esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. Compared with traditional joints, the ductility and the energy-consuming performance of the new joint were improved to a certain extent, and the ultimate bearing capacity was greatly improved. The seismic performance of the concrete beam-column joint connected using newly assembled steel sections proved to be superior to that of the traditional cast-in-place joint. This joint demonstrated excellent performance in seismic conditions, which meets the practical needs of modern precast concrete beam-column joint seismic performance analysis.
2. According to the nephogram analysis of the joints, it can be seen that the prefabricated joint had distinctive characteristics of concrete and reinforcing steel stress changes in the elastic stage, the ultimate bearing capacity stage, and the damage stage, and the damage mode was clear. Compared with the traditional cast-in-place joint, the performance indexes were superior, and the joint design was reasonable.
3. This study thoroughly examined and analyzed the influences of a variety of factors on the distinct properties of the new joint. This study observed that an increase in the axial compression ratio and the strength of the concrete had a positive impact on the ultimate load-carrying capacity and the energy dissipation capacity of the joint but led to a decrease in ductility. Therefore, it is crucial to reasonably set the axial compression ratio and concrete strength to ensure the ductility of joints in practical projects. Then, as the strength of the steel reinforcement in the joint increased, the ultimate load-carrying capacity and ductility also improved, while the energy dissipation capacity initially increased and then decreased. Therefore, an excessively high steel reinforcement strength may not necessarily be advantageous in enhancing the seismic capacity of joints. Furthermore, since the strength of steel section connectors is significantly higher than that of concrete, the use of high-strength components for steel section connectors does not produce a substantial improvement in seismic performance.  
Methodology, software, data analysis, and writing, Y.X.; funding acquisition, resources, and writing, M.Y.; software, data analysis, and writing, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Scientific Research Projects Of Hunan Provincial Education Department (grant number 20A522 and 21A0601) and the National Natural Science Foundation Of China (grant number 32371807).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Enhancing Sensitivity of Double-Walled Carbon Nanotubes with Longitudinal Magnetic Field
## 2 Theoretical Basis
\begin{split} P_{1}=\frac{1}{2}\Big{(}EI_{1}&+(e_{0 }a)^{2}\eta A_{1}H_{x}^{2}\Big{)}\int_{0}^{l}\left[\frac{\partial^{2} \mathcal{O}_{1n}(x)}{\partial x^{2}}\right]^{2}dx+\frac{1}{2}c(e_{0}a)^{2} \int_{0}^{l}\left[\frac{\partial\mathcal{O}_{2n}(x)}{\partial x}\right]^{2} dx\\ &-\frac{1}{2}\Big{(}c(e_{0}a)^{2}\int_{0}^{l}\left[\frac{\partial \mathcal{D}_{1n}(x)}{\partial x}\right]^{2}dx+\eta A_{1}H_{x}^{2}\Big{)}\int_ {0}^{l}\left[\frac{\partial\mathcal{O}_{1n}(x)}{\partial x}\right]^{2}dx\\ &+\frac{1}{2}\int_{0}^{l}c[\mathcal{O}_{1n}(x)]^{2}dx-\frac{1}{2 }\int_{0}^{l}c[\mathcal{O}_{2m}(x)]^{2}dx.\end{split}  \tag{18}\]  
Meanwhile, the potential energy of the outer-walled DWCNT is  
\[ \begin{split} P_{2}=\frac{1}{2}\Big{(}EI_{2}&+(e_{ 0}a)^{2}\eta A_{2}H_{x}^{2}\Big{)}\int_{0}^{l}\left[\frac{\partial^{2} \mathcal{O}_{2n}(x)}{\partial x^{2}}\right]^{2}dx\\ &+\frac{1}{2}\int_{0}^{l}c[\mathcal{O}_{2n}(x)]^{2}dx-\frac{1}{2 }\int_{0}^{l}c[\mathcal{O}_{1n}(x)]^{2}dx\\ &-\frac{1}{2}\Big{(}(c+k)(e_{0}a)^{2}+\eta A_{2}H_{x}^{2}\Big{)} \int_{0}^{l}\left[\frac{\partial\mathcal{D}_{2n}(x)}{\partial x}\right]^{2}dx \\ &+\frac{1}{2}(e_{0}a)^{2}\int_{0}^{l}c\left[\frac{\partial\mathcal{ D}_{1n}(x)}{\partial x}\right]^{2}dx+\frac{1}{2}\int_{0}^{k}k[\mathcal{O}_{2n}(x) ]^{2}.\end{split}  \tag{19}\]  
Hence, the total potential energy of the DWCNT is the sum of the potential energies of the inner and outer nanotubes. Therefore,  
\[P_{t}=P_{t}+P_{O}. \tag{20}\]  
Finally, according to the potential energy equivalences, through substituting Equations (19) and (20) into (21) we obtain  
\[ \begin{split} k_{eq}=\frac{z^{4}}{B}\Big{[}EI_{1}&+ EI_{2}+(e_{0}a)^{2}\eta H_{x}^{2}(A_{2}+A_{1})\Big{]}-\frac{z_{0}^{2}}{l} \left(\eta H_{x}^{2}(A_{2}+A_{1})-k(e_{0}a)^{2}\right)\\ &+kl+c(\xi_{1m}-\xi_{1n})+c(\xi_{2n}-\xi_{2m})-c(e_{0}a)^{2}(\xi_ {1m}-\xi_{1n})\\ &+c(\xi_{2n}-\xi_{2m}).\end{split}  \tag{21}\]  
Due to the boundary condition of the cantilevered DWCNT and the location of the absorbed mass, the fundamental resonant frequency is  
\[f_{n}=\frac{1}{2\pi}\sqrt{\frac{k_{eq}}{m_{eq}}}. \tag{22}\]  
The resultant frequency of the DWCNT without the attached mass is  
\[f_{on}=\frac{1}{2\pi}\sqrt{\frac{k_{eq}}{\rho l(A_{1}+A_{2})}}. \tag{23}\]  
Using Equations (22) and (23), the frequency shift can be expressed as follows:  
\[\Delta f=f_{on}-f_{n}=f_{on}-\frac{f_{on}}{\sqrt{1+\beta_{m}M}}\, \tag{24}\]  
where \(\beta_{m}=\frac{\mathcal{O}_{n}(\zeta)\mathcal{O}_{n}(\zeta)}{\rho l(A_{1}+A_{2 })}\). Rearranging Equation (24) gives the following equation:  
\[M=\frac{k_{eq}}{\beta_{m}\Big{(}k_{eq}-2\pi\Delta f\sqrt{\rho l(A_{1}+A_{2})} \Big{)}^{2}}-\frac{1}{\beta_{m}}\;. \tag{25}\]
## 3 Bit Error Rate Performance
Consequently, the asymptotic BER can be found as follows:  
\[P_{\text{e,GG}}^{\infty}=\frac{2^{\frac{\beta}{2}-1}\Gamma\Big{(}\frac{\beta}{2} +\frac{1}{2}\Big{)}\Big{(}\frac{\alpha\beta}{A_{0}h_{1}}\Big{)}^{\beta}}{\Gamma (\alpha)\Gamma(\beta)\sin((\alpha-\beta)\pi)\Gamma(-(\alpha-\beta)+1)}\frac{1} {|\gamma^{2}-\beta|\beta}\Gamma\bigg{(}\frac{\beta+1}{2}\bigg{)}\sqrt{\pi} \gamma^{2}\bar{\gamma}^{-\frac{\beta}{2}}. \tag{34}\]  
It is interesting to find that when \(\gamma^{2}>\alpha\), the diversity order is \(G_{d}=\beta/2\). This suggests that in high average SNR regions, the GG fading effect has a greater impact on BER performance than the pointing error effect.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 4 Methodology and Model Development
### Diversion Model
#### 4.4.3 Neighborhood Impacts  
It is essential to eliminate truck traffic from suburban areas with high population densities to preserve the health and quality of life of the neighborhood. Areas near schools are overcrowded during morning and afternoon hours, and a heavy vehicle redirected to the vicinity of a school would present a risk to schoolchildren.  
Data were collected from the ESRI database and extracted as a shapefile then clipped to the study area. A 0.5-mile buffer was created around school locations, and a polygon barrier was added to network analysis as a scaled cost. Segments that intersect with school polygon barriers were assigned a scaled cost value of 2. Hospital location data were collected from the ESRI library and extracted as a point shapefile. Data were clipped to the study area, and a 0.5-mile buffer was created around each hospital location. A polygon barrier was added to the network analysis as a scaled cost barrier.
# Influence of CeO\({}_{2}\) Addition to Ni-Cu/HZSM-5 Catalysts on Hydrodeoxygenation of Bio-Oil
## 3 Results and Discussion
### Effect of CeO\({}_{2}\) on the Catalytic Yield
#### 3.1.3 GC-MS Analysis  
In order to explore the changes of chemical compositions in bio-oil before and after an HDO reaction, the composition of bio-oil and upgrading oil was determined by GC-MS. The results obtained are sorted out as shown in Table 3. The main components of bio-oil before HDO were phenols, ketones, and acids; after an HDO reaction with different catalysts, some chemical compositions changed in the upgrading oil. For the upgrading oils, their common changes were (1) that the content of acid, alcohol, and ketone decreased significantly and (2) that the content of esters and hydrocarbons increased significantly. For the upgrading oil with 15%CeO\({}_{2}\)-Ni-Cu/HZSM-5, the content of acid was the lowest, while the esters content reached the highest and the hydrocarbons also reached the maximum.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reaction Temperature** & **High Heating Value** & **Water Content** & **Oxygen Content** & **Loss Weight of Catalyst** \\ (\({}^{\circ}\)**C**) & **(MJ/kg)** & **(wt\%)** & **(wt\%)** & **(wt\%)** \\ \hline
250 & 32.86 & 2.76 & 22.22 & 7 \\
270 & 32.95 & 2.38 & 21.66 & 15 \\
300 & 33.14 & 2.01 & 20.26 & 48 \\
330 & 35.25 & 1.15 & 17.41 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The properties of upgrading oil at different temperatures with 15%CeO\({}_{2}\)–Ni–Cu/HZSM-5 catalyst.  
Figure 3: The thermogravimetric profiles of the 15%CeO\({}_{2}\)–Ni–Cu/HZSM-5 catalysts after hydrodeoxygenation (HDO) at different temperatures at a reaction time of 1 h.
## 3 Methods
### Resampling Module
#### 3.2.2 SMOTE Oversampling Process  
After obtaining node representations in the embedding space through the feature extractor, we oversample the minority class to generate representations for new samples. SMOTE, a basic concept, interpolates the target minority class samples with their nearest neighbors from the same class in the embedding space. Using Equation (2), we find the closest node \(\mathrm{ne}(\mathrm{v})\) that belongs to the same class as \(\mathrm{h}_{\mathrm{v}}^{1}\).  
For the nearest neighbor node, we generate a new node according to Equation (4) and obtain the node embedding of the new node, \(\mathrm{h}_{\mathrm{n}(\mathrm{v})}^{1}\).  
\[\mathrm{h}_{\mathrm{n}(\mathrm{v})}^{1}=(1-\delta)\cdot\mathrm{h}_{\mathrm{v} }^{1}+\delta\cdot\mathrm{h}_{\mathrm{ne}(\mathrm{v})}^{1} \tag{4}\]In Equation (4), \(\delta\) is a random variable uniformly distributed within the range [0, 1]. Since \(\rm h_{v}^{1}\) and \(\rm h_{n(v)}^{1}\) belong to the same class and their distributions are close to each other, the generated synthetic node \(\rm h_{nv}^{1}\) should also belong to the same class. This allows us to obtain labeled synthetic nodes.  
After the above steps, an augmented dataset is obtained in which the majority class samples are reduced using the ENN method and the minority class samples are increased using the SMOTE method. This sampling method can make the dataset more balanced and improve the performance of the classifier.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 5 Aviation Big Data Multi-Layer Security Sharing Framework
### Blockchain Optimization Deployment and Consensus Mechanism for ATM
#### 5.1.2 Clustering-Based Grouped PBFT Consensus Mechanism  
Leveraging ATM's spatiotemporal characteristics, the K-means++ clustering algorithm groups nodes by spatial location. An intra-cluster to inter-cluster consensus strategy narrows the consensus scope, reducing per-round communication rounds, lowering overhead, and improving efficiency and throughput.
# Audio-Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model
## 4 Experimental Results and Discussions
### Decoding Examples  
Table 3 shows some decoding results for 0-dB-SNR noisy reverberant data. Using video-only modality provided inferior performance to the others due to its inherent ambiguity in visual speech recognition. Using audio-only modality with speech contamination, it was difficult to recognize similar pronunciations such as \"to\"-\"two\", \"that\"-\"bad\", \"of the\"-\"off a\". The methods using both the modalities mitigated the disadvantages for the uni-modalities by fusing audio and video information. In particular, our model that achieved superior performance to the others predicted correct words successfully except a very unusual word \"antiquarans\".
## 2 Materials and Methods
### Electron Relaxation Model
#### 2.2.2 Thermalized Carriers
Thus, using Equations (25), (26), (29), and (30), we can define the following system of equations:  
\[f_{h}(\varepsilon,F_{h},T)=1-f_{v}(\varepsilon)=\frac{1}{1+\exp\biggl{(}\frac{ F_{h}-\varepsilon}{k_{B}T}\biggr{)}}\quad,\quad\varepsilon<0. \tag{17}\]8 \[f_{h}(\varepsilon,F_{h},T)=1-f_{v}(\varepsilon)=\frac{1}{1+\exp\biggl{(}\frac{ F_{h}-\varepsilon}{k_{B}T}\biggr{)}}\quad,\quad\varepsilon<0. \tag{17}\]9  
enabling us to determine the transient evolution of \(F_{e}(t)\) and \(T(t)\). Yet, it remains to determine the holes' chemical potential. By virtue of the local charge neutrality, we can simply define a third equation as  
\[\dot{n}_{\text{t}}(\vec{\tau},t) =\frac{n_{\text{nt}}(\vec{\tau},t)}{\tau_{ee}}+D\
abla^{2}n_{\text {t}}(\vec{\tau},t)-\frac{n_{t}(\vec{\tau},t)-n_{0}}{\tau_{r}}\ ; \tag{18}\]0  
which means that we can also compute the transient evolution of the holes' chemical potential. After getting excited, the non-thermalized carriers undergo electron-electron scattering events. Theoretical work [48] and experimental evidence [4] indicate that these scattering processes are much more probable in the first few femtoseconds than the electron-phonon ones. As a result, the distribution of the non-thermalized carriers is broadened and, owing to the bottleneck shape of the Dirac cone, evolves toward a Fermi-like distribution [5] much faster than the carriers can lose energy to the lattice. This idea is expressed through Equations (20) and (22): the energy of the non-thermalized electrons, \(\varepsilon_{\mathrm{nt}}\), is transferred to the thermalized carriers essentially at the same rate as their population, \(n_{\mathrm{nt}}\), namely, \(1/\tau_{\mathrm{ee}}\). In fact, as a consequence of Equation (22), \(\varepsilon_{\mathrm{nt}}\) obeys a balance equation in the same form as Equation (15) for \(n_{\mathrm{nt}}\). Thus, in our model, only the thermalized (hot) carriers transfer energy to the lattice.
# Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls
## 1 Introduction
For example, a novel technique for cell-free massive MIMO deployment considers the use of radio stripes, which are a sort of cable with several antenna elements embedded and connected sequentially [15]. This solution has also been considered for indoor wireless energy transfer [16, 17]. Additionally, in [18], the authors analyze an uplink scenario of a cell-free massive MIMO system that is implemented with a limited-capacity radio stripe. However, previous studies have overlooked the impact on power consumption when transitioning from dedicated front-haul links, which have been the focus so far, to serial front-haul links, as proposed for radio stripes. In this sense, we foresee a lower power consumption associated with lowering the number of dedicated links. This aspect will be discussed in the following sections. In this context, the main contribution of this paper is to propose some adaptations for the traditional power consumption models to make them valid in the downlink for dedicated, serial, and hybrid dedicated/serial front-haul-based deployments. These adaptations consider that the total power consumed in the front-haul does not scale directly with the number of APs connected to the CPU since the front-haul is shared. The modified model does not reduce power consumption but better matches the physical characteristics of the network by giving more accurate consumption values. The remainder of the paper is organized as follows. Section 2 analyzes the model for dedicated front-haul and why the model needs to be adjusted when serial front-haul links are used in the downlink. Section 3 introduces the adaptations of the power consumption model. In Section 4, the power consumption for a specific deployment case is calculated by applying the proposed adaptations to the original model. Moreover, the results obtained for the original and modified models are compared. Finally, the conclusions are drawn in Section 5.
# An Enhanced Multi-Constraint Optimization Algorithm for Efficient Network Topology Generation
## 5 Results
### Test Methods and Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Orbit** & **Satellite Name** & **Inf1 Code** & **Perigee (km)** & **Apogee (km)** & **Orbital Period (min)** \\ \hline \(1\) & ONEWEB-0012 & 2019-010A & 1203.7 & 1206.1 & 109.4 \\ \(1\) & ONEWEB-0010 & 2019-010B & 1204.0 & 1205.8 & 109.4 \\ \(1\) & ONEWEB-0008 & 2019-010C & 1203.2 & 1206.6 & 109.4 \\ \(1\) & ONEWEB-0007 & 2019-010D & 1207.5 & 1210.3 & 109.5 \\ \(1\) & ONEWEB-0006 & 2019-010E & 1207.6 & 1210.2 & 109.5 \\ \(2\) & ONEWEB-0011 & 2019-010F & 1207.1 & 1210.7 & 109.5 \\ \(2\) & ONEWEB-0021 & 2020-008D & 1227.4 & 1230.3 & 109.9 \\ \(2\) & ONEWEB-0022 & 2020-008E & 1227.3 & 1230.4 & 109.9 \\ \(2\) & ONEWEB-0023 & 2020-008F & 1226.9 & 1230.8 & 109.9 \\ \(2\) & ONEWEB-0024 & 2020-008G & 1227.4 & 1230.3 & 109.9 \\ \(3\) & ONEWEB-0025 & 2020-008H & 1227.3 & 1230.4 & 109.9 \\ \(3\) & ONEWEB-0028 & 2020-008K & 1223.3 & 1226.3 & 109.8 \\ \(3\) & ONEWEB-0032 & 2020-008L & 1227.4 & 1230.3 & 109.9 \\ \(3\) & ONEWEB-0033 & 2020-008M & 1227.5 & 1230.2 & 109.9 \\ \(3\) & ONEWEB-0035 & 2020-008N & 1227.3 & 1230.3 & 109.9 \\ \(4\) & ONEWEB-0038 & 2020-008Q & 1223.6 & 1226.1 & 109.8 \\ \(4\) & ONEWEB-0044 & 2020-008V & 1227.3 & 1230.4 & 109.9 \\ \(4\) & ONEWEB-0045 & 2020-008W & 1223.4 & 1226.3 & 109.8 \\ \(4\) & ONEWEB-0047 & 2020-008X & 1227.3 & 1230.4 & 109.9 \\ \(4\) & ONEWEB-0048 & 2020-008Y & 1227.2 & 1230.5 & 109.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed Parameters of 20 Walker Constellation Satellites. Figure 4: Initialization of potential links in constellation network topology. The MCOTG algorithm proposed in this paper is compared with the Shortest Link Initialization (SLI) topology generation algorithm and the Longest Connection Time (LCT) topology generation algorithm in terms of average link length and average link lifetime. The LCT strategy algorithm prioritizes the selection of satellite nodes with the longest connection duration for establishing links, while the SLI strategy algorithm prioritizes the selection of satellite nodes with the shortest link length for establishing links. We use average link length and average link lifetime as evaluation metrics to analyze the communication efficiency and stability of the generated network topology.
## 3 Results and Discussion
### Fatigue Test  
The fatigue lines (Strain--Number of Load Cycles) obtained for all of the series are very similar, reaching notably high R\({}^{2}\) values; although, in the case of the C-Series, it is slightly lower. This may be due to the fact that the temperature interaction process is longer than in the A-Series and B-Series.  
In the A-Series, for low values of strain, the submerged series, especially A1, obtain more load cycles than the dry specimens (Figure 4).  
In the case of the series subjected to five freeze-thaw cycles (B-Series), the one that is submerged in salt water (B2) obtains more load cycles for any strain range than the other two series (Figure 5). This fact may be due to two main reasons. The first is that salt water offers a cushion effect on the temperature variations due to its thermal conductivity, which is smaller than that of distilled water [36]. The second is that the specimens of this series are in contact with frozen water for less time, which, as corroborated by the rest of the tests, is one of the most harmful effects for the mixture.  
Figure 4: Fatigue test specimens at constant temperature of 20 \({}^{\circ}\)C.  
The series that have been subjected to a year outdoors (C) obtain similar values for all specimens, being for C1 the number of load cycles slightly lower for high strain values (Figure 6).
## 3 Experimental Setup  
The experimental setup of the UWOC system based on direct current-biased optical OFDM (DCO-OFDM) is established and demonstrated in Figure 2. The key parameters of the system are listed in Table 1. On the transmitter side, the digital OFDM signal is generated offline with DSP algorithms in MATLAB. Firstly, the generated pseudo-random binary sequence (PRBS) of \(2^{17}-1\) is mapped into \(m\)-QAM symbols and two phase-conjugated pilots are inserted for SFO estimation. Then, Hermitian symmetry and inverse fast Fourier transform (IFFT) guarantee real signal transmission. The CP and CS are added for each OFDM symbol, and two training sequences (TSs) are added to realize symbol synchronization and channel equalization. Subsequently, the generated digital OFDM signal is loaded into the ROM of a Xilinx AX545 FPGA board and sampled by an 8-bit DAC (AD9280) with a 10 MS/s sampling rate. A variable electrical attenuator (VEA) is added to adjust the amplitude of the OFDM signal. The output analog signal is boosted by an electrical amplifier (AMP-OPA657) and is combined with direct current (DC) bias via a Bias-Tee to drive the blue LD (OSRAM, PL 450B), which can convert an electrical signal into an optical signal for underwater transmission.  
Figure 2: Experimental setup of the UWOC system based on DCO-OFDM. **(a)** The electrical spectral of the transmitted signal; **(b)** the electrical spectra of the received signal; **(c)** the transmission light path of the system.  
On the receiver side, according to the divergence angle of LD and the attenuation coefficient of tap water in [31], the estimated receiving optical power after 1 m underwater transmission is approximately \(-22.59\) dBm. To further improve the receiving optical power, the output beam is concentrated through the convex lens and fed into a commercial PIN (Hamamatsu, S10784). Then, the detected electrical signal is amplified by the amplifier (AMP-OPA656) and then captured by a digital storage oscilloscope (KEYSIGHT, DSO-S 804A, America). The DSO collects 5,000,000 sampling points each time for MATLAB offline processing. The main DSP flow sequentially includes symbol synchronization, CP and CS removal, 64-point FFT, channel estimation and equalization, SFO estimation and compensation, EVM calculation, QAM de-mapping and BER calculation. Due to the limitation of the receiving amplifier circuit, the 3 dB bandwidth of the system is 5.36 MHz. To investigate the SFO effect, the DAC and ADC are clocked by the same 10 MHz reference clock, which can avoid the difference between reference clocks. Different SFOs can be obtained by changing the frequency of the external clock source for the DAC.