esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Acoustic-Assisted CCS Experiments: \(\sigma_{1}\) = \(\sigma_{2}\)  
In Experimental series B (i.e., acoustic-assisted CCS experiments where \(\sigma_{1}\) was set equal to \(\sigma_{2}\)) a total of eight CCS tests were performed on the numerous rock types collected from the Kizildere site (Table 3). Figure 6 presents the P-wave velocity as a function of increasing \(\sigma_{1}\) = \(\sigma_{2}\). The S-wave velocity, as a function of increasing \(\sigma_{1}\) = \(\sigma_{2}\), is shown in Figure S4 (see Supplementary Materials).  
Similar to the results of the performed UCS experiments (Section 3.2), the acoustic results shown in Figure 6 also indicate that the largest increase in P-wave velocity occurred at the lowest absolute stress levels. Most probably, as previously discussed, this is a consequence of potential closures of microcracks within the assessed porous media. Once again, the weakest material investigated, i.e., siltstone, reveals the lowest P-wave velocity. In fact, the eight rock types investigated can be classified into the following groups: soft and hard porous media. The soft materials, i.e., micaschist, silt- and mudstone, are characterised by a relatively low bulk density (\(\leq\)2.67 \(\pm\) 0.02 g/cm\({}^{3}\)) and high porosity (\(\geq\)8.52 \(\pm\) 0.37%). They reveal relatively low P-wave velocities. The hard materials, with the exception of the limestone, are defined by a high bulk density (\(\geq\)2.67 \(\pm\) 0.02 g/cm\({}^{3}\)) and low porosity (\(\leq\)2.77 \(\pm\) 0.16%). They show high seismic velocities of \(>\)4630 m/s for all stresses studied. Furthermore, in line with the observations made in Section 3.2, the limestone sample behaves somewhat differently compared to the other formations: it demonstrates a relatively constant seismic velocity as a function of increasing \(\sigma_{1}=\sigma_{2}\). The data points symbolised by circles in Figure 6, representing active-source acoustic measurements during the unloading stage at the end of an experiment, generally follow the loading trend. The latter suggests that the tests were conducted in the elastic regime and no permanent deformation, that might significantly affect the seismic velocity, occurred.  
Figure 5: P-wave velocity as a function of axial stress, and thus depth below surface, for all UCS experiments performed. Axial stress–depth relationship is taken from [35].
# Cooperative Power-Domain NOMA Systems: An Overview
## 2 Non-Orthogonal Multiple Access  
Several MA schemes are presented in the introduction section; however, there exist multiple constraints and trade-offs in any particular MA scheme. For brevity, let us investigate only two such constraints. First, the number of users served at the same time is limited. Second, user scheduling and robust feedback mechanisms are necessary to ensure orthogonality. These constraints are overcome by NOMA, which is a superior resource allocation approach to OMA as it has the ability to maximise system's spectral efficiency. The basic concept and brief mechanism of NOMA is discussed next.
## 1 Introduction
In addition, due to the lower cost and greater proliferation of scanning electron microscopes compared to high-resolution microtomography scanners, fiber length examination using a scanning electron microscope may be preferable for a series of repetitive tasks, such as input and output material inspection. Obtaining two-dimensional microscopy images requires simpler equipment than computed microtomography; however, it is a more difficult task to recognize fiber patterns because of their possible layering on top of each other [22; 25; 26]. This problem can be solved by applying neural networks. A previous study [27] developed a deep learning model for processing optical microscope images. One of the main architectures for using the instance segmentation method is an architecture based on convolutional neural networks called Mask R-CNN [28; 29]. Various studies have demonstrated the versatility of Mask R-CNN, e.g., for bacterial colony counting [30], vehicle counting [31], cattle counting [32], plant counting, and size measurement [33], among others. A feature of each solved problem was the large sample of instances in the image and the presence of many overlapping instances. Thus, Mask R-CNN can detect, count, and measure short fibers. In a previous study [34], a two-stage image segmentation technique was developed to solve the problem of fiber detection in electron microscope images. The first stage involves semantic segmentation using DeepLabv3+ [35] or PVT [36] neural networks. The second stage is based on instance segmentation using SAM [37] or SEEM [38] neural networks. By using these techniques together, a significant improvement in mask prediction can be achieved [39; 40]. In [41], a method was proposed to describe the spatially varying microstructural behavior of a fiber, obtaining local anisotropic stiffness and strength that can be integrated into a finite element structure for the volumetric estimation of effective stiffness and strength. The results of this methodology are presented for an additively manufactured carbon-fiber-reinforced polymer. This paper is devoted to the analysis of the sizes of short glass fibers in polyamide-6 composites using scanning electron microscopy and neural networks for the segmentation and dimensioning of the obtained images, as well as to the estimation of the error from considering the aspect ratio of these fibers in models of homogenization characteristics of a representative volume of the material. The novelty of this paper lies in the effective fiber length evaluation based on a comparative analysis of fiber aspect ratios using scanning electron microscopy (SEM) and the mean field homogenization approach. The peculiarity of this method is the large number of processed images with glass fibers, which increased the accuracy of the calculated parameters. The effective fiber length evaluation obtained in this work will allow us, on the basis of scanning electron microscope fiber length measurements, to make refinements to the parameters of mean field homogenization models used both in calculations [42] and in the design [43] of structures made of composite materials.
## 2 Materials and Methods
### Characterisation of Thin Film and Device Performance  
Raman spectroscopy was performed to investigate the semiconducting phase of MoTe\({}_{2}\). All the Raman spectra were obtained using a Horiba Jobin Yvon LabRAM ARAMIS IR\({}^{2}\) spectrometer with a 532 nm excitation laser, a laser power of 5 mW, and a laser spot of diameter 200 \(\upmu\)m. The height profile of the channel was measured using an atomic force microscope (CE-100; Park Systems, Suwon-si, Korea). The electrical characterisations of the MoTe\({}_{2}\) FETs were performed using a semiconductor parameter analyser (B1500A, Keysight, Santa Rosa, CA, USA). All the transfer characteristics were measured under a drain bias of 1 V.
## 3 A Feasible Solution and the Mathematical Formulations
### Mathematical Formulations
#### 3.2.1 Parameters  
* \(D_{ij}\): The shortest path distance between nodes \(i\) and \(j\in V\) in the network. Notice that matrix \(D=(D_{ij})\) is symmetric, and its diagonal has only zero values. Moreover, notice that these distances are computed using [PERSON]'s algorithm [PERSON] (1996).
## 3 Numerical Calculation Settings
### Nuclei Distribution Settings  
The initial sizes of nuclei have significant influences on the prediction of tip vortex cavitation inception, as a nucleus with larger size is prone to be captured by a tip vortex and grows to an incipient cavitation bubble [56], leading to a higher predictive value of the incipient cavitation number. The literature [57] argues that the initial radius of a nucleus R = 100 \(\upmu\)m can be used as the maximum nucleus radius in the practical calculation of a cavitation tunnel. In the literature [58], nuclei with three different initial diameters are selected for the calculation, and it is found that the initial radius of a nucleus R = 50 \(\upmu\)m can satisfy the discrimination criterion of cavitation inception in engineering applications. The existing experimental measurement results indicate that the diameters of nuclei in the water are mainly distributed between 40 \(\upmu\)m and 60 \(\upmu\)m. In summary, in order to eliminate the influence of the nucleus initial size and make the proposed cavitation inception discrimination method based on bubble dynamics in this paper more applicable, the nucleus initial diameter selected in this paper is a fixed value of 50 \(\upmu\)m by referring to the nuclei spectrum given by the experiments in [55]. There are 14,040 nuclei in total, which are released randomly in an annular region 0.2R-1.0R away from the upstream of the propeller disk surface. The initial release region of nuclei is shown in Figure 4.
## 5 Results
### Topology 2
#### 5.2.1 Scenario 7  
In this test scenario, each link on the path h26->h30->h27 was configured with a delay of 100 ms, and h26->h29->h27 was configured with a delay of 50 ms with a 10% packet loss. The link between h29 to h30 had no delay or packet loss. The static method was configured to use the path h26->h29->h27.  
Figure 15a shows the average response time with snnr being 1-30% better than snndp and 2-25% better than snnd. Both snnr and snndp performed worse than static at lower bundles, but showed 36-70% and 36-67% better response for bundle rates of 9-10 bundles/s. snndp showed up to 20% better performance than snnd. snndp and snnr showed up to 100% and 90% worse performance than snnp.  
Figure 15b shows the packet loss percentage with snnr being 2-46% and 2-25% better than snndp and snndp had 8% better performance than snnd. snnp showed up to 100% better packet loss performance than snnr and snndp. snnr and snndp showed 41-70% 29-43% better performance than static.  
Figure 15c depicts that the throughput with snnr being up to 4-31% and 2-27% better than snndp and snnd. snnp showed up to 30% better throughput than snnr and snndp showed up to 10% better throughput than snnd. snnr and snndp showed 105-17% and 92-144% better throughput than static.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **h26-h29** & **h29-h27** & **h26-h30** & **h30-h27** \\ \hline Scenario 7 & 50 ms loss 10\% & 50 ms loss 10\% & 100 ms & 100 ms \\ Scenario 8 & 50 ms loss 10\% & 50 ms loss 10\% & 100 ms & 100 ms \\ Scenario 9 & 200 ms & 50 ms loss 10\% & 50 ms loss 10\% & 200 ms \\  & & rate 640 kbit & rate 640 kbit & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Delay and packet loss configurations tested with Topology 2.  
Figure 14: Link selection at Node h26: Scenario 6.
## 3 Proposed Approach
### Routing Protocol Description
```
0:\(SA\_view\), node \(n\) running the algorithm
1:procedureHDA_Routing(\(SA\_view\), \(n\))
2: Group friendly nodes from \(SA\_view\) in a set \(\mathcal{F}\)
3: Create an empty set \(\mathcal{L}\) that will store links between friendly nodes
4: Group remaining nodes from \(SA\_view\) in a set \(\mathcal{A}\)
5: Use the sets \(\mathcal{F}\) and \(\mathcal{L}\) to define a directed topology graph \(\mathcal{G}(\mathcal{F},\mathcal{L})\)
6:for each node \(src\in\mathcal{F}\)do
7:for each node \(dst\in\mathcal{F}\) other then \(src\)do
8:if\(dst\) in Tx range from \(src\)then
9: Add the directed link \((src,dst)\) to set \(\mathcal{L}\)
10: Set link cost: \((src,dst)\).weight \(\gets 1\)
11:else
12: continue
13:endif
14:\(f\leftarrow\)Is_Forbidden_Sector(\(src,dst,\mathcal{A}\))
15:if\(f\) = True then
16: Set link cost: \((src,dst)\).weight \(\leftarrow\) 5000
17:break
18:endif
19:endfor
20:endfor
21:\(SPT\leftarrow\)Dijkstra(\(n,\mathcal{G}(\mathcal{F},\mathcal{L})\))\(\triangleright\)Shortest paths from \(n\) to other nodes in \(\mathcal{F}\)
22: Use \(SPT\) to update the IPv4 routing table entries at \(n\)
23:endprocedure
```  
**Algorithm 4** Hostile-Direction Aware Routing (HDARP) at node \(n\).
## 2 Geological Setting  
The southern Sichuan Basin is located in the southeast of the Upper Yangtze block (Figure 1). In the Precambrian, the Yangtze block was one part of the South China Plate and separated from the Tarim and North China Plates in the Early Paleozoic. The Wufeng-Longmaxi interval was deposited during the collapse of the South China Basin and the formation of the South China orogenic belt [10; 11]. Convergence between the Cathaysia and Yangtze blocks during the Guangxi orogen led to the uplift of the Southeast Yangtze platform and the Jiangnan Basin at the end of the Cambrian [12; 13]. This was followed by the development of a passive continental margin in the Yangtze region [11].  
During the Early to Middle Ordovician, a carbonate platform dominated the Yangtze platform's sedimentary facies [14; 15]. By the Late Ordovician, the platform was covered by a mixed carbonate-clastic epicir sea. During the Ordovician-Silurian transition, global sea levels fluctuated significantly [16; 17]. From the Early Silurian, the expansion of the Cathaysia Plate and the uplift of much of South China caused substantial changes in land and sea distribution (Figure 1b). Regional tectonism created ancient landmasses and subaqueous highs, transforming the Upper Yangtze region into a semi-closed sea [7,17]. This barrier resulted in prolonged anoxic and stagnant conditions in the Upper Yangtze region [17-19].  
The Wufeng-Longmaxi interval is widespread across the Upper Yangtze region, with a thickness ranging from 0 to 550 m (Figure 1c). The Wufeng Formation is relatively thin, typically between 0 and 13 m [20]. It predominantly consists of black, graptolite-rich shale, except for a thin bioclastic limestone layer in the Guanyinqiao bed of the uppermost part (Figure 2). The Wufeng Formation overlies the Baota Formation and is overlain by the Longmaxi Formation. It contains graptolite bizoones _Dicellognptus complantus_ (WF1), _Dicellognptus complexus_ (WF2), _Paraorthognptus pacificus_ (WF3), and _Metabolognptus extraordinarius_ (WF4) from bottom to top [21,22].  
Figure 1: The paleogeographic background of the study area. (**a**) Global Paleogeography of the Ordovician-Silurian transition; (**b**) Early Silurian paleogeographic map of the Yangtze epicontinental sea in southern Sichuan Basin; (**c**) Shale thickness in the Sichuan Basin of the Ordovician-Silurian transition.  
Figure 2: Descriptive chart and bedding characteristics of Wufeng Formation in the southern Sichuan Basin, China.
## 4 Online Icing Prediction Model Based on _Robust STL-BTSF_ and IBWO-SVR  
### Forecasting Process  
The foundational concept underpinning the modeling approach involves the projection of meteorological datasets, encompassing variables such as ambient temperature, relative humidity, and wind speed, into an expansive, high-dimensional feature space. This is achieved through the establishment of a nonlinear mapping. Subsequently, linear regression analyses are conducted on the icing phenomena within this intricately defined feature space. Features are extracted from time series data to obtain feature vectors. Then, using kernel functions, these feature vectors are mapped to a higher dimensional feature space, making the data easier to fit with linear models. Finally, use the SVR model to train and predict the mapped data. The procedural methodology of the icing prediction model, based on the integration of _Robust STL-BTSF_ and IBWO-SVR, is delineated in Figure 5. This figure provides a visual representation of the sequential steps and analytical processes involved in the model's formulation.  
Accordingly, the online icing prediction model can be divided into the following six parts:  
Step 1: Meteorological data preprocessing based on _Robust STL-BTSF_.  
Step 2: Establish the LSSVR model. Choose the kernel function, the regularization constant \(c\) kernel function, and the hyperparameter \(g\).  
Step 3: Optimize the model parameters. A multi-strategy method is proposed to improve the BWO and use the IBWO to find the optimal value mentioned in Step 2.  
Step 4: Construct an IOL online model. An incremental learning algorithm is used to construct an online prediction model for ice cover.  
Step 5: Establish and train the IBWO-LSSVR online prediction model. The processed data is fed into the model, building the icing prediction model of transmission lines.  
Step 6: Test and model evaluation. Test the performance and choose evaluation indexes to evaluate the model and verify the effectiveness of the model.
## 2 Materials and Methods
### Monte Carlo Computation
#### 2.1.2 Energy Transfer Modeling  
To track individual energy transfer events and calculate the transfer efficiency for different configurations, we used the Monte Carlo code described previously [24; 30]. Based on the distances of the individual fluorophores, it calculates the transfer probabilities and, based on these, decides the subsequent course of each \"excitation\" in the system. In each system, one type of protein/QD was designated as a donor and initially excited. After that, each molecule could be both donor and acceptor, depending on the current situation (homo-transfer was also possible). In each case, we used 10,000 different starting configurations of proteins within layers to calculate FRET efficiency. It is worth noting that the FRET efficiency is determined by not only the intermolecular distances but also the orientation factor, \(\kappa\)2. In this study, we assumed a \(\kappa\)2 value of 2/3, which is typical for rapidly rotating fluorophores in free solution. However, in our system, while the orientation of the fluorophores is completely random, it remains static. This distinction may introduce discrepancies when comparing computational results with experimental observations.
## 4 Performance Analysis
The overhead ratio of the proposed protocol has the smallest out of all the considered protocols since the proposed protocol restricts the forwarded messages efficiently and has a smaller number of forwarded messages and also has a higher delivery ratio. Figure 21 shows the delivery latency. Delivery latencies of all the protocols decrease as the number of cars increases since messages can be forwarded quickly with the help of more message forwarders and delivered to destination nodes more quickly. The proposed protocol has the smallest  
Figure 7:3 The delivery latency for different numbers of cars. Figure 7:4 The overhead ratio for different numbers of cars. delivery latency since the proposed protocol restricts forwarded messages efficiently, and thus, has a smaller number of messages in the buffer, which results in a faster message delivery to the final destination nodes.
## 2 Related Work
### Autoencoders
Again, the Conv.LSTM layers are expected to capture the temporal dynamics of video sequences along with spatial information. The experimental results indicated that OF-ConvAE-LSTM is effective in detecting anomalies in videos. In addition, the model was able to outperform the oldest approaches. However, as previously mentioned, optical flow increases the model complexity. In [22], a spatio-temporal residual autocoder (R-STAE) architecture was proposed to obtain visual patterns more accurately than other types of deep spatio-temporal autocoders. The R-STAE architecture is composed of layers common in other autocoder architectures used in video anomaly detection tasks such as: 3D convolution, deconvolution and Conv.LSTM layers. The difference in R-STAE are the residual blocks added to the architecture, used to avoid the vanishing gradient problem. The architecture is composed of three convolution layers and one Conv.LSTM layer in the encoder component, while the decoder consists of three deconvolution layers and one Conv.LSTM layer. The residual blocks are located between the encoder and decoder components. As the preceding methods, R-STAE is built as an end-to-end learning framework guided by the reconstruction loss. The experimental results on anomaly detection in surveillance videos showed that the addition of residual blocks to the network helps achieve lower reconstruction loss compared to the network with no residual blocks. In addition, R-STAE outperformed other methods considered state-of-the-art. However, due to the different types of layers involved, the setup of some hyperparameters is difficult and may critically affect the performance of the model. For instance, a very low number of hidden units in the Conv.LSTM layer can lead to information loss, whilst the opposite can introduce redundancy in the latent representation.
## 2 Research Methodology  
### Car-Following Model  
A basic car-following model expresses the human-driven vehicle's longitudinal dynamics through observations of the vehicle and its corresponding leading vehicles--namely, its velocity, its relative distance, and the headway between vehicles. A basic car-following model is described as follows:  
\[a_{i}=f(h_{i},h_{i},v_{i}), \tag{1}\]  
where \(a_{i}\) indicates the vehicle's acceleration, \(f(0)\) indicates the nonlinear approximation, \(v_{i}\) indicates the leading vehicle's speed, \(\dot{h}_{i}\) indicates the relative speed, and \(h_{i}\) expresses the headway between vehicles.  
To improve the realistic driver modeling, the IDM that is a subset of the ACC system conducts human-driven behavior through the longitudinal dynamic. In this study, the \"get\" function sets the vehicle speed, the leading vehicle's identification (ID), and the headway between vehicles. Accordingly, the vehicle acceleration command is expressed as follows:  
\[a_{IDM}=a\left[1-\left(\frac{v}{v_{0}}\right)^{\delta}-\left(\frac{s^{*}(v, \Delta v)}{s}\right)^{2}\right], \tag{2}\]  
where \(a_{IDM}\) indicates the vehicle acceleration, \(v_{0}\) indicates the desired velocity, \(\delta\) indicates the acceleration exponent, \(s\) indicates the headway between vehicles, and \(s^{*}(v,\Delta v)\) expresses the desired headway. In particular, the desired headway is shown as follows:  
\[s^{*}(v,\Delta v)=s_{0}+max\left(0,vT+\frac{v\Delta v}{2\sqrt{ab}}\right), \tag{3}\]  
where \(S_{0}\) indicates the minimum gap, \(T\) indicates the time gap, \(\Delta v\), indicates the difference between the current velocity and the lead velocity, \(a\) indicates the acceleration term, and \(b\) indicates the comfortable deceleration.  
Based on Treiber and Kesting [36], the typical IDM parameters in the context of city traffic are expressed in Table 1.
## 3 Testing Results Analysis
### Analysis of Experimental Results of Lower Step Specim
#### 3.2.2 Characteristics of Acoustic Emission Energy  
Figure 14 is the acoustic emission absolute energy and accumulated energy diagram of the lower step specimen. Among them, T1, T2 and T3 were the key points at which the absolute energy of the three acoustic emissions changed abruptly. In general, the absolute energy and accumulated energy curves of acoustic emission of the lower step specimen and upper step specimen had a high similarity. The difference was that when the lower step specimen was disturbed in the fourth stage, a large acoustic emission energy release occurred, and the released energy was about \(1.17\times 10^{7}\) J. At this time, the shear cracks appearing in the specimen intersected with each other during the expansion process, forming a local penetration and resulting in a \"logarithmic spiral\" partition fracture phenomenon and a trend of flake spalling. When the fifth-order perturbation was applied, there was also a significant release of acoustic emission energy, which was about \(2.0\times 10^{7}\) J,representing the development of the \"logarithmic spiral\" zonal disintegration phenomenon at the right arch footing. With the continuous development and penetration of cracks, when the sixth-order disturbance was applied, the acoustic emission signal was concentrated and the energy was huge, releasing a large amount of energy, and finally rockburst occurred. When the rockburst was destroyed, the absolute energy of acoustic emission of the lower step specimen reached 2.08 \(\times\) 10\({}^{8}\) aJ, and the accumulated energy of acoustic emission could reach 2.67 \(\times\) 10\({}^{8}\) aJ.