esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 5 Discussion
### Considering Different Evaluation Methods  
In assessing the accuracy of earthquake maximum magnitude prediction models, the method of limiting the error between the predicted magnitude and the true magnitude to \(\pm\)0.5 may have some practical limitations. Even with a magnitude difference of 0.5, there can be significant differences in the amount of energy released by earthquakes and in the potential damage they can cause. For example, a magnitude 6.5 earthquake may vastly differ from a magnitude 6 earthquake in actual impact. Therefore, to consider the impact of earthquake destructiveness, another assessment method based on magnitude intervals is proposed in this study. Specifically, when the predicted magnitude falls in the same interval as the true magnitude, this is considered an accurate prediction. This method can better reflect the actual impact of earthquakes.  
Using the above method, we focused on predicting accuracy within the moderate to strong earthquake range. As shown in Figure 10, for Region A, within the test set time window, there were 67 earthquake events with magnitudes of 5 < \(M_{\rm L}\)\(\leq\) 6, of which the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **5 < \(M_{\rm L}\)\(\leq\) 6** & \(M_{\rm L}\) > 6** \\ \hline \hline \multirow{8}{*}{5 < \(M_{\rm L}\)\(\leq\) 6} & None & 0.84 \\  & \(M_{\rm mean}\) & 1.00 \\  & \(E^{1/2}\) & 0.40 \\  & \(b_{1}\) & 0.64 \\  & \(a\) & 0.40 \\  & \(b_{2}\) & 0.46 \\  & \(\sigma\) & 0.44 \\  & \(\Delta M\) & 0.40 \\  & \(Lon_{mean}\) & 1 \\  & \(Lat_{mean}\) & 0.64 \\  & \(Lon_{E}\) & 0.40 \\  & \(Lat_{E}\) & 0.84 \\  & None & 0.64 \\  & \(M_{mean}\) & 0.25 \\  & \(E^{1/2}\) & 0.61 \\  & \(b_{1}\) & 0.61 \\  & \(a\) & 0.64 \\ \(M_{\rm L}\) > 6 & \(b_{2}\) & 0.61 \\  & \(\sigma\) & 0.61 \\  & \(\Delta M\) & 0.64 \\  & \(Lon_{mean}\) & 0.61 \\  & \(Lat_{mean}\) & 0.61 \\  & \(Lon_{E}\) & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Computed values of R scores for different predicted magnitudes, using different input vectors obtained by removing one parameter at a time for Region B.  
LSTM model successfully predicted 57, resulting in an accuracy of approximately 85%. For earthquakes with magnitudes greater than \(M_{\rm L}\) > 6, there were 19 events, and the LSTM model successfully predicted 2 of them, with an accuracy of about 11%. For Region B, there were 49 earthquake events within the 5 < \(M_{\rm L}\)\(\leq\) 6 range and the LSTM model successfully predicted 42 of them, with an accuracy of approximately 86%. There were 45 earthquake events with \(M_{\rm L}\) > 6, and the LSTM model successfully predicted 20 of them, resulting in an accuracy of about 44%. These results indicate that the model performs well in predicting earthquakes within the 5 < \(M_{\rm L}\)\(\leq\) 6 range.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 5 HARRF LNA Design
### X-Band LNA Design Procedure  
The X-Band LNA design covers a frequency range of \(8\,\mathrm{GHz}\) to \(12\,\mathrm{GHz}\) in the electromagnetic spectrum. A three-stage LNA was implemented using the \(0.15\,\mathrm{\SIUnitSymbolMicro m}\) low noise InGaAs pHEMT process. The small-signal parameters were extracted under a bias condition of  
\[V_{gs}=-0.3\,\mathrm{V},\quad V_{ds}=2\,\mathrm{V}. \tag{38}\]This bias point was chosen to minimise current dissipation while providing the desired trade-off between gain and noise figure. Each stage was biased to maintain current uniformity across the amplifier. The design centre frequency was  
\[f_{d}=10\,\mathrm{GHz}. \tag{39}\]  
Figure 7 depicts the three-stage LNA structure for the X-band. Each transistor stage has distinct gate and drain lines for proper inter-stage matching and to ensure overall stability. Inductive source degeneration (feedback) is used in the first stage to bring the optimum noise match \(\Gamma_{\mathrm{opt}}\) closer to the gain match point, which helps in achieving a lower noise figure.
## 2 Modeling of Compromised LFC System and False Data Injection Attacks
### Attack Templates of FDI Attacks on LFC  
In this subsection, we discuss different types of false data injection attack on an LFC system. The typical fixed FDI attacks can be categorized into two types: [17]:  
* Exogenous attack: \[\Delta P_{i}^{tm}=\Delta P_{i}^{t}+D_{p}\] (7) where \(D_{p}\) represents the value of the data injection added to the measurements of tie-line power.
* Scaling attack: \[\Delta P_{i}^{tm}=K_{p}\Delta P_{i}^{t}\] (8) where \(K_{p}\) is the scaling attack parameter.  
Due to the fixed parameters of typical FDI attacks, the features of compromised data can be exploited. Based on the classifier, defenders can distinguish the compromised data from the normal ones. In fact, sophisticated attackers can adjust injection parameters flexibly to achieve attack targets. In this paper, we further study flexible FDI attacks on \(\Delta P_{i}^{tm}\), which can be written as:  
\[\Delta P_{i}^{tm}=k_{p}(t)\Delta P_{i}^{t}+d_{p}(t) \tag{9}\]  
where \(k_{p}(t)\) and \(d_{p}(t)\) are time-varying variables. By launching the proposed FDI attacks, the attacker can tamper the data of \(\Delta P_{i}^{tm}\) in the current operating condition into the false data with characteristics of real data in other operating situations. Thus, the controller can be misled to issue control commands that are not applicable to the current operating situation. It is necessary to relate the true data of tie-line power to accessible known-secured variables under the current operating situation.
# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
## 2 Methodology
### Long Short-Term Memory (LSTM)  
The LSTM network structure is built on the foundation of recurrent neural networks (RNNs) and also includes forget gates, input gates, and output gates, allowing the model to selectively remember relevant information and discard irrelevant information. This structure addresses the issues of gradient explosion and vanishing gradients commonly found in RNNs. The LSTM network model enhances learning of long-term dependencies, enabling it to derive the state at the next time step from the previous state, thus achieving a \"memory\" function that effectively improves the model's predictive accuracy.  
Figure 3 shows the general single-layer LSTM network's structure.  
Figure 3: LSTM network structure.  
For a given sequence, the iterative formulation of the RNN structure is as stated in Equations (7) and (8).  
\[h_{t}=f_{a}(w_{xh}x_{t}+w_{hh}h_{t-1}+b_{h}) \tag{8}\]  
\[y_{t}=w_{thy}h_{t}+b_{y} \tag{9}\]  
where \(w\) is the matrix of weight coefficients; \(b\) is the bias vector; \(f_{a}\) represents the activation function; and the subscript \(t\) denotes the moment.  
The basic unit structure of LSTM is shown in Figure 4.  
To solve the problem of a vanishing and exploding RNN gradient, the deep neural network, unfolding in time, becomes easy to train by introducing control gates, and its forward computation process is shown in Equations (9)-(13).  
\[i_{t}=\sigma(w_{xi}x_{t}+w_{hi}h_{t-1}+w_{ci}c_{t-1}+b_{i}) \tag{10}\]  
\[f_{t}=\sigma(w_{xf}x_{t}+w_{hf}h_{t-1}+w_{cf}c_{t-1}+b_{f}) \tag{11}\]  
\[c_{t}=f_{t}c_{t-1}+i_{t}\text{tanh}(w_{xc}x_{t}+w_{hc}h_{t-1}+b_{c}) \tag{12}\]  
\[o_{t}=\sigma(w_{xo}x_{t}+w_{ho}h_{t-1}+w_{ca}c_{t}+b_{o}) \tag{13}\]  
\[h_{t}=o_{t}\text{tanh}(c_{t}) \tag{14}\]  
where \(i\) is the input gate; \(f\) is the forgetting gate; \(c\) represents the cell state; \(o\) is the output gate; \(w\) stands for the corresponding weight coefficient matrix; \(b\) stands for the corresponding bias term; \(\sigma\) represents the activation function; and tanh denotes the hyperbolic tangent activation function.
## 3 Results of Global-Local Analysis
### Results
#### 3.3.5 Crack Tip Displacement  
In Table 3, the displacement of the tip crack is presented for all cases analyzed. A monolithic solution with the most refined mesh was used as a reference.  
The results for the different cases are similar for each element size.  
For the same element size, case III has a slightly higher error than case I and case II. This may be attributed to the projection process between the global and local models, where increasing the size of the local domain, a larger local model domain encompassing a broader portion of the structure, may induce a different distribution of internal loads on  
Figure 7: Von-Mises stress \(KPa\) for the different studied cases. (**a**) Element size: 1 mm. (**b**) Element size: 0.625 mm. (**c**) Element size: 0.5 mm.  
the local model. However, the difference in accuracy is relatively small, suggesting that both configurations yield consistent results within an acceptable error range.  
An oscillatory convergence behavior with mesh refinement can be observed. Although non-monotonic behavior is observed, the amplitude of the variations decreases with refinement, suggesting a trend towards asymptotic convergence. This behavior can be attributed to the following factors:  
* Interpolation effects between meshes of different refinement levels, which leads to local over- or underestimation of displacement, particularly when displacement fields exhibit steep gradients or discontinuities associated with crack propagation.
* Mesh quality in the cohesive zone, particularly the aspect ratio and distortion of the elements aligned with the crack path, may significantly affect the accuracy and stability of the results. Poor mesh quality in this region can lead to artificial stiffness or premature damage localization, which in turn influences the calculated displacements and contributes to the oscillatory convergence behavior observed in the results.
* Discrete positioning of the crack front, as mesh refinement improves the accuracy of the crack front representation. This may cause the crack to interact differently with the structure and boundary conditions, resulting in slight variations in the magnitude of the maximum displacement.  
It can be concluded that the error in the tip crack displacement does not depend on the size of the local problem or the position of the interfaces, even if they are close to the zone of interest, but depends only on the size of the elements. Finally, for all cases, it can be said that the larger the mesh, the larger the error.
## 4 Active Distribution Network Scheduling Model
### Objective Functions  
#### 4.1.1 Minimize Total Operating Costs  
According to the general principles of optimal scheduling, the first optimization objective is to achieve minimum total operation cost of the ADN, including electricity purchase cost \(C_{buy,t}\), DG generation cost \(C_{\text{DG},t}\), storage battery operation cost \(C_{\text{ESS},t}\), network reconfiguration cost \(C_{\text{SW},t}\), and compensation cost for users \(C_{comp,t}\). The total operating costs are as Equation (8):  
\[\min f_{1}=\sum_{k=1}^{T}\Big{(}\Big{(}C_{buy,t}+C_{DG,t}+C_{comp,t}+C_{ESS,t}+ C_{SW,t}\Big{)}\Delta t\Big{)} \tag{8}\]  
The electricity purchase cost \(C_{buy,t}\) at time \(t\) included two parts, which can be calculated as Equation (9):  
\[C_{buy,t}=c_{grid,t}P_{grid,t}\Delta t+\sum_{i=1}^{N_{DC}}c_{\text{DG},t}P_{ \text{DG},t}\Delta t \tag{9}\]  
The DGs in this paper include photovoltaic (PV) cells, wind turbines (MTs), and microturbines (MTs). The controllable DGs considered in this paper are MTs. DG generation cost at time \(t\)\(C_{\text{DG},t}\) mainly includes fuel cost \(F_{\text{DG},t}\), operation maintenance cost \(M_{\text{DG},t}\), and depreciation cost \(D_{\text{DG},t}\)[37; 38] as Equation (10):  
\[\left\{\begin{array}{l}C_{\text{DG},t}=F_{\text{DG},t}+M_{\text{DG},t}+D_{ \text{DG},t}\\ F_{\text{DG},t}=c_{\text{DG}}\frac{P_{\text{DG},t}}{\eta_{\text{DG},t}}\Delta t \\ M_{\text{DG},t}=K_{DG}P_{\text{DG},t}\Delta t\\ D_{\text{DG},t}=\frac{c_{\text{DG},t}(1+t)^{\eta_{\text{DG}}}P_{\text{DG},t} \Delta t}{8760k_{\text{DG}}(1+t)^{\eta_{\text{DG}}}-1}\end{array}\right. \tag{10}\]  
where \(\eta_{\text{DG},t}\) can be calculated as \(\eta_{\text{DG},t}=m_{3}(P_{\text{DG},t})^{3}+m_{2}(P_{\text{DG},t})^{2}+m_{1} P_{\text{DG},t}+m_{0}\).  
The operation cost of storage batteries at time \(t\)\(C_{\text{ESS},t}\) can be expressed as a quadratic function:  
\[C_{\text{ESS},t}=c_{\text{ESS}}P_{\text{ESS},t}^{2} \tag{11}\]  
The network reconfiguration cost at time \(t\)\(C_{\text{SW},t}\) can be expressed as Equation (12):  
\[C_{\text{SW},t}=c_{\text{sw}}|sw_{t}-sw_{t-1}| \tag{12}\]  
The compensation cost for users at time \(t\)\(C_{comp,t}\) can be expressed as Equation (13):  
\[C_{comp,t}=\sum\limits_{i=1}^{N_{ll}}c_{IL,i}P_{IL,i}\Delta t \tag{13}\]
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 4 Experiments  
This section presents extensive experiments conducted on 11 benchmark datasets for four restoration tasks, including defocus deblurring, motion deblurring, dehazing, and low-light enhancement. Summaries of all the datasets used in these experiments are provided in Table 1. In the result tables, the best and second-best scores for each dataset are highlighted in **bold** and underlined, respectively.  
Implementation Settings. We trained separate models to restore images degraded by different degradation factors. Considering the complexity of each problem, we redesigned the proposed FreANet by adjusting the number of encoder-decoder layers and residual blocks [L\({}_{1}\), L\({}_{2}\), L\({}_{3}\), \(N\)]. We set [1; 2; 3; 10] for defocus/motion deblurring, [1; 2; 3; 3] for dehazing, and [1; 2; 2; 1] for low-light enhancement. Additionally, to ensure a fair evaluation for dehazing, we designed a small version of FreANet [1; 1; 1; 2]. Unless mentioned otherwise, the following tunable parameters were used in all experiments. We adopted the Adam optimizer [47] with an initial learning rate of \(1\times 10^{-4}\), which decreases to \(1\times 10^{-6}\) in a cosine annealing manner [48]. We used a patch size of 256 \(\times\) 256 with random horizontal flips and rotations for data augmentation. We trained and evaluated the proposed restoration models for all degradation types on a single NVIDIA H100 GPU (NVDIA, Santa Clara, CA, USA). FLOPs were measured on data with a resolution of \(256\times 256\).
# A Collocation Method for the Numerical Solution of Nonlinear Fractional Dynamical Systems
## 1 Introduction  
Presently, fractional differential problems are well established models to describe a great variety of real-world phenomena [1; 2; 3; 4]. Since their analytical solution can be obtained in just a few cases, numerical methods are required for its approximation. A key point to construct efficient numerical methods is the ability to reproduce the nonlocal behavior of the fractional derivative. This is a challenging goal especially in cases of nonlinear problems.  
In the literature several methods, such as finite difference methods, spectral methods, finite element methods, collocation methods, were proposed to solve fractional differential problems [5; 6; 7]. In this context, collocation methods are revealed particularly attractive since their global nature is suitable to catch the non-locality of the fractional derivative.  
The aim of this paper is to describe in detail how to apply the fractional collocation method introduced in [8] for the numerical solution of nonlinear dynamical systems with time derivative of fractional order. The method was already used to numerically solve some linear and nonlinear problems with fractional time derivative [9; 10; 11]. Here, we will describe how to use the method to solve differential problems of a more general form and discuss its implementation issues. To allow the reader to easily follow the description of the algorithm, we limit ourselves to the case when the approximate solution belongs to the spline space. In particular, we will show some numerical tests when using both linear and cubic splines and provide two Mathematica Notebooks [12] that can be used to solve such test problems.  
The paper is organized as follows. In Section 2 the fractional differential problem we are interested in and the collocation method we use are briefly recalled. In particular, the spline space we use to construct the approximate solution is described in Sections 2.2 and 2.3; the analytical expression of both the basis functions and their fractional derivative is given in Sections 2.3 and 2.4; the algorithm of the collocation method is described in Section 2.5. The results of the numerical tests are shown in Section 3 and briefly discussed in Section 4. Finally, in Section 5 some conclusions are given.
# CNN-Based Ti-6242 Impeller Forging Process Design for Uniform Strain Distribution
## 1 Introduction
The method of forging the impeller in two steps involves designing the preform shape on the basis of the repeated finite element method [30; 31], experience [28], or methods like upsetting [32] and then forging the impeller. Since these methods concentrate on a specific shape, the consumption of material and time occurs. To address this issue, other approaches [33], including utilizing a CNN, can be employed [25]. However, this method focuses only on the design of the preform shape for the target impeller and does not provide a method for processing the billet for preforming. This method was studied using AISI 1060 material and may result in reduced product durability and defects due to concentrated strain in the forging of other complex shapes using materials that are difficult to form, such as Ti-6242 [34]. We designed an initial billet that underwent three steps--upsetting, preform forging, and target impeller forging--aiming to achieve uniform strain distribution in the forged product. To systematically design this process, we utilized a CNN to train the model on the basis of the strain distribution after each forging step, thereby enabling the derivation of an appropriate preform shape that could homogenize the strain distribution throughout the forging process. Three different impeller shapes, which had not been previously trained in the CNN model, were employed to validate the effectiveness of the proposed method. The efficacy of our proposed forging process design was evaluated by comparing both the strain distribution during the process and in the final product with those observed in existing studies.
# Evolution of System Embedded Optical Interconnect in Sub-Top-of-Rack Data Center Systems
## 3 LightningValley--First-Generation Optically Enabled Data Center System  
The LightningValley system (Figure 4b) was adapted from a 4U24 OneStor data storage array platform (Figure 4a). The system enclosure is 4U (177.8 mm) high and 19\({}^{\prime\prime}\) (482.6 mm) wide and supports an array of 24 3.5\({}^{\prime\prime}\) hard disk drives, which gives rise to the prefix 4U24. The LightningValley platform was modified to allow 12 bidirectional 12 Gb/s SAS channels to be conveyed optically from the expander chip of one controller daughtercard across an optical bridge on the midplane to the expander chip of the other controller daughtercard in the system (Figure 4c).  
Figure 3: Low port-count transceivers on end node interface cards: (**a**) Avago McLight 2 + 2 transceiver on ThunderValley2 disk drive interface card, (**b**) Murata FOT 2 + 2 transceiver on Pegasus end node interface card.  
Figure 2: Finisar midboard optical transceiver module package: (**a**) schematic view of Finisar “BOA” module [3], (**b**) photo of Finisar BOA module assembled directly onto a PCB, (**c**) photo of Finisar BOA module on a pluggable small mezzanine card [12].  
The LightningValley platform has been described in previous work [12; 13; 14]. In this section we focus on the optical interconnect technology and performance.
# Durability of Subsea Tunnels under the Coupled Action of Stress and Chloride Ions
###### Abstract  
The durability of subsea tunnels under the coupled action of stress and chloride ions was analyzed to estimate the service life and provide a theoretical foundation for durability design. The influence coefficient of the stress on chloride ion transmission at lower stress levels was discussed according to the material mechanics, and was verified by experimental data. A stress calculation model of a subsea tunnel's lining section is proposed based on the plane-section assumption. Considering the space-time effect of the convection velocity, a partial differential equation was constructed to calculate the chloride ion transfer condition under the coupled action of stress-convection-diffusion. The numerical solution of the partial differential equation was solved and the sensitivity of the parameters was analyzed. The subsea tunnel's time-varying reliability index was calculated following the Monte Carlo method, and was used to predict the service life. The results show that the chloride ion concentration calculated by considering the coupled action is larger and the reliability index is lower than calculated only considering diffusion. Our findings contribute to the conclusion that durability designs of subsea tunnels should consider the coupled action of stress-convection-diffusion. An effective method to improve the service life of a subsea tunnel is to reduce the water-binder ratio or increase the thickness of protective cover.  
Keywords:subsea tunnels; durability; convection; diffusion; stress; carbonation +
Footnote †: journal: Applied Sciences
## 2 Methods and Hardware for Application of subTHz Instruments
### Prospects for Gyrotrons on the Sufffa Antenna for Radar and Telecommunication Applications
A two-order increase in the gyrotron power yields an increase by an order of magnitude in the data transfer rate according to the Shannon-Hartley Formula (4):  
\[C=B\ log_{2}(1+SNR), \tag{5}\]  
where \(C\) is the bit rate of the channel, \(B\) is the spectrum bandwidth, and \(SNR\) is the signal/noise ratio. With the MW power sources presented here, one can obtain an increase by three orders of magnitude, as compared with those used till now [4] and at the 10 kW level of high-power deep-space station signals. Another order of magnitude, according to Equation (4), can be gained by using the W-band antenna (Suffa, for example) instead of the X-band ones, which are currently used for the Exo-Mars mission [4]. Two more orders of magnitude in \(SNR\) can be won by using the low-noise superconducting receivers, which are now capable of cryogen cooling to tens of K of the noise temperature compared to the not-cooled receivers used for space communication now. Currently, there are some groups in the USA [36; 37] and Europe [23; 29; 38] who develop and use receivers of the quantum level of sensitivity in combination with actual onboard and terrestrial subTHz antennas. There also exist lots of receiving subTHz systems tested only in laboratory.
## 3 Case Studies of Pilot and Full-Scale STRBs
### Sewage Sludge with High Chromium Content (Greece)
A higher Cr concentration was also found in the drained water, compared to the other beds receiving SAS without Cr addition, indicating that a larger portion of Cr leaves the bed through drainage. Cr losses in the drained water were found to be the main fraction (42-48%) in the Cr mass balance. Accumulation in the residual sludge accounted for 26-34% of Cr losses, followed by adsorption to gravel media (20-25%) [50]. In general, the pilot STRB managed to handle the excessive Cr load without showing any visible toxicity signs. However, the EU legal limit values (Table 1) were quickly exceeded, prohibiting a potential reuse of the residual sludge.