esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Establishment of the Atmospheric Wind Field Model
### Spectrum Function  
Quadrotor UAVs generally fly at low altitudes, and considering the difficulty in decomposing the square root of the Karman turbulence model, the Dryden turbulence model with a relatively simple calculation process is adopted. Based on the above assumptions, the correlation function of the turbulent field is only related to the relative distance (\(\zeta\)) between the turbulent velocity components. When \(\zeta\) is in the same direction as the velocity component, it is velocity correlated, and when \(\zeta\) is perpendicular to the velocity component, it is horizontally correlated. Through the measured and statistical data in the wind field samples, the exponential vertical and horizontal correlation functions are obtained as:  
\[f(\zeta)=e^{-\frac{\zeta d}{L}} \tag{1}\]  
\[g(\zeta)=e^{-\frac{\zeta d}{L}}(1-\frac{0.5\zeta d}{L}), \tag{2}\]  
where \(L\) is the turbulence-related scale, and \(d\) is the flight airspeed. The free atmospheric turbulence component is denoted by \(U,V,W\). The spectral density function of Dryden model is obtained by the Fourier transformation of (1) and (2). \(\sigma\) is defined as turbulence intensity. According to the conversion relationship between space frequency \((\Omega)\) and time frequency \((\omega)\): \(\Phi(\Omega)=\Phi(\omega/d)/d\), the time domain spectral expression of the model can be obtained:  
\[\Phi_{U}(\omega)=\frac{1}{d}\sigma_{U}2^{2}\frac{L}{\pi}\frac{1}{1+(L_{U}\omega ^{\omega}/d)^{2}}\]  
\[\Phi_{V}(\omega)=\frac{1}{d}\sigma_{V}2^{2}\frac{L}{\pi}\frac{1+12(L_{V}\omega /d)^{2}}{[1+4(L_{V}\omega/d)^{2}]^{2}} \tag{3}\]  
\[\Phi_{W}(\omega)=\frac{1}{d}\sigma_{W}2^{2}\frac{L}{\pi}\frac{1+12(L_{W}\omega /d)^{2}}{[1+4(L_{W}\omega/d)^{2}]^{2}}.\]
## 2 Materials and Methods
### Image Classification Methods
#### 2.3.3 Liquid Time-Constant Networks
Furthermore, the inclusion of the function \(f(x(t),I(t),t,\theta)\) allows for a more comprehensive modeling approach, incorporating the influence of both the current hidden state and external inputs on the dynamics of the system over time. Based on the above modification, the researchers of [35] proposed the Liquid Time-Constant recurrent neural networks using an alternative formulation inspired by synaptic transmission mechanisms where they replace the neural network function \(f(x(t),I(t),t;\theta)\) with the nonlinearity function \(S(t)=f(x(t),I(t),t;\theta)(A-x(t))\) that is used to approximately calculate synaptic currents to the cell in the ready state, forming a new equation to calculate the hidden state as below:  
\[\frac{dx(t)}{dt}=-\bigg{[}\frac{1}{\tau}+f(x(t),I(t),t;\theta)\bigg{]}x(t)+f(x (t),I(t),t;\theta)A, \tag{5}\]  
With this approach, the neural network function not only governs the rate of change of the hidden state but also acts as an input-dependent time constant for the learning system. This dynamic time constant enables each element of the hidden state to adapt to the specific input features present at each time point, allowing the model to identify specialized dynamical systems tailored to the input data. The LTC model offers several key benefits. Firstly, LTCs enable differentiable computational graphs, facilitating their training with various gradient-based optimization algorithms similar to ODEs. This enhances their adaptability and ease of optimization. Moreover, LTCs ensure stability in output dynamics by bounding the state and time constant within a finite range, which is essential for maintaining reliable performance, particularly when faced with continuously increasing inputs. Additionally, LTCs exhibit superior expressivity compared to other time-continuous models, as they offer a universal modeling framework capable of capturing complex temporal patterns effectively. Finally, in the research, LTCs demonstrate improved performance in time-series modeling tasks compared to modern RNNs, showcasing their practical utility and efficacy in real-world applications.
## 3 Results and Discussion
### Roller Bearings of Rotary kilns  
#### 3.5.1 Tested Object  
The possibility of conducting AE testing of roller bearings of rotary kilns without their decommissioning was investigated (Figure 15). Using AE monitoring for rotating equipment is an alternative solution to vibrodiagnostics [19; 20]. In the case of slowly rotating elements, the use of the AE method is more effective for the diagnosis of rolling bearings, rotating shafts, rollers and others [21; 22]. Experimental work was carried out at an alumina plant in the sintering and calcination shops and also in the repair shop.  
The kiln is a cylinder with a diameter of up to 5 m and a length of up to 185 m, installed with a slope. The kiln is supported by 7-8 pairs of roller bearings. Each pair of roller bearings consists of 2 support blocks, mounted on a common foundation. Each block consists of a shaft pressed onto it by a supporting roller and 3 bearing assemblies mounted in 2 bearing housings (Figure 16). The rotation period of the kiln is 50 s; the rotation period of the support roller is 12.5 s. The lifetime of the monitored supports was from 1 to 13 years.  
Roller support is a complex load carrying element of the kiln. In case of its breakdown, an emergency stop of the equipment occurs, followed by the replacement of defective components. Of the 200 supporting blocks at the plant, on average 5 blocks per year are replaced because of broken bearings and 1 more block due to the fracture of the shaft. Therefore, it is necessary to ensure the detection of defects of all its elements such as shaft, roller and bearings.  
The roller is a part of the support block and is in direct contact with the kiln. Thus, the main defects on it are scoring and metal chipping from rolling contact under load.  
Figure 16: Roller support.  
Figure 15: Rotary kiln on paired roller bearings. 1: kiln; 2: roller bearings; 3: support foundation.  
The shaft is a cast steel component. Since roller bearings are operated under unfavorable temperature conditions, with a high level of vibration and under cyclic loading, the most likely defect of the shaft is the formation of fatigue cracks. Since the shaft is a monolithic all steel construction with numerous protrusions and thickness differences, it is difficult to carry out the NDT of shaft by traditional scanning methods, even for roller bearings in decommissioned state. The main bearing elements include an outer ring, an inner ring, cylindrical rolling bodies and a separator. Destruction of the bearing occurs for 3 main reasons: incorrect mounting of the bearing, insufficient quantity or low quality of lubricants, as well as fatigue failure of the bearing. Due to the low angular velocity (1 rotation in 12.5 s), a well-known method of vibrometry is not quite suitable for testing the roller bearings and AE is expected to provide a good solution.
## 4 Results and Discussion
### Comparative Analysis of Univariate Models  
In this section, short-term forecasting was performed for six hours forecast horizon using seven different models based on the persistence model, classical time series model, and machine learning models. The results of short-term forecasting for four different case study sites are presented in Tables 6-9. The LSTM model emerged as the clear winner for the short-term wind speed prediction for all four case study sites. The LSTM model can produce a result with a MAPE as low as 3.53%, MAE as low as 0.4, and RMSE as low as 0.51. WEBSEC is also competitive with the LSTM model with a MAPE as low as 9.82, MAE as low as 1.09, and RMSE as low as 1.26. However, for site IV, whose wind speed distribution is not well described by the Weibull probability distribution function, the WEBSEC also does not give a good result. The SVR model is also competitive compared to the LSTM and WEBSEC models.
## 2 Overview of Network Architecture and Networking Technologies  
This section is an overview of our network architecture and networking technologies to accommodate a wide range of network services that support a variety of academic applications. Examples of resource allocation and QoS control are also shown.
## 4 Results
### Driving Safety Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Section** \\ **ID** \\ \end{tabular} } & \multicolumn{3}{c}{**AV Pair**} & \multicolumn{3}{c}{**Mixed Pair**} & \multicolumn{3}{c}{**MV Pair**} \\ \cline{2-10}  & **AN** & **Std.LP** & **Avg.HW** & **AN** & **Std.LP** & **Avg.HW** & **AN** & **Std.LP** & **Avg.HW** \\ \hline
0 & 0.01\({}^{-2}\) & 0.04\({}^{-5}\) & 5.90 & 0.05 & 0.02 & 3.97 & 0.10 & 0.05 & 3.86 \\ \hline
1 & 0.05\({}^{-2}\) & 0.11\({}^{-1}\) & 2.67 \({}^{\star}\) & 0.11 & 0.09 & 3.94 & 0.20 & 0.17 & 3.10 \\  & (400) & (2,749,900) & (\(-55\)) & (175) & (350) & (\(-1\)) & (89) & (240) & (\(-20\)) \\ \hline
2 & 0.04\({}^{-2}\) & 0.08\({}^{-1}\) & 2.97 & 0.08 & 0.09 & 3.68 & 0.17 & 0.16 & 3.44 \\  & (300) & (1,999,900) & (\(-50\)) & (100) & (350) & (\(-7\)) & (61) & (220) & (\(-11\)) \\ \hline
3 & 0.05\({}^{-2}\) & 0.05\({}^{-1}\) & 3.32 & 0.08 & 0.09 & 3.73 & 0.14 & 0.17 & 3.81 \\  & (400) & (1,249,900) & (\(-44\)) & (100) & (350) & (\(-6\)) & (32) & (240) & (\(-1\)) \\ \hline
4 & 0.04\({}^{-2}\) & 0.03\({}^{-1}\) & 3.62 & 0.07 & 0.08 & 3.38 & 0.16 & 0.16 & 3.80 \\  & (300) & (749,900) & (\(-39\)) & (75) & (300) & (\(-15\)) & (51) & (220) & (\(-1\)) \\ \hline
5 & 0.01 \({}^{\star}\) & 0.05\({}^{-5}\) & 3.84 & 0.06 & 0.05 & 3.21 & 0.14 & 0.09 & 3.55 \\  & (9900) & (25) & (\(-35\)) & (50) & (150) & (\(-19\)) & (32) & (80) & (\(-8\)) \\ \hline
6 & 0.01 \({}^{\star}\) & 0.01\({}^{-4}\) & 3.98 & 0.10 & 0.08 & 3.27 & 0.22 & 0.17 & 3.38 \\  & (9900) & (150) & (\(-33\)) & (150) & (300) & (\(-18\)) & (108) & (240) & (\(-12\)) \\ \hline
7 & 0.01 \({}^{\star}\) & 0.01\({}^{-4}\) & 4.08 & 0.05 & 0.03 & 3.02 & 0.11 & 0.06 & 3.34 \\  & (9900) & (150) & (\(-31\)) & (25) & (50) & (\(-24\)) & (4) & (20) & (\(-13\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of evaluation index by road sections. A higher rate of change in the evaluation index refers to poorer driving safety. The section with the highest rate in the evaluation index is defined as the hazardous road section of driving safety in this study. The hazardous road sections for the mixed pairs of vehicles are presented in Table 4. It was identified that the longitudinal driving safety was poor in the road section where the length of the horizontal curve was 1000 m and at 3% grade. With regard to lateral driving safety, a road section with a 500 m right curve and 1% downhill is relatively vulnerable in terms of the driving safety of the mixed pair. Inter-vehicle safety would deteriorate in a combined alignment section where the length of the horizontal curve is 1000 m while going downhill. \begin{table}
###### Abstract  
This paper presents a novel approach to addressing the issue of temperature-induced instability in an optical, single-sideband transmitter based on a micro-ring resonator (MRR) suitable for millimeter-wave (mmW) radio-over-fiber (RoF) communications. We propose utilizing the drop port of the MRR to provide a feedback signal to the closed-loop control (CLC) system. The latter serves to maintain the optimal alignment between the laser's carrier and the MRR's resonant wavelength, thus mitigating the adverse effects of chromatic-dispersion-induced power fading at the receiving end. Since the feedback information is extracted from the otherwise-wasted resonant energy at the drop port, the control system does not compromise the delicate optical signal at the through port. A CLC was synthesized, designed, and prototyped to provide real-time wavelength tuning of the heat-pump-controlled laser based on the feedback signal. Experimental evaluations demonstrate that the wavelength of the laser could be successfully locked to the MRR's resonance with a wavelength of less than 0.004 nm (\(\sim\)491 MHz). This allowed us to limit the power-penalty deterioration to less than 2 dB for a RoF link with a 2.5-km standard telecommunication single-mode fiber (SMF), a modulation frequency of 37.8 GHz, and a carrier wavelength of 1563.97 nm (\(\sim\)191.820 THz). The proposed solution offers an alternative approach for the carrier and the MRR's resonant wavelength interlocking without the need for complex photonics like thermo-optic or electro-optic structures to control the temperature or phase velocity, respectively.  
microwave photonics; micro-ring resonator; radio over fiber; wavelength locking; closed-loop stabilization; heat pump; DFB laser 2023103390/photonics10121341  
1
###### Abstract  
Traditional command and control (C2) frameworks struggle with evasion, automation, and resilience against modern detection techniques. This paper introduces covert C2 (C3), a novel C2 framework designed to enhance operational security and minimize detection. C3 employs a decentralized architecture, enabling independent victim communication with the C2 server for covert persistence. Its adaptable design supports diverse post-exploitation and lateral movement techniques for optimized results across various environments. Through optimized performance and the use of the native messaging API, C3 agents achieve a demonstrably low detection rate against prevalent Endpoint Detection and Response (EDR) solutions. A publicly available proof-of-concept implementation demonstrates C3's effectiveness in real-world adversarial simulations, specifically in direct code execution for privilege escalation and lateral movement. Our findings indicate that integrating novel techniques, such as the native messaging API, and a decentralized architecture significantly improves the stealth, efficiency, and reliability of offensive operations. The paper further analyzes C3's post-exploitation behavior, explores relevant defense strategies, and compares it with existing C2 solutions, offering practical insights for enhancing network security.  
command and control; C2; browser; post-exploitation; EDR; lateral movement; evasion; privilege escalation; network security
## 2 Data Source Integrity and Parametric Space Definition
### Output Variable Definitions and Calculation Methods  
The two output variables used as model targets--total unit cost and CO\({}_{2}\)-equivalent emissions--were not directly observed in all cases but derived from consistent physical formulations to ensure cross-source comparability. Both were expressed per cubic meter of compacted steel slag base material to align with road design volumetric specifications.  
The total unit cost (Y\({}_{1}\)) in USD/m\({}^{3}\) was decomposed into three components: transportation cost C\({}_{\rm T}\) processing energy cost C\({}_{\rm E}\), and operational overhead C\({}_{\rm O}\). The summation is provided in Equation (4):  
\[\rm Y_{1}=C_{T}+C_{E}+C_{O} \tag{4}\]  
here, C\({}_{\rm T}\) is computed based on fuel consumption per ton-km and prevailing fuel prices, C\({}_{\rm E}\) is based on electricity consumption (from X\({}_{2}\)) multiplied by regional tariffs (USD/kWh), and C\({}_{\rm O}\) includes labor, maintenance, and depreciation, standardized using industry unit rates. In scenarios where data were missing, the component estimates were reconstructed using proportional regressions anchored to known benchmark cases.  
The total CO\({}_{2}\)-equivalent emission (Y\({}_{2}\)), in kg/m\({}^{3}\), was calculated as the sum of two subcomponents: emissions from diesel-based transport and emissions from electricity-based processing. This is formalized in Equation (5):  
\[\rm Y_{2}\ =\ (X_{1}\ \rho\ E{F}_{\rm diesel})\ +\ (X_{2}\cdot X_{5}) \tag{5}\]  
X\({}_{1}\) and X\({}_{2}\) are defined as before, \(\rho\) is the material density (taken as 2.1 ton/m\({}^{3}\) for compacted slag), and EF\({}_{\rm diesel}\) is set at 0.13 kg CO\({}_{2}\)/ton-km. The second term reflects emissions from grid electricity, with X\({}_{5}\) being region-specific (e.g., 0.24-1.05 kg CO\({}_{2}\)/kWh). This equation ensures that the emission metric accounts for both mobile (Scope 1) and stationary (Scope 2) sources as defined under the GHG Protocol, enhancing the relevance of the results for carbon policy planning and environmental impact assessments.  
To assess the plausibility of the computed output values, model-derived estimates for total unit cost and CO\({}_{2}\)-equivalent emissions were benchmarked against published field studies and empirical datasets. Specifically, transport- and processing-related cost values were cross-compared with unit rates reported in case studies from [28; 29], which analyzed slag-based pavement construction.
# Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults
## 3 Fault Water Inrush Model of Igneous Rock
### Control Equations for Fault Core  
Intensified stress concentration in the fault core area results in the absorption of a significant amount of energy and deformation, leading to the scattered distribution of rock mass within the fault core, as shown in Figure 3a [35]. According to the correlation between representative volume element (REV) and engineering scale, the continuum model (CM) is used to study the solid deformation and fluid seepage patterns in the fault core.  
The equation for stress equilibrium in saturated fluid is as follows:  
\[\
abla\cdot\left(\sigma_{fc}-\alpha_{fc}p\mathbf{I}\right)+\mathbf{f}=\mathbf{0} \tag{1}\]  
where \(\sigma_{fc}\) is the stress, \(\alpha_{fc}\) is the Biot's coefficient, \(p\) is the pore pressure, \(\mathbf{I}\) is the second-order unit tensor, and \(\mathbf{f}\) is the body force vector. The constitutive law of the fault core is:  
\[\sigma_{fc}=\mathbf{C}:\varepsilon \tag{2}\]  
where \(\mathbf{C}\) is the solid skeleton stiffness tensor, \(\varepsilon=\
abla^{s}\cdot\mathbf{u}\) is the linearized strain tensor, and \(\mathbf{u}\) is the displacement for the solid matrix.  
In this section, the Darcy model is employed to simulate the overall seepage field within the fault fracture zone. The Darcy model is chosen due to its ability to balance accuracy and computational efficiency, making it more suitable for engineering-scale seepage simulation research when compared to the Navier-Stokes flow model [23].  
The continuity equation for single-phase fluid flow in continuous media is given by [36]:  
\[\
abla\cdot-\frac{k_{fc}}{\mu_{w}}(\
abla p-\rho_{w}g)+\left(\frac{\alpha_{fc }-\varphi_{fc}}{K_{fc}}+\frac{\varphi_{fc}}{K_{w}}\right)\frac{\partial p}{ \partial t}+\alpha_{fc}\frac{\partial\varepsilon_{v}}{\partial t}=W \tag{3}\]  
where, \(k_{fc}\) is the permeability of the fault core; \(\mu_{w}\) is the fluid viscosity, \(\rho_{w}\) is the fluid density, \(\frac{\alpha_{fc}-\varphi}{K_{fc}}\) is the compression term of rock skeleton; \(\frac{\varphi_{fc}}{K_{w}}\) is the compression term of fluid; \(K_{fc}\), \(K_{w}\) is the bulk modulus of the fault core and fluid respectively; \(\varphi\) is the porosity; \(\alpha_{fc}\frac{\partial\varepsilon_{v}}{\partial t}\) is the volumetric strain term; and \(W\) is the source term.  
Under the assumption of incompressible fluid, the equation of motion is given by [PERSON]'s law as:  
Figure 3: Generalized dual porosity coupling model: (**a**) continuum model (CM) at fault core (FC); (**b**) equivalent continuum model (ECM) at damage zone (DZ); (**c**) generalized dual continuum model in heterogeneous water-rich fault zones.  
\[\mathbf{v}=-\frac{k_{fc}}{\mu_{w}}\
abla p \tag{4}\]  
Based on the results of [PERSON]'s experiment, cross coupling term between the above two equations is as follows [37]:  
\[k_{fc}=k_{fc0}e^{\lambda(\sigma_{fc}-\alpha_{fc}p)} \tag{5}\]  
where \(k_{fc0}\) is the initial permeability of the fault core and \(\lambda\) is the influence coefficient.
## 3 Results and Discussion
### Stability of the Material: Leachates Analysis  
The stability of the prepared carbonaceous adsorbent was examined and aimed at the analysis of the plausible metals released in an aqueous solution. Table 6 summarizes  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Parameter** & **Char** & **char-CA-1.0** & **AC** & **AC-CA-0.5** & **AC-CA-1.0** & **AC-CA-1.5** \\ \hline Langmuir & q\({}_{\text{max}}\) & 11.07 & 23.26 & 77.77 & 62.00 & 90.77 & 71.66 \\  & K\({}_{\text{L}}\) & 0.01 & 0.00 & 0.53 & 0.27 & 0.00 & 0.29 \\  & R\({}^{2}\) & 0.943 & 0.970 & 0.997 & 0.951 & 0.947 & 0.968 \\ \hline [PERSON] & K\({}_{\text{F}}\) & 0.91 & 0.05 & 33.09 & 20.16 & 2.67 & 22.91 \\  & n\({}_{\text{F}}\) & 2.73 & 1.31 & 6.93 & 5.36 & 2.12 & 5.27 \\  & R\({}^{2}\) & 0.941 & 0.964 & 0.917 & 0.844 & 0.979 & 0.858 \\ \hline Sips & q\({}_{\text{max}}\) & 14.87 & 7.28 & 81.27 & 60.97 & 100.00 & 70.46 \\  & K\({}_{\text{S}}\) & 0.02 & 0.01 & 0.71 & 0.15 & 0.01 & 0.15 \\  & n\({}_{\text{S}}\) & 1.50 & 1.76 & 1.71 & 0.67 & 1.31 & 0.57 \\  & R\({}^{2}\) & 0.957 & 0.942 & 1.000 & 0.958 & 0.949 & 0.980 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model fitting parameters of acetaminophen isotherm onto the char and the activated carbon after citric acid modification.  
Figure 6: (**A**) Adsorption isotherms of acetaminophen with char and the activated carbon after citric acid modification. The dashed lines correspond to the Sips modeling. (**B**) Maximum experimental uptake of acetaminophen registered.  
the concentrations of the major and minor metallic elements of a selection of the materials prepared, i.e., char, char-CA-1.0, AC, and AC-CA-1.0. The leachate analysis revealed that the activation with Na\({}_{2}\)CO\({}_{3}\), and the further modification with citric acid, significantly alters the concentration of the released species in the solution. Na\({}_{2}\)CO\({}_{3}\) generally increases the levels of major elements like Ca and P, detected on the surface by the XPS technique. The citric acid treatment influences minor elements such as Cd, Co, and Sb. The sequential activation with Na\({}_{2}\)CO\({}_{3}\) and citric acid functionalization tends to maximize the concentration of several components, especially Ti and Sb. The findings suggest that using plastic residues as precursors for the preparation of activated carbon requires a detailed investigation of the leaching of metallic impurities incorporated in the polymeric structure of this residue as an additive [59]. The components that are liviviated to a larger extent, such as Ca, P, Si, and Ti, do not display any problems for environmental applications in water due to the lack of toxicity. In contrast, the metals that might compromise the toxicity of the released effluent were detected at low concentrations, bearing in mind that the presented data in Table 6 correspond to a solid-water (\(m/v\)) ratio of 1:10, an abnormally high dosage.
## 4 Results Analysis
### Noise Data Collection and Assessment
The L\({}_{10}\) (96.72 dB) and L\({}_{90}\) (74.97 dB) demonstrate that even during relatively quieter periods, the noise level remains within a high range, denoting the persistence of the noise environment. The dynamic range (LR) is 71.57 dB, which reflects a substantial difference between the maximum and minimum sound pressure levels and demonstrates considerable noise variability during train operation. For frequency metrics, the main frequency (MF) averages 24.68 Hz, which suggests that low-frequency noise predominates in the driver's cab. The total energy accumulation (TE) is 7,753,212 joules and reveals a high cumulative noise energy over the observation period. The frequency of specific sound pressure level events (NE\({}_{70}\), NE\({}_{75}\), NE\({}_{80}\)) shows frequent occurrences of noise events exceeding 70 dB, 75 dB, and 80 dB, respectively. The above results show that the noise levels are generally high and exhibit significant fluctuations; low-frequency noise predominates, and there are frequent occurrences of high-intensity noise events, potentially posing long-term health risks and operational safety concerns for the driver.