esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Rock Slide Geometry on Stability Behavior during Reservoir Impounding
## 4 Impact of Rock Slide Geometry on Reservoir Impounding Behavior
### Selected Case Studies
Movements were first recognized in the 1960s during initial reservoir impoundment and have been recorded since then with a surface rate of approximately 4 to 6 mm/year. It is not clear whether the rock slide has been reactivated as a consequence of reservoir filling or if the movements existed already, but reservoir level fluctuations clearly influence the deformation rates. The fractured rock mass is composed of graisses and mica schists, and the basal shear zone is composed of fault breccia and gouges, up to a maximum thickness of 20 m.  
On 9 October 1963, the 270 million m\({}^{3}\) large Vajont rock slide failed and slid rapidly into the Vajont reservoir, producing an enormous wave that overtopped the dam, destroying several villages and leading to more than 2000 fatalities [5]. The rock slide scarp area is located in a complex sequence of carbonate rocks with thin clayey inter-layers. Since this event, numerous studies have been published that led to different geometrical models and interpretations (e.g., [30; 35; 36]). For this study, the original model of [PERSON] and [PERSON] [35] was taken (Figure 4f). The Little Chief slide is located 3 km upstream of Mica Dam in British Columbia, Canada, and is composed of mica gneiss, mica schist, and hornblende gneiss [9]. The rock slide reaches a maximum thickness of roughly 300 m at the slope toe and has a volume of approximately 800 million m\({}^{3}\). Slope movement has been monitored since 1969, with a consistent rate of 4-14 mm/year [9]. The rock slide did not show any impact due to the filling of the Mica Dam reservoir in the 1970s, and the basal shear zone is composed of clay-rich gouges ranging from a few mm to about 200 mm in thickness [37]. The geometry proposed by [PERSON] et al. [9] serves as the model for our calculation (Figure 4g). Additionally, two generic rock slide models were established for this analysis, one representing a pure translational rock slide oriented parallel to a planar slope with a dip angle of 32\({}^{\circ}\) (Figure 4h) and another one, a compound-type model, with a massive mass located at the slope foot over a gentle inclined basal shear zone ([38], Figure 4i).
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 5 Experimental Evaluation
### Parameter Selection and Tuning
We execute 10 runs of Algorithm 1 for each sparse dataset and each parameter combination (we have 5 choices for \(\gamma\) times 4 choices for \(\alpha\), which makes 12 different parameter combinations for each dataset), showcasing the average CPU time and the average Adjusted Rand Index (ARI) against the ground truth clustering provided in the UCR archive. In each run, the initial set of \(k\) cluster representatives is chosen randomly. Nevertheless, the seeds were predefined for each run to ensure reproducibility and facilitate fair comparisons between different methods. To assess our approach against the standard \(k\)-means, we opted to compute a baseline ARI for each original dataset from scratch by executing 10 runs of Algorithm 3 for each of them with the standard DTW distance (with \(\beta=0\)). As for the sparse case, the initial set of \(k\) cluster representatives is chosen randomly with predefined seeds. For each original dataset, we logged the average CPU time and the average Adjusted Rand Index (ARI) against the ground truth clustering. Therefore, we ensure consistent initialization and CPU time reporting across all runs, facilitating a comprehensive comparison and evaluation of run times.
# Integrating Classical Preprocessing into an Optical Encryption Scheme
## 3 Results
### Symmetric-key Encryption Using Mesoscopic Coherent States
For example, for \(\langle n\rangle=100\) and \(\beta=5\), the minimum probability of error is \(P_{e}^{E}\approx 0.476\). So \(H(P|\tilde{C})=-P_{e}^{E}\log_{2}P_{e}^{E}-(1-P_{e}^{E})\log_{2}P_{e}^{E} \approx 0.998\). Since \(H(P)=1\), this will imply AlphaEta is \(\epsilon\)-secure for \(\epsilon=0.002\) for the chosen parameters._  
_Moreover, the mutual information of the plaintext bit and reconstructed ciphertext is equal to \(\epsilon\), since \(I(P,\tilde{C})=H(P)-H(P|\tilde{C})\). A graph of \(P_{e}^{E}\) and \(I(P,\tilde{C})\) as a function of \(\langle n\rangle\) and \(\beta\) can be found in [10], where it can be seen how they can be made as close to \(1/2\) and \(0\), respectively, as desired by choosing the appropriate parameters._  
Once the secret key is exposed, previous transmissions using the exposed key could become vulnerable. An adversary may have obtained previous ciphertexts, which, knowing the secret key, could potentially be decrypted and the messages would be known. Owing to the fact that in a symmetric-key encryption scheme using mesoscopic coherent states, parts of the ciphertext may be non-classical and we assume the adversary to conduct measurements on those parts, we can hope for some type of forward secrecy, however:  
**Definition 6**. : _For \(t\in\mathbb{N}\), let \(P_{i}^{t}\) be the random variable taking the value of a plaintext bit, and \(C_{i}^{t}\) the random variables taking the value of classical ciphertext bits at time \(t\). Let \(\tilde{C_{1}^{t}},\ldots,\tilde{C_{\ell}^{t}}\) represent the random variables taking the value of the reconstructed ciphertext bits, i.e., \(\tilde{C_{i}^{t}}\)s correspond to the bits obtained after the corresponding measurement of coherent states at time \(t\). Let \(K^{1},\ldots,K^{t}\) represent the sequence of random variables taking the values of the symmetric key, i.e., values in \(\{0,\ldots,\beta-1\}^{s}\). We assume after time \(t\) (when the ciphertext has been sent and the measurements without knowing \(K^{t}\) have been realized), \(K^{1},\ldots,K^{t}\) is revealed. A symmetric-key encryption scheme using mesoscopic coherent states is \(\epsilon\)-forward secure if for all \(t\) we have_  
\[|H(P_{i}^{t}|\tilde{C_{1}^{t}},\ldots,\tilde{C_{\ell}^{t}},C_{\ell+1}^{t}, \ldots,C_{s}^{t},K^{1},\ldots,K^{t})-H(P_{i}^{t})|\leq\epsilon. \tag{6}\]
## 4 Application
### Result Analysis  
The constraint diagram with stall speed limits superimposed after power conversion can be obtained under the given design parameters using the design system established  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Zero-Lift Drag** & **Aspect Ratio, AR** & **Ground Friction Constant, \(\mu\)** & **Lift Coefficient During Take-Off Run, \(C_{L\_TO}\)** & **Drag Coefficient During** \\ \hline
0.025 & 9 & 0.04 & 0.7 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic design parameters.  
in this article, as shown in Figure 5. From the diagram, it was shown that the wing load is 1380 N/m\({}^{2}\) to be an optimum, and the electric propulsion power required at this point is 596 kW; these are the optimal combination points of the design domain. Here, we can read that the \(C_{\rm{Lmax}}\) of about 2.27 is required to meet that restriction of stall speed, 61 KCAS (31.4 m/s), for the aircraft. These values are reasonable and easily attained with a simple high-lift system design.  
In the derivation of Figure 5, not only can the \(P/W\) and \(W/S\) be obtained; the cruise power-to-weight ratio can also be obtained. The battery weight coefficient of 0.36 can be obtained according to Equation (5); then, the weight of each part can be iteratively solved by the weight analysis module, as is shown in Figure 6. When the payload required is 1000 kg, the total weight of the electric cargo UAV is 3446 kg; the battery weight is 1241 kg, with a proportion of 0.36; the empty weight is 1207 kg, with a proportion of 0.35, of which the airframe structure is estimated to be 769 kg; the power system is estimated to be 238 kg; and the airborne equipment is 200 kg, accounting for 0.22, 0.07, and 0.06, respectively. The estimated distribution of weight will have great guiding significance for subsequent detailed design and manufacturing.  
The market demand range is 500 km, as described in Section 4.1. It can be deduced that the cruise lift-drag ratio, \(L/D\), is 14.2 according to the range analysis module. From the range relationship diagram in Figure 3, where the design lift-to-drag ratio and expected battery energy density are given, the battery weight fraction and the aircraft weight can be iterated in reverse. Assuming that the cruise lift-to-drag ratio is 12, and the other initial input parameters are the same as above, the relationship between the total aircraft  
Figure 5: Constraint diagram with stall speed limits superimposed.  
Figure 6: The weight and coefficient composition of the electric cargo UAV.  
weight and the energy density of the battery system can be obtained using the design system, as shown in Figure 7, which provides an important reference for the aircraft weight assessment, range design, and battery selection.
# rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
## 3 rSEM Algorithm
For \(s\)-th (\(s=1,\ldots,N-N^{ch}\)) selection, only \(\left(N-N^{ch}-\left(s-1\right)\right)\times v\) cases need to be considered, where \(s-1\) is the number of cluster member nodes that have been connected to the network. The \(s\)-th selection is  
\[\mathbf{L}_{s}=\underset{\mathbf{L}_{s,i}\in\Psi}{\text{arg min}}\,[H^{sys}( \mathbf{L}_{s,i})], \tag{6}\]  
where \(\Psi\) represents the set formed by the above \(\left(N-N^{ch}-\left(s-1\right)\right)\times v\) cases where each unconnected member node has \(v\) cases. \(\mathbf{L}_{s,i}\in\mathbb{R}^{\left(N^{ch}+s+1\right)\times\left(N^{ch}+s+1 \right)}\) is obtained from \(\mathbf{L}_{s-1}\) by extending it by one dimension and adding the corresponding degrees and connections. When \(s=1\), the algorithm operates the extending and adding on the matrix \(\mathbf{L}^{ch}\). The specific algorithm steps are in Algorithm 1. ```
0:\(N\), \(N^{ch}\), \(v\), location of the nodes
0:L
1: According to \( \begin{pmatrix}N\\ N^{ch}\end{pmatrix} \) cases, the set \(\Omega\) is obtained;
2:Selecting the \(N^{ch}\) cluster heads from the set \(\Omega\) as (5) to get \(\mathbf{L}^{ch}\);
3: Let \(\mathbf{L}_{0}\) = \(\mathbf{L}^{ch}\);
4: For every remaining \(N-N^{ch}\) member node, \(v\) candidate cluster head nodes are selected based on the minimum distance principle for it;
5: According to the above \(\left(N-N^{ch}\right)\times v\) cases, the initial set \(\Psi\) is obtained;
6:for\(s=1\) to \(N-N^{ch}\)do
7: Selecting a member node from current \(\Psi\) as (6);
8: Adding this member node to \(\mathbf{L}_{s-1}\) to get \(\mathbf{L}_{s}\);
9: Deleting the \(v\) cases about this member node to get an updated \(\Psi\);
10:endfor
11: Let \(\mathbf{L}\) = \(\mathbf{L}_{N-N^{ch}}\);
12:return\(\mathbf{L}\)
```  
**Algorithm 1** Pseudocode of the rSEM Algorithm  
Obviously, the usage of the parameter \(v\) and the greedy strategy will lead to a reduction in the discussed cases, and the case with the minimum system entropy may not be included. Therefore, if we want to obtain the minimum entropy of the network system, we need to traverse more cases, that is, a larger \(v\). In the experiment of this paper, \(v=2\) is used, which is a trade-off between complexity and performance. The trade-off not only ensures enough traversal cases but also ensures the complexity of the algorithm within an acceptable range.
# Recycling of Low-Quality Carbon Black Produced by Tire Pyrolysis
## Materials and Methods
### Methods  
#### 2.2.1 Measurements  
Various analyses and measurements were conducted on the raw material, employing a comprehensive approach. The techniques included thermogravimetry, incineration tests, XRD/XRF, SEM, simple chemical analyses, and the determination of specific surface area. The elemental composition of carbon black was assessed using X-ray fluorescence spectrometry with the Bruker Tracer S5 XRF instrument (Bruker, Billerica, MA, USA). XRD and XRF are excellent nondestructive techniques that correspondingly provide detailed information about the crystallographic structure and elemental composition of the studied objects. The composition of polyaromatic hydrocarbons in carbon black was determined at the Estonian Environmental Research Centre through gas chromatographic methods with mass spectrometric detection (GC-MS), following ISO 18287-2006 [27]. Thermogravimetric analyses were conducted with a Netzsch STA 449 F3 Jupiter thermal analyzer (AZom, Riyadh, Saudi Arabia). Observations of the carbon black microstructure were carried out using a Hitachi TM-3000 (Hitachi High-Technologies Corporation, Tokyo, Japan) scanning electron microscope (SEM). Scanning electron microscopy (SEM) was used to characterize the elemental composition and geometrical structure of the surface of materials. The calorific value of solid fuel briquettes made of rCB-peat mixtures (Section 3.4) was examined following EN 14918 standard [28] methods. Concrete test specimen compressive strength with added carbon black was tested using a cone apparatus according to standard methods (ASTM C803) [29]. The method is indicative, but sufficient for the purposes of the current study.
# AI-Based Approach to Firewall Rule Refinement on High-Performance Computing Service Network
## 4 Result and Discussion
The validation accuracy (blue line) begins at 95.39% and increases to approximately 97.7% at the end. Both models showed a consistent decrease in both the training and validation losses; this result indicates that they were learning effectively. The ten-feature model outperformed the four-feature model in terms of losses, indicating that the inclusion of additional features improved the model's ability to generalize unseen data. Regarding accuracy trends, both models showed consistent improvement over the epochs. The ten-feature model outperformed the four-feature model in terms of accuracy on both the training and validation sets. This indicates that the model can learn more and perform better as a result of the additional information provided by the extra features. There seems to be a slight indication of overfitting in both models, particularly towards the later epochs, where the training loss continues to decrease, whereas the validation loss plateaus or slightly increases. However, this effect  
Figure 9: F1-score average accuracy: improvement comparison of machine-learning algorithms and deep-learning model with different feature selections. was more evident in the four-feature model than in the ten-feature model. This implies that the 10-feature model was more robust and less overfitting. Based on the provided data, the 10-feature model appeared to perform better in terms of loss and accuracy in both the training and validation sets. This model provides better generalization and predictive ability than the four-feature model. In conclusion, the 10-feature model exhibited better performance in terms of loss and accuracy, suggesting that it was the preferred model for this dataset. However, to make a more informed decision, it is essential to consider factors such as computational resources, model complexity, and practical application requirements.
## 2 Development of FBG Stress Sensor
### Design of Sensing Units  
The pressure sensing unit is the key component of the sensor, and its design is directly related to the measurement accuracy of the sensor. After comparison and selection of various schemes, the final design scheme for the sensing unit is shown in Figure 4. The upper part of the sensing unit is a cylindrical pressure head and the lower part is an elliptic strain ring. A rectangular cantilever beam is arranged on both sides of the long axis of the strain ring. A groove, which is used to set optical fiber installation, is arranged at the bottom of the cantilever beam. The FBG passes through the elliptic strain ring in the direction of the long axis and is then fixed by the fiber installation groove. The bottom of the elliptic strain ring is provided with a square base to maintain the stability of the sensing unit. When the top of the pressure head is subjected to an external pressure, the force will be transferred to the strain ring, resulting in tensile deformation along the long axis and contraction deformation along the short axis. The FBG fixed on the cantilever beam at both ends of the strain ring will produce synchronous stretching deformation, which will finally cause the wavelength change of the FBG.  
Due to the three-dimensional geometric structure of the sensing unit, it is difficult to directly establish the relationship between the external pressure and the FBG strain through analytical solution. Therefore, numerical simulation is a preferable option. The finite element calculation model is consistent with the design scheme of the sensing unit (Figure 5). The material of the sensing unit is elastic stainless steel, with an elastic modulus and Poisson's ratio of 192 GPa and 0.3, respectively. During the simulation, the bottom of the sensing unit was restrained, and the top was subjected to uniform pressure, \(P\), with values of 5, 10, 15, 20, 25, and 30 MPa, respectively.  
Figure 4: Design of sensing unit: a, cylindrical pressure head; b, elliptic strain ring; c, rectangular cantilever beam; d, fiber installing groove; e, square base; f, FBG.  
The simulation results are shown in Figure 6. It was found that within the design range of 0 to 30 MPa, there was a strong linear relationship between the FBG strain and the external pressure. As shown in Equation(2), the wavelength shift also increased linearly as strain increases. Therefore, there is also a linear relationship between the external pressure and the wavelength shift.
# Heat Conduction Plate Layout Optimization Using Physics-Driven Convolutional Neural Networks
## 3 Methodology
### Physics-Driven Training Approach  
Recently, the physics-driven training methods for deep learning have shown particular promise for the physical field prediction work for their advantages that reduce the heavy requirement of a large amount of training data [49]. In the typical data-driven method, the mathematical formulation of the loss function compares the difference between the prediction result and the ground truth as:  
\[L_{data}=|T_{out}-T_{truth}| \tag{1}\]  
As shown in Equation (1), the subscript \"data\" denotes data-driven, and the \(T_{out}\), \(T_{truth}\) are output and true temperature fields, respectively. In this paper, the loss function is built based on [PERSON]'s law. The physics law of heat conduction can be expressed with a second-order PDE, the Laplace equation. The system assumes no inner heat source contribution, and the thermal conductivity is considered constant. Under this assumption, the physical law of heat conduction can be expressed with a second-order PDE Laplace equation. the two-dimensional form is defined as shown in Equation (2):  
\[\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{\partial y^{2}}=0 \tag{2}\]  
In the physics-driven method, the Laplace equation is used to drive the training process as the loss function, as shown in Equation (3):  
\[L_{PHY}=\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{\partial y^{ 2}}=e \tag{3}\]  
The Dirichlet boundary conditions are applied in the temperature field, where the temperatures of the outer and inner of the plate are constant at zero degrees.  
As proposed previously for simple PDEs [50], the Laplace equation can be calculated through finite differences using suitable convolutional filters. The backpropagation of the loss can be achieved with convolutions. The weights of the first- and second-order differential kernels are shown in Equation (4):  
\[\mathrm{W}_{\frac{\partial}{\partial\kappa}}=\left[\begin{array}{ccc}0&-0.5 &0\\ 0&0&0\\ 0&0.5&0\end{array}\right],\mathrm{W}_{\frac{\partial^{2}}{\partial x^{2}}}= \left[\begin{array}{ccc}0&1&0\\ 0&-2&0\\ 0&1&0\end{array}\right]. \tag{4}\]  
The multiple cases training is shown in Figure 2. Ten temperature fields and ten masks are the two inputs of the U-net architecture. Then, the backpropagation process calculates the loss function's gradient and updates the multilayer CNN's weights to satisfy the loss function, which is the residual of the Laplace equation. Finally, when the threshold of the loss function is reached, accurate field solutions are generated.  
Figure 2: The physics-driven training approach. One batch has ten layout masks.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
###### Abstract  
Banking malware poses a significant threat to users by infecting their computers and then attempting to perform malicious activities such as surreptitiously stealing confidential information from them. Banking malware variants are also continuing to evolve and have been increasing in numbers for many years. Amongst these, the banking malware Zeus and its variants are the most prevalent and widespread banking malware variants discovered. This prevalence was expedited by the fact that the Zeus source code was inadvertently released to the public in 2004, allowing malware developers to reproduce the Zeus banking malware and develop variants of this malware. Examples of these include Ramnit, Citadel, and Zeus Panda. Tools such as anti-malware programs do exist and are able to detect banking malware variants, however, they have limitations. Their reliance on regular updates to incorporate new malware signatures or patterns means that they can only identify known banking malware variants. This constraint inherently restricts their capability to detect novel, previously unseen malware variants. Adding to this challenge is the growing ingenuity of malicious actors who craft malware specifically developed to bypass signature-based anti-malware systems. This paper presents an overview of the Zeus, Zeus Panda, and Ramnit banking malware variants and discusses their communication architecture. Subsequently, a methodology is proposed for detecting banking malware C&C communication traffic, and this methodology is tested using several feature selection algorithms to determine which feature selection algorithm performs the best. These feature selection algorithms are also compared with a manual feature selection approach to determine whether a manual, automated, or hybrid feature selection approach would be more suitable for this type of problem.  
banking malware; Zeus malware variants; machine learning; binary classification algorithms; automated feature selection; manual feature selection
# Medical Devices for Tremor Suppression: Current Status and Future Directions
## 6 Other Devices
### Haptic Stimulation Systems  
The Emma Watch (Microsoft, Redmond, WA, USA) is a wrist-worn device consisting of several vibration-generating actuators aimed at providing haptic stimulation to the wrist. Mechanosensitive receptors, such as the Pacinian and Meissner corpuscles in the upper limbs, deliver afferent signals to the cuneate nucleus in response to vibratory stimuli [135; 136]. Proprioceptive inputs from the cuneate nucleus are projected to the thalamus [136; 137; 138], implicating its possible role in the neural pathway associated with tremors [139]. However, a study involving 18 patients with ET reported that a mechanical vibration to the hand and forearm via piezoelectric actuators resulted in no homogenous effect in the tremor amplitude [139]. Across different frequencies of vibratory stimuli, 50-72% of patients experienced an increase in tremor amplitude, while 5-22% of patients showed a decrease [139]. The Emma Watch will require clinical validations, since the use of haptic stimulation for tremor suppression has, to date, only led to questionable efficacy.