esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### SHAP and LIME for Explainability and Interpretability
Notably, while the top contributing features remain largely the same, their relative magnitudes vary slightly across instances. For example, instance 18,339 exhibits a stronger positive influence from smean, while 133,349 and 45,127 show more balanced contributions from the next-ranked features. Additionally, features such as dbytes, sybytes, and tcprtt appear with negative contributions, consistently pushing against the predicted class, which may reflect patterns associated with benign traffic, in contrast to anomalous flows. These findings indicate a level of interpretability and coherence in the model's behavior, where key features maintain relevance across cases, yet respond adaptively to the characteristics of each instance. This reinforces the usefulness of local explainability techniques in validating the reliability and reasoning of black-box models in cybersecurity applications. Although certain ROC curves (e.g., for XGBoost) suggest low classification performance, their inclusion in this study is intentional and methodologically relevant. The primary objective of our work is not only to assess predictive performance, but also to analyze the behavior and reliability of explainability methods such as SHAP and LIME under varying learning conditions. Evaluating these tools in models that fail to generalize well offers valuable insights into the stability and limitations of post hoc interpretability techniques. Moreover, in real-world forensic scenarios, it is common to encounter poorly performing or partially trained models. Understanding how explanations behave under such conditions contributes to building more robust, transparent, and trustworthy systems. Thus, presenting these results is justified as part of a comprehensive evaluation framework.
# Antivirus Evasion Methods in Modern Operating Systems
## 4 Antivirus Evasion Mechanisms
### Obfuscation  
Obfuscation is a well-known technique employed by attackers to modify the code and signature of a virus to make it difficult for antivirus software to interpret the malicious code. This technique involves adding redundant code, modifying the case of a script, and reordering commands to create confusion and evade detection. Although obfuscation does not affect the malware's functionality, it may reduce its effectiveness [24]. However,relying solely on obfuscation to deceive antivirus software may not be sufficient, as modern antivirus software utilizes heuristic and behavior-based approaches to detect potential threats. To bypass antivirus detection, attackers often use obfuscation in combination with other techniques, such as polymorphism or metamorphism.  
Another research study using a systematic approach for antivirus evasion using malware obfuscation was presented in [25]. The authors focused on comparing different mechanisms of obfuscation for bypassing virus detection on different platforms. Obfuscation is a wide term, so new kinds of methods for code obfuscation are presented in the literature, such as ROP gadgets used for antivirus evasion presented in [26].
## 4 Acoustic Loading Test of the Deployable Reflector Antenna in STM Phase  
Figure 6 shows the test configuration and procedure of the acoustic load test in STM phase. The acoustic load test is a test to evaluate the structural integrity of the satellite or payload against the high-level reverberation field formed inside the launch vehicle fairing during the launch process. To mimic the reverberation field generated inside the launch vehicle fairing, a high sound pressure is created in the reverberation room environment using an acoustic modulator. The required specification of the acoustic load test defined in Table 2 can be realized by feedback control by several control microphones. As mentioned in Section 2, the acoustic load test is performed with \(-4\) dB level of required specification before the full level test to protect the test sample and prevent damage to the equipment. The acoustic load test is conducted in two stages, and a low-level acoustic random test (LLART) is performed with \(-8\) dB level of the original specification before/after each test as shown in Figure 6b. The pass/fail of the acoustic load test is determined by calculating the frequency and amplitude shift of the fundamental mode frequency caused by the acoustic load through the LLART. Similar with the sinusoidal vibration test, if the frequency and amplitude shift of the fundamental mode is more than 5% before/after each test, it is considered that there is a problem with the test specimen [15].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Center Frequency** & **Sound Pressure Level** & **Tolerance** \\ (1/1 Octave Band) & & \\ \hline
31.5 & 0.00 dB & \(-2\) dB/+4 dB \\
63 & 0.00 dB & \(-1\) dB/+3 dB \\
125 & 0.00 dB & \(-1\) dB/+3 dB \\
250 & 0.00 dB & \(-1\) dB/+3 dB \\
500 & 0.00 dB & \(-1\) dB/+3 dB \\
1000 & 0.00 dB & \(-1\) dB/+3 dB \\
2000 & 0.00 dB & \(-1\) dB/+3 dB \\
4000 & 0.00 dB & N/A \\
8000 & 0.00 dB & N/A \\ OASPL & 144 dB or more & \(-1\) dB/+3 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Required specification of the acoustic load test.  
Figure 6: Concept of the acoustic load test; (**a**) Test configuration with the acoustic zig; (**b**) Test procedure.  
However, since it is difficult to control the sound pressure level (SPL) in the high-frequency range, there is any tolerance requirement of the SPL in the 4000-8000 Hz frequency band. Therefore, pass/fail of the acoustic load test is determined by not only SPL within the frequency band of 31.5-2000 Hz but also the overall sound pressure level (OASPL). In the acoustic load test conducted in this study, the sound pressure level used in the test was measured by the reference microphone as shown in Figure 7a. As shown in Figure 7b, the acoustic load test satisfies the test tolerance in the frequency range of 31.5-2000 Hz, and the measured OASPL showed an output that was 0.35 dB higher than the sound pressure level of the required specification. As shown in Figure 7c, the results of LLART before/after the full-level acoustic load test show a frequency shift of less than 5% and an amplitude shift of less than 10% [15].
# On the Development of a Neural Network Architecture for Magnetometer-Based UXO Classification
## 4 Experimental Setup
### Resilience Analysis  
The final experimental stage focused on model resilience to input signal distortions, simulating real-world sensor anomalies.  
Three perturbations were introduced to the test set:  
* Additive noise: An independent and independently distributed noise was added as \(signal^{(out)}=signal^{(in)}+random\times\frac{\max(signal^{*})}{\min( signal^{*})}\), with noise levels \(\{0,0.01,0.05,0.1,0.2\}\), where \(signal^{*}\) refers to all signals in the input data. Examples are provided in Figure 4a-e.
* Time warping: Signals were divided into three random segments, each stretched or compressed based on a parameter \(max\_speed\_ratio\). Here, the \(t\)'th sample of an output signal was generated as \(signal^{(out)}(t)=\text{Interpolate}(signal^{in}(t+\Delta t))\), where Interpolate() is a function returning interpolation of a signal around \(t+\Delta t\). For interpolation, the Piecewise Cubic Hermite Interpolating Polynomial method was used, which assures shape-preserving interpolation. Example distortions for \(max\_speed\_ratio\in\{0,1.1,1.25,1.5\}\) are shown in Figure 5a.
* Signal drift: Drift was introduced into the signals at various amplitude levels, \(A\in\{0,0.01,0.05,0.1,0.2\}\) (Figure 6a). The distortion was performed additively according to the formula \(\text{signal}^{(\text{out})}=\text{signal}^{(\text{in})}+\text{drift}\), where the nonlinear drift component is generated as \(\text{drift}=(\text{cumsum}(\mathcal{N}(0,1|W)))\cdot\left(\text{max}(\text{ signal}^{(\text{in})})-\text{min}(\text{signal}^{(\text{in})})\right)\cdot A\). Here, \(\text{cumsum}(\cdot)\) denotes the cumulative sum function applied to a sequence and normalized to have a maximum equal 1. \(\mathcal{N}(0,1|W)\) represents a sequence of samples drawn from a standard normal distribution (mean zero, standard deviation one), with the number of samples \(W\) equal to that of the original signal. The amplitude factor \(A\) controls the overall magnitude of the added drift relative to the original signal's peak-to-peak range.  
The perturbed dataset was evaluated using the best model obtained from classical machine learning methods, as well as the developed neural network. All data transformations were implemented using the TsAug library [23]. For the experiments, a simple 80/20 train/test split was applied. To evaluate the impact of input signal distortions, the network's prediction performance was measured for increasing levels of input signal perturbations. In these experiments, balanced accuracy (detailed in the following section) was utilized as the primary performance indicator.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
###### Abstract  
The energy-efficient and reliable delivery of data packets in resource constraint underwater wireless sensor networks (UWSNs) is one of the key considerations to enhance the network lifetime. The traditional re-transmissions approach consumes the node battery and increases the communication overhead, which results in congestion and affects the reliable data packet delivery in the network. To ensure the reliability and conserve the node battery, in this paper, we propose adaptive forwarding layer multipath power control routing protocol to reduce the energy dissipation, achieve the data reliability and avoid the energy hole problem. In order to achieve the reliability, tree based topology is exploited to direct multiple copies of the data packet towards the surface through cross nodes in the network. The energy dissipation is reduced by a substantial amount with the selection of low noise path between the source and the destination including the information of neighbors of the potential forwarder node. Extensive simulation results show that our proposed work outperforms the compared existing scheme in terms of energy efficiency and packet received ratio (PRR).  
Keywords:re-transmissions; energy efficiency; energy hole; multipath; layered path; embedded systems; feasible regions; cross node; reliable data delivery +
Footnote †: journal: Sensors
## 2 Problem Formulation  
In order to design the proposed controller, the following equation is adopted.  
\[\left\{\begin{array}{l}x^{(n)}=f(\mathbf{x},t)+g_{1}(\mathbf{x},t)u(t)+g_{2} (t)d(t)\\ y=x\end{array}\right. \tag{1}\]In the above, \(d(t)\) is the unknown time-dependent external disturbances. In Equation (2), \(f(.)\) and \(g_{1}(.)\) become unknown nonlinear functions, which need to be identified. In this work, a new type of control law via estimating the \(\hat{d}(t)\) of \(d(t)\) and the \(\hat{f}(.)\) and \(\hat{g}_{1}(.)\) of \(f(.)\) and \(g_{1}(.)\) is built. By this way, the effectiveness of estimating UAD can be improved, since the time varying rate of \(d(t)\) is low. This approach has two combined phases, as shown in Figure 1. This figure represents a basic controller called a \(B\)-controller and a disturbance estimator DE. Thus, the control signal \(u(t)\) for the plant is to be totalized as follows: \(u(t)=u_{s}(t)+\hat{d}(t)\). In order to construct the total controller, the FL and SMC methods are to be used, in which the unknown functions \(f(.)\), \(g_{1}(.)\) in Equation (1) are approximated by the fuzzy structures. It is noted that although \(d(t)\) is ignored, the impact of model error on the system during the calculation process is compensated by the fuzzy structure of the \(B\)-controller. In order to investigate the DE for estimating \(\hat{d}(t)\) of \(d(t)\), the known functions \(f_{0}(.)\) and \(g_{0}(.)\) are utilized.  
The second issue relates to the function approximation. In order to establish \(\hat{f}(.)\) and \(\hat{g}_{1}(.)\), a sliding surface \(S(\mathbf{x})\) defined via the error \(\mathbf{e}(t)\) between \(\mathbf{x}(t)\) and \(\mathbf{x}_{d}(t)\) is formulated as follows [12; 18]:  
\[u_{s}(t)=\frac{1}{\hat{g}_{1}(\mathbf{x},t)}\left(-\hat{f}(\mathbf{x},t)+{x_{ d}}^{(n)}+F(\mathbf{e}(t),S)-\rho G(S,t)\right) \tag{2}\]  
where, \(F(\mathbf{e}(t),S)\) denotes the expression related to \(\mathbf{e}(t)\) and \(S\); \(G(S,t)\) is the function of \(S\) and \(t\); and \(\rho\) is the gain parameter. It can be observed from the equation that since \(\hat{g}_{1}(\mathbf{x},\varphi_{g})\) is generated online, there is no guarantee such that \(\hat{g}_{1}(\mathbf{x},\varphi_{g})^{-1}\) remains during the operating process. This method is liable to appear in singular cases in the calculating process. In order to avoid the singular cases, in this work, a solution via the role of \(\varepsilon_{0}\lambda^{T}(\mathbf{x})\lambda(\mathbf{x})\) will be proposed (refer to Theorem 1). The last issue relates to the calculating cost. The fuzzy gain \(\rho\) presented in [12] or [18] is an essential option to reduce the chattering phenomenon. This, however, causes the increment of the calculating time, which results in increased time delay. Due to this aspect, an adaptive gain \(\rho_{ad}\), which can be calculated directly and updated adaptively based on the convergent status of the sliding surface, \(S(\mathbf{x})\to 0\), will be determined in this work.
## 5 Discussions of Wave Velocities, Dispersion and Attenuation Characteristics
### Dispersion and Real Wave Velocity  
Dispersion is a procedure where waves are gradually separated with different frequencies. Thus, the wave velocity, initial waveform, and vibration mode are changed during the propagation process.  
In the case of the finite permeability, theoretical velocities of the three waves are uniformly expressed by the complex number C. Thus, the velocity can be written in the following form:  
\[C_{j}(\omega)=\frac{\omega}{I_{j}(\omega)}=C_{rj}(\omega)+C_{ij}(\omega)i \tag{45}\]  
where \(j\) is the root of the dispersion equation. \(J=1,2,3\). \(C_{r}\) and \(C_{i}\) represent the real and imaginary parts of the root, respectively. In Equation (36), real and imaginary parts of the velocity are expressed as a function of the circle frequency, which reflects the dispersion of the waves.  
The one-dimensional plane wave is employed to simplify the analysis progress. Equation (42) is uniformly written as the form shown below:  
\[\varphi_{j}=A_{j}\exp(i\omega(\frac{1}{C_{j}(\omega)}x+t)) \tag{46}\]  
where,  
\[\frac{1}{C_{j}(\omega)}=\frac{C_{rj}(\omega)}{C_{rj}(\omega)^{2}+C_{ij}(\omega )^{2}}-\frac{C_{ij}(\omega)}{C_{rj}(\omega)^{2}+C_{ij}(\omega)^{2}}i \tag{47}\]  
Equation (47) is substituted into Equation (46), and the following equation is obtained:  
\[\varphi_{j}=A_{j}\exp\left(\frac{\omega}{\widetilde{C}_{ij}(\omega)}x\right) \exp\left[i\omega\left(\frac{1}{\widetilde{C}_{rj}(\omega)}x+t\right)\right] \tag{48}\]  
where,  
\[\widetilde{C}_{ij}(\omega)=\frac{C_{rj}(\omega)^{2}+C_{ij}(\omega)^{2}}{C_{ij }(\omega)} \tag{49}\]  
\[\widetilde{C}_{rj}(\omega)=\frac{C_{rj}(\omega)^{2}+C_{ij}(\omega)^{2}}{C_{rj }(\omega)} \tag{50}\]  
For the problems of a one-dimensional plane wave, the potential functions of three waves in a saturated porous medium can be expressed in a unified form of Equation (48). In Equation (48), the term \(A_{j}\exp\left(\frac{\omega}{C_{ij}(\omega)}x\right)\) describes the attenuation of the wave propagation, and the term \(\exp\left[i\omega\left(\frac{1}{C_{rj}(\omega)}x+t\right)\right]\) reflects the propagation of the waves in a simple harmonic mode. Therefore, the real part of Equation (50) is the real wave velocity for the finite permeability case in a saturated porous medium.
## 4 Performance Evaluation  
This section is going to discuss the performance of the proposed TCN-TS-SW in the following case studies:  
1. The validation of TCN-TS-SW versus other machine learning (ML) algorithms like multi-look back LSTM, LSTM, GRU in terms of training loss and training accuracy.
2. The effectiveness of the proposed two-stage sliding window technique is investigated as the proposed scheme is compared to the classic TCN model presented in [33] in terms of training loss and accuracy.
3. The forecasting capability of the TCN-TS-SW. The results are presented using performance metrics like RMSE, MAE, and \(R^{2}\) and the predicted data is plotted agains the actual data.  
Also, it is an important note that in every experiment conducted, there are different set of data are utilized and, in each section, the structure is be explained. To analyze whether the proposed architecture is performing well in terms of accurately providing the forecasted nozzle temperature values, the models presented in this paper were evaluated using the metrics like root-mean-square error (RMSE), mean absolute error (MAE) and \(R^{2}\).  
\[\text{RMSE}=\left[\sum_{i=1}^{N}\frac{\left(v_{i}-y_{i}\right)^{2}}{N}\right] ^{1/2} \tag{2}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\left|v_{i}-y_{i}\right| \tag{3}\]  
\[R^{2}=1-\frac{\sum\left(v_{i}-\hat{g}\right)^{2}}{\sum\left(v_{i}-y_{i}\right) ^{2}} \tag{4}\]  
Presented in Equation (2), RMSE is the squared-root value of mean squared error that reveals the deviation between the actual temperature reading \(v_{i}\) to the predicted data \(y_{i}\) at time \(i\), and \(N\) is the total number of observations involved. Another metric is the mean absolute error (MAE) in (4), that asserts the absolute average residual between \(y_{i}\) and \(v_{i}\). Lastly, r-squared \(R^{2}\) or the coefficient of determination in Equation (4), is an indication of how well the predicted values replicate the actual observation. The values of \(R^{2}\) can be on the less than zero [40].
# Green Project Planning with Realistic Multi-Objective Consideration in Developing Sustainable Port
## 5 Numerical Experiments and Analyses
### Experimental Results and Comparison  
The problem is solved by the weight sets-based multi-objective evolutionary optimization approach. We can obtain a set of single-objective optimal solutions to each weight set-based single-objective optimization problem. The solutions in the first front of non-dominated sorting are the final solutions to the problem. The experimental results show that only two solutions can be found by solving this problem as 21 single objective optimization problems. In Table 2, Column 2 presents the optimal project construction sequence under the corresponding weight combination, while Column 3 presents the objective function value of the solution for the two objectives. In fact, the two solutions obtain the same results after decoding, so the step of non-dominated sorting for each single-objective optimal solution is omitted. We use the heuristic rules in Section 4.3.2 for decoding, and the resulting values for the three decision variables can be draw in Figure 4. The abscissa represents the time and the ordinate represents the project number, the number in the box is the construction ratio. For example, the construction of Project 1 is started in the first year, so that \(X_{11}=1\). The construction of Project 3 is started and finished in second year, so that \(X_{23}=1\), \(Z_{23}=1\) and \(Y_{23}=1\).  
To verify the validity of the proposed approach, NSGA-II was used to directly solve the same problem and the Pareto front was found as the final solution. Through calculations, we find that the solution number of Pareto frontier is 66 (see Table A1 in Appendix A), but there are multiple construction sequences for the same construction plan, such as the decoding schemes of Individuals 1 and 2 are the same. Therefore, after the merger of the same decoding scheme, there are only five different construction plans after decoding. For the specific construction scheme, see Figures A1-A5 in Appendix A.  
By comparing the results obtained by the two methods, the solution found by our method is one of the Pareto optimal solutions generated by the NSGA-II. It is found that the conversion of multiple problems to single-objective problem solving under a given combination of weights can simplify the multi-objective optimum-seeking process very well, and obtain the solution in the Pareto optimal front, and avoids the complex process of making decisions in many Pareto optimal solutions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Project No.** & **Unit Numbers** & \begin{tabular}{c} **Unit Cost** \\ **(Thousand Yuan)** \\ \end{tabular} & \
\begin{tabular}{c} **CO2 Emission** \\ **Reduction (ton)** \\ \end{tabular} & \(M_{p}\) &
\begin{tabular}{c} \(F_{p}\) \\ **(Thousand Yuan)** \\ \end{tabular} \\ \hline
1 & 17 & 6200 & 1484 & 0 & 620 \\
2 & 1040 & 2.59 & 0.5 & 1 & 0.26 \\
3 & 38 & 180 & 25 & 1 & 18 \\
4 & 2 & 500 & 9 & 1 & 50 \\
5 & 1 & 18,950 & 3142 & 0 & 1895 \\
6 & 23 & 5400 & 301 & 0 & 540 \\
7 & 10 & 300 & 10
## 1 Introduction
Due to the presence of MgO in the castables, aids are also used that influence the hydration process, like amorphous silica, chelate compounds, etc. These compounds strongly modify the hydration process. Chelates like citric and tartaric acids modified the hydration process in this system. In a high pH environment, which is characteristic for systems with MgO, citric as well as tartaric acids dissociated. The dissociation of these compounds depends on the pH of the environment and for pastes from MgO-Al\({}_{2}\)O\({}_{3}\)-H\({}_{2}\)O system, it exceeds 10. In such conditions, both the citric and tartaric acids are present in the fully dissociated forms, C\({}_{6}\)H\({}_{5}\)O\({}_{7}\)\({}^{3-}\) and C\({}_{4}\)H\({}_{4}\)O\({}_{6}\)\({}^{2-}\). This proudsethen with the ability to adsorb the positive charges on the surface of the grains and create a complex with metal ions in solution. The neutralization of the net surface charge and coordination of the Mg/Al ions prevent magnesium oxide grains fromdissolution and precipitation of the hydroxides and decrease the rate of hydration process in a water solution. However, during the heat treatment at an elevated temperature, reactions in this system are accelerated by the high-water vapor pressure that prevails in castables [13; 14; 15; 16; 17]. Reactions that occurat the early stage of the heat treatment of the castables have a key role in shaping the properties of thesematerials. This, in consequence, decides about the course of many high-temperature reactions like the spinel synthesis. The aim of this study was to evaluate the effect of the two most popular chelating agents used in castables, citric and tartaric acids, on the phases coexisting in the MgOAl\({}_{2}\)O\({}_{3}\)-H\({}_{2}\)O system, under hydrothermal conditions. Hydrothermal conditions are designed to simulate conditions that are prevailing in large volume castables precast units during the heat treatment.