esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application of the Design Method
### Design of the Superstructure  
The superstructure of the isolated building is designed as a fixed-base structure with a seismic intensity of 7.5 degree, along with a near field coefficient of 1.25. The corresponding PGA values of the SLE, DBE and MCE are also 68.75 cm/s\({}^{2}\), 187.5 cm/s\({}^{2}\) and 387.5 cm/s\({}^{2}\), respectively. The seismic design of the superstructure is conducted using the structural design software PKPM [39] under the SLE.  
The appropriate \(T_{\mathrm{f}}\) is calculated using Equation (1) with a value of 1.77 s. Hence, the superstructure is designed with a \(T_{\mathrm{f}}\) of 1.798 s and 1.697 s for the \(Y\)-direction and the \(X\)-direction, respectively,  
Figure 11: Three-dimensional view of the isolated RC frame-core tube building.  
Figure 12: Three-dimensional view of one typical floor.  
which can approximately satisfy the requirement. The section size and the concrete strength of the superstructure are listed in Table 4. The total gravity load of the entire structure is 617338 kN. The distribution of the inter-story drift ratio of the superstructure under response spectrum analysis is presented in Figure 13. The corresponding maximum inter-story drift ratios of the \(X\) and \(Y\) directions are 0.066% and 0.072%, respectively, which are both lower than the limit value (i.e., 0.125%) specified in the GB50011-2010 [17].  
It is worth mentioning here that the design of the seismically isolated building is mostly conducted using ETABS. Therefore, a reliable refined FE model should be built using ETABS. To validate the reliability of this model, typical analytical results obtained from ETABS are compared with those from PKPM. After conducting the modal analysis and the response spectrum analysis under the SLE, the fundamental periods and distribution of the inter-story drift ratios predicted by ETABS and PKPM are found to agree well with each other, with only 3% of difference. This value can be considered acceptable and therefore, validates the reliability of the refined FE model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Story** & **Concrete** & **Section/mm** \\ \hline \multirow{6}{*}{Column} & 1–8 & C50 & 1200 \(\times\) 1200 \\  & 9–10 & C50 & 1000 \(\times\) 1000 \\  & 11–15 & C40 & 1000 \(\times\) 1000 \\  & 16–18 & C40 & 800 \(\times\) 800 \\  & 19–27 & C30 & 800 \(\times\) 800 \\ \hline \multirow{2}{*}{Frame beam} & 1–15 & C30 & 600 \(\times\) 900 \\  & 16–27 & C30 & 500 \(\times\) 900 \\ \hline \multirow{2}{*}{S Shear wall} & 1–27 & C30 & 350 \(\times\) 750 \\ \cline{2-4}  & 1–10 & C50 & 400 & 300 \\  & 11–15 & C40 & 400 & 200 \\  & 16–18 & C40 & 300 & 200 \\  & 19–27 & C30 & 300 & 200 \\ \hline \multirow{2}{*}{Height of coupling beam} & 1–3 & \multirow{2}{*}{Same as shear wall} & External & Internal \\  & 4–27 & & 1000 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Concrete strength and section size of components.  
Figure 13: Distribution of inter-story drift ratio of superstructure.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 4 Result and Discussion
### Comparison between Auto-Grounding Method and the Data Mining-Decision Tree Algorithm Method  
Based on the two simulations, the two islanding detection methods are compared in accordance with their respective accuracies in Figure 7. While the auto-grounding method is more accurate in detecting islanding conditions with 95.2% accuracy compared to 94.4% accuracy of the data mining-decision tree algorithm method, the data mining-decision tree algorithm method is vastly more accurate in predicting the non-islanding conditions with the accuracy of 94.7% compared to the accuracy of auto-grounding method being only 90.4%. Taking both islanding and non-islanding conditions, the auto-grounding method is only 92.8% accurate while the data mining-decision tree algorithm method is 94.5% accurate. Auto-grounding takes a long time to detect the islanding condition (1.705 s), while the data mining-decision tree method is quicker to detect the islanding condition (0.162309 s).
# Modulation of Inflammatory Cytokine Production in Human Monocytes by cGMP and IRAK3
## 4 Materials and Methods
### Transfection of Wild Type and Mutant IRAK3 Constructs to IRAK3 Knockdown Cells  
The IRAK3 wild type clone and IRAK3 mutant R372L clone were generated as described [14]. The IRAK3 mutant W74A and R97A clones were generated by site-directed mutagenesis of the IRAK3 wild type construct using specific primers (0.5 \(\upmu\)M) (Supplemental Table S5) and Phusion High-Fidelity DNA Polymerase (New England BioLabs). The IRAK3 mutant clones were transformed to competent cells, and plasmids of these clones were purified as described [14]. All the constructs were sequenced (Micromon Monash University) and those with correct in frame insertions were kept and independently recombined into the Gateway expression vector pcDNA 6.2/C-EmGFP-DEST for mammalian cell expression (Thermo Scientific, Waltham, MD, USA) using the Gateway LR Clonase II Enzyme mix [14].  
IRAK3 gene knockdown THP-1 cells grown in 6-well plates were transiently transfected with IRAK3 wild type or R372L, R97A, W74A mutant constructs in pcDNA 6.2/C-EmGFP-DEST vector [7; 14], using the Lipofectamine 3000 Reagent (Thermo Fisher Scientific) following the manufacturer's protocol. After transfection, cells were treated with 1 \(\upmu\)g/mL LPS for 24 h, supernatants from cell cultures were collected and measured to detect the level of human cytokines (TNF-\(\upalpha\) and IL-6) present using ELISA as described above. Transfection efficiency was first checked using the Countess II Automated cell counter (Thermo Fisher Scientific), and then by measuring fluorescence intensity (excitation: 487 nm, emission: 510 nm) using the CLARIOstarPlus plate reader. The total protein concentration of the transfected cells was quantified by method of [61]. The quantified cytokine level of control (untransfected or transfected with empty vector) and IRAK3 wild type or mutant--transfected cells were normalised based on fluorescence intensity and total protein concentration of these cells.
## 3 Results
### Antifungal Activity and Minimal Inhibitory Concentration (MIC)  
The antifungal activity of vancomycin-functionalized gold nanoparticles (PEI-AuNP@Van) and non-functionalized gold nanoparticles (PEI-AuNPs) was assessed using the agar well diffusion method and by obtaining the minimum inhibitory concentration values of these materials. As shown in Figure 3a,b, the non-conjugated AuNPs and vancomycin did not show any zone of inhibition; however, the conjugated AuNPs demonstrated potent activity against _A. fumigatus_ (Figure 3a). In addition, the material exhibited fungistatic activity against _A. flavus_ (Figure 3b). The MIC values of PEI-AuNPs and PEI-AuNP@Van, along with voriconazole and vancomycin, against _A. fumigatus_ and _A. flavus_ were obtained using the broth microdilution method. The MIC values for PEI-AuNPs against _A. fumigatus_ were higher than those for PEI-AuNP@Van; the values for PEI-AuNPs and PEI-AuNP@Van were 37.5 and 4.68, respectively. Notably, voriconazole and vancomycin provided MIC values of 1.2 \(\upmu\)g/mL and no activity, respectively. In contrast, The MIC values of PEI-AuNPs, PEIAuNP@Van, voriconazole, and vancomycin against _A. flavus_ were 75.0, 18.75, 1.5 \(\upmu\)g/mL and no activity, respectively (Figure 3). The higher MIC value of the non-functionalized AuNPs indicated an unusual role for vancomycin in germination inhibition and antifungal activity; however, vancomycin without conjugation did not show any effect on fungal growth. Owing to the high MIC of PEI-AuNPs and the ineffectiveness of vancomycin, the remaining studies were performed with PEI-AuNP@Van.  
Figure 1: (**A**) Physical characterization of PEI-AuNP@Van nanoparticles. (**a**) UV-Vis spectrum, (**b**) TEM micrograph, (**c**) high resolution image of nanoparticles, (**d**) mean nanoparticle size, (**e**) XRD diffractogram, and (**f**) dynamic light scattering data. (**B**) Zeta potential distribution histograms for non-functionalized gold nanoparticles (PEI-AuNPs) and vancomycin-functionalized gold nanoparticles (PEI-AuNP@Van).  
Figure 2: Fluorescence spectroscopic confirmation of vancomycin functionalization of PEI-stabilized gold nanoparticles. Adopted and modified under CC BY (2023) [33].
# A Finite Regime Analysis of Information Set Decoding Algorithms
## 3 A Finite Regime Analysis of Information Set Decoding Techniques
### Lee-Brickell
We report, in Table 1, a summary of all the computational complexities of the examined Information Set Decoding (ISD) algorithms, together with the parameters which should be optimized to achieve the best running time for each one of them. ## 4 Quantitative Assessment of ISD Complexities

In this section, we analyze the computational complexities to solve the Syndrome Decoding Problem (SDP) and the Codeword Finding Problem (CFP) for sets of code parameters relevant to post quantum cryptosystems. To this end, we select the proposals which were admitted to the second round of the NIST post quantum cryptography standardization effort [24] relying on a Niederreiter cryptosystem, or its McEliece variant. In particular, we consider Classic McEliece [43] and NTS-KEM [44], which employ Goppa codes, and BIKE [34] and LEDAcrypt [31], which employ quasi cyclic codes, to assess our finite domain approach on both quasi-cyclic codes and non-quasi-cyclic codes. The parameters for the reported cryptosystems are designed to match the computation effort required to break them to the one required to break AES-128 (Category 1), AES-192 (Category-3), and AES-256 (Category 5). We report the code length \(n\), code dimension \(k\), number of errors to be corrected \(t\) and size of the circulant block \(p\) for
## 3 Time-Gated SPAD Optimization Technology
### Gate-Width Optimal Technology  
At the receiving end, the gate-ON time directly determines the performance of the time-gated SPAD. Appropriate gate-ON time can not only effectively reduce the background photons, but also suppress ISI distortion, to improve the communication performance. However, the signal photon is not detectable in the gate-OFF time, and the gated mode may cause the signal photon counting loss, resulting in a decrease in receiver performance. Therefore, signal photons, background photons, and ISI distortion should be considered in the performance analysis of the time-gated SPAD. Taking the minimization of BER as the optimization target, the blocking time, signal photons, and background photons as constraint conditions, the appropriate gate-ON time is selected to obtain the best BER performance. This optimization problem can be expressed as:  
\[\begin{array}{c}\arg\min_{T_{g}}BER\\ K_{s}=(\lambda_{1}+\lambda_{\text{b}})T_{g}\\ K_{b}=\lambda_{b}T_{g}\\ S.t.\hskip 14.226378 pt0\leq T_{g}\leq T_{s}\\ 0\leq T_{Bg}\leq T_{g}\end{array} \tag{19}\]  
In order to solve the optimization problem, according to Equations (6) and (7), the random variable blocking time vectors \(\mathbf{T_{Bg}}\) also affects the optimal gated-ON time in addition to \(K_{S}\) and \(K_{b}\). ISI distortion can only be eliminated when the gate-OFF time exceeds the blocking time. In addition, the concavity and convexity of the BER function are affected by \(K_{S}\) and \(K_{b}\). In this paper, the optimal gate-ON time is obtained by numerical simulation, and the accuracy of the results is verified.
###### Abstract  
The longitudinal equivalent continuous model generally only studies the stiffness of shield tunnels under longitudinal bending moments, considering it a constant. However, in actual engineering, shield tunnels are exposed to complex environments where seismic events, uneven settlement, etc., may cause simultaneous axial forces and bending moments between segmental rings, necessitating consideration of the longitudinal stiffness of shield tunnels under coupled axial force and bending moment effects. Therefore, based on the influence of different axial forces and bending moments on the separation effect between segmental rings, this study establishes a longitudinal nonlinear equivalent continuous model. Using Guangzhou Metro Line 18 as a case study background, a numerical model of segment ring-bolt is established for comparative analysis. The results show that the contact states between segmental rings can be classified into three modes: completely separated, completely in contact, and partially in contact. Longitudinal bending stiffness remains constant in modes 1 and 2 but decreases with decreasing \(\varepsilon\) in mode 3. The numerically simulated \(\varphi-\varepsilon\) curves are consistent with the theoretical results. At the special point \(\varepsilon_{0}\), the numerical simulation result is \(-57.27^{\circ}\) compared to the theoretical result of \(-59.66^{\circ}\); at point \(\varepsilon_{g0}\) (\(-0.3036\)), the numerical simulation result is close to \(0^{\circ}\). The longitudinal bending stiffness curve shows an overall decreasing trend. When \(\varepsilon\leq-\frac{\pi}{r}\), which corresponds to mode 2, the longitudinal bending stiffness remains constant at \(\pi r^{3}E_{c}t\). As the longitudinal axial pressure decreases, the longitudinal bending stiffness continues to decrease when \(-\frac{2}{r}<\varepsilon\leq\varepsilon_{g0}\). When the longitudinal axial pressure decreases to 0, then the tensile force gradually increases \((\varepsilon_{g0}\leq\varepsilon\leq\frac{7}{r})\). \(-\frac{2}{r}\leq\varepsilon\leq\frac{2}{r}\) belongs to mode 3, and the equivalent bending stiffness is \(\frac{2(1+sin\varepsilon_{0})^{2}E_{c}t}{A_{c}^{2}-A_{c}^{2}\pi r}\). As tension continues to increase, when \(\varepsilon\geq\frac{2}{r}\), the stiffness no longer decreases, and the longitudinal bending stiffness is \(\frac{\pi r^{3}E_{c}t}{u+1}\), which belongs to mode 1. The overall trend of the tensile and compressive stiffness curves is an inverse proportional function, with the middle mutation point at \(\varphi=0\), i.e., \(\varepsilon_{g0}=\frac{-4\pi}{(2+u)\pi r}\approx-0.3036\). The findings of this study can provide a basis for the rational calculation of longitudinal forces in shield tunnels in engineering applications.  
shield tunnel; longitudinal nonlinear equivalent continuous model; ratio of axial force to bending moment; mechanical parameters +
Footnote †: journal: Applied Sciences
## 3 Experimental Setup
### Comparison Methods  
In this paper, we compare a range of advanced dual-temporal change detection (CD) models, including those based on Convolutional Neural Networks (CNNs) and Transformer architectures:  
* FC-EF [35]: This model is based on the U-Net architecture and utilizes a pure Convolutional Neural Network for image-level fusion. The network takes as input a concatenation of a pair of dual-temporal images.
* FC-Siam-conc [35]: A variant of the FC-EF network, FC-Siam-conc employs a Siamese network structure to extract multi-layer image features, which are then concatenated using two fully connected layers.
* DTCDSCN [36]: This model is a variant of the FCN network that incorporates semantic-level and attention mechanisms. By focusing on channel and spatial features, it captures additional contextual information to discern changes in the images.
* MFPNet [30]: A Siamese network based on attention mechanisms, MFPNet uses multi-path fusion and adaptive weighted fusion strategies to integrate features effectively.
* DSIFN [29]: This model employs a differential discriminative network for change detection and integrates multi-layer features with image differential maps through attention mechanisms.
* BiT [38]: Designed with a Transformer-based encoder-decoder architecture, BiT models contextual information and effectively captures important features from the global feature space.
* MSCANet [39]: This model combines CNNs and Transformers in a multi-scale change detection network. It utilizes CNNs to capture local contour features at different scales and employs Transformers to encode and decode contextual information. These models represent some of the most advanced approaches in the field, each contributing unique methodologies for enhancing change detection performance.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 2 Related Work
After the mapping is completed, the following effects can be achieved: the communication node function can be modeled and reproduced by the twin node, and the physical resources required by the twin node can be carried by the physical node. The communication link function can be modeled and reproduced by the twin link, and the physical resources required and information transmitted by the twin link can be transmitted by the physical link. The amount of resources consumed by the functions carried and transmitted by each physical node and the physical link is basically the same, and the cost of power and energy consumed is minimal.
## 5 Nanoindentation of Diffusion-Bonded Interfaces
### Steel/Tungsten
Charpy impact tests and tensile tests (at ambient temperature and at 550 \({}^{\circ}\)C) both highlighted the brittle nature of the V\({}_{2}\)C and \(\sigma\) phase reaction layers. Post-test analyses revealed that crack propagation was initiated from this region, i.e., between the steel and the V interlayer. Both the Charpy impact toughness and UTS of the bonded specimen were markedly inferior to those of W. Its toughness at 550 \({}^{\circ}\)C was not significantly greater than its ambient temperature value; in contrast, the toughness of W increased by a factor of four from that tested at ambient temperature. [PERSON] et al. [60] investigated the use of a V/Ni composite interlayer to bond W to steel. V and Ni foils, of thicknesses of 0.2 mm and 0.1 mm, respectively, were placed between the two parent metals to form an assembly of W/V/Ni/steel. Diffusion bonding was carried out in a hot-pressing furnace at 1050 \({}^{\circ}\)C for 1 h. Nanoindentation measurements were made across the interface of 30 mN to evaluate the hardness distribution across the interface. SEM of the interface revealed that the bonding had been achieved, with no indications of cracking or debonding at the interface. On the contrary, interdiffusion of the different metals had appeared to cause the formation of intermediate layers, although, in some cases, they were of solid solutions rather than intermetallic compounds. This was the case with the Ni/steel and V/W interfaces. In contrast, the Ni/V interface appeared to consist of up to six discrete sub-layers, which were identified as being various Ni-V intermetallic compounds such as Ni\({}_{3}\)V, Ni\({}_{2}\)V, Ni\({}_{2}\)V\({}_{3}\) and Ni\({}_{3}\). Nanoindentation of the various interfaces (see Figure 10) indicated the various diffusion zones to exhibit elevated hardness values compared to the adjacent parent metals. This was even seen in the case of the V/W interface, which is in contradiction to the observation of [PERSON] and [PERSON] [58], despite there being no intermetallic formed. [PERSON] et al. [61] used a V/Cu composite interlayer to bond W to steel. The thicknesses of the V and Cu were 0.2 mm and 0.1 mm, respectively. Diffusion bonding was carried out at 1050 \({}^{\circ}\)C for 1 h. SEM examination of the interface indicated that bonding was successful, while nanoindentation of the interfaces revealed that the hardness of the W/V interdiffusion zone was between that of W and V. In contrast, the diffusion zone separating the V and Cu had a hardness that was higher than that of either V or Cu; the same observation was reported for the Cu/steel diffusion zone. X-ray diffraction (XRD) showed that no intermetallic compounds had formed at any of the interfaces. The W/steel joint had a strength of \(\sim\)402 MPa; fracture occurred at the W substrate near the W/V interface during tensile testing. Figure 9: Nanoindentation hardness measurements across the bond interfaces of (**left**) steel/vanadium and (**right**) vanadium/tungsten [58].
## 3 Materials and Methods
### Fundamental Theory  
Feature extraction from audio data is a critical step in the preprocessing phase of speech recognition. It involves converting audio data into feature representations that  
Figure 1: Overall flowchart of the methodology.  
can be utilized by deep learning models. This study employs the commonly used Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction, as it effectively simulates human auditory characteristics, enhancing the efficiency of model training and improving generalization capabilities. The process is illustrated in Figure 2.  
The model structure employed in this study is the Conformer network, a novel hybrid neural network architecture designed specifically for capturing both local and global dependencies in complex sequential data. The Conformer network ingeniously combines the local feature extraction capabilities of convolutional neural networks (CNNs) with the long-range dependency handling abilities of Transformers. By integrating convolutional layers into its structure, the Conformer is capable of capturing short-term phonetic features crucial for speech recognition tasks, while its self-attention mechanism allows it to understand and process the overall semantic structure of longer passages. This combination makes the Conformer particularly effective in tasks such as natural language processing and speech recognition, which require both detailed local observation and broad global understanding. The specific structure of the Conformer is illustrated in Figure 3.  
Figure 3: Conformer network architecture.  
Figure 2: MFCC processing flow.  
Building on this, two variant structures are also employed in this study: Unified_Conformer and U2++_Conformer. These three architectures are similar in design. Unified_Conformer introduces improvements in the encoding layer by implementing a dynamic block training method and utilizing a causal convolution module to support streaming recognition, thereby enhancing training effectiveness. U2++_Conformer builds on the causal convolution by adding a right-to-left attention decoder in the decoder section, enabling bidirectional decoding and improving decoding accuracy. The subsequent experiments will focus on these three structures.  
During the model training process, the calculation of the loss value is a hybrid of two types: CTC (1) and attention mechanism decoding (2). The structure is illustrated in Figure 4.  
\[LCTC=-\sum_{t=1}^{T}\log P_{ctc}(y_{t}^{true}\mid X) \tag{1}\]  
\[L_{att}=-\sum_{t=1}^{T}\log P_{att}(y_{t}^{true}\mid y_{1:t-1},X) \tag{2}\]  
where \(y_{t}^{true}\) represents the true label information, \(X\) represents the input audio feature sequence, and \(y_{1:t-1}\) represents all target labels before the time step \(t\).  
By using \(\lambda\) as a weighting coefficient, the two types of loss are combined to ultimately obtain the total loss value:  
\[L_{total}=\lambda L_{CTC}+(1-\lambda)L_{att} \tag{3}\]