esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
In this paper, we discuss the user stance detection problem and redefine stance. Therefore, we propose a stance detection model based on commonsense knowledge and surrounding environmental information to find the tweets sent by users regarding a particular event. In the commonsense knowledge module, we have extended the ordinary BiLSTM to better and more effectively integrate emotional commonsense when the sequence is encoded into a vector. Additionally, we added the topic information and the users' neighbors' information into the model. At the same time, we propose an attention mechanism to better integrate history content and social contextual information. Finally, we use a softmax layer for the stance classification of user information. Compared with ordinary tweet-level stance detection, our model can automatically track stances related to topics. In order to verify the effectiveness of our model, we conduct numerous experiments on two Twitter datasets, BREXIT and ELECTION. Experimental verification shows that our model has a significant improvement in accuracy compared with the baseline method. To verify the effectiveness of the modules in the model, we conducted ablation experiments to show that external commonsense knowledge and neighbor information has a specific effect on improving stance detection performance.  
In our research, we only discussed a small number of eigenvalues. Although our model is significantly better than several other baseline methods, this method still has much room for improvement on the model. In future work, we will consider more representative feature values and compare their performance. In addition, we will also consider adopting new deep learning methods to improve the performance of stance detection.  
Methodology, software, data curation and writing--original draft, P.J.; supervision, writing--review and editing, funding acquisition, and formal analysis, Y.D.; investigation, and validation, J.H.; formal analysis and software, H.L.; funding acquisition and formal analysis, X.L.; formal analysis, X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation (Grant Nos. 61872298, 61802316, and 61902324) and the Sichuan Regional Innovation Cooperation Project (Grant No. 2021 YFQ008).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request. All source codes and experiment details are available at [[https://github.com/[PERSON]/ECKEI](https://github.com/[PERSON]/ECKEI)]([https://github.com/[PERSON]/ECKEI](https://github.com/[PERSON]/ECKEI)), (accessed on 4 September 2022).  
The authors would like to thank all editors and anonymous reviewers for their valuable comments and suggestions, which have significantly improved the quality and presentation of this paper.  
The authors declare no conflict of interest.  
Figure 14: The influence of \(\alpha_{1}\) and \(\alpha_{2}\) on the Avg\({}_{-}\)MF1, Avg\({}_{-}\)Acc and Avg\({}_{-}\)Recall of the dataset ELECTION.
# Effect Mechanism of Connection Joints in Fabricated Station Structures
## 3 Structure Introduction and Ground Motion Input
### Ground Motion Input  
According to the soil characteristics of the site where the station is located, three real seismic waves from the PEER (Pacific Earthquake Engineering Research Center) were selected as the input ground motions, namely, the Parkfield wave, Kobe wave, and Taft wave. Usually, the peak acceleration of the vertical component is half to two-thirds of the peak acceleration of the horizontal component [39]. The peak accelerations of different horizontal components (0.1 g, 0.2 g, 0.3 g) and seismic excitations in different directions were considered. The peak acceleration of the vertical component was two-thirds that of the horizontal component. The recorded seismic wave time history and Fourier spectrum are shown in Figure 8.  
In EERA software, the equivalent linearization method was used to calculate the equivalent shear modulus and damping ratio by calculating the G\({}_{\text{max}}\)/G-shear strain and damping ratio-shear strain relationship of soil, which were used in the dynamic time history analysis of the numerical model. Figure 9 shows the equivalent linearization param  
Figure 8: Acceleration time histories and Fourier spectra of the seismic waves.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Density (kg/m\({}^{3}\)) & 2500 \\ Elastic modulus (GPa) & 34.5 \\ Poisson’s ratio & 0.25 \\ Dilation angle (\(\theta\)) & 30 \\ Compression stiffness recovery parameter & 1.0 \\ Tensile stiffness recovery parameter & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Plastic damage model parameters of prefabricated components.  
eters of soil under different seismic motions. Figure 10 shows the equivalent linearization parameters of soil under different seismic intensities.  
Under the action of seismic waves with the same peak seismic acceleration, the frequency of the seismic waves had a greater effect on the maximum shear strain of the soil, which also showed the necessity of using different seismic waves for the seismic analysis of underground structures. As the peak acceleration of the seismic wave increased, the maximum shear strain of the soil increased, but it was not linearly related. Therefore, the seismic peak acceleration and the seismic response of the underground structure were also not linearly related.
###### Abstract  
The study of deep soil mechanics is the basis of deep shaft construction. Exploring the nonlinear permeability mechanism of deep confined aquifers in depth is the prerequisite and foundation for carrying out calculations of the hydrophobic consolidation settlement of thick alluviums and preventing and controlling deep-well-damage disasters. Against the background of shaft damage caused by hydrophobic consolidation settlement of the bottom aquifer of thick alluviums, a joint HPLC-HPNP (high-pressure long-term consolidation and high-pore-pressure nonlinear permeability) test was carried out on the bottom aquifer of thick alluviums based on the ETAS test system. This paper studied the evolution law of the permeability coefficient (\(k_{v}\)) of bottom aquifers under different heads of confined water, confining pressures (\(\sigma_{r}\)), permeability hydraulic gradients (\(i\)) and loading-unloading methods. The internal pore structure characteristics of clayey sand were obtained by using low-field nuclear magnetic resonance (NMR) technology to explore the clayey sand's nonlinear permeability micro-mechanism. The research results showed that the bottom aquifer seepage volume (\(\Delta Q_{i}\)) under high stress is affected by the head pressure difference and pore water dissipation, and \(k_{v}\) decreases with an increasing \(\sigma_{r}\) according to the power function relationship. The influence of the hydraulic gradient (\(i\)) on \(k_{v}\) is significantly influenced by \(\sigma_{r}\). When \(\sigma_{r}<4\) MPa, \(k_{v}\) decreased with an increasing \(i\), and when \(\sigma_{r}>4\) MPa, \(k_{v}\) increased with an increasing \(i\) first, then decreased, before then tending to be stable. Under different stress states, the \(T_{2}\) spectrum of clayey sand showed a bispectrum peak type, and the adsorbed water content decreased linearly with an increasing \(\sigma_{r}\), while the capillary water decreased according to the power function. The content of capillary water in the permeable pores plays a key role in the permeability of clayey sand, and it has a power function relationship with \(\sigma_{r}\). The research results of this paper provide a good experimental method for the study of deep soil permeability characteristics and parameter determination, provide a theoretical basis for deep alluvial hydrophobic consolidation and settlement, and further make up for the shortcomings of existing deep soil mechanics in permeability characteristics.  
thick alluvium; bottom aquifer; clayey sand; permeability; pore structure +
Footnote †: journal: Applied Sciences
## 4 Experiments
### Experimental Setup  
The AVRS experimental setup consists of four independent vibration isolation units, controller, payloads, PC, etc. Each isolator is integrated with a steel spring, geophones, and voice-coil motors. The schematic diagram of the experiment is shown in Figure 8.  
In this experiment, excitation signals generated by the LMS signal system were amplified and acted on the AVRS substrate by the MB shaker. All internal signals were acquired by the AD port in the DSP controller and, after frequency expansion, signal decoupling, and algorithm processing, they were output from the DA port as motor drive signals.  
In addition, the PC was used to establish an intelligent agent, which communicates with the DSP in the controller through the serial port and the CAN port. The CAN port realizes vibration signal transmission, and the serial port is used to execute the parameter's updated commands. The online network parameters are updated at the end of each 100-step episode. Before starting self-learning control, 2000 random samples should be stored in replay memory. When the sample size reaches the set value, the agent randomly selects 32 mini-batch samples for DDPG reinforcement learning. Next, two external high-resolution VSE-15D servo velocity seismometers are used to verify the vibration reduction effect of the AVRS. Table 2 shows the parameters of the AVRS.  
Figure 8: Experimental system: (**a**) experimental setup and (**b**) sketch of the experiment.
# Pharmacokinetics of Pullulan-Dexamethasone Conjugates in Retinal Drug Delivery
## 3 Results
### Retinal Penetration and Distribution  
#### 3.5.1 Ex Vivo Studies  
Distribution of pullulan-based formulations was studied in ex vivo mouse retinal explants (Figures 10 and 12). The conjugates, Cy3-pullulan and Cy3-pullulan-DEX, were present in GCL, INL and ONL layers. The formulations were visualized in radial sections (red punctas, Figures 10 and 12) through retinal layers overlapping with the Muller cells (Figure 12). Interestingly, the pullulan-based materials followed the route of [PERSON] glial cells from the GCL to the distal part of the ONL, close to the retinal pigment epithelium (RPE) (Figure 12, 3D visualization in Supplementary Materials). Thus, this finding suggests that the pullulan-based conjugates may bypass the inner limiting membrane (ILM) via Muller cell phagocytosis in mouse retinal explants.  
Figure 11: Fundus and optical coherence tomography (OCT) images of rat vitreous and retina. The figures are before and after intravitreal injections (1, 3 and 5 days) of BDP-pullulan (**A**) and BDP-pullulan-DEX (**B**). The length of scale bar for fundus images are 200 \(\upmu\)m. In the case of OCT, vertical and horizontal scale bars in OCT are 110 and 130 \(\upmu\)m, respectively.  
The retinal distribution of pullulan conjugates was also studied in ex vivo bovine retinal explants. In this tissue model, the nanoparticles were mainly localized at ILM. However, several sections showed a few signals (green punctas) from BDP-pullulan-DEX in GCL of the retina (Figure 13).  
Figure 12: Müller glial cells in sections of the ex vivo mouse retina labelled with an antibody against glutamine synthetase (green). Untreated retina, retina treated with Cy3-pullulan-DEX (15 \(\mu\)L, 1.9 mg/mL) and Cy3-pullulan (15 \(\mu\)L, 1.7 mg/mL) are shown. Cy3 fluorescence is shown as red. Nuclei were stained with DAPI (blue). RG: overlay of red and green channels for Müller glial cell, nanoparticle or conjugate colocalization. RGB: overlay of red, green and blue channels. Bar size: 20 \(\mu\)m.  
Figure 13: Retinal distribution of BDP-pullulan-DEX (100 \(\mu\)L, 5 mg/mL) in the vitreous-retinal ex vivo bovine explant 24 h after intravitreal injection. Representative confocal microscopy images of cryosections display the penetration of BDP-pullulan-DEX in the retinal layers (green). The inner limiting membrane (ILM) was labelled with rabbit anti-collagen type IV antibody (Col-IV ab, red). The vitreous can be seen as transparent layer in transmission imaging which is well aligned along the ILM while it appears in bright green color due to the high load of BDP-pullulan-DEX in merged channel mode. Nuclei are stained with Hoechst (blue). The white bar on the right bottom corner of each picture indicates the bar size: 50 \(\mu\)m. Abbreviations indicated in image: ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), overlay of red/green/blue channels (RGB).
## 6 Data Transfer and the Creation of a 3D Model for VR and AR
### AR Visualization
#### 6.3.2 Phase 3 and Finalization of the Project  
As with the use of 3D models for VR, the development for AR is fast and the whole process is greatly simplified, especially with more complex tools such as Agisoft's Metashape 2.2 software. The process was the same as for the 3D model for VR, and the final 3D model of 110,000 polygons was uploaded from Xcode software 16.1, which allows the 3D model to be edited and prepared for loading and displaying on iOS devices using the AR suite, which has been integrated into iOS devices since iOS 11 (Figure 22). Subsequently, the 3D model was exported as Universal Scene Description Zip format (USDZ). The development of mobile devices should not be forgotten, as mobile phones and tablets currently have no problem loading a model almost twice as large as the 60,000-polygon model tested in 2018/2019, Figure 22.  
The workflow enhancements based on the features added to Agisoft Metashape software are shown in Figure 23. A test was conducted to see if this process using Instant Mesh and xNormal software could be applied to a 3D model for VR. However, the testing did not achieve the results we would expect; multiple normal maps for a single object could not be created in xNormal. Thus, with the Unreal Engine's limitation to texture only at 8K, the surface was not of sufficient quality. This is why the solution of slicing the 3D models into multiple strips was originally chosen.  
Figure 22: QR for AR model (accessed on 6 November 2024), iOS device only (from left—Phase 1, Phase 2, Phase 3, 1:100).
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 3 Mathematical Model
### DSDV Path Duration  
When receiving new information about a new assigned path, all intermediate nodes that establish the full path between the source and the destination will wait for a specific time period, called the settling time. It is used by the node to make sure that it has obtained the shortest path before retransmitting the new updates. For estimating the path duration, the waiting time of every intermediate node will be excluded. Consequently, the DSDV path duration is calculated using the following equation (Equation (26)):  
\[\text{DSDV}_{T_{path}}=E_{T_{path}}-(E_{h}-2)\times\text{settling Time} \tag{26}\]  
All nodes deploying the DSDV protocol can update their routing information on a specific time period, called the update period. This update period is determined by the arrival time of the update, and in case the path duration is expired, the routing information will remain unchanged until the expiry of this period. Therefore, the percentage of path useful time is represented by Equation (27).  
\[\text{useful time}=\frac{\text{DSDV}_{T_{path}}}{\text{Update period}} \tag{27}\]
# Towards Rack Utilization in Internet Datacenters: An Approach Based on Dynamic Programming
## 4 Our Approach
### The Scale of Server Placement Problem  
All servers and racks in datacenter should be taken as input when solving the optimal server placement strategy \(deploy_{opt}\). However, the simultaneous processing of all servers and racks cannot be achieved due to the following two reasons:  
* the servers hosting the same or similar businesses are usually deployed in closer racks to reduce the expense of network. However, if the server placement strategy is implemented for all servers and racks, the servers with similar businesses may be deployed dispersedly in racks with long physical distance, resulting in huge communication expense.  
Figure 3: Model of the IDC’s leasing cost.  
* IDC is equipped with a huge number of servers and racks. Suppose \(m_{z}\) and \(n_{z}\) are the number of servers and racks, and the complexity of iteration for the optimal server placement strategy is \(O(n_{z}^{m_{z}})\). If all servers and racks are taken as the input of optimal server placement strategy, the scale of problem will be so large that the solving time will be very long and even the problem cannot be solved. Therefore, in the production system, taking a region consisting of a certain number of servers and racks as the input of server placement strategy is necessary. It should be noted that the region must be racks with short physical distance and a set of servers running similar businesses, thus reducing the cost of server migration during deploying servers and the cost of communication. Here, we first need to figure out what the size of region suitable for server placement strategy is. In the Tencent network, there are over 4700 kinds of businesses deployed in dedicated servers. The number of racks used by each business in one machine room are shown in Figure 4. The number of racks used by more than 90% businesses is within 10, and that used by over 80% businesses does not exceed 5. Therefore, when optimizing the original server placement strategy, an integer between 5 and 10 can be used as the number of racks \(n_{z}\), and the number of existing servers inside these \(n_{z}\) racks can be used as \(m_{z}\).
# ICN-Based Enhanced Content Delivery for CDN
## 3 Proposed Design
### Workflow  
Hereafter, we elaborate on our proposed mechanisms for enabling seamless collaboration between CDN and ICN to achieve efficient content delivery as illustrated in Figure 2.  
Upon a cache miss on the CDN edge server, the content request is redirected to IG. This can be achieved by configuring IG as a proxy (steps 1-2 in Figure 2). IG parses the HTTP content request and initiates a name resolution request to NRS, querying if the corresponding chunk ID is registered (steps 3-4 in Figure 2). If registered, NRS returns the list of NAs of chunk replicas, and then IG selects the optimal replica using the nearest replica mechanism (steps 5-6 in Figure 2). If not registered (as in the dotted box), NRS returns NULL and the content needs to be retrieved from the origin server: first, IG initiates a chunk request to EG; second, EG handles the chunk request and generates an HTTP request of the content; third, EG requests the content from the origin server and sends the content back to IG in the form of a chunk. While forwarding, the chunk is cached along path (steps 5\({}^{\prime}\)-6\({}^{\prime}\) in Figure 2). The details of the conversion between the HTTP and ICN paradigm will be discussed in the following section. Once IG retrieves the content, it serves the CDN edge server (steps 7-8 in Figure 2).  
Figure 2: Flow diagram of content request and retrieval process.
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 3 Energy-Efficient Subcarrier Pairing and Power Allocation Algorithm
### Solution of the Master Problem  
Using the subgradient method, the dual variables \(\lambda\), \(\mathbf{\mu}\), and \(\mathbf{\
u}\) can be iteratively updated as  
\[\lambda^{(l+1)} =\left[\lambda^{(l)}-\varepsilon_{2}^{(l)}\left(P_{max}-\sum_{i=1 }^{K}\sum_{j=1}^{K}\rho_{i,j}^{\star}\left(P_{S,i}^{\star}+P_{R,j}^{\star} \right)\right)\right]^{+} \tag{29}\] \[\mu_{i}^{(l+1)} =\left[\mu_{i}^{(l)}-\varepsilon_{3}^{(l)}\left(1-\frac{\Omega_ {i}^{\star}\sigma_{R,i}^{2}}{P_{S,i}^{\star}\left|h_{SR,i}\right|^{2}}\right) \right]^{+}\] (30) \[\
u_{i}^{(l+1)} =\left[\
u_{i}^{(l)}-\varepsilon_{4}^{(l)}\left(1-\frac{\Omega_ {j}^{\star}\sigma_{D,j}^{2}}{P_{R,j}^{\star}\left|h_{RD,j}\right|^{2}}\right) \right]^{+} \tag{31}\]  
where \(\varepsilon_{i}^{(l)}\), \(i\in\{2,3,4\}\), are positive step sizes. The parameter \(\Psi(t)\) can be updated as [6]:  
\[\Psi(t)=\frac{\frac{1}{2}\sum\limits_{i=1}^{K}\sum\limits_{j=1}^{K}\rho_{i,j} \left(\frac{\alpha_{i}^{d}}{\ln 2}\ln\Omega_{i}^{\star}+\beta_{i}^{d}\right)-\sum \limits_{j=1}^{K}\frac{\alpha_{j}^{e}}{\ln 2}\left(\ln P_{R,j}^{\star}+\ln \left(\left|h_{RE,j}\right|^{2}\right)-\ln\left(\sigma_{E,j}^{2}\right) \right)-\beta_{j}^{e}}{\left(\sum_{i=1}^{K}\sum_{j=1}^{K}\rho_{i,j}\left(e^{ \beta_{S,i}}+e^{\beta_{R,j}}\right)+P_{SC}\right)} \tag{32}\]  
The \(\Psi\)-based iterative EEM algorithm for joint power allocation and subcarrier pairing is finally summarized in Algorithm 1.  
The quality-of-service (QoS)-based joint subcarrier pairing and power allocation design for maximizing the secrecy EE can be formulated as:  
\[\begin{array}{ll}\max_{\mathbf{P}_{S},\mathbf{P}_{R},\boldsymbol{\rho}}&\Sigma_ {E}\left(\mathbf{P}_{S},\mathbf{P}_{R},\boldsymbol{\rho}\right)\\ \text{s.t.}&\left(\text{C.1}\right)\sum_{i=1}^{K}\sum_{j=1}^{K}\rho_{i,j} \left(P_{S,i}+P_{R,j}\right)\leq P_{max}\,;\\ &\left(\text{C.2}\right)\sum_{j=1}^{K}\rho_{i,j}=1,\,\,\,\,\forall i\,;\\ &\left(\text{C.3}\right)\rho_{i,j}\in\left\{0,1\right\},\,\,\,\forall i,j\,; \\ &\left(\text{C.4}\right)\frac{1}{2}\log_{2}\left(1+\min\left\{\Gamma_{SR,i}, \Gamma_{RD,j}\right\}\right)\geq R_{(i,j),min},\forall(i,j);\\ &\left(\text{C.5}\right)P_{S,i}\geq 0,P_{R,j}\geq 0,\,\,\,\,\forall i,j\,. \end{array} \tag{33}\]  
Note that the constraint (C.4) denotes the quality-of-service (QoS) metric for a user pair on the \((i,j)\)th subcarrier pair. Accordingly, we can resolve the new optimization problem in a same way as the problem in (**P.1**).
## 4 Experimental Research
### Comparison of Multi-Feature Extraction Methods
#### 4.6.1 Comparison with Multiple Nonlinear Dynamic Methods  
The proposed IMFE and the other nonlinear dynamic methods, including ApEn, SampEn, FE, PE, WPE, MSE, MPE, and MFE, were used to extract the features of MI-EEG. The experimental results are given in Figure 10.  
As seen from Figure 10, the classification results of ApEn and SampEn are relatively poor, because they use the Heaviside function to measure the similarity definition of reconstructed vectors. FE replaces the Heaviside function with fuzzy membership function, and the recognition rate has been improved. The classification accuracy of WPE is higher than that of PE. This is because WPE also contains amplitude information besides the order structure of MI-EEG, compared with PE. Compared with ApEn, SampEn, FE, and PE, the classification rates of MSE, MPE, and MFE have been greatly improved. That is because ApEn, SampEn, FE, and PE can only estimate the complexity of time series based on a single scale and MSE, MPE, and MFE can measure the complexity of time series on multiple scale factors. IMFE is improved by adding the parameters' independent optimization to MFE, and it can adaptively extract more and deeper information so that the classification accuracy can be further improved. In addition, compared to other nonlinear dynamic methods, its standard deviation (\(\pm\)2.1) is the smallest, which means that the improved MFE method has better stability.