esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Framework
### Evaluation Measure Selection  
Several measures of distance/similarity between probability distributions can be applied to measure the distance/similarity between label distributions. We propose to use a set of six measures when comparing different LDL algorithms:  
* Chebyshev Distance: is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
* Clark Distance: the Clark distance also called coefficient of divergence is the squared root of half of the divergence distance.
* Canberra Metric: is a numerical measure of the distance between pairs of points in a vector space. It is a weighted version of Manhattan distance. The Canberra distance is a metric function often used for data scattered around an origin.
* Kullback-Leibler Divergence: which is closely related to relative entropy, information divergence, and information for discrimination, is a non-symmetric measure of the difference between two probability distributions \(p(x)\) and \(q(x)\). Specifically, the Kullback-Leibler divergence of \(q(x)\) from \(p(x)\) is a measure of the information lost when \(q(x)\) is used to approximate \(p(x)\).
* Cosine coefficient: is a metric used to measure how similar two non-zero vectors are irrespective of their size. It measures the cosine of the angle between two vectors projected in a multidimensional space. The smaller the angle, higher the cosine similarity.
* Intersection similarity: has its largest value, 1, when all the terms of the first probability distribution are identical to the corresponding terms of the second probability distribution. Otherwise, the similarity is less than 1. In the extreme case, when both distributions are very different, then the similarity will be close to 0.  
Each of these measures come from a different syntactic family: [PERSON] family, the \(X^{2}\) family, the \(L_{1}\) family, the [PERSON]'s entropy family, the inner product family, and the intersection family, respectively [68]. Taking into account that they come from different families and are significantly different in both syntax and semantics, they have a good chance to reflect different aspects of an LDL algorithm. Supposing that the real label distribution is \(D=\{d_{1},d_{2},...,d_{c}\}\), and the predicted label distribution is \(\hat{D}=\{\hat{d}_{1},\hat{d}_{2},...,\hat{d}_{c}\}\), then the formulation of the six measures is summarized in Table 2.
## 2 Research Method
### Method 2 of 4, [PERSON] et al.'s Compiled Functions: Linear  
The second method used herein to estimate the temperature-dependent electron-neutral collision cross sections is based on a published study [41] regarding the theoretical calculation of fourteen thermodynamic and electric properties of combustion gases and their plasma for a range of temperatures from 0 K to 4000 K. In that study, expressions or constant values for the electron-neutral collision cross sections were listed for 23 species. All these published functions were either a zeroth-degree polynomial (a constant value) or a first-degree polynomial (a linear function). Thus, this method for computing the approximate electron-neutral collision cross sections is designated here by the word \"Linear\" in the corresponding plots of results, because its complexity does not go beyond a linear function in the electron absolute temperature. For the six covered gaseous species in the present study, the corresponding published temperature-dependent functions are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline
**Index** & **Species** & **Mathematical Expression** \\ \hline
1 & CO & \(\overline{Q}=0.18933824\)\(\sqrt{T_{e}}\) \\ \hline
2 & CO\({}_{2}\) & \(\overline{Q}=3.8431220+\frac{24,068.535}{T_{e}}\) \\ \hline
3 & H\({}_{2}\) & \(\overline{Q}=8.2352614+0.12899968\)\(\sqrt{T_{e}}\) \\ \hline
4 & H\({}_{2}\)O & \(\overline{Q}=\frac{141.5796.62}{T_{e}}\) \\ \hline
5 & K & \(\overline{Q}=\frac{25.7544.48}{\sqrt{T_{e}}}\) \\ \hline
6 & O\({}_{2}\) & \(\overline{Q}=5.0326598\) \\ \hline \end{tabular}
\end{table}
Table 2: Derived expressions for the average electron–neutral collision cross section (\(\overline{Q}\), in Å\({}^{2}\)) in the first method as a direct function of the electron absolute temperature (\(T_{e}\), in K).  
\begin{table}
\begin{tabular}{c c c} \hline
**Index** & **Species** & **Mathematical Expression** \\ \hline
1 & CO & \(\overline{Q}=8.6+0.0009\)\((T_{e}-2000)\) \\ \hline
2 & CO\({}_{2}\) & \(\overline{Q}=30-0.01\)\((T_{e}-2000)\) \\ \hline
3 & H\({}_{2}\) & \(\overline{Q}=11+0.001\)\((T_{e}-2000)\) \\ \hline
4 & H\({}_{2}\)O & \(\overline{Q}=63-0.015\)\((T_{e}-2000)\) \\ \hline
5 & K & \(\overline{Q}=250\) \\ \hline
6 & O\({}_{2}\) & \(\overline{Q}=3.9\) \\ \hline \end{tabular}
\end{table}
Table 3: Expressions for the average electron–neutral collision cross section (\(\overline{Q}\), in Å\({}^{2}\)) in the second method as a function of the electron absolute temperature (\(T_{e}\), in K).
## 3 Materials and Methodology
### Construction of the Numerical Simulation Model  
To study the effect of solid backfilling on strata and ground subsidence beneath a city, a model measuring 500 m \(\times\) 265 m \(\times\) 150 m (length \(\times\) width \(\times\) height), with 23 layers of strata from top to bottom, was established in FLAC3D based on the actual geological conditions of the 5 th coal seam of the Tangshan coal mine, as shown in Figure 5. In the model, a Mohr-Coulomb model was adopted to analyze the rock mass; horizontal constraints were applied to its four sides; horizontal and vertical constraints were applied to its bottom boundary; and no constraint and load of 17 MPa was applied to the top boundary. The mechanical parameters of each layer are listed in Table 5. In the model, the 2.5 m thick 5 th coal seam was mined at a strike length of 300 m from the open-cutting face, a dip length of 65 m, and backfilled with a filling rate of 80%. A schematic diagram of the 3D numerical model is shown in Figure 5.  
Figure 4: Pressure gauges’ layout.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{\lambda}\)**} & \multirow{2}{*}{**Lithology**} & \multirow{2}{*}{**Density**} & **Bulk** & **Shear** & **Cohesion** & **Internal Friction** & **Tensile** \\  & & **/m\({}^{3}\)** & **Modulus/GPa** & **Modulus/GPa** & **/MPa** & **Angle/\({}^{c}\)** & **Strength/MPa** \\ \hline
1 & Fine-grained sandstone1 & 2.54 & 2.7 & 1.6 & 3.2 & 42 & 1.29 \\ \hline
2 & Mudstone1 & 2.55 & 8.8 & 4.3 & 2.0 & 8 & 2.0 \\ \hline
3 & Sandy mudstone1 & 2.60 & 5.1 & 3.3 & 1.0 & 28 & 2.0 \\ \hline
4 & Medium-grained sandstone1 & 2.56 & 4.2 & 2.9 & 5.0 & 34 & 1.3 \\ \hline
5 & Sandy mudstone2 & 2.60 & 5.1 & 3.3 & 1.0 & 28 & 2.0 \\ \hline
6 & Medium-grained sandstone2 & 2.56 & 4.2 & 2.9 & 5.0 & 34 & 1.3 \\ \hline
7 & Mudstone2 & 2.55 & 8.8 & 4.3 & 2.0 & 8 & 2.0 \\ \hline
8 & Medium-grained sandstone3 & 2.56 & 4.2 & 2.9 & 5.0 & 34 & 1.3 \\ \hline
9 & Sandy mudstone3 & 2.60 & 5.1 & 3.3 & 1.0 & 28 & 2.0 \\ \hline
10 & Fine-grained sandstone2 & 2.54 & 2.7 & 1.6 & 3.2 & 42 & 1.29 \\ \hline
11 & Sandy mudstone4 & 2.60 & 5.1 & 3.3 & 1.0 & 28 & 2.0 \\ \hline
12 & 4 th coal seam & 1.41 & 1.73 & 0.82 & 0.18 & 20 & 0.2 \\ \hline
13 & Fine-grained sandstone3 & 2.54 & 2.7 & 1.6 & 3.2 & 42 & 1.29 \\ \hline
14 & Sandy mudstone5 & 2.60 & 5.1 & 3.3 & 1.0 & 28 & 2.0 \\ \hline
15 & Fine-grained sandstone4 & 2.54 & 2.7 & 1.6 & 3.2 & 42 & 1.29 \\ \hline
16 & Mudstone3 & 2.55 & 8.8 & 4.3 & 2.0 & 8 & 2.0 \\ \hline
17 & Fine-grained sandstone5 & 2.54 & 2.7 & 1.6 & 3.2 & 42 & 1.29 \\ \hline
18 & Sandstone1 & 2.52 & 23.3 & 13.2 & 8.3 & 32 & 3.1 \\ \hline
19 & Mudstone4 & 2.55 & 8.8 & 4.3 & 2.0 & 8 & 2.0 \\ \hline
20 & 5 th coal & 1.41 & 1.73 & 0.82 & 0.18 & 20 & 0.2 \\ \hline
21 & Mudstone5 & 2.55 & 8.8 & 4.3 & 2.0 & 8 & 2.0 \\ \hline
22 & Fine-grained sandstone6 & 2.54 & 2.7 & 1.6 & 3.2 & 42 & 1.29 \\ \hline
23 & Mudstone6 & 2.55 & 8.8 & 4.3 & 2.0 & 8 & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mechanical parameters of each layer in the model.  
Figure 5: Schematic diagram of the 3D numerical model.
# Time-Division Color Holographic Projection in Large Size Using a Digital Micromirror Device +
## 2 Methods
### Proposed Method: Time-Division Reconstruction Method  
In a holographic projection [7], the reconstructed image is enlarged by setting the pixel pitch of the original image larger than that of the CGH. Thus, we obtain a large reconstructed image without a zoom lens. However, with increased pixel pitch and a fixed number of pixels, the image quality deteriorates.  
A time-division reconstruction method that divides the original image into blocks after up-sampling without changing the pixel pitch is proposed, and projects the CGHs of the divided blocks by switching them rapidly. As the pixel pitch is not manipulated, projecting a large-screen reconstruction image with higher quality than the method for changing the pixel pitch is possible [7].  
Figure 3 shows an example of calculating CGH when the original image is divided into four after adding a random phase. As the centers of each divided block have an offset from the center of the CGH, it is necessary to use off-axis diffraction to calculate the propagation from divided blocks of CGH. Several off-axis calculations have been proposed [19; 20], but this study used that proposed in [20]. Figure 4 shows the reconstruction process. High-speed DMD was used and, by switching the CGHs at high speed, we obtained the entire reconstructed image.  
Figure 5 shows our optical system. For color reconstruction, a time-division color reconstruction method was used [21]. RGB semiconductor lasers were used as light sources and switched the RGB lasers using the synchronizing signals outputted from the DMD. A 4f optical system and a spatial filter were placed between the DMD and the screen to eliminate direct light.  
Figure 3: Recording of CGH using time-division reconstruction method: (**a**) recording of divided original image 1, (**b**) recording of divided original image 2, (**c**) recording of divided original image 3, and (**d**) recording of divided original image 4.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 3 Mesoscale Modeling of Concrete by FEM
### Validation by Expansion-Compression Analysis  
Next, a uniaxial compression analysis was performed on the analytical model described in Section 3.2.1 with ASR degradation, i.e., aggregate expansion, to examine the validity of the model in reproducing changes in the mechanical properties associated with ASR degradation. In the experiments described in Section 2, two series were considered in the case of expansion and degradation, with alkali contents of 4.0 kg/m\({}^{3}\) and 5.5 kg/m\({}^{3}\), respectively, and approximately 2000 \(\upmu\) and 3000 \(\upmu\) of surface expansion were measured as a result of ASR expansion, respectively. Therefore, in this analysis, two cases of alkali content of 4.0 kg/m\({}^{3}\) and 5.5 kg/m\({}^{3}\) were considered, and the aggregate expansion was given such that the surface expansion was measured in these cases. The results of the uniaxial compression tests on the ASR-degraded cylindrical specimens are shown in Figure 16. In addition, Figure 17 shows surface cracks before loading.  
Figure 16 shows that the trend of the change in compressive properties is generally consistent between the experimental and analytical results. The stress-strain data under ASR expansion and deterioration showed decreased initial stiffness and maximum load due to expansion cracks. The stress-strain data for the ASR expansion-deteriorated condition showed decreased initial stiffness and maximum load due to expansion cracking. The results of the reduction in the static modulus of elasticity of concrete subjected to alkali-silica reaction are consistent with previous studies [36]. The stress-strain curves of the concrete were found to be more convex than those of the healthy case. This is considered to be due to the fact that a cavity is generated inside the specimen as a result of cracking. When the cavity closes due to loading, a reaction force is less likely to be generated. The analytical results were more convex downward than the experimental results, but this may be due to the condition of the loading surface, such as the effect of the unevenness of the contact surface and the specimen due to expansion and deterioration.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 2 Received Signal and Proposed HCTC Model
### Proposed HCTC Model  
The design of the proposed HCTC model is shown in Figure 1. The model uses input signals in the form of in-phase (I) and quadrature (Q) signals along with the individual components of the I and Q signals. The proposed model comprised three stages: (A) a convolutional layer for the extraction of spatial features, (B) a transformer encoder layer for the extraction of temporal features, and (C) a deep neural layer for feature mapping.
# Method for Constructing a Facade Dataset through Deep Learning-Based Automatic Image Labeling
## 6 Discussion
### Limitations  
Several limitations are acknowledged in this study. First, there was a limitation that made automatic labeling difficult, in the case of high noise such as street trees and streetlights. To this end, we are studying ways to increase the facade information accuracy calculation using NVDIA Image Inpainting [24], a technology that automatically removes noise and by securing high-quality readings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Facade Information** & **CMP Test Data** & **Distortion Correction Data** \\ \hline Dominant Color & 0.92 & 0.83 \\ Secondary Color & 0.73 & 0.80 \\ The Number of Floors & 0.89 & 0.93 \\ The Number of Buildings & 0.81 & 0.90 \\ Average & 0.84 & 0.87 \\ \hline \hline \end{tabular}  
* The accuracy of the secondary color calculation was high because the ratio of the area of the window class, which has a relatively high mIoU, is high and the color of the window is often calculated as a secondary color because it is included in the facade.
* Compared to the CMP database, the shape of the window is clear and designed for each floor; therefore, the number of floors calculation accuracy of the building is high.
* The boundaries between buildings were ambiguous in the CMP database, but the test data were clear. Therefore, despite the higher mIoU of the wall and background classes in the CMP database, the number of buildings calculation accuracy of the buildings in the test data was higher.  
\end{table}
Table 16: Facade information accuracy of CMP test and distortion correction data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data** & **Pixel Accuracy (Average)** \\ \hline CMP Test Data & 0.77 \\ Distortion Correction Data & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Pixel accuracy of CMP test and distortion correction data.  
Secondly, the SegNet model exhibited low quality learning due to only using 600 images of the CMP database as training data. To this end, we will build large-scale databases by increasing the accuracy of labeling to a certain level from the road-view images.  
These issues in part provide a general perspective to the limitations of the deep learning approach. The approach requires careful data collection and preparation.
## 4 Lignin Biodegradation and Ligninolytic Enzymes
### Lignin Peroxidase (LiP)  
L Lignin peroxidase (LiP: EC 1.11.1.14) was the first reported lignin-degrading oxidase. This oxidase is an extracellular heme glycoprotein with approximately 35-50 kDa and pI of 3.0-4.0 belonging to the heme oxidase II family [97,98]. Since LiP activity was identified in a white-rot fungus P. chryssopporum, most lactose-produced mushroom species, including C. cinerea, L. edodes, P. eryngii, P. ostreatus, P. sajor-caju, and T. versolcor, can produce LiP and/or other oxidase enzymes together [43,98]. The number of LiP coding genes for isoenzymes, their expression level, and their productivity depend on the fungal growth condition, inducers, growth stage, medium composition, etc. These LiPs play an active role in lignin depolymerization, similar to other extracellular oxidases including lactose, manganese peroxididases (MnP), and versatile peroxididases (VPs) [99,100,101,102,103]. In lignin depolymerization, LiPs attack C\({}_{\alpha}\)-C\({}_{\beta}\) linkages in phenylpropanol units and \(\beta\)-O-4 ether linkages between the side chain and the next subunit to open aromatic rings [17]. Since the mechanism of lignin degradation by these extracellular class II peroxidase has been determined by using protein structure models with biochemical characterizations [17,98], the basic reaction is identified as a heme oxidase involving H\({}_{2}\)O\({}_{2}\). In the depolymerization of lignin, this oxido-reduction reaction is initiated when H\({}_{2}\)O\({}_{2}\) accesses the heme prosthetic group via the heme channel, acts as an electron acceptor, and then reduces to water (Figure 2a). Interestingly, LiP has a higher redox potential (E\({}_{0^{\prime}}\approx\) 1.2 V versus Standard hydrogen electrode, SHE) than other oxidases and therefore can oxidize both phenolic and non-phenolic compounds in the absence of mediators [17,104]. This advantage of LiPs displayed the broad substrate specificities for a variety of applications to degrade recalcitrant petroleum-based chemicals [105,106,107].
## 6 Calculation Method of Tunnel Face Support Pressure Based on Seepage Control
### Calculation Method  
In existing research, the tunnel face water pressure is often set to zero, which is unreasonable for shield tunnel construction that requires seepage control [21; 22; 23; 24; 25]. To achieve seepage control, it is necessary to first calculate a reasonable tunnel face water pressure coefficient \(\eta\). This coefficient is then used in seepage and limit analysis calculations to determine appropriate tunnel face support pressures, as depicted in Figure 16.  
Figure 16: Flowchart for calculating the ultimate support pressure of tunnel face based on seepage control.  
Figure 15: Relationship between \(\eta\) and \(Q\) under different \(k\).  
To demonstrate the impact of seepage control on \(\sigma_{t}\), the calculation method proposed in this paper is utilized. Examples are computed for unit area seepage rates of 1.0, 1.5, and 2.0 m\({}^{3}\)/h/m\({}^{2}\) at the tunnel face. Calculation parameters include \(c\) = 0 kPa, \(\varphi\) = 25\({}^{\circ}\), \(D\) = 10 m, \(\gamma_{sat}\) = 20 kN/m\({}^{3}\), and \(k\) = 5 \(\times\) 10\({}^{-5}\) m/s. After conversion, \(Q\) is obtained, and normalized tunnel face support pressures \(\sigma_{t}/\gamma_{sat}D\) are calculated for various ratios of water level to tunnel diameter (\(h_{w}/D\)), as shown in Figure 17.  
Observations indicate that when \(h_{w}/D\) is small and the seepage control requirement is low, even with \(\eta\) = 0, the requirements can be met. In such cases, \(\sigma_{t}/\gamma_{sat}D\) increases slowly with \(h_{w}/D\). However, as \(\eta\) increases, \(\sigma_{t}/\gamma_{sat}D\) increases rapidly with \(h_{w}/D\). Neglecting seepage control by continuing to assume \(\eta\) = 0 in the calculation of \(\sigma_{t}/\gamma_{sat}D\) can lead to exceeding seepage limits and even accidents.
## 3 Discussion on the Existing Shear Capacity Formula
### Existing Shear Capacity Formula  
(1) [PERSON]'s formula [22]  
Figure 7: Tension damage distribution diagram and crack development process; (**a**) load to point A; (**b**) load to point B; (**c**) load to point C.  
Based on experimental and theoretical research, [PERSON] from the Massachusetts Institute of Technology proposed a shear resistance formula of the keyed dry joints, which is as follows:  
\[V_{u}=A_{j}\Big{(}0.647\sqrt{f_{c}^{\prime}}+1.36\sigma_{n}\Big{)} \tag{5}\]  
where, \(\mathbf{f_{c}}\) is the compressive strength of the concrete cylinder, \(\sigma_{n}\) is the lateral prestress on the joint, and \(A_{j}\) is the total area of the joint interface.  
(2) AASHTO (2003) provisions [15]  
The formula adopted by AASHTO [15] divides the shear capacity into two parts, one is the resistance provided by the friction force on the smooth surfaces of the key, the other is the resistance provided by the concrete key roots. The expression is as follows:  
\[V_{u}=A_{k}\sqrt{f_{c}^{\prime}}(0.205\sigma_{n}+0.996)+0.6A_{sm}\sigma_{n} \tag{6}\]  
where, \(\mathbf{A_{k}}\) is the area of the key root, \(\mathbf{A_{sm}}\) is the smooth area of the key interface.  
(3) ATEP provisions [25]  
In ATEP provisions, the shear capacity of the keyed joint is proportional to the area of the entire joint interface, which is expressed as follows:  
\[V_{u}=A_{j}(1.14\sigma_{n}+0.0564f_{cd}) \tag{7}\]  
where, \(\mathbf{f_{cd}}\) is the design value of concrete compressive strength.  
(4) [PERSON]'s formula [23]  
With a safety factor introduced, the shear capacity formula of the keyed dry joint proposed by [PERSON] is expressed as:  
\[V_{u}=\Big{(}\mu\sigma_{n}A_{j}+0.14f_{ck}A_{k}\Big{)}/\gamma_{F} \tag{8}\]  
where, \(\mu\) is the friction coefficient, take as 0.65; \(\gamma_{F}\) is the safety factor, take as 2.0; \(\mathbf{f_{ck}}\) is the standard value of concrete compressive strength.  
(5) [PERSON]'s formula [17]  
Based on the experimental results of lots of groups of the test specimen, [PERSON] did some revision on the empirical formula of concrete tensile strength in AASHTO. Consequently, a new formula about the shear capacity of the keyed dry joints was proposed as follows:  
\[V_{u}=A_{k}f_{ck}^{2/3}(0.07\sigma_{n}+0.33)+0.6\sigma_{n}A_{sm},f_{ck}\leq 50 \tag{9}\]  
\[V_{u}=A_{k}ln(1+0.1f_{cm})(0.49\sigma_{n}+0.233)+0.6\sigma_{n}A_{sm},f_{ck}>50 \tag{10}\]  
where, \(\mathbf{f_{cm}}\) is the average value of concrete compressive strength.
## 2 Materials and Methods
### RNA Extraction and Quantitation  
The stimulated cells underwent 30 or 60 m/d of ES over 5 days; RNA was extracted from the cells immediately after the last period of ES on day 5. The RNeasy Mini Kit (Qiagen, Manchester, UK) was used as per the manufacturer's instructions for cells cultured in 2D. The quantity of RNA was determined using the Qubit RNA Broad Range Assay Kit and the Qubit fluorometer (ThermoFisher, Paisley, UK). The purity of the RNA was validatedby the Nanodrop One machine (ThermoFisher, Paisley, UK), as per the A260/280 ratio. The acceptable RNA purity value was taken to be \(\geq\)2.