esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 2 Related Work
### Addressing Prevention Gaps  
Existing ransomware defense solutions often focus narrowly on operational and/or managerial aspects, but they ignore situational factors that significantly influence the effectiveness of these strategies. To address this limitation, we propose a comprehensive framework that combines both approaches, allowing the development of situationally aware solutions designed for the context of specific attacks. Moreover, the proposed framework emphasizes predictive situational awareness to trigger active defenses and assess the effectiveness of hardening strategies. By integrating evidence from diverse sources, the model could identify potential ransomware threats early and allow for timely preventive measures. This framework not only provides direction for future research but also serves as a baseline for evaluating current investigations and future work that can build on our framework to create adaptive and context-aware solutions for ransomware prediction and prevention.  
To summarize, the capacity of ransomware to masquerade as legitimate software and irreversibly encrypt data renders early detection paramount yet arduous. The reliance on robust cryptographic mechanisms and the rapid evolution of attack patterns impedes conventional detection methodologies, frequently resulting in failures due to data insufficiency within the critical pre-encryption window and the malware's polymorphic nature. To mitigate these challenges, we use a dual-pronged strategy: (1) leveraging GANs to augment sparse datasets with realistic synthetic samples, and (2) employing adaptive feature selection via mutual information to dynamically prioritize salient features. This approach addresses data scarcity and dimensionality complexities, ensuring model efficacy against evolving ransomware tactics. Furthermore, DBNs are investigated as detection architectures, with training optimizations such as early stopping to mitigate overfitting. This integrated approach enhances detection accuracy, synergistically combining robust modeling techniques with adaptive strategies to counter ransomware's dynamic behavior.
## 3 Results
### Analysis of Hole Size Analysis and Circularity Error
Furthermore, adding magnesium to aluminium alloys contributes to maintaining a high degree of ductility/formability but is also known to be thermodynamically unstable at higher temperatures. This, in return, means that lower cutting parameters are more suited for softer aluminium alloys like AA5083 [26]. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Hole Size**} & \multicolumn{2}{c}{**Circularity**} & \multicolumn{2}{c}{**Cylindricity**} & \multicolumn{2}{c}{**Perpendicularity**} \\ \cline{2-9}  & \(\mathbf{p}\)**-Value** & **Contribution** & \(\mathbf{p}\)**-Value** & **Contribution** & \(\mathbf{p}\)**-Value** & **Contribution** & \(\mathbf{p}\)**-Value** & **Contribution** \\ \hline Model & 0.001 & 96.08\% & 0 & 97.06\% & 0.024 & 90.24\% & 0.009 & 92.60\% \\ Linear & 0 & 64.19\% & 0 & 54.54\% & 0.01 & 47.06\% & 0.001 & 81.59\% \\ \(n\) & 0.007 & 9.71\% & 0.804 & 0.16\% & 0.046 & 11.35\% & 0.122 & 5.12\% \\ \(f\) & 0.12 & 2.74\% & 0.281 & 1.10\% & 0.619 & 1.25\% & 0.218 & 3.43\% \\ Alloy type & 0 & 51.75\% & 0 & 53.28\% & 0.002 & 34.46\% & 0 & 73.04\% \\ \(2\)-Way Interactions & 0.012 & 31.88\% & 0.002 & 42.52\% & 0.066 & 43.18\% & 0.521 & 11.02\% \\ \(n\times f\) & 0.386 & 2.33\% & 0.389 & 1.73\% & 0.218 & 8.89\% & 0.779 & 1.61\% \\ \(n\times\) Alloy type & 0.002 & 24.81\% & 0 & 29.20\% & 0.045 & 19.54\% & 0.388 & 4.37\% \\ \(f\times\) Alloy type & 0.134 & 4.74\% & 0.007 & 11.59\% & 0.086 & 14.75\% & 0.328 & 5.04\% \\ Error & - & 3.92\% & - & 2.94\% & - & 9.76\% & - & 7.40\% \\ Total & - & 100.00\% & - & 100.00\% & - & 100.00\% & - & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of variance for hole quality metrics analyzed in the study. Figure 2: Effect of cutting parameters on hole size. The minimum circularity error of 0.0197 mm was obtained for AA2024 at the \(n\) of 2015 rpm and \(f\) of 0.14 mm/rev. For AA5083, the minimum circularity error was 0.0231 mm, resulting from the \(n\) of 2015 rpm and \(f\) of 0.04 mm/rev. Similarly, in the case of AA6061, the lowest circularity error was 0.0237 mm at the \(n\) of 3025 rpm and \(f\) of 0.08 mm/rev. A comparison between the results reported in this study and previous studies [24; 25] on the same alloys and drill diameter, with the annotation that different feed rates, spindle speeds, tool geometry, and coatings were used, showed that hole circularity in AA2024 and AA6061 alloys was not critically influenced by the cutting tool coating or the level of cutting parameters. This conclusion is further supported by the ANOVA results shown in Table 3, which showed that both cutting parameters did not significantly affect hole circularity and displayed a non-linear trend at the circularity error, which could be confirmed by \(p\)-values of more than 0.05. Table 3 also shows that alloy type was a significant factor of ANOVA. The alloy type had the highest contribution (53.28%), followed by the interaction of the spindle speed and alloy type, which showed a contribution of 29.20% in affecting the circularity error.
## 2 Materials and Methods
### Determination of Antioxidant and Oxidoreductase Activities
#### 2.6.2 ABTS Test  
The antiradical capacity against the cation radical of 2,2-azo-bis (3-ethyl benzothiazazoline-6-sulfonic acid (ABTS**+**) for the tested extracts was measured based on the Sridhar method [12], with minor modifications. ABTS**+** solution was prepared by mixing 7 mM ABTS stock solution with 2.45 mM potassium persulfate solution and incubating at room temperature (23 \({}^{\circ}\)C) in the dark for 16-24 h. The ABTS**+** solution was diluted with phosphate buffer (0.1 M) to give an absorbance of 0.700 \(\pm\) 0.05 at 734 nm. A measure of 0.02 mL of the extract to be tested was added to 1.0 mL of the diluted ABTS**+** solution. The absorbance at 734 nm was read exactly 10 s after mixing the test extract with the ABTS**+** solution. The antiradical activity was calculated from the calibration curve and expressed as the mg Trolox equivalent (TE) for 1 g of raw material used. All the samples were analyzed in duplicate.
## 4 Design Methodology for SOA-Enabled HONoC
### Worst-Case Crosstalk Noise and SNR Analysis
#### 4.2.3 SNR Analysis inside Regions without SOAs  
As previously described, the SNR can be improved when the optical signal passes through multiple SOAs with suitable SOA gain control. However, if the optical signal path forms within a certain \(REG\), the SNR can degrade compared to a non-SOA HONoC, even if the condition \(G<\left|L_{avg}\right|\) is satisfied.  
When there is a path that travels along the boundary of \(REG\), as shown in Figure 9, both the crosstalk noise occurring inside \(REG\) and the optical signal will not pass through any SOA, whereas the crosstalk noise originating from the outside of \(REG\) is amplified by SOAs.  
The worst-case SNR of the SOA-integrated HONoC under the corresponding traffic condition and that of the HONoC without SOAs under the same condition can be expressed as follows:  
(1) \[\begin{split} SNR_{1}\approx\frac{L^{t_{x}+t_{y}-1}/K}{\left[ \begin{array}{c}GL\Big{(}2\sum_{i=1}^{t_{x}}L^{t_{x}+t_{y}-1-i}+\sum_{i=2}^{ t_{y}}L^{t_{y}-i}\Big{)}+2\Big{(}\sum_{i=1}^{t_{x}-2}L^{t_{x}+t_{y}-i}+\sum_{i=1}^{ t_{y}-2}L^{t_{y}-i}\Big{)}\\ +\Big{(}L^{t_{y}+1}+L^{t_{y}}+L\Big{)}+\Big{(}\sum_{i=1}^{t_{x}+t_{y}-2}L^{t_{ x}+t_{y}-2-i}\Big{)}\end{array}\right]}\end{split}\] (2)  
_Case 2_: \(h=2m-1\)  
\[SNR_{2}\approx\frac{L^{t_{x}+t_{y}-1}/K}{\left[\begin{array}{c}L\Big{(}2 \sum_{i=1}^{t_{x}}L^{t_{x}+t_{y}-1-i}+\sum_{i=2}^{t_{y}}L^{t_{y}-i}\Big{)}+2 \Big{(}\sum_{i=1}^{t_{x}-2}L^{t_{x}+t_{y}-i}+\sum_{i=1}^{t_{y}-2}L^{t_{y}-i} \Big{)}\\ +\Big{(}L^{t_{y}+1}+L^{t_{y}}+L\Big{)}+\Big{(}\sum_{i=1}^{t_{x}+t_{y}-2}L^{t_{ x}+t_{y}-2-i}\Big{)}\end{array}\right]} \tag{3}\]  
Case 1 represents the SOA-integrated HONoC and Case 2 denotes the HONoC without SOAs. In Equation (3), the first term in the denominator is the crosstalk noise introduced from outside of \(REG\) and amplified by the SOA gain (\(G\)). Assuming the same optical signal power for both cases, the SNR  
Figure 9: Worst-case signal to noise ratio (SNR) of the non-SOA region surrounded by SOA-placed links.  
of the SOA-integrated HONoC is less than that of the HONoC without SOAs. Therefore, if the SOAs are not placed on every link, there is a path along which the SNR decreases.
## 4 Simulation Results  
In the simulations, we determine end-to-end reliability, latency, and availability in a typical low duty cycle WSN. As the synchronization affects the performance, we first study the effect of beacon losses. Then, we simulate the effect of network load, packet errors, and buffer sizes to the performance. For generalizable results, the simulations are performed both with ZigBee and TUTWSN protocols.
## 3 Identification of SSO Parameters of the Fitting-Based ERA  
### The Methodology of the ERA  
Utilizing the synchrophasor model's distinct features, the ERA formulates the Hankel matrix. Firstly, it constructs two shifted Hankel matrices and then derives the system matrix using the distinct interdependence linking the two translated Hankel matrices.  
Therefore, two translated Hankel matrices must be formulated by using a synchrophasor sequence \(\hat{X}(k)\), which are denoted as \(\mathbf{H}\) and \(\mathbf{H}^{\prime}\), and their expressions are as follows:  
\[\mathbf{H}=\begin{bmatrix}\hat{X}(k)&\hat{X}(k+1)&\cdots&\hat{X}(k+s-1)\\ \hat{X}(k+1)&\hat{X}(k+2)&\cdots&\hat{X}(k+s)\\ \vdots&\vdots&\ddots&\vdots\\ \hat{X}(k+r-1)&\hat{X}(k+r)&\cdots&\hat{X}(k+s+r-2)\end{bmatrix};\mathbf{H}^{ \prime}=\begin{bmatrix}\hat{X}(k+1)&\hat{X}(k+2)&\cdots&\hat{X}(k+s)\\ \hat{X}(k+2)&\hat{X}(k+3)&\cdots&\hat{X}(k+s+1)\\ \vdots&\vdots&\ddots&\vdots\\ \hat{X}(k+r)&\hat{X}(k+r+1)&\cdots&\hat{X}(k+s+r-1)\end{bmatrix} \tag{12}\]  
where \(s+r\) represents the number of synchrophasors and each row and column in the Hankel matrix has time continuity.  
According to Section 2.1, the synchrophasor model under SSO is formed by a linear combination of four modes. Therefore, the Hankel matrix is further decomposed, and it is expressed as follows. When \(k\) is zeri, the Hankel matrix \(\mathbf{H}\) is further decomposed by singular values [20], which are expressed as follows:  
\[\mathbf{H}=\mathbf{Y}_{1}\mathbf{R}\mathbf{Y}_{2} \tag{13}\]  
\[\mathbf{Y}_{1}=\begin{bmatrix}1&1&1&1\\ e^{\omega_{1}}&e^{\omega_{2}}&e^{\omega_{3}}&e^{\omega_{4}}\\ \vdots&\vdots&\vdots&\vdots\\ e^{\omega_{1}(r-1)}&e^{\omega_{2}(r-1)}&e^{\omega_{3}(r-1)}&e^{\omega_{4}(r-1 )}\end{bmatrix};\mathbf{Y}_{2}=\begin{bmatrix}1&e^{\omega_{1}}&\cdots&e^{ \omega_{1}(s-1)}\\ 1&e^{\omega_{2}}&\cdots&e^{\omega_{2}(s-1)}\\ 1&e^{\omega_{3}}&\cdots&e^{\omega_{3}(s-1)}\\ 1&e^{\omega_{4}}&\cdots&e^{\omega_{4}(s-1)}\end{bmatrix} \tag{14}\]  
\[\mathbf{R}=diag([R_{1},R_{2},R_{3},R_{4}]) \tag{15}\]  
where the matrix \(\mathbf{R}\) is the eigenvalue matrix.  
According to the characteristics between two shifted Hankel matrices, Hankel matrix \(\mathbf{H}^{\prime}\) can be expressed as shown in Equation (16):  
\[\mathbf{H}^{\prime}=\mathbf{Y}_{1}^{\prime}\mathbf{R}\mathbf{Y}_{2}^{\prime}= \mathbf{Y}_{1}\mathbf{R}\mathbf{Y}_{0}\mathbf{Y}_{2} \tag{16}\]  
where \(\mathbf{Y}_{0}\) is a system matrix and \(\mathbf{Y}_{0}=diag[e^{\omega_{1}},e^{\omega_{2}},e^{\omega_{3}},e^{\omega_{4}}]\).  
Utilizing Equation (16) as a basis, the determination of the system matrix \(\mathbf{Y}_{0}\) is facilitated by applying the subsequent formula:  
\[\mathbf{Y}_{0}=\mathbf{R}^{-\frac{1}{2}}\mathbf{Y}_{1}^{-1}\mathbf{H}^{\prime }\mathbf{Y}_{2}^{-1}\mathbf{R}^{-\frac{1}{2}} \tag{17}\]  
Consequently, the acquisition of angular frequency values necessitates resolution of the \(\mathbf{Y}_{0}\) matrix as the sole means.
# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 4 Result and Performance Evaluation  
### Traffic Accident Severity Prediction System  
Traffic accidents arise due to diverse causes, such as weather, time of day, driver's condition, and driving career. The severity of traffic accidents is predicted based on vehicle damage and the number of deaths. Traffic accident severity is subjectively determined by an expert, its reliability is low, and it is difficult to make an immediate decision based thereon. To solve this problem, this study applies the weight feedback-based harmonic MDG-ensemble model for the prediction of traffic accident severity to establish a traffic accident severity prediction system. The operating system and hardware specifications of the system are as follows: Window10 Education, Intel(R) Core(TM) i5-4690 3.50 GHz, 32 GB RAM. Figure 7 shows the traffic accident severity prediction system using the weight feedback-based harmonic MDG-ensemble model. When a traffic accident occurs, a user collects traffic accident factors. Without pre-processing the factors, the user fills in each of the items in the system, similar to a survey. In the model information, the user selects a classification model version. When the user completes data input, the system inputs the data into the traffic accident severity prediction model. A categorical value of traffic accident severity is then derived.  
Figure 7: Traffic accident severity prediction system using weight-feedback based harmonic MDG-ensemble model.  
The traffic accident severity prediction system in Figure 7 consists of traffic accident factor collection, position preview, model selection, traffic accident severity derivation, and model information. The traffic accident factors selected through the MDG are the categories or values attributable to the eleven variables. A user can find information on an input position visually through the system. In addition, the traffic accident severity prediction system updates a prediction model when data collection is completed or a data update occurs. Therefore, a user can select and use models with different versions. Based on the collected information, the model derives the severity of a traffic accident. If the value is 0, it means slight-moderate injuries; if it is 1, it means serious injuries; if it is 2, it means very serious injury. In addition, the system provides information on the single models that constitute the traffic accident severity prediction model.  
For example, as shown in Figure 7, traffic accident severity data are collected according to regions. The MDG extracts the importance of each variable that meets the 500 threshold. Subsequently, a classification model is selected and the updated weight is calculated using the harmonic MDG-based weight feedback. As a result, the traffic accident's severity is derived from an ensemble model with calculated weights. Thus, users can find the severity of traffic accidents in a region.
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 4 Industrial Applications of Cavitating Jets
### Borehole Cavitating Jet Hydraulic for Oil-Shale Mining  
Oil-shale has been widely used as a supplement to conventional energy resources. For deep and low-grade oil-shale mining, borehole hydraulic mining is considered the most suitable technology [74; 75; 76; 77; 78; 79]. During this process, high-pressure jets are used to break the oil shale [80]. Therefore, the high performance of the water jet is critical for the overall success of this drilling process [80; 81; 82]. This method presents many advantages such as the reliability and simplicity of operation. In addition, it does not require removing the overlying strata.  
[PERSON] et al. [80] used the organ pipe nozzle shown in Figure 19. It is a typical self-resonating cavitating jet nozzle where a standing wave develops in the cavity of the nozzle and excites the eddies of the jet shear layer resulting in large-scale vortex rings. The high kinetic energy of the separated vortex rings can enhance the cavitation erosion effect of the jet [31; 32]. [PERSON] et al. [80] found that for the non-submerged condition, the performance of the self-excited oscillating pulsed jet nozzle is always greater than the straight cone nozzle. They also found that the striking force produced by the self-excited oscillating jet decreases when the distance from the jet exit increases. For the cone nozzle, the striking force increases with the jet distance then it decreases with further increase in the jet distance.  
Figure 19: Schematic diagram of the self-resonating cavitating jet nozzle structure and operating principle [80].
## Data and Methods
### Wetland Probability Mapping Algorithm
#### 2.3.2 Ndwi  
The Normalized Difference Water Index (NDWI) measures surface water content based on the spectral reflectance properties of water [61]. There are various ways to calculate NDWI using a combination of visible green, near-infrared, or mid-infrared wavelengths. We use a modified NDWI equation proposed by [62] that emphasizes open water features by utilizing the visible green and mid-infrared reflectance bands [61]:  
\[\text{NDWI}=\ \frac{\text{green}\ -\ \text{MIR}_{1}}{\text{green}\ +\ \text{MIR}_{1}}. \tag{2}\]  
Since water reflects visible green light and absorbs mid-infrared wavelengths, NDWI values above or equal to 0 correspond to water, while negative values represent non-water surfaces [62]. This index is effective in reducing vegetation, soil, and built-up land noise, but may provide slight overestimates in water content [61].
###### Abstract  
This study analyzes the turbomachinery flow of a gas turbine using OpenFOAM, an open-source CFD code. While foam-extend, a version of OpenFOAM, includes tools for turbomachinery analysis, some of its codes are incomplete, resulting in incorrect results. Consequently, this study required the investigation and correction of the solvers and libraries. Specifically, foam-extend-4.1 and a compressible multi-reference-frame solver were utilized. Two primary errors related to temperature calculation were identified. The first error involved temperature discontinuity at the interface between the stator and rotor domain when using the mixingPlane. The second error was related to temperature rising at the wall. To address the temperature discontinuity problem, the rothalpy jump equation in the enthalpyjump code was modified from a scalar product to an inner product of vectors. To resolve the high-temperature problem at the wall, modifications were made to the energy equation code in iEqn.H. A rothalpy separation was introduced, and the rothalpy equation was adjusted to mimic the enthalpy equation. The results obtained with the corrected codes were consistent with those from the commercial code, demonstrating the effectiveness of the modifications.  
compressible flow; computational fluid dynamics (CFD); gas turbine; multiple reference frame (MRF); OpenFOAM; turbomachinery +
Footnote †: journal: Applied
## 2 Module Design  
The frame of the MIR module was made from a single block of 7075 aluminum alloy for its high strength-to-weight ratio. As a result, we managed to construct the whole module with a weight of less than 2.5 kg. The configuration and schematic of our module is shown in Figure 1, and it was designed to be quickly aligned with any continuous-wave (CW) pump laser at around 1064 nm. The pump path into the OPO cavity was aligned to two irises in our module, and two high-reflection mirrors R1 and R2 can be used to steer the pump beam for the input alignment. Once the pump light matches with the irises, the module is ready for MIR generation. Inside the module is a symmetric ring OPO cavity. Cavity mirrors M1-M4 are all high-reflection-coated (R > 99.5%) at 1.35-2 um (signal wavelength) and high-transmission-coated at 1064 nm (pump wavelength, T > 95%) and 2.3-5 um (idler wavelength, T > 90%). M1 and M2 on both ends of the short arm are plane mirrors, while M3 and M4 on the long arm are concave mirrors with a curvature radius of 100 mm. The nonlinear medium is a periodically poled lithium niobate crystal (MgO:PPLN) doped with 5 mol % MgO with dimensions of 50 \(\times\) 10 \(\times\) 1 mm\({}^{3}\) and two channels with poling periods of 30.49 and 31.59 um, for the dual band MIR generation around 2.7 and 3.3 um, respectively. Both ends of the crystal are antireflection-coated at pump, signal, and idler wavelengths. The crystal temperature is controlled using a homemade oven, where the crystal mount is temperature-controlled with an accuracy of \(\pm\)0.1 \({}^{\circ}\)C in a range between 20 and 170 \({}^{\circ}\)C. It is suspended from the oven enclosure by three ceramic tubes, which form a 2 mm air gap to reduce the thermal conductivity and further stabilize the temperature of MgO:PPLN. The surface of the crystal mount is finely ground for good thermal contact with the MgO:PPLN. A homemade miniaturized linear translation stage and electric actuator were built into the temperature-controlled oven for channel switching for the MgO:PPLN, and together they weigh only 10 g.
## 4 Experimental Results and Analysis
### Network Architecture  
The network architecture of the proposed CMADDPG algorithm utilizes a fully connected, double-layered neural network with 300 and 400 hidden neurons along with Rectified Linear Units (ReLU) in between every layer. A tanh unit in the last is continued by the actor's output. The state and action are provided to the initial layers of the critic. A mini-batch consisting of 100 uniformly sampled transitions is selected and updated at a learning rate of \(10^{-3}\) by utilizing the Adam optimizer, which in turn is expected to reduce the algorithmic loss. The delay parameter for updating the policy of actor and target critics is assigned to be \(d=2\) iterations with \(\tau=0.005\).  
The off-policy exploration is leveraged by adding Gaussian noise \(\mathcal{N}(0,0.1)\) to the actions determined by the target actors in order to trade-off exploitation and exploration; later, the noise is clipped to \(\mathcal{N}(-0.5,0.5)\) to achieve target policy smoothing. However, exploring tasks are carried out rather than training for experience acquisition. The symmetry between the under and overestimation biases is attained by assigning the weight factor \(\beta\) to 0.95. Similarly, the approximation errors are addressed by calculating the median values of target action \(K\) for each update, and minimal variation in the K value enhances the stability of continuous actions.