_{k})]^{-1}\mathbf{H}^{T}(\pi_ {k})\mathbf{R}^{-1}\Delta\pi_{k} \tag{33}\]  
\[\pi_{k+1}=\pi_{k}+\Delta\pi_{k} \tag{34}\]  
Furthermore, according to (9)-12), the HBE can be rearranged as,  
\[\begin{array}{l}f(T)=I^{2}R(T)+q_{s}-q_{r}(T)-q_{c}(T)=\\ I^{2}R_{r}[1+\alpha_{l}(T-T_{d})]+q_{s}-A_{r}[(T+273)^{4}-(T_{a}+273)^{4}]-A_{c} (T-T_{a})=0\end{array} \tag{35}\]  
where \(A_{r}\) and \(A_{c}\) are coefficients of radiated heat loss and convective heat loss, respectively.  
In the (\(k\)+1)th iteration, the estimation of line temperature can be calculated by the Newton method. According to (35), the calculation process can be represented as,  
\[T_{k+1}=T_{k}+\Delta T_{k}=T_{k}-\frac{f(T_{k})}{f^{\prime}(T_{k})} \tag{36}\]  
\[f^{\prime}(T_{k})=\alpha_{l}I_{k+1}^{2}R_{r}-4A_{r}(273+T_{k})^{3}-A_{c} \tag{37}\]  
\[I_{ij,k+1}^{2}=[G_{ij,k}^{2}(T_{k})+B_{ij,k}^{2}(T_{k})](U_{k,k+1}^{2}+U_{j,k +1}^{2}-2U_{i,k+1}U_{j,k+1}\cos\theta_{ij,k+1}) \tag{38}\]  
With the combination of the (17), (18), and (36)-(38), the procedures of the proposed ITS-ETC-SE approach can be demonstrated in Figure 4, and listed as,  
1. Obtain system topology and network parameters.
2. Input the measured electrical and meteorological data.
3. Initialize the voltage phasors, line temperature, and corresponding parameters.
4. Form the admittance matrix and Jacobian matrix, calculate the correction vector \(\Delta\mathbf{x}\) by (33), and update the state vector \(\mathbf{x}\) by (34).
5. Calculate the current of transmission lines by (38), and the correction vector of temperature \(\Delta T\) by (36) and (37).
6. If \(\|\Delta\mathbf{x}\|_{\infty}\)\(\leq\)\(\xi\) and \(\|\Delta T\|_{\infty}\)\(\leq\)\(\xi\), output the state vector \(\mathbf{x}\), otherwise, update the line parameters according to (31) and (32), and return to step (4).  
Figure 3: Framework of the proposed ITS-ETC-ES approach.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 6 Practical Challenges in Underwater SLAM
### Computational Resources  
Underwater SLAM systems require real-time processing for obstacle avoidance, precise navigation, and stable control in dynamic and unpredictable underwater conditions. Unlike surface or aerial platforms with greater power and cooling capabilities, underwater AUVs typically have limited CPU/GPU resources and battery lifespans. Deep learning-based underwater SLAM algorithms introduce significant challenges due to their computational demands. For instance, underwater images suffer from distortion, turbidity, and variable lighting, requiring extensive processing for meaningful feature extraction [94]. Moreover, modern deep SLAM models, using convolutional, recurrent, and transformer-based architectures with millions of parameters, are highly computationally demanding, especially when fusing multi-modal data [95].  
Consequently, real-time inference demands efficient model optimizations, while the high memory and bandwidth requirements of deep learning models challenge resource-limited underwater embedded systems. This necessitates the development of lightweight architectures [96] and the use of techniques like pruning, quantization, and knowledge distillation (KD) [97].
## 4 Contribution of Thermodynamic Calculations  
Due to the implementation of high temperature chemical reactions in TCES processes, considerations related to thermodynamic equilibria are of high interest for the design of the most performant materials. Indeed, a precise thermodynamic description of the system MMO\({}_{(\alpha\mathrm{ox})}\)-MMO\({}_{(\mathrm{red})}\)-O\({}_{2}\)(g) makes it possible to determine the maximum achievable conversion yield, to target the most suitable compositions and operating conditions, and to understand experimental results obtained at lab scale.  
Figure 6: Mass variation (OSC), enthalpy change and onset temperatures of redox reactions measured for the Mn-Co-O system.  
However, as illustrated by the case of the mixed oxides considered in this work, a full thermodynamic description of the systems is not always available. There are three major levels of thermodynamic description for such systems:  
* Existence of an accurate phase diagram for the pseudo-binary system,
* Existence of thermochemical data such as enthalpy of mixing between phases or heat capacity functions (\(\mathrm{c}_{\mathrm{p(T)}}\)) for all phases of the system,
* Existence of a model established with the Calphad method, allowing equilibrium computations with a dedicated software.  
When a full model is available, various equilibrium computations can be performed, such as the plot of phase diagrams or the calculation of the system equilibrium at various compositions and temperatures. This provides, for each composition and at each temperature step, the phase assemblage, the cationic distribution in each solution phases, the theoretical mass change (i.e., oxygen storage capacity), as well as the enthalpy variations of the system. As summarized in Table 2, three of the five systems considered here (Co-Fe-O, Co-Cu-O and Mn-Fe-O) benefit from a complete thermodynamic description including one or several models. However, this is not the case for the Mn-Co-O system, for which the phase diagram at temperatures below 800\({}^{\circ}\)C is not firmly established [49], nor for the Mn-Cu-O system, for which phase diagrams with large discrepancies have been published [50; 51]. Among the global Mn-Co-Fe-Cu-O system, the last ternary sub-system, Cu-Fe-O, benefits from a large amount of experimental data and a robust model [52].  
Depending on the level of description of the mixed oxide systems, several applications of thermodynamics are described in the following sections. The calculations were carried out with the FactSage software [59].
## 2 Challenges in Underwater Communications
### Underwater Noise  
In general, underwater noise adds to the desired data and degrades the quality of communications. As a remedy, routing protocols have to select the paths less affected by noise. The underwater ambient noise is constituted by four components: shipping, wave, thermal and turbulence noise [12]. The power spectral density \(N\) of the ambient noise in dB is the sum of the power spectral densities of the four noise types and is given by:  
\[N=N_{sh}+N_{wv}+N_{tb}+N_{th} \tag{1}\]  
where \(N_{sh}\), \(N_{wv}\), \(N_{tb}\) and \(N_{th}\) are the power spectral densities of the shipping, wave, turbulence and thermal noise, respectively. They are modeled by:  
\[N_{sh}=40+20(s-0.5)+26\log(f)-60\log(f+0.03) \tag{2}\]  
\[N_{wv}=50+7.5^{w}+20\log f-40\log(f+0.4) \tag{3}\]  
\[N_{tb}=27-30\log f \tag{4}\]  
\[N_{th}=-27+\log f \tag{5}\]  
where \(f\) is the frequency in kHz, \(w\) is the speed of wind in m/s and \(s\in[0,1]\) determines the extent of shipping activities in water. Turbulence in water generates noise that corrupts frequencies below 20 Hz. The noise generated by shipping activities ranges from 20-200 Hz. Waves generated by wind blowing at the surface of water with speed \(w\) result in noise generation from 200-200 kHz. Temperature-generated thermal noise affects acoustic frequencies above 200 kHz.
## 5 Experiment Setup and Metrics
### Experiment Settings  
Experiments were conducted on a high-performance desktop workstation running Windows 10 Professional, equipped with an Intel Core i7 processor, 16 GB of RAM, and a dedicated GPU, providing reliable performance for computational tasks, with Python as the primary programming language. Python libraries, such as NumPy (2.2.4), Pandas (2.2.3), Matplotlib (3.10.1), ScikitLearn (1.6.1), TensorFlow (2.19.0), and Keras (3.9.2), were utilized for data manipulation, model development, and evaluation.  
To ensure the validity and generalizability of the proposed seizure detection model, a patient-independent classification protocol was adopted. This means that data segments from the same subject were not used in both the training and testing sets, thereby preventing data leakage and overly optimistic performance estimates.  
As mentioned in Section 4.1, the dataset comprises 500 subjects, with each class containing EEG recordings from 100 distinct individuals. During preprocessing, each 23.6-s EEG recording was segmented into 23 1-s epochs, resulting in a total of 11,500 segments. To preserve patient independence, the data was split at the subject level rather than at the segment level. Specifically, 80% of the subjects were used for training, and the remaining 20% were reserved for testing. For example, if a subject's EEG file contributed 23 segments, all 23 segments were either in the training set or in the test set--never both. This ensured that the classifier learned from EEG patterns of distinct individuals and was tested on completely unseen subjects.  
This protocol better reflects real-world clinical scenarios, where seizure detection systems must generalize to new patients not seen during training. It also supports the claim of developing a patient-independent detection system as opposed to patient-specific models that often suffer from poor generalizability.
# Research on Environmental Vibration Induced by High-Speed Maglev Transportation
## 4 Environmental Ground Surface Vibration
### Vibration Propagation in Whole Ground Surface
#### 4.3.1 Vibration Propagation 30 Degrees from Maglev Line  
To investigate the vibration propagation in the 30-degree direction, five test points were chosen with distances to the foundation of 0 m, 7.5 m, 10 m, 12.5 m, and 15 m (points G, M, N, O, and P in Figure 4) in sequence. The ground acceleration responses and frequency spectra are shown in Figure 10 (listed in order of distance).  
Figure 10: Acceleration time history and frequency spectra of each test point in the 30-degree direction.  
Figure 9: Acceleration time history and frequency spectra of test points perpendicular to the maglev line.  
In the time history records above, it can be seen that, when the ground vibration propagates 30 degrees from the maglev line, the trend of attenuation rapidly reduces and then maintains relative stability. At the first 10 m away from the foundation center, the peak value of acceleration rapidly reduces from near 1.5 m/s2 to about 0.01 m/s2, and then it maintains this peak value until the farthest test point. Meanwhile, as the test point layout is oblique to the maglev line, the vibration propagating from the column requires more time to reach each test point, leading to tiny time lags between the different subgraphs.  
In order to determine the reason for this phenomenon, the frequency spectra of the logarithmic coordinates shown in Figure 10 were examined. It can be seen that, when the distance reaches 7.5 m, the frequency components over 200 Hz almost all disappear, and then at 10 m away, only vibration components under 10 Hz remain. As low-frequency vibrations attenuate quite slowly in the ground, it can be inferred that the ground vibration will continue at this level for a quite long distance.
## 2 Materials and Methods  
### Experimental Work  
We have previously reported microfluidic mix-and-inject devices, which were optimised for molecular imaging studies at the European XFEL [40,41]. The microfluidic mix-and-inject devices were fabricated using a novel SUS-on-glass method that has not been reported previously. The efficiency of the serpentine micro-mixer component was determined using the fluorescent intensity analysis technique. The devices were also tested for their jetting performance, and 3 distinct jetting regimes were identified and reported. In the present paper, the goal is to explore, using numerical simulation, the mixing characteristics and minimum achievable mixing time that is possible using our integrated mix-and-inject sample delivery platform. The devices investigated here consist of an integrated serpentine micro-mixer channel and a gas dynamic virtual nozzle (GDVN), fabricated using our recently developed SU8 on glass technique [40,41]. The nominal width (W) and height (H) of the serpentine microchannel is 25 \(\upmu\)m and 45 \(\upmu\)m, respectively. The mixing performance was experimentally characterised using fluorescence measurements at five different positions along the micromixer channel, starting at just after the inlets. During the experimental measurements, three different types of the liquid jet (cylindrical, ribbon, and planer) were observed; the schematic in Figure 1 is an approximate representation of the regime (in terms of flow rate and gas pressure) over which combined homogeneous mixing and stable jetting can be achieved using our devices.  
For the simulation study, the mixing behaviour of the microfluidic devices was investigated over a range of conditions for which both homogeneous mixing and stable jetting was observed experimentally shown in Figure 1. One of the key goals of the simulations was the characterisation of the mixer in terms of the minimum time required to achieve homogeneous mixing as a function of the flow rate and length of the serpentine mixer. This, in turn, determines the maximum time-resolution possible using these devices.
## 3 Route Determination for Ship Passage Using Capability Plots
### Route Determined Using [PERSON]'s Algorithm and the Capability Plot  
The passage route determined by [PERSON]'s algorithm for the navigational scenario presented in Figure 5 is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Phase**} & **S** & **V** & **t** & **Course** & **True** & **Env. Force** & **Sum** & **Fuel** & **Energy** \\  & **[NM]** & **[knots]** & **[h]** & **[\({}^{\circ}\)]** & **Wind** & **[X-Axis;** & **[X-Axis;** & **[X-Axis]** & **[KN]** & **[L]** & **Consumption** \\  & & & & & & & & & \\  & & & & & & & & & \\  & & & &  
In the figure above, the starting point of the determined route is marked in red, and as each waypoint gets closer to the destination, the color gradually shifts to green. The final passage route is presented in Figure 13.  
The cumulative weight of transitions for the identified route is 0.8476, which is the lowest value obtainable given the parameters of the anchorage. It is possible to find alternative routes that yield the same sum, however, from the algorithm's operational perspective, there is no difference between them.  
The value of the route cost function is presented in Table 4.  
Figure 12: The route determined using [PERSON]’s algorithm.  
Figure 13: Determined passage route.  
The energy consumption for the route determined by the algorithm was 4231.127 [kWh], which is 7.58% less than the direct route to the end point.
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Data Confidentiality of a Channel between the User and Robot
#### 7.1.3 Last, One-to-One Open Communication  
In this method, the user and the robot communicate directly via voice, gestures, signs, etc., without using any form of additional communication technology. This method of communication is natural, seamless, and easy to adopt.  
The means of communicating in the first two methods (which uses technology) can be secured easily by adopting any current state-of-the-art encryption security mechanisms to maintain data confidentiality. However, one-to-one open communication does not adopt any technology as a medium to interact between the user and the robot. It is impossible to secure the channel if direct voice communication is used during the interaction, because it could be heard by anyone within audible range. Signs and gestures could also be easily intercepted and decrypted by anyone within the user and robot's sensing area. Thereby, information could easily be leaked to both unintended and inappropriate audiences, notwithstanding any intruders. Coded signs, signals, and words could also  
Figure 6: Security Measures for Safeguarding and Protecting User’s Data and Privacy.  
be used to maintain some form of confidentiality during the interaction in the presence of any third-party individual. However, this may even lead to the making of unintended decisions by the robot due to ambiguity. In addition, such codes and signs would neither be as secure as expected nor convenient for the users, especially if the users have mental health issues or some form of serious disability. Therefore, the robot needs to know when and how it should communicate with the user, to create a safe environment to protect the user's data and safeguard their privacy.  
In an open communication between the user and the robot, information could easily be leaked to three categories of unauthorised user within the sensing range namely: unintended, inappropriate, and intruder. If the user approves the presence of any individual (family, friends, doctors, or nurses), privacy concerns would not arise or apply in such a scenario. In a remote access platform, any form of unauthorised access could be an attempt to steal, tamper with, or alter information, so unintended or inappropriate scenarios may not arise.