esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Out-of-plane p-n heterojunctions based on two-dimensional layered materials (2 DLMs) with unusual physical characteristics are attracting extensive research attention for their application as photodetectors. However, the present fabrication method based on 2 DLMs produces out-of-plane p-n homojunction devices with low photoresspativity and detectivity. This work reports an ultrasensitive phototransistor based on a laser-induced p-doped WSe\({}_{2}\)/MoS\({}_{2}\) van der Waals heterojunction. The laser treatment is used for p-doping WSe\({}_{2}\) nanoflakes using high work function WO\({}_{\rm x}\). Then, an n-type MoS\({}_{2}\) nanoflakes is transferred onto the resulting p-doped WSe\({}_{2}\) nanoflake. The built-in electric field of p-doped WSe\({}_{2}\)/MoS\({}_{2}\) is stronger than that of pristine WSe\({}_{2}\)/MoS\({}_{2}\). The p-n junction between p-doped WSe\({}_{2}\) and MoS\({}_{2}\) can separate more photogenerated electron-hole pairs and inject more electrons into MoS\({}_{2}\) under laser illumination than pristine WSe\({}_{2}\)/MoS\({}_{2}\). Thus, a high photoresponsivity (\(R\)) of \(\sim\)1.28 \(\times\) 10\({}^{5}\) A\(\cdot\)W\({}^{-1}\) and high specific detectivity (\(D^{*}\)) of \(\sim\)7.17 \(\times\) 10\({}^{13}\) Jones are achieved under the illumination of a 633 nm laser, which is approximately two orders higher than the best phototransistor based on a WSe\({}_{2}\)/MoS\({}_{2}\) heterojunction. Our work provides an effective and simple method to enhance photoresponsivity and detectivity in two-dimensional (2D) heterojunction phototransistors, indicating the potential applications in fabricating high-performance photodetectors based on 2 DLMs.  
photodetector; heterojunction; MoS\({}_{2}\); WSe\({}_{2}\); p-doping +
Footnote †: journal: Applied Sciences
# Mdpli Mitigation of Routing Congestion on Data Networks: A Quantum Game Theory Approach
## 5 Conclusions  
Communication networks are facing increasing congestion problems associated with the tremendous growth of the number of packets sent over the networks. In order to get an improvement on the communication network performance classical and quantum network protocols based on game theory are proposed in this work. Classical game protocols lead to a routing and traveling time constraint that deteriorates the network performance for an increasing number of packets.  
Figure 11: Effect of real devices in the trade-off barrier by quantum protocol. Congestion can be mitigated by making use of IBM NISQ quantum computers. \(p\) values between 0.3 and 0.7.  
The quantum model is obtained with quantum strategies as proposed by the EWL protocol of quantum games. Quantum strategies are represented by a three-parameter one-qubit quantum gate model. Then, the players strategies are extended when passing from classical to quantum.  
The trade-off barrier between routing and traveling time present in the classic probabilistic protocol is surpassed by many quantum game strategies leading to an enhancement of the network performance for increasing packet number. The stability of the quantum strategies is associated with Nash equilibrium. As pure quantum strategies are not Nash equilibrium, mixed strategies have been studied. In this way, a mixed strategy that is Pareto optimal and seems to be Nash equilibrium was shown. Additionally, it was also shown that under the influence of simulated noise and real quantum devices the quantum protocol benefits still remain.  
We have shown that a quantum game formalism applied to the communication network enhances its efficiency when dealing with congestion problems. Consequently, a new world of opportunities could emerge in these types of complex systems when taking advantage of the possibilities offered by quantum computing. Moreover, they might bring with them solutions that create remarkably more efficient systems.  
Conceptualization, A.S.; Formal analysis, A.S.; Investigation, A.S., O.G.Z. and C.M.A.; Project administration, C.M.A.; Software, A.S.; Supervision, O.G.Z. and C.M.A.; Validation, O.G.Z.; Writing--original draft, A.S. and O.G.Z.; Writing--review & editing, C.M.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data and scripts that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 3 Methods
### Physical Layer of OFDM Communication System
#### 3.1.2 Channel Estimation  
Channel estimation is crucial to obtain information about the Channel Impulse Response (CIR). Pilot-aided channel estimation is often used at the receiver by inserting known data sequences (pilots) in the form of block, comb, or scatter patterns to sample the channel distortions [48; 49]. These pilots are recognizable at the receiver and are either constant or low auto-correlation sequences, such as Zadoff-Chu sequences. For channel correction, the received bit sequence is compared with the known ones at the receiver to estimate the CIR, which is then equalized to mitigate the channel distortions [48; 50].  
The most common pilot-aided channel estimation method is the least square (LS) technique. The LS method increases the sampling rate of the channel frequency responses at the pilot subcarriers, and then uses interpolation, such as linear, spline, or cubic, to estimate the Channel Impulse Response (CIR) at the data subcarriers [44; 46; 49]. The LS estimator of the channel is given by:  
\[\hat{X}(k)=\frac{Y(k)}{\hat{H}_{p}^{LS}}, \tag{9}\]  
\[\text{where, \ }\hat{H}_{p}^{LS}=\mathcal{F}\Big{(}\frac{Y_{p}}{X_{p}}\Big{)}. \tag{10}\]  
\(\hat{X}(k)\) is the estimated channel, \(\hat{H}_{p}^{LS}\) contains the LS estimate, \(Y_{p}\in\mathbb{C}^{N_{p}\times 1}\) is the received pilots, \(X_{p}\in\mathbb{C}^{N_{p}\times N_{p}}\) is a matrix with known transmitted pilot symbols on its diagonals, and \(\mathcal{F}(\cdot)\) is the interpolation operation, such as linear, cubic, spline, etc. [48]. The LS algorithm is simple because it disregards noise and ICI, therefore, it is calculated with minimal complexity without any channel statistics knowledge; however, it results in high means square error (MSE).  
Minimum Mean Square Error (MMSE) channel estimation technique utilizes the second-order statistics of the channel conditions (prior channel knowledge) and performs channel estimation [51; 52]. It uses channel auto convenience to reduce the MSE, as shown below  
\[\hat{H}_{p}^{MMSE}=R_{H}[R_{H}+\left(X_{p}X_{p}^{H}\right)^{-1}]^{-1}\hat{H}_{ p}^{LS}, \tag{11}\]  
where \(R_{H}\) is the frequency-domain correlation matrix at the pilot-symbols with \(R_{H}=E\big{[}H_{p}H_{p}^{\top}\big{]}\) and the channel coefficient matrix can be obtained via interpolation [51]. In our experiment we have considered both LS and MMSE channel estimation with linear interpolation. Linear interpolation utilizes the frequency response of two neighboring pilots to estimate the frequency response of the data subcarriers in between them, therefore, it has simple computational complexity [51]. Overall, MMSE channel estimation outperforms LS estimator, especially in lower \(\text{E}_{\text{b}}/\text{N}_{0}\) regions. However, as the number of subcarriers in an OFDM system increases, the computational complexity of the MMSE estimator also increases due to the computations required for matrix inversions [53].
# Contact Fatigue State Identification of Specim Based on Heterogeneous Data and Evidence Theory
###### Abstract  
In order to accurately realize the contact fatigue state identification of specimen, a new method based on vibration and image heterogeneous data, as well as on D-S evidence theory, is proposed. Firstly, combined with the bearing public data set from CWRU, the vibration signal imaging methods such as SDP, GAF and GRI, as well as neural network models such as VGG16, ResNet and S-T, were compared and analyzed. It is determined that the SDP method is used to visualize the vibration signal, and the two state identification evidence bodies based on the vibration information source are obtained through the VGG16 and ResNet models. Secondly, combined with image monitoring signals, the fatigue defect identification method based on automatic weighting threshold and the detection error dynamic compensation method based on fatigue defect edge features are used to quantify the fatigue damage area and obtain the state identification evidence body based on the image information source. On this basis, a state identification network model based on vibration and image spatiotemporal heterogeneous data is constructed, and the D-S evidence theory is used to realize the contact fatigue state identification of the specimen. The results show that fusion of vibration and image data can achieve information complementarity and may identify the contact fatigue state of the specimen more accurately. The accuracy of state identification after fusion is 98.67%, which is at least 3% higher than that of a single information source. This research is of great significance for the accurate acquisition of material contact fatigue properties and has certain reference value for the heterogeneous data fusion from different sources.  
multi-source information fusion; pattern recognition; deep learning; D-S evidence theory +
Footnote †: journal: Applied Sciences
# Uniaxial Testing of Soil-Cement Composites to Obtain Correlations to Be Used in Numerical Modeling
## 1 Introduction  
Deep soil mixing (DSM) technologies are being widely used in foundation (civil) engineering, with a large variety of execution modes [1, 2] and engineering applications [3, 4]. In environmental engineering, DSM technologies are mainly applied in seepage control [5, 6] and contamination immobilization and spreading prevention [7].  
DSM is an in situ group improvement technique that enhances the parameters of a weak subsoil by introducing a hydraulic binder into its structure, e.g., lime [8, 9, 10], cement [11, 12, 13, 14, 15, 16], fly ashes [17, 18, 19, 20], or a combination of different types of binders and activators [21, 22, 23]. It must be noticed here that the issue of this study is very much related to the production of backfill materials for the mining industry. Strengthening weak soils and the reuse of mining and construction waste for geotechnical works are currently widely discussed and tested. The purpose of it is to compose valuable backfill, removing harmful waste from the ground surface [24, 25]. Currently, the leading soil mixing technologies are DSM columns and wet or dry mass stabilization [26, 27, 28]. Both wet and dry methods make it possible to mix the soil with a hydraulic binder; the difference is the way that the binder (cement, lime) is transferred into the ground. The wet method uses the slurry, and in the case of dry mixing, the binder needs the groundwater to initiate the hardening (setting) process. The problems that are still under study, due to the diversity of combinations of tested soils, binders and mixing technologies, are issues related to the final product quality control [29, 30, 31, 32] and further applications of measured or presumed parameters in numerical studies [33, 34, 35, 36]. At the same time, the requirement of continuous technological progressin the aspect of mixing techniques and used binders makes unconventional technologies more and more popular. The observed diversity of results, depending on soils and binders, brings the need for calibration and optimization of technology, especially when we consider the carbon footprint and the need for sustainable development. It must be underlined that the former authors' experiences with DSM production and testing [26; 28; 29; 30; 31; 32] formed the basis for the current research program. The current research directions focus on promoting sustainable development, which is understood as reducing the broadly understood carbon emissions while ensuring the required effect of soil reinforcement and reliability of the implemented design solution.  
One of the core aims of this research is to show how various technological issues affect the quality of the final product (soil-cement composite), aiming to determine the most efficient and sustainable way of production using the same machine. The presented study focuses on just one aspect, meaning the possibility to estimate material stiffness on the basis of its measured compressive strength, with regard to a large variability of the obtained results.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 2 Material and Methods
### Vital Sign Detection Using a Mono-Static UWB Life-Detection Radar
#### 2.2.3 Algorithm for Stationary Operating Mode
As the OS-CFAR method is the threshold-dependent method, some distances might be produced by the clutters or other interferences. Therefore, the correlation output \(\mathbf{s}_{m}\) is designed to eliminate these wrong distances. The estimated distances in \(\mathbf{s}_{0m}\) are assumed to be from the quasi-static trapped human beings in the case that the correlation between \(\mathbf{s}_{0m}\) and \(\mathbf{s}_{m-1}\) equals to non-zero. The calculation of the correlation output \(\mathbf{p}_{m}\) follows the same way as \(\mathbf{s}_{m
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 5 Experiment and Result Analysis
### Experimental Comparison  
To assess the performance of the proposed BPDM-GCN algorithm for fast recovery of faulty links, this experiment compared BPDM-GCN with other contemporary methods for faulty link recovery. The comparison algorithms include network recovery for large-cale failures in smart grid by reinforcement learning (NRLF_RL) [11], low interruption ratio link fault recovery scheme (LIR_LFR) [15] and implementation failure recovery mechanism using VLAN ID in software defined networks (FR_VLAN) [17]. The primary comparison focuses on key metrics such as fault link recovery delay, fault link recovery success rate, and other relevant indicators.
## 2 Materials and Methods
The tested turbulence models' base line is two transport equations for the turbulence kinetic energy (k) and the turbulence dissipation (\(\varepsilon\) or \(\omega\)). The \(k\)-\(\varepsilon\) model is very powerful in the simulation of different flow conditions but the lack of sensitivity to adverse pressure gradients is one of the well-known shortcomings. The \(k\)-\(\varepsilon\) model predicted significantly higher shear stresses which delay or completely prevent separation. On the other hand, the \(k\)-\(\omega\) model had a better performance in relation to the adverse pressure gradient [48] which led to the achieved velocity fluctuations. It can be concluded that the \(k\)-\(\omega\) turbulence model is capable of capturing the flow oscillations and vortex shedding at the downstream side of the pier. The \(k\)-\(\omega\) turbulence model results are more accurate than the \(k\)-\(\varepsilon\) and \(RNG\)\(k\)-\(\varepsilon\) turbulence models and are achievable with affordable computational resources compared to the required resourced for the \(LES\) and \(DES\) turbulence models. Accordingly, the \(k\)-\(\omega\) turbulence model was selected to simulate the turbulence effect on the flow pattern in the current study. Figure 6: Validation versus [30] experimental measurements at the plane of symmetry. Figure 8: \(K\)-\(\omega\) turbulence model resulted in longitudinal velocity oscillation at the pier’s downstream side for 10 s with 0.5 s interval. Figure 7: Validation versus [30] experimental measurements at the plane of symmetry. In all the seven \(CFD\) simulations that were performed (\(d_{S}\)/\(d_{Se}\) = 0, 30, 47, 69, 78, 92, and 100%) the following model parameters were assigned; (0.5 cm grid size in the three Cartesian directions, depth-averaged approach velocity \(U_{o}\) = 0.247 m/s at the velocity inlet boundary, water depth \(H_{o}\) = 30 cm at the domain's upstream and downstream sides, and the \(k\)-\(\omega\) turbulence model). The flow velocities, vorticities, and bed shear stress were obtained from each simulation.
## 3 Experimental Setup
### Experimental Details  
The experimental setup is designed to rigorously evaluate the proposed method across diverse datasets and tasks. The experiments were conducted on a high-performance computing environment equipped with NVIDIA A100 GPUs (NVIDIA, Santa Clara, CA, USA), using PyTorch 3.8 as the primary deep learning framework. The training process employed the Adam optimizer with a learning rate initialized at \(1\times 10^{-4}\) and scheduled decay using a cosine annealing schedule. A batch size of 64 was utilized for training, balancing computational efficiency and convergence stability. The number of epochs was set to 100, ensuring sufficient training iterations for model convergence. Data preprocessing steps included normalization to zero mean and unit variance, along with data augmentation techniques such as random cropping, flipping, and rotation to improve model generalization. The input data dimensions varied depending on the dataset, with all inputs resized to a consistent resolution to standardize processing. For material datasets, specific domain knowledge, such as structural encoding or composition features, was integrated into the preprocessing pipeline.The proposed model incorporates a multi-scale feature extraction backbone, leveraging self-attention mechanisms to capture both local and global information. The training loss combined categorical cross-entropy and regularization terms tailored to the dataset's properties, such as structural consistency loss for material datasets. Hyperparameter tuning was performed using grid search over critical parameters, including learning rates and regularization coefficients. Evaluation metrics included accuracy, precision, recall, F1-score, and mean squared error (MSE) where applicable. For datasets with imbalanced classes, weighted metrics were calculated to ensure fair comparisons. Model performance was compared against state-of-the-art benchmarks using identical train-test splits, and statistical significance testing was employed to validate the results. Visualizations of attention maps and feature distributions were included to provide qualitative insights into the model's performance and decision-making processes. All experiments were repeated five times with different random seeds to ensure robustness and reproducibility of results (as shown in Algorithm 1).
## 4 Experiment
### RNN vs. BO Experiment
In the Medium Load condition, the BO method resulted in an average entry time of 211 s with a maximum of 559 s, and vibration amounts averaging 11.4 mm with a maximum of 16.5 mm. The RNN method, on the other hand, produced an average Entry Time of 332 s with a maximum of 1033 s, and vibration amounts averaging 12.0 mm with a maximum of 17.9 mm. The T-Test results showed statistically significant differences in entry time (T-Statistic: 3.040, \(p\)-value: 0.0029), while the difference in vibration was not significant (T-Statistic: 0.907, \(p\)-value: 0.3673). Figure 12: Max vibration verification results (RNN vs. BO). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Load**} & **Output** & **T****T****T****T** & **Average** & **BNN** & **T****T****T** & **B0** & **T****T****T** & **B0** & **T****T****T** & **B-Test** & **(B-Test)** \\  & **Parameter** & **Train** & **AvAvgnog** & **Max** & **St.Dev.** & **Z-Value** & **Total** & **Avgnog** & **Max** & **St.Dev.** & **Z-Value** & **Total** & **T-Statistic** & **p-Value** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Light \\ Load \\ \end{tabular} } & Energy Time & 12.4 & 501 & 105 & 7 & 8 & & 77 & 245 & 60 & 14 & 0 & 6.488 & 4.1 \(\times\) 10\({}^{-9}\) \\  & \begin{tabular}{c} Vibration \\ [mm] \\ \end{tabular} & 100 & 10.8 & 127.7 & 3.8 & 1.9 & 3 & 32 & 6.7 & 9.1 & 1.3 & 8.7 & 0 & 9.191 & 8.3 \(\times\) 10\({}^{-16}\) \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Medium \\ Load \\ \end{tabular} } & Energy Time & 332 & 1033 & 201 & 3 & 0 & 211 & 589 & 182 & 5 & 0 & 3.040 & 0.0029 \\  &
\begin{tabular}{c} Vibration \\ [mm] \\ \end{tabular} & 65 & 32.0 & 1033 & 201 & 3 & 0 & 31 & 11.4 & 16.5 & 2.5 & 2.6 & 0 & 0.007 & 0.3673 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Verification test results of the entry time and Max Vibration (RNN vs. BO). These test results confirm that the BO showed a more efficient and stable performance compared to the RNN in terms of the entry time and vibration amount.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 2 Principles
### Algorithms of Temporal Phase Unwrapping  
For the two different frequencies phase map, the wrapped phase maps are \(\phi_{l}\) and \(\phi_{h}\), the corresponded unwrapping phase maps are \(\Phi_{l}\) and \(\Phi_{h}\), and the corresponding frequencies are \(f_{l}\) and \(f_{h}\), respectively. The fringe period \(T\) express as frequency f is \(T=1/f\). The subscripts \(l\) and \(h\) represent low-frequency and high-frequency, respectively. Obviously, the relationship between the unwrapped phase and the wrapped phase can be written as  
\[\left\{\begin{array}{l}\Phi_{l}(x,y)=\phi_{l}(x,y)+2\pi k_{l}(x,y)\\ \Phi_{h}(x,y)=\phi_{h}(x,y)+2\pi k_{h}(x,y)\end{array}\right., \tag{4}\]  
where the \(k_{l}\) and \(k_{h}\) are the integer fringe orders, respectively. It is easy to prove that the high-frequency phase order \(k_{h}\) can be calculated by the low-frequency phase order \(k_{l}\) as the following relationship  
\[k_{h}(x,y)=NINT\left[\frac{(f_{h}/f_{l})\phi_{l}(x,y)-\phi_{h}(x,y)}{2\pi} \right], \tag{5}\]  
where \(NINT\) is the calculation of the closest integer value. Once one fringe pattern is projected and covers the high-frequency fringe, the unwrapping phase of low-frequency \(\Phi_{l}\) must be required as \(\Phi_{l}=\phi_{l}\). This means the unwrapping phase of a high-frequency can be determined from Equation (4) and Equation (5) as illustrated in Figure 2. This approach is also called multi-frequency temporal phase unwrapping.  
Figure 2: The illustration of high-frequency fringe order determination based on one fringe pattern in multi-frequency temporal phase unwrapping.  
The multi-wavelength temporal phase unwrapping approach means the use of fringe projection with different wavelengths. Since the reference phase is also calculated by the two different wrapped phase maps as  
\[\phi_{eq}(x,y)=\phi_{h}(x,y)-\phi_{l}(x,y), \tag{6}\]  
The equivalent fringe period \(T_{eq}\) can be described as  
\[T_{eq}=\frac{T_{l}T_{h}}{T_{l}-T_{h}}, \tag{7}\]  
Very obviously, the high-frequency fringe order of \(k_{h}\) can be referred in Equation (5) as  
\[k_{h}(x,y)=NINT\bigg{[}\frac{(f_{h}/f_{eq})\phi_{eq}(x,y)-\phi_{h}(x,y)}{2\pi} \bigg{]}, \tag{8}\]  
If the frequency difference between the high and low frequencies is one, the equivalent phase \(\phi_{eq}\) must be one fringe period as described in multi-frequency temporal phase unwrapping \(\Phi_{eq}=\phi_{eq}\); see Figure 3. The unwrapping phase of high-frequency can be determined from Equations (6) and (8). The phase unwrapping process is shown in Figure 2. This approach is also called multi-wavelength temporal phase unwrapping.  
The reference wrapped phase with one fringe period is calculated by two different phase patterns with one frequency difference; see Equation (6). Then the high-frequency wrapped phase is unwrapped by the reference phase, as shown in Figure 2.