esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Multipath Transmission in ICN
The ICN architecture based on the standalone name resolution approach,such as Data-Oriented Network Architecture (DONA) [26], Mobilityfirst [27], Network of Information (Netinf) [28], SD-ICN [29] and On-Site, Elastic, Autonomous Network (SEANet) [30], can be compatible with the existing network and its underlying routing supports the routing method of the existing network, which can facilitate the smooth evolution of the network architecture. Therefore, some researchers try to combine the ICN architecture based on the standalone name resolution approach with multi-homing technology to realize multipath transmission in ICN [31; 32]. The authors in [31] proposed a network-assisted ICN multipath transmission scheme based on Mobilityfirst, which requires that terminal devices have multiple network interfaces, and registered the globally unique identifier (GUID) mapping to multiple network addresses with the global name resolution service (GNRS). Based on the IP-compatible ICN architecture, the author in [32] used MPTCP in the transport layer to support multipath transmission in ICN. However, as mentioned above, this approach requires that at least one terminal device has multiple network interfaces and corresponding network settings at the terminal. Combined with the above research work, we design a general framework of multipath transmission system to realize multipath data transmission in ICN. Compared with the existing ICN multipath scheme, our scheme is easy to implement and deploy since it has no specific requirements for the network settings of terminal devices and does not require modification of the existing network devices.
## 3 Results and Discussions
The characteristics of the generated tri-comb in this mode are illustrated in Figure 5. In Figure 5a, the pulse trains are shown, designated as IV, V and VI. From the spacing among the pulse trains, it can be confirmed that only pulse train IV comes from R-1, whereas R-2 outputs the pulse trains of V and VI. Again, the relative intensity of pulse trains corresponds very well with the optical spectrum shown in Figure 5b. Thus, the spectral peaks can be easily identified. In this case, the center wavelength of comb IV is approximately 1564.5 nm and the center wavelengths of combs V and VI are approximately 1546.1 nm and 1568.1 nm, respectively. Accordingly, the optical spectrum bandwidth of combs IV, V and VI are 1.9 nm, 1.4 nm and 1.7 nm, respectively. With the same RBW of 180 Hz as before, the RF spectra of combs IV, V and VI were detected as well. In Figure 5c, the only spectral peak reveals a repetition rate of 47.00200 MHz for comb IV from R-1. In comparison, the repetition rates of combs V and VI in Figure 5d can be confirmed as 49.59920 MHz and 49.59646 MHz with the same methods discussed above, respectively. A constant frequency difference of 2.74 kHz can be found between the spectral peaks shown in Figure 5d. Due to the difference in sub-ring length, the minimum and maximum repetition rate differences are changed from 2.38 kHz and 2.59526 MHz to 2.74 kHz and 2.59720 MHz merely by switching the mode of tri-comb generation. This further proves the variability of the proposed multidimensional multiplexing mode-locked laser. The continuous mode-locking of both the tri-comb generation modes can last for over 10 h. In future studies, we will develop a stabilization system for the proposed dual-ring mode-locked laser. Figure 4: The schematic of RF spectrum generation for dual-combs I and II from sub-ring 1. (**a**) The individual RF spectrum of combs I and II. (**b**) The final RF spectrum output from sub-ring 1.
# Mixed-Carrier Communication for Technology Division Multiplexing
## 6 Conclusions  
MCC is introduced as a novel VLC PHY design for connecting receivers with different complexities simultaneously. Embedding different modulation schemes for technology division multiplexing in a PWM-like envelope comes with energy and spectral efficiency trade-offs. The transmitter and receiver designs for both low-speed and high-speed communication are proposed. Individual formulas for BER per modulation scheme are deduced to set an FEC BER of \(3.8\times 10^{-3}\) threshold as a performance metric. An optimization problem is formulated to maximize MCC spectral efficiency and define MCC optimum variables including modulation orders, PWM frequency, and SNR per modulation scheme. A spectral efficiency of around 3 bits/sec/Hz is obtained at MCC SNR of almost 12 dB.  
Conceptualization, A.F.H. and H.E.; methodology, A.F.H. and H.E.; software, A.F.H.; validation, A.F.H., D.S. and H.E.; formal analysis, A.F.H.; investigation, A.F.H., D.S. and H.E.; resources, A.F.H., D.S. and H.E.; writing--original draft preparation, A.F.H.; writing--review and editing, D.S. and H.E.; visualization, A.F.H., D.S. and H.E.; supervision, H.E.; project administration, D.S. and H.E.; funding acquisition, D.S. and H.E. All authors have read and agreed to the published version of the manuscript.  
This project is partially funded by the National Science Foundation (NSF), Award number: 1823225, titled \"CRI: II-NEW: CHRONOS: A Cloud based Hybrid RF-Optical Network Over Synchronous Links\".  
The authors declare no conflicts of interest.
###### Abstract  
The stability of a tunnel face and the rationality of its supporting structures are the guarantees for safe tunnel construction. This paper established a quantitative analysis model of tunnel face stability, obtained the calculation formula of the tunnel face stability coefficient based on the silo theory of surrounding rock, and then realized the quantitative description of stability of the tunnel face under the condition of a pipe roofing support, bolting support, grouting support and reserved core soil. Finally, a tunnel face stability discrimination and support optimization system was developed, its supporting effects were quantitatively evaluated, and the support measures were optimized based on a buried tunnel of Chongqing rail transit passing through the suburban expressway. The results show that the grouting support increased the stability coefficient by 103-412%, and its supporting effect is the most significant. The reinforcement with reserved core soil has the lowest cost. The tunnel face stability discrimination and support optimization system carries out a rapid judgment of tunnel face stability, and then provides a quantitative evaluation method for the assessment of the tunnel face. On-site monitoring indicates that the cumulative displacement gradually increased with monitoring time; the farther from the tunnel surface, the smaller the cumulative displacement. The cumulative displacement reached 34.50 mm before the optimization of the reinforcement scheme. The optimization scheme of pipe roofing support + reserved core soil + grouting support led to the gradual convergence of cumulative displacement. The final surface settlement displacement was reduced to 15.50 mm, which was about 44.93% of that before the optimization of reinforcement scheme, ensuring the safe construction of the buried tunnel. This research has a certain theoretical significance for the quantitative evaluation and analysis of the tunnel face stability of shallow buried tunnels.  
shallow buried tunnel; stability coefficient of tunnel face; cumulative settlement displacement; support optimization
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 4 System Performance Analysis
### Outage Probability and Its Asymptotic Behavior
Therefore, when \(\mu_{r}=\rho_{r}=\overline{\gamma}\rightarrow\infty\), based on Equation (31), \(F_{\gamma^{DF}}(\gamma_{th})\) can be asymptotically expressed as:  
\[F_{\gamma^{DF}}^{\infty}(\gamma_{th})\rightarrow\frac{\Delta_{1}\gamma_{th}^{ \frac{\xi_{1}^{2}}{\tau}}}{\frac{\xi_{1}^{2}}{\tau}}+\frac{\Delta_{2}\gamma_{ th}^{\frac{\xi}{\tau}}}{\overline{\gamma}^{\frac{\xi}{\tau}}}+\frac{\Delta_{3} \gamma_{th}^{\frac{\xi}{\tau}}}{\frac{\xi}{\overline{\gamma}^{\frac{\xi}{\tau} }}}+\frac{\Delta_{4}\gamma_{th}^{\frac{\xi_{1}^{2}}{\tau}}}{\frac{\xi_{2}^{2}}{ \overline{\gamma}^{\frac{\xi}{\tau}}}}+\frac{\Delta_{5}\gamma_{th}^{\frac{ \varpi}{\tau}}}{\overline{\gamma}^{\frac{\varpi}{\tau}}} \tag{34}\]  
From the above equation, it can be seen that when the detection technique is fixed, i.e., the value of \(r\) is fixed, the performance of the OP is determined by the magnitude of the five parameters \(\xi_{1}^{2}\), \(\xi_{2}^{2}\), \(\alpha\), \(\beta\), \(ac\), which are the pointing errors of the two hops and the parameters characterizing atmospheric and oceanic turbulence. Specifically, the pointing error of the FSO link will dominate the system OP performance when \(\xi_{1}^{2}\) takes the smallest value, while, when \(ac\) takes the minimum value, the turbulence in the UWOC link has the greatest impact on the OP.
# An Improved Target Network Model for Rail Surface Defect Detection
Generally, while machine vision and neural networks have been widely employed in industrial inspection tasks, there remains a relative scarcity of research focused on detecting rail surface defects characterized by intricate noise patterns and diverse sample sets. To further improve the accuracy of surface defect detection on railway tracks, this paper proposes a lightweight improved network MobileNet-YOLOv7. The primary contributions are as follows:  
1. A simplified network structure with a MobileNetV3 feature extraction network, reduced model parameters, an enlarged receptive field, more effectively extracted local features of samples, and improved computational efficiency and that has detection accuracy. 2. To address problems of slow convergence and overfitting in the model, we use the k-means++ clustering algorithm to adjust the anchors for object detection, improving the alignment between anchor boxes and real samples. The results show that this method can effectively accelerate network convergence speed and improve detection accuracy while mitigating sample imbalance concerns. 3. In order to tackle the challenges of oscillation and slow convergence in the loss function during algorithm training, we opted to substitute the conventional loss function with the EIOU function. By integrating the EIOU function, the algorithm can retain the essential features of the loss while minimizing the difference between the width and height of the target and anchors, consequently enhancing localization performance. The method proposed in this paper is shown in Figure 1.
## 3 Results and Discussion
### GPP Distribution and Projected Changes
The seasonal GPP anomalies under the 1.5 and 2 \({}^{\circ}\)C warming scenarios are shown in Figures 4 and 5, respectively. The spatial modes between the two warming scenarios are similar, noting that the magnitude in the 2 \({}^{\circ}\)C warming is much larger than in the 1.5 \({}^{\circ}\)C warming. In spring and summer, the GPP anomalies are the most prosperous, as they correspond to the growing season in China, while they drop to become the weakest in winter. In spring and winter with all ESMs, the GPP all over China generally shows a positive anomaly, with only sporadic negative points. Some negative changes occur in summer and autumn, especially with BCC-CSM2-MR and MPI-ESM1-2-HR. For BCC-CSM2-MR in summer, the negative GPP anomalies concentrate in the Huaihe River, which divides the subtropical-tropical monsoonal and temperate monsoonal regions. However, there were no negative changes in GPP at the zone scale. For MPI-ESM1-2-HR in summer and autumn, we observed some negative changes over the grassland in the temperate continental, which is similar to the results of [PERSON] et al. [30]. They found that large areas in Northern China showed a decreasing trend in NPP under global warming, although the overall NPP increased significantly. The fact that only one ESM obtained similar results also indicates the large inter-model spread in representing the future GPP change. The negative changes in the temperate continental were weakened under the 2 \({}^{\circ}\)C warming scenario (Figure 5). Figure 2: (Left) Spatial distribution of GPP anomaly (gC m\({}^{-2}\) yr\({}^{-1}\)) and (right) the respective regional GPP changes under 1.5 °C of global warming. (**a,b**) BCC-CSM2-MR, (**c,d**) CMCC-CM2-SR5, and (**e,f**) MPI-ESM1-2-HR. Numbers in the right-hand panels represent the relative change in comparison with regional GPP from 1986 to 2005. Figure 3: (Left) Spatial distribution of GPP anomaly (gC m\({}^{-2}\) yr\({}^{-1}\)) and (right) the respective regional GPP changes under 2 \({}^{\circ}\)C of global warming. **(a,b)** BCC-CSM2-MR, **(c,d)** CMCC-CM2-SR5, and (**e,f**) MPI-ESM1-2-HR. Numbers in the right-hand panels represent the relative change in comparison with regional GPP from 1986 to 2005.
## 4 Results and Discussion
### Experiment Settings
#### 4.1.2 Baselines  
To thoroughly validate the predictive performance of the proposed model, this study selects baselines from five perspectives: (1) forecasting models based on spatiotemporal graph neural networks, like dynamic graph convolutional recurrent networks (DGCRNs) and adaptive graph convolutional recurrent networks (AGCRNs); (2) forecasting models  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wind Power Data** & **\#1** & **\#2** & **\#3** \\ \hline Minimum & 0.215 & 0.228 & 0.198 \\ Mean & 325.796 & 416.174 & 289.531 \\ Maximum & 1632.475 & 1589.344 & 1602.128 \\ Standard deviation & 315.188 & 299.148 & 345.179 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic statistics of these data.  
based on transformers, such as inverted transformers (iTransformer) and cross-dimension transformers (Crossformer); (3) forecasting models based on MLPs, like time series mixer (TSmixer) and Rlinear; (4) hybrid forecasting models based on ensemble learning, such as [PERSON]'s model and [PERSON]'s model; and (5) classic forecasting models, like ARIMA and support vector machine (SVM). The introductions of all baselines are as follows:  
* DGCRN [55]: it introduces dynamic graph convolutional networks to improve the performance of recurrent neural networks.
* AGCRN [56]: it introduces adaptive graph learning to improve the forecasting power of GRUs.
* iTransformer [57]: it uses multi-head attention to mine the correlations between different variables, and then uses the full connection layer to mine the context of the time series.
* Crossformer [58]: it uses transformer encoders to mine the context information of the time series, and then uses routing attention to mine the correlations between different variables.
* TSmixer [59]: it uses the MLP framework to mine both variable correlations and temporal information
* Rlinear [60]: it uses Revin and channel independence to improve the forecasting performance of MLP models.
* [PERSON]'s model [61]: it combines deep reinforcement learning and multi-objective optimization algorithms to propose dynamic ensemble learning strategies.
* [PERSON]'s model [62]: it uses reinforcement theory to integrate two different spatiotemporal graph neural networks to improve the forecasting performance of the model.
* SVM [63]: it is a classical kernel-based machine learning model.
* ARIMA [64]: it is a classical statistical model based on autoregression.
## 4 Conclusions  
In this work, it has been experimentally verified that UHRE is an effective method on the extraction of four flavonoids in Mairei Yew leaves. After single factor experiments and BBD experiments, the optimized best condition was 20 min for extracted time, 200 W for ultrasonic power and 26 mL/g for a liquid-solid ratio. By UHRE, the yields of QU, KA, GI and SC were, respectively, 0.109, 0.406, 0.031 and 0.355 mg/g, and total yield of four flavonoids was 0.901 mg/g, which were, respectively, 1.23-fold and 1.25-fold higher than those by using HRE and UAE. The reason may be that the coupling effect of ultrasound and heat-reflux increased the yield of target compounds. The extraction time was only 20 min by UHRE, relatively short, and the yields of four flavonoids were higher than those by HRE and UAE. Thus, UHRE shows an obvious advantage over HRE and UAE. Therefore, UHRE is an advantageous extraction method and it can be used well in the extraction of natural products from plants.  
Methodology, C.Z.; software, X.R.; validation, formal analysis and data curation, X.R., H.J., J.G., W.S., Y.L., Y.T., T.W. and S.L.; writing--original draft preparation, C.Z. and X.R.; writing--review and editing, C.Z. and C.L.; supervision, C.L.; project administration, C.Z.  
This research was funded by the National Key Research and Development project of China (2017 YFD0601306), the Fundamental Research Fund for Central Universities (2572017 CA11), the National Natural Science Foundation (31870609) and the Natural Science Foundation of Heilongjiang province (LC2017005).  
We sincerely thank two anonymous reviewers for their constructive suggestions which would help us both in English and in depth to improve the quality of the paper.  
The authors declare no conflict of interest.  
Figure 7: SEM images of Mairei Yew leaves samples. (**A**) is raw materials; (**B–D**) are, respectively, samples after HRE, UAE and UHRE.
## 2 Methods and Data
### Pseudo-Measurement Generation
#### 2.2.2 Clustering  
It is common practice to classify consumers based on their annual energy consumption. For the purpose of selecting an appropriate, characteristic time series dataset for each load of the network during simulation, residential datasets were divided into four clusters. The boundaries of the clusters are marked by the aggregated annual energy in the dataset. These boundaries were first estimated by clustering the annual consumption values of the loads for all four demo networks and the aggregated annual consumption of the original datasets using the \(k\)-means method. Then, the boundaries were set based on the heuristic analysis of the clustering results, as illustrated in Table 1.
# Green's Function, Earthquakes, and a Fast Ambient Noise Tomography Methodology
## 2 Materials and Methods  
The data consist of 10 days (9 October 2021-19 October 2021) of continuous seismogram recordings from 15 seismic stations of the Hellenic Unified Seismological Network (HUSN), which were retrieved using the ObsPy framework [23]. Any gaps in these measurements were replaced with zeros. The Z components were used in this analysis to obtain Rayleigh waves. We applied the following standard preprocessing steps [24], which were implemented asynchronously while retrieving the data: First, the waveforms were decimated from 100 to 10 Hz, tapered, demeaned, and detrended by subtracting a third-order spline with 500 samples between the spline nodes. Later, the instrument response was removed, after applying a band-pass filter with corner frequencies 0.05, 0.1, 4, 4.5 Hz, and a 1-bit amplitude normalization procedure, under which we applied the sign function to the signal. According to [PERSON] [25], the derivative of the cross-correlation is as follows:  
\[C_{a,b}(t)=\int a\big{(}t^{\prime}\big{)}b\big{(}t+t^{\prime}\big{)}dt^{\prime} \tag{1}\]  
Between the recordings, \(a\) and \(b\) of the two stations can be related to the Green's function \(g_{a,b}(t)\) between them. Namely, this is found as follows, with \(*\) denoting convolution:  
\[\frac{-dC}{dt}\propto g_{a,b}(t)*\widetilde{\alpha}(t) \tag{2}\]  
Figure 1: The area of interest along with the Hellenic Unified Seismological Network (HUSN) stations denoted with red triangles.  
By the convolution theorem, assuming the autocorrelation \(\widetilde{\alpha}(t)\) is a delta-like function, with \(A\), \(B\) denoting the Fourier transforms of the recordings \(a\), \(b\) and \(\overline{A}\) being the complex conjugate of \(A\), this is equivalent to the following:  
\[G(\omega)\propto-i\omega\overline{A(\omega)}B(\omega) \tag{3}\]
# Two-Dimensional Modelling Approach for Electrokinetic Water Transport in Unsaturated Kaolinite
## 2 Materials and Methods
### Description of the Simulation Exercises
The boundary conditions depend on the configuration of the system: electrodes located in electrolyte reservoirs and electrodes inserted in the soil. These conditions are applied either on the electrolyte reservoir or the electrode surface, respectively. The electric boundary conditions are intended to simulate the application of an external potential gradient between two points. For this purpose, Dirichlet-type boundary conditions that set the electric potential at the cathode/catholyte reservoir to zero have been imposed, while the electric potential applied in the anode/anolyte reservoir (\(\psi_{\text{anode}}\)) is calculated as the product of the electric potential gradient and the minimum distance between pairs of anodes/cathodes. Non-flux boundary conditions are set for all the external walls of the simulated domain. For hydraulic boundary conditions, a non-flux condition (Neumann type) was applied in the simulations where the electrodes are inserted into the soil, as no addition/extraction of water occurs in this configuration, and to the external walls. However, when using electrolytic wells, the usual practice is to maintain constant electrolyte levels in the reservoirs, which are usually open to the atmosphere. This can be simulated by applying a Dirichlet boundary condition such as \(P_{\text{L}}=P_{\text{atm}}=100\) kPa (see Table 2). In both configurations, a sensitivity analysis of both the void ratio (simulating three cases with different void ratio values [47]: 0.7, 0.9 and 1.2) and the applied electric potential gradient (simulating three potential gradients: 1, 10 and 50 Vcm\({}^{-1}\)) was carried out. Table S1 presents the values of the physical parameters as a function of the void ratio range studied. Second, the influence of the geometrical arrangement of the electrodes when they are placed inside electrolyte reservoirs was assessed. For this purpose, 12 additional cases were simulated with different configurations, both in terms of the number of electrodes and their spatial distribution (Figure 1). These cases were analysed with two different electric potential gradients, 1 and 50 Vcm\({}^{-1}\), in a soil with a void ratio of 0.9. The initial and boundary conditions used are specified in Table 2. The parameters used to compare the performance between these electrode configurations were the energy consumption required to reach a state of total saturation of the soil and the time necessary to reach this state.