esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Countries such as Korea adopt design codes, evaluation criteria and specifications from standards originating abroad; this leads to a lack of distinction of the separate applications of dynamic stability evaluation parameters between various track structures of different track moduli. This paper discusses the applicability of the dynamic stability evaluation method of railway track structures by assessing 10 different types of railway track sections of a newly constructed railway operation line (5 ballasted and 5 concrete type track structures) by field instrumentation testing. Parameters of track support stiffness (TSS), wheel load fluctuation, derailment coefficient, and rail displacement are measured. The respective results are first compared to the standard criteria (design specification) and comparisons between the different track types are presented as ratios. Findings show that while all of the tracks satisfy the design specification requirements, each track type measurement result varies by a noticeable degree, particularly when comparing between concrete and ballast type track structures. Results of the study demonstrate that using the same dynamic stability evaluation criteria can lead to an incorrect assessment of the track performance evaluation of track structure, and a separate evaluation parameter for ballasted and concrete track structures is required.  
dynamic amplification factor; total spring constant of track; field measurement; railway system management; railway maintenance; different track structures +
Footnote †: journal: Applied Sciences
# From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning
## 1 Introduction
### Deep-Learning Approaches  
Deep-learning approaches currently dominate the state-of-the-art of document image binarization. Convolutional neural networks are the most popular implementation, followed by vision transformers, but both produce competitive results.  
SAE [10] is an auto-encoder that harnesses a convolutional neural network to encode the original image into a probability map. A global threshold is then applied to the probability map to generate the binarized image. MRAM [11] uses a convolutional random field to generate the binarized image from a probability map produced by a multi-resolution attention model.  
DeepOtsu [12] applies [PERSON]'s thresholding algorithm on an enhanced image generated by a convolutional neural network that iteratively removes noise from the original image. 2 DMN [13] is a bidimensional morphological network that removes noise from the original image using simple operations such as dilation and erosion.  
DD-GAN [14] is a dual discriminator generative adversarial network comprising two networks, one to process the image globally and one locally. A threshold is applied to the generated image to obtain the binarized image. [PERSON] [15] uses six generative adversarial networks: four colour-independent ones that extract information from the input image and two that transform the output images of the first four into the final binarized image.  
cGANs [16] use conditional generative adversarial networks to combine multi-scale information. First, text pixels are extracted from the original image at various scales, and then, the multi-scale binarized images are combined to obtain the final binarized image. DE-GAN [17] also uses conditional generative adversarial networks.  
D2 BFormer [18] uses a vision transformer for semantic segmentation, transforming the original image into a two-channel feature map, one channel for the foreground and the other for the background. They then use the feature map to binarize the original image.  
DocDiff [19] consists of a coarse predictor, which recovers primary low-frequency information, and of a high-frequency residual refinement module, which predicts high-frequency information using diffusion models.  
DocEntr [20] and DocBinFormer [21] use vision transformers in an encoder-decoder architecture. The encoder transforms pixel patches into latent representations without using any convolutional layers, and the decoder generates the binarized image from the latent representations.  
GDB [22] uses gated convolutions that, unlike regular convolutions, precisely extract stroke edges. First, a coarse sub-network generates a precise feature map, and then, a refinement sub-network uses gated convolutions to further refine the feature map. GDBMO is a version of GDB that combines local and global features to further improve the results.
## 3 Results and Discussion
### Training Hyperparameters, Evaluation Settings, and Data Preprocessing  
Table 4 presents the main training hyperparameters, such as batch size, learning rate, and number of epochs, as well as the configuration for the triplet loss function. Additionally, it specifies how the model was evaluated using metrics like Top-K accuracy and how data were processed or split (e.g., validation set ratio, chunk length, and maximum segments per document).  
The Training Hyperparameters section defines key elements such as the batch size (_Batch Size_), number of epochs (_Number of Epochs_), and the learning rate (_Learning Rate_). It also includes the triplet loss margin (_Triplet Loss Margin_) and a scaling factor for a custom variant of this loss (_Gradual Triplet Scaler_). Under Evaluation and Logging, we list the K values for computing Top-K accuracy, the sample size for t-SNE visualization, and file naming conventions for saving both the main and the best model states. Finally, the Data Handling and Preprocessing rows indicate how the validation split was configured (_Validation Set Ratio_) and specify text chunk parameters (_Embedding Chunk Lengths_ and _Max Segments per Document_) for embedding generation. All these parameters collectively ensure that the training, evaluation, and preprocessing steps are clearly documented and reproducible.
## 3 Results and Discussion
### In Vitro and In Vivo Tyrosine Activity Inhibition  
Figure 6 shows that the antityrosinase activities in mushroom and HEMn cells increase with an increase in AuNPs concentration. Regarding the in vitro antityrosinase activity against mushroom tyrosinase, the IC\({}_{50}\) values of BLAuNPs, ECAuNPs, and Chemicals-5 nm were 14.2, 10.2, and 24.1 mg/L, respectively (Figure 6a). Moreover, the antityrosinase activity of biogenic AuNPs was superior to that of Chemicals-5 nm. It was suggested that some bioactive compounds produced by bacteria are coated on the AuNPs surface without capping agents, thereby enhancing the tyrosinase inhibition activity [38]. Furthermore, our results were more favorable than those for the control arbutin (IC\({}_{50}\): 18.2 mg/L) and AuNPs synthesized by _Panax ginseng_ leaves (IC\({}_{50}\): 16.06 mg/L) [38], but inferior to that for _P. ginseng_ berries (IC\({}_{50}\): 6.6 mg/L) [5].  
Figure 5: Cell viability of (**a**) HEMn cells and (**b**) CCD966 SK cells treated by BLAuNPs, ECAuNPs, and Chemicals-5 nm at various concentrations after 72 h treatments.  
The results of antityrosinase activity in human cells better reflected the actual effect compared with the results of the mushroom tyrosinase inhibition. The IC\({}_{50}\) values of BLAuNPs and ECAuNPs for inhibition of tyrosinase activity in HEMn cells were 15.4 and 11.8 mg/L, respectively (Figure 6b). The tyrosinase inhibition in human cells (in vivo) was slightly weaker than the mushroom tyrosinase inhibition (in vitro). This finding might be attributed to the difference in the chemical structure of tyrosinase between mushrooms and human cells [39].  
The inhibition of melanin production in HEMn cells decreased in a dose-dependent manner (Figure 7). Melanin was completely inhibited by ECAuNPs and BLAuNPs at 26 and 30 mg/L, respectively. The trend of melanin content inhibition shown in Figure 7 was similar to that of tyrosinase inhibition in shown in Figure 6b. As ECAuNPs and BLAuNPs were noncytotoxic at such concentrations (Figure 5), the inhibition of melanogenesis may be attributed to the inhibition of tyrosinase or related enzymes, rather than melanocyte death. The underlying mechanism must be confirmed through Western blotting. ECAuNPs and BLAuNPs at a concentration of 10 mg/L reduced the melanin contents in HEMn to 46.5% and 33.5%, respectively (Figure 7)--higher than the inhibitions observed when HEMn were treated with the same concentration of _P. ginseng_ leaf-mediated AuNPs (25%) and _P. ginseng_ berry-mediated AuNPs (16%) [5, 38]. These results suggested that biogenic AuNPs synthesized in this study have the potential to be used in skin whitening agents.  
Figure 6: Effects of AuNP concentrations on antityrosinase activities in (**a**) mushroom tyrosinase and (**b**) HEMn cells.  
Figure 7: Effects of AuNP concentrations on the melanin content of HEMn cells. Cells were treated with different concentrations of BLAuNPs and ECAuNPs for 24 h.
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
## 3 Results and Discussion
### Influence of the Trap-Door Width  
To investigate the influence of trap-door width, the maximal earth pressure acting on the model box wall at 30 cm height was selected for analysis, which was marked as \(p_{\text{max}}\). To be conveniently compared, the static earth pressure at a 30 cm height was noted as \(p_{0}\). The range where the earth pressure change was influenced by the movement of trap-door is denoted as \(B\). The tendency of \(p_{\text{max}}\) and \(B\) varying with the trap-door width is indicated in Figure 16.  
Figure 16: \(p_{\text{max}}\) and \(B\) varying with trap-door width. (**a**) Relation between \(p_{\text{max}}/p_{0}\) and \(w/H\). (**b**) Relation between \(B/w\) and \(w/H\).  
Figure 15: Diagram of soil arching evolution.  
It can be observed from Figure 16a that the peak load acting on the fixed wall increased with the width of the trap-door expanding for T and RT mode. However, for RT mode, it increased when \(w/H\) was less than 0.43 and then descended gradually. The maximal value of \(p_{\text{max}}/p_{0}\) was about 1.37, emerging on T mode at \(w/H\) = 0.8. It is evident that the exertion degree of the soil arching effect was strongest in T mode, followed by RB mode, and was weakest in RT mode.  
The variation of \(B/w\) as a function of \(w/H\) is presented in Figure 17. It is clear from Figure 16b that \(B/w\) decreased with the increasing value of \(w/H\) for the three movement modes. For most cases, the \(B/w\) was from 3 to 5. From the analysis above, the \(p_{\text{max}}/p_{0}\) and \(B/w\) were mainly influenced by the width of the trap-door. Thereby, for the partial-displacement retaining wall, the increase in the extra load transferred from the moveable wall to the adjacent fixed wall would be controlled by the movement range and displacement amount.
## 2 Fundamental Theory
### Measure Index  
The SNR is often used as a measure of the SR model, however, in this paper the output SNR is different from the traditional output SNR [55]. The specific calculation of the output SNR is as follows:  
For a group of discrete signals \(x=\{x_{1},x_{2},\cdots x_{N}\}\) (\(N\) means the signal length), in the frequency domain, we can acquire \(X(k)\) by FFT and amplitude sequence \(Y(k)\) by the following expression:  
\[\begin{array}{l}X(k)=\sum\limits_{n=1}^{N}x(n)e^{-j\pi(k-1)(n-1)/N},1\leq k \leq N\\ Y(k)=2\frac{\sum\limits_{X(k)}}{N},1\leq\frac{N}{2}\end{array} \tag{4}\]  
Let the peak number of the spectrum at the signal frequency \(f_{0}\) be \(K_{0}\), which can be gained from the following formula:  
\[K_{0}=\frac{f_{0}\times N}{f_{\rm s}}+1 \tag{5}\]  
The sampling frequency of the signal is \(f_{\rm s}\), from which the output SNR can be expressed below:  
\[SNR=10\log\frac{Y(K_{0})}{\sum\limits_{K=1}^{N}Y(K)-Y(K_{0})} \tag{6}\]  
Figure 1: The potential function of \(U(x,y)\).
# Width Dependent Two-Photon Absorption in Monolayer Black Phosphorus Nanoribbons
###### Abstract  
Black phosphorus nanoribbons (BPNs) might offer alternatives to narrow-gap compound semiconductors for tunable optoelectronics in infrared region. In this work we present a quantum perturbation theory on two-photon absorption (TPA) in monolayer armchair-edged black phosphorus nanoribbons (acBPNs) employing the reduced two-band model within the long-wavelength BP Hamiltonian. The matrix elements for one-photon transition have been derived and the TPA spectrum associate with intra conduction band transition and inter band transition have been drawn. The calculations predict that the TPA coefficient in acBPNs is in the magnitude of \(10^{-6}\) m/W in visible region, which is 4 orders higher than the conventional semiconductor quantum dots. And in infrared region, there is a giant TPA coefficient, which is mainly contributed from intra band transitions and can reach up to\(10^{-1}\) m/W. The TPA peaks can be tuned both by the width of BPNs and the electron relaxation energy.  
two-photon absorption; black phosphorus; nonlinear optics Article
# Optimization and Experimental Verification of Lower
## 2 Optimization Model for Lower Articulation
### Design Variable of Lower Articulation  
#### 2.2.1 Structural Analysis  
The lower articulation must ensure structural stability, as it plays a crucial role in enabling the safe operation of the low-floor tram by withstanding forces from three axes in  
Figure 1: Gangway system.  
Figure 2: Diagram of lower articulation.  
the vertical, longitudinal, and lateral directions. In this study, ANSYS Workbench Release 18.1 [19] was used as the commercial software. The finite element analysis (FEA) was performed by modeling the lower articulation, as shown in Figure 3. The entire finite element model employed SOLID45, with the number of elements totaling 237,422 and the number of nodes totaling 414,593. The bolted connections between individual components were modeled using tie elements, while the areas expected to experience contact were modeled using surface contact elements. The material of the FRT Frame and RR Frame is SCM440, while the Bearing and Bush are made of STB2 and SM45C, respectively. The mechanical properties of these materials are listed in Table 1. For the boundary conditions, all directions on the end surface of the FRT Frame were constrained. The loading conditions were determined based on an analysis by the vehicle manufacturer, as well as relevant standards and specifications. A tensile load case of 600 kN and a compressive load case of 1000 kN were applied to the RR Frame. Figure 4 illustrates the loading and boundary conditions for the lower articulation.  
The finite element analysis (FEA) was performed using tensile and compressive loads applied to the lower articulation. Figure 5 shows the stress distribution obtained from  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Young’s Modulus** & **Poisson’s Ratio** & **Density** \\  & **[GPa]** & & **[kg/m3]** \\ \hline SCM440 & 205 & 0.29 & 7850 \\ STB2 & 210 & 0.30 & 7850 \\ SM45C & 205 & 0.29 & 7850 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties.  
Figure 4: Load and boundary conditions: (**a**) tensile case; (**b**) compression case.  
Figure 3: FEM model of lower articulation.  
the structural analysis. As indicated in the figure, the maximum stress was calculated to be 317 MPa under tensile load case and 529 MPa under the compressive load case, both of which are below the allowable stress limit of 980 MPa. Therefore, it can be concluded that the lower articulation is structurally safe. Furthermore, design variables were selected based on the results of the structural analysis.
## 2 Methodologies and Dataset
### Rf-Imf  
#### 2.3.1 Empirical Mode Decomposition  
[PERSON] first proposed EMD in 1998. Non-stationary and nonlinear signals are decomposed into a finite number of components using EMD. For the original signal, these components constitute a nearly orthogonal basis. The decomposed signals are called the intrinsic mode functions (IMFs) [24]. IMFs have characteristic time scales of oscillations defined by the local maxima and local minima of the data. IMFs are derived from the data themselves without using any functional form. IMFs are locally stationary, oscillating around zero. Additionally, each IMF has a characteristic oscillation period. In short, with EMD, the complexity of the data is reduced by separating trends and oscillations at different scales.  
EMD is a useful technique for breaking down the nonlinear and non-stationary time series into intrinsic mode functions and a monotonic function known as trend or residue. The signal may be reconstructed by combining the parts together because this decomposition is additive. EMD's components or IMFs can be utilized as input to any regressionor classification model. EMD and random forest regression were coupled in this work to forecast annual rainfall. An IMF has: (i) Only one extreme between zero crossings; (ii) Mean value of IMF is zero; (iii) The number of extremes are either equal or differs by a maximum of one. The processsfor obtaining IMFs from the time-series signal is as follows: Let \(y(t)\) be the input time-series signal 1. Initialization \[i=1Y(t)=y(t)\] (5) 2. Locate all local maximum and local minimum values of the input signal \(Y(t)\). Find the upper envelope \(y_{\max}(t)\) by connecting all the local maxima by a spline curve; do the same with all the local minima and find \(y_{\min}(t)\). 3. Calculate the local mean \[m_{i-1}(t)=\frac{(y_{\min}(t)+y_{\max}(t))}{2}\] (6) 4. Find the difference between the input signal and the mean \[h_{i}(t)=Y(t)-m_{i-1}(t)\] (7) 5. If the stopping criteria are satisfied by \(h_{i}(t)\) then \[\text{IMF}_{i}=h_{i}(t)\text{ else }Y(t)=h_{i}(t)\] (8) and start again from step 1 6. \[Y(t)=Y(t)-\text{IMF}_{i},\ i=i+1\] (9) 7. The sifting process is stopped if the residual signal \(Y(t)\) becomes a monotonic or residue function (\(r(t)\)). The original signal can be written as the summation of all IMFs and residue function: \[Y(t)=\sum_{i=1}^{n}h_{i}(t)+r(t)\] (10) where \(n\) denotes the number of IMFs and \(r(t)\) is the trend signal. Figure 1 shows the flowchart for empirical mode decomposition.
# Polarization Sensitive Imaging with Qubits
## 2 Materials and Methods  
### Direct Quantum State Tomography (DQST) Overview  
Quantum state tomography is a technique that reconstructs density matrix \(\rho\) through multiple measurement of relevant quantum states. The measured probability \(p_{i}\) for projection operator \(A_{i}\) is given by  
\[p_{i}=Tr[\rho A_{i}], \tag{1}\]  
The direct state reconstruction of a density matrix from the coincidence rate of the entangled photons projected on to a set of sixteen polarization states follows  
\[\rho=\sum_{i=1}^{16}M_{i}p_{i} \tag{2}\]  
where the matrix \(M_{i}\) is determined from projection operators and listed in the appendix of [4]. The density matrix \(\rho\) of a quantum state must be Hermitian, positive, and semidefinite with unit trace. Directly reconstructed density matrix, Equation (2), fails to be positive and semidefinite. Numerical inverse optimization technique addresses this problem [4].
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 4 Experimental Results and Discussion
### Image Quality  
The peak-signal-to-noise-ratio (PSNR) is used to measure the visual quality of marked images. The PSNR definition for an 8-bit grayscale image is given in Equation (8) previously. In ours experiments, the mean-square-error (MSE) of marked image \(I^{\prime}\) with respect to the cover image \(I\) is given as  
\[\text{MSE}=\frac{1}{H\times W}\sum\
olimits_{i,j}\bigl{(}I^{\prime}{}_{ij}-I_{ ij}\bigr{)}^{2}. \tag{10}\]  
The PSNR values for the four test images are listed in Table 2. We can see that the PSNR decreases with the increase in number of users. The major reason is that hiding a large number of participants' respective secret messages by CDMA multiplexing leads to greater distortion in the marked images. That is, a higher multiplexing level yields a lower PSNR value. This phenomenon indicates that there is a tradeoff between imperceptibility and multiplexity.  
We also observe that the results closely coincide with the expected PSNR values in Table 1, which means that the PSNR performance is independent of the characteristics of the original cover image. Take Lena image for example. Figure 5 shows the original Lena image and the marked images with different values of \(N\). As shown in the figures, with the number of users \(N\) less than 16, the PSNR is kept above 34 dB and the visual quality of the marked image is quite good. Even when \(N\) reaches 16, the visual distortion is minor and the PSNR is still higher than 28 dB.
## 3 Results and Discussion
### Results of Calorimetric and Thermogravimetric Tests
In the case of nitrogen oxides, the emission exceeded 120 mg\(\cdot\)m\({}^{-3}\) for two variants (P2 and P6). The smallest amount of nitrogen oxides was recorded in the case of combustion of pellets made of tobacco stalks (P3) (Table 5). In the case of waste from agricultural production, the emission of nitrogen oxides at the level of about 50 mg\(\cdot\)m\({}^{-3}\), nitrogen dioxide at the level of 1.5%, and carbon monoxide at about 300 mg\(\cdot\)m\({}^{-3}\) was recorded [36]. During the incineration of waste in the form of sunflower hus, the emission of sulfur dioxide was recorded at 0.289 mg\(\cdot\)m\({}^{-3}\), nitrogen oxides at 122.65 mg\(\cdot\)m\({}^{-3}\) and carbon monoxide at 118.85 mg\(\cdot\)m\({}^{-3}\). In addition, the combustion of coniferous sawdust showed an increase in the emission of nitrogen oxides and a decrease in the emission of sulfur and carbon oxides compared to the emission during the combustion of agricultural waste biomass, such as sunflower husk or rapeseed straw [37]. Coniferous wood waste is characterized by a smaller amount of nitrogen oxides emitted into the atmosphere during combustion compared to deciduous wood waste. During the combustion of pellets from pine wood waste, 30% less nitrogen oxides were emitted into the atmosphere compared to the combustion of pellets from deciduous wood waste [38]. According to [PERSON] et al. [39] and [PERSON] [35] during the combustion of biomass, the nitrogen contained in it is almost completely converted into gaseous nitrogen (N\({}_{2}\)) and nitrogen oxides (NO\({}_{\mathrm{x}}\))-nitric oxide (NO), nitrous oxide (N\({}_{2}\)O) and nitrogen dioxide (NO\({}_{2}\)). Numerous studies [40; 41; 42] have shown that the formation of nitrogen oxides is highly differentiated and depends on the type of combustion boiler and the type of fuel. Nitrogen oxides can be produced by the reaction of nitrogen with reactive oxygen species at temperatures >1300 \({}^{\circ}\)C [43]. Due to the relatively low temperature in solid biofuel combustion installations (about 800-1200 \({}^{\circ}\)C), the thermal NOx formation is transient and of little importance. In addition, nitrogen oxides NOx are formed as a result of fuel oxidation. This is the most critical mechanism for the formation of nitrogen oxides during biomass combustion. Therefore, NOx emission increases with the nitrogen content in the fuel [39], which is important when burning nitrogen-containing biomass [39; 44]. In addition, the automatic fuel feeder, the geometry of the boiler chamber, and the type of combustion technology are the main variables affecting the formation of NOx [45]. [PERSON] et al. [46] report that reducing excess air can help reduce NOx emissions and increase carbon monoxide (CO) emissions. [PERSON] [35] also reports that pollutants such as NOx and particulate matter are caused by fuel components such as N, K, Cl, Ca, Na, Mg, P and S, therefore biomass heating devices have relatively high NOx emissions.