esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. The brick granule content in RAIW have great effect on its performance. As a large quantity of tiny particles are contained in brick powder, the apparent density and bulk density of recycled aggregate constantly decreases as the brick powder content (brick-concrete ratio) increases, while the mud content and water absorption increase.
2. For RAIW, the cross-section failure mode of the flexural test is similar to those of the compressive strength test, in which the substrate broke and the interface fell off. In addition, the failure mode of the compressive test presented inverted 'V'-shaped oblique cracks, while the flexural strength test presented a longitudinal crack perpendicular to the loading surface.
3. The order of influencing factors on RAIW is as follows: the water-cement ratio, the aggregate-cement ratio, the lime powder content, the brick granule content in the aggregate, the replacement rate.
4. The water-cement ratio is the primary factor that influences the compressive strength, followed by the aggregate-cement ratio and the lime content. While the replacement ratio and the brick granule contained in the aggregate presented a wave tendency, this effect was not significant. The same rule was found for the analysis of variance and range for the flexural strength.
5. The water-cement ratio at level of 0.7-0.8 was especially effective for improving the mechanical properties of RAIW in this experiment.
6. The flexural-compressive ratio of RAIW ranged from 0.184 to 0.398. The analysis of the variance and range indicated that the aggregate-cement ratio has the greatest impact on the flexural strength of RAIW.
7. It was found that a good linear relationship exists between the mechanical properties of RAIW and the five factors examined in this experiment: brick granule content, the water-cement ratio, the aggregate-cement ratio, the lime content of the mixtures and the replacement rate. The proposed strength calculation formula presented better scientificity, which can provide a reference and can be applied in the relevant studies.  
**Author Contributions:** [PERSON] conceived the experiments, [PERSON] designed the experiments and wrote the initial draft of the manuscript. [PERSON], [PERSON] and [PERSON] analyzed the data and wrote the final manuscript.  
This paper forms part of the research supported by the National Natural Science Foundation of PR China (grant 51578163), the project of Natural Science Research Foundation of Guangxi Province (grants 2012 GXNSFAA053203 and 2013 GXNSFDA019025), and the Key project of Polytechnic and Science Experimental Center of Guangxi Province (grant LGZX201102). The authors are grateful for the above supporters.  
The authors declare no conflict of interest.
## 3 General Framework and Methodology
### The Self-Adaptive-Attention-Prompt-BERT-RCNN Model
#### 3.3.3 The BERT-RCNN Classification Model  
1. BERT Layer  
In the field of medical text processing, BERT, with its deep bi-directional Transformer architecture, has demonstrated a strong ability to capture complex semantic relationships between words in medical texts. In the task of classifying short medical question and answer texts, the BERT model first transforms the input text sequence into word embeddings, which are then deeply processed by multiple Transformer encoder layers. The clever combination of self-attention mechanisms and feed-forward neural networks in these encoder layers enables the model to finely analyze the word embeddings and generate context-sensitive vectors that fully capture the information of the entire text sequence. These vectors retain the rich semantic information of the original text and significantly increase the sensitivity to the specific language patterns in the medical domain, thus laying a solid foundation for the medical text classification task (Figure 5).  
Figure 4: Schematic of fine-tuned contrast cue learning based on BERT modeling.  
Figure 5: BERT structure diagram.  
RCNN layer  
RCNNs (Recurrent Convolutional Neural Networks) have a unique advantage in text classification tasks, which can effectively solve the problem of insufficient text window sizes and bring better classification results compared with traditional window-based neural networks. When processing medical Q&A text, the original text is segmented into a number of words, which are subjected to a left-right contextual convolution operation to extract the corresponding contextual information. The extracted contextual information is then processed into a circular convolutional layer to generate new feature representations. Then, the key feature information is extracted through the maximum pooling layer, and finally the classification results are generated through the fully connected layer to realize the accurate classification of medical Q&A text (Figure 6).
# ZONE-Based Multi-Access Edge Computing Scheme for User Device Mobility Management
## 2 Related Works  
To solve the aforementioned problems of mobile user's movement in the MEC environment, there are studies related to mobility management scheme in MEC environment. [PERSON] et al. developed a MEC platform with a migration support system for edge servers [26]. Their proposed platform uses IP mobility support gateway that applies the extended virtualized Mobility Support Gateway (vMSG) to achieve mobility management requirement. They focused on requirements for continuous and transparent communication as well as reduced latency between the edge server and the mobile user. This platform provides a low-latency response when the mobile user moves, but there is lack of consideration for the movement of the mobile user requiring task migration. [PERSON] et al. predicted user mobility with Kalman filter for estimation of the connectivity [27]. They used mobility prediction to enable mobile users to select stable MECS during task requests and task collection and the success rate of collecting results was improved. However, if the mobile user moves frequently, they should select stable MECS at the new location each time. [PERSON] et al. proposed the solution to decompose the data in the cloud into a set of files and services [28]. They performed caching on the user mobile device to provide frequently requested files and services quickly. Their proposed solution is able to rapidly provide cloud composite services to end users while maintaining Quality of Service (QoS) requirements, but there is a lack of consideration for task migration situations that occur when users move frequently. [PERSON] et al. proposed the solution to provide fast service in situations where users demand data or services at the same time in a high density environment such as the stadium or the subway station [29]. They used service-specific overlays to provide mobile devices with faster data access by using data replication methods from the cloud to the edge in the congested environment. Although the methods provide efficient solutions for managing data and services in congested situations, they do not provide efficient solutions to the use of MECS resources in situations where mobile users are frequently moving and generating tasks. [PERSON] et al.  
Figure 1: The existing cloud computing architecture and Multi-Access Edge Computing (MEC) architecture.  
proposed the mobile device cloud system architecture to solve the buffering problem of mobile devices in the dense and congested environment [30]. To this end, they utilized MPTCP (MultiPath-TCP) in their proposed system architecture. They also proposed an OS-side architecture that can manage the traffic coming from different flows. They have demonstrated the reduction in buffer occupancy using balking techniques that enable better queue management. They also modeled agents that meet Kleinrock's laws and demonstrated their effectiveness through experiments. The OS-side architecture and MPTCP-based methods allow efficient use of cloud resources, but lacks consideration of the MEC environment.
# Ultrafast Electron Dynamics in Magnetic Thin Films
## 3 Results  
Through the 3 THz filter, the maximum achievable THz fluence on the sample is 17.3 mJ/cm\({}^{2}\), leading to an instantaneous demagnetization (dM/Ms) of 60%, as shown in Figure 1d, where the time axis of the signals was shifted such that maximum demagnetization is observed at 0, and the timescale of remagnetisation can be compared for all pulse fluences. Extending the spectrum with the 6 THz filter, presented in Figure 1e, allows for much higher peak fluence up towards 66.8 mJ/cm\({}^{2}\) (equating to a peak incident magnetic field of 3.7 T in free space). This offers the possibility of complete demagnetization without sample damage at 47 mJ/cm\({}^{2}\), as alluded to in Figure 1f, where magnetisation is shown to recover. Measurements of the reflected pulse show that around 90% of the pulse fluence was reflected. This suggests that roughly 10% of the incident fluence is absorbed. Therefore, this corresponds to an absorbed fluence of \(\sim\)4.7 mJ/cm\({}^{2}\).  
At low fluence, the extent of demagnetization increases with the excitation fluence almost linearly, suggesting a linear THz absorption mechanism in Figure 1f.  
Figure 1: (**a**) The experimental setup for THz pump and optical MOKE probe. (**b**) The triggering unfiltered THz pulse. (**c**) Fourier transform of the unfiltered THz pulse **(d**)** Terahertz-induced demagnetization taken with 3 THz low pass filter and THz excitation levels of 4.3, 7.7, 11.2 & 17.3 mJ/cm\({}^{2}\). (**e**) The demagnetization measurements taken with 6 THz low pass filter for 8, 16.7, 24.5, 29, 33.3, 39, 47.3 & 66.8 mJ/cm\({}^{2}\). (**f**) The fluence-dependent demagnetization taken with 6 THz low pass filter for delay of 0,10 and 200 ps, with a straight line as a guide for the eye to showcase the near linear response.  
The dynamics under the different biases, characterised as the symmetric and antisymmetric components of the signal, are shown in Figure 2a for an excitation fluence of 190 mJ/cm\({}^{2}\). Individually, these dynamics contains all three degrees of freedom mentioned. If the signals under opposite magnetic biases are summed, we receive a bias-averaged total signal with features that do not depend on (the initial magnetisation state, and hence) the magnetic response of the sample. Consequently, the difference of opposite magnetic bias signals would give only the magnetic response of the material. Hence, the magnetic spin dynamics response (Figure 2b) could be separated from the charge and lattice degrees of freedom (shown in Figure 2c). The lattice and charge dynamics were further separated in Figure 2d by fitting Figure 2b to Figure 2c, with an overall amplitude variable, and calculating the difference.
## 4 Test Results and Analysis
### Parameter Determination  
According to the value range of each physical parameter in Table 1, the proportion scheme of similar materials of each ore and rock of multi-layer deposit was determined, as shown in Table 10. Samples of similar materials were prepared according to the reference ratio, and the physical and mechanical indexes of three groups of reference groups were measured by the same method. The test results are shown in Table 11.  
After calculation, the actual similarity constants of similar materials are shown in Table 12. The determined similar materials had a relatively dense structure and small porosity. The similar constants of similar materials met the requirements of test similar conditions, and the error range was less than 1.62%, which met the requirements of mineral and rock materials of similar multi-layer shale deposits.
# Low-Cost Active Anomaly Detection with Switching Latency
## Appendix A Proof for Theorems
### Order-Optimality of DMSC Policy  
In this subsection, we focus on Scenario 2 where the parameter \(s\) satisfies \(s=\Omega(B)\) as \(\alpha\to 0\). In this scenario, \(\triangle(M,L)\) can not be ignored as \(\alpha\to 0\).  
If DMSC is in Case I,  
\[\frac{2D(g||f)}{L+1}+\triangle(M,L)\geq\frac{D(f||g)}{M-L} \tag{101}\]  
holds. In this situation, based on (101), we can not infer whether the term \(c\frac{L(L+1)B}{2D(g||f)}+cs\frac{L(L-1)}{2}\) of (101) is greater or less than the other term \(c\frac{L(M-L)B}{D(f||g)}+csL(M-L-1)\). Thus, the asymptotic lower bound on the expected cumulative may be the value \(c\frac{L(L+1)B}{2D(g||f)}+cs\frac{L(L-1)}{2}\) or the value \(c\frac{L(M-L)B}{D(f||g)}+csL(M-L-1)\). If the asymptotic lower bound is equal to the former expression, we can upper bound the ratio of the expected cumulative cost of DMSC policy to \(\mathbb{C}^{*}\) by applying (100) and yield  
\[\begin{split}&\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{ \mathbb{C}^{*}}\\ &\leq\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C_{LB}^{*}}\\ &\leq\lim_{\alpha\to 0}1+\frac{r_{1}}{\frac{L(L+1)}{2D(g||f)}+ \frac{L(L-1)}{2B}}\frac{s}{B}\\ &\leq\lim_{\alpha\to 0}1+\frac{r_{1}s}{\frac{L(L+1)}{2D(g||f)}B}=O(1). \end{split} \tag{102}\]  
If the asymptotic lower bound is equal to the latter expression, then the upper bound is given by  
\[\begin{split}&\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{ \mathbb{C}^{*}}\\ &\leq\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C_{LB}^{*}}\\ &\leq\lim_{\alpha\to 0}\frac{c\frac{L(L+1)}{2}\frac{B}{D(g||f)}+cs \frac{L(L+1)}{2}T_{s}}{c\frac{L(M-L)B}{D(f||g)}+csL(M-L-1)}\\ &=\lim_{\alpha\to 0}r_{2}+\frac{r_{3}s}{(M-L)B+s(M-L-1)D(f||g)},\end{split} \tag{103}\]  
where \(r_{2}=\frac{(L+1)D(f||g)}{2(M-L)D(g||f)}\) and \(r_{3}=r_{2}\cdot((M-L)D(g||f)T_{s}-(M-L-1)D(f||g))\). Then,  
\[\begin{split}&\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{ \mathbb{C}^{*}}\\ &\leq\lim_{\alpha\to 0}r_{2}+\frac{r_{3}s}{(M-L)B}=O(1).\end{split} \tag{104}\]  
Thus, it has been proved that \(\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{\mathbb{C}^{*}}=O(1)\) in Case I.  
Then, if DMSC is in Case II, i.e.,  
\[\frac{2D(g||f)}{L+1}+\triangle(M,L)<\frac{D(f||g)}{M-L} \tag{105}\]  
holds. Then, we can infer that the asymptotic lower bound is equal to \(c\frac{L(M-L)B}{D(f||g)}+csL(M-L-1)\) since \(\frac{L(L+1)B}{2D(g||f)}+s\frac{L(L-1)}{2}\geq\frac{L(M-L)B}{D(f||g)}+sL(M-L-1)\) holds in this situation. Then,\[\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C^{*}} \tag{120}\] \[\leq \lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C_{LB}^{*}}\] \[\leq \lim_{\alpha\to 0}\frac{cL(M-L)\frac{B}{D(f||g)}+csL(M-L)T_{s}}{c \frac{L(M-L)B}{D(f||g)}+csL(M-L-1)}\] \[= \lim_{\alpha\to 0}1+\frac{sLT_{s}}{\frac{L(M-L)B}{D(f||g)}+sL(M-L-1)}.\]  
Since \(\lim_{\alpha\to 0}s/B=O(1)\) and \(T_{s}\) is a constant, then,  
\[\lim_{\alpha\to 0}\frac{E(C|\text{DMSC})}{C^{*}} \tag{121}\] \[\leq \lim_{\alpha\to 0}1+\frac{T_{s}s}{\frac{(M-L)}{D(f||g)}B}=O(1).\]  
Therefore, the proof of the order-optimality is completed.
## 5 Conclusions  
A Raman anti-Stokes scattering light-based distributed optical fiber temperature sensor is proposed and experimentally demonstrated. A calibration zone is set at the front end of the sensing fiber, which helps to obtain and correct the Raman anti-Stokes power value and temperature information. In addition, a calibration algorithm is proposed to correct the power variance of the scattering light caused by the fluctuation of output power in the pulse laser and the instability of the gain factor of the APD. Moreover, a reference about the power and temperature information along the sensing fiber is obtained, and, according to the derived temperature demodulation formula, the temperature value along the sensing fiber can be computed. In the experiment, the new sensing system is constructed with a sensing fiber length of approximately 3.5 km; a 24 dB dynamic range is achieved; temperature testing in a range from 30.0 \({}^{\circ}\)C to 80.0 \({}^{\circ}\)C achieves a maximum temperature deviation range of \(-1.5\)\({}^{\circ}\)C to \(+1.6\)\({}^{\circ}\)C; and the RMS error in the whole temperature range is 0.3 \({}^{\circ}\)C. The new temperature sensor avoids the walk-off effect that must be corrected in commonly used temperature demodulation schemes adopting both Raman Stokes light and anti-Stokes light. Therefore, it saves a signal sampling channel and some photo-electric devices, such as an APD, which has benefits for the integration of hybrid distributed optical fiber sensing systems for multi-parameter monitoring by using different light spectra.  
Conceptualization, L.L. and C.L.; methodology, L.L. and Y.W.; software, Y.W.; validation, L.L. and M.H.; formal analysis, L.L.; investigation, J.F., X.S. and C.L.; resources, L.L.; data curation, Y.W.; writing--original draft preparation, L.L. and C.L.; writing--review and editing, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of China with Grant No. 51977001.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Comparison with Other P300 Detectors
#### 4.3.1 Flda  
The FLDA classifier finds a linear decision boundary having the form of \(\mathbf{w}^{t}\)\(\mathbf{x}+b=\ 0\) to separate two classes [26]. The weight vector in FLDA is determined by  
\[\mathbf{w}=\mathbf{S}_{w}^{-1}\left(\mathbf{m}_{1}-\mathbf{m}_{2}\right) \tag{12}\]  
where  
\[\mathbf{S}_{w}=\sum_{k=1}^{2}\sum_{\mathbf{x}_{i}\in\mathbb{C}_{k}}\left( \mathbf{x}_{i}-\mathbf{m}_{k}\right)\left(\mathbf{x}_{i}-\mathbf{m}_{k}\right) ^{t} \tag{13}\]  
is the within-class scatter matrix [37], \(\mathbf{m}_{1}\) is the mean of the training data belonging to class 1 (P300 class), \(\mathbf{m}_{2}\) is the mean of the training data belonging to class 2 (non-P300 class), \(\mathbf{x}_{i}\)\({}^{\prime}s\) are training data, and \(C_{k}\) denotes the \(k\)th class. In order to prevent the within-class matrix from suffering the small-sample-size (SSS) problem, _i.e._, the matrix \(\mathbf{S}_{w}\) becomes nonsingular and thus its inverse does not exist if the dimension of \(\mathbf{x}_{i}\) is larger than the number of training data, we add a sufficiently small value to every diagonal element of \(\mathbf{S}_{w}\), as suggested in [32]. For an unseen data \(\mathbf{x}\), its output can be obtained by the sign of the FLDA decision function  
\[f\left(\mathbf{x}\right)=\mathbf{w}^{t}\mathbf{x}+b \tag{14}\]  
Figure 8: P300 classification rates of different methods.  
where \(b\) is a bias which needs to be determined experimentally [26]. The test data \(\mathbf{x}\) is classified as class 1 if \(f\left(\mathbf{x}\right)>0\); otherwise, it is classified as class 2. In our experiment, the optimal bias \(b\) is determined using a grid search and 10-run two-fold cross validation procedure. The best value of \(b\) gives the highest average classification rate.
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 2 Materials and Methods
### Power Law Gradation of Non-Porous FG Shaft  
There are various gradation laws such as the power law, sigmoidal law and exponential law for material gradation. Amongst these gradation laws, the power law is the most widely used and is the simplest for controlling the gradation of material properties. As FGM is especially made for high-temperature conditions, it is considered that material properties vary nonlinearly with temperature (\(T\)). Thus, NLTD is always used alongside the power law. For an FG shaft with a circular cross-section, the power law is expressed as in Equation (5) [28].  
\[P(r,T)=P_{m}+(P_{c}-P_{m})\bigg{[}\frac{r-R_{i}}{R_{o}-R_{i}}\bigg{]}^{k} \tag{5}\]  
where \(P(r,T)\) represents the varying material properties along with the radius. \(R_{i}\) and \(R_{o}\) represent the inner and outer radius of the shaft, \(r\) is the radial coordinate of the shaft and \(k\) is the power law index. The value of \(k\) can range from 0 to \(\infty\).
# Deep Learning Methods in Image Matting: A Survey
## 7 Challenges and Future Directions
### Robust Trimap-Free Matting  
The current trimap-free methods combined with the powerful feature extraction capability of CNN and increasing number of open-source datasets can already generate fine alpha mattes without the need for a trimap. However, they still have limitations, and their performance is not ideal in some special scenarios. We randomly experimented with some open-source trimap-free matting models [29; 30; 34; 35], and the results are shown in Figure 6. When processing moving foreground objects, the edges of the objects in motion may blend and overlap with the background, creating blurry areas that greatly increase the difficulty of image processing. The model fails to match them correctly. In addition, current models also struggle to perfectly extract small foreground objects with different scales when they appear in images. This is because most matting datasets contain images with large foreground objects that dominate the image. The model tends to overlook smaller foreground objects within an image when there are foreground objects of varying scales present. It is currently a major challenge for trimap-free methods to extract foreground objects with completely different scales and to deal with motion blur.
# Wireless Power Transfer System Based on Strapping Resonators
## 4 Discussion  
The comparison of the proposed system and the system proposed by MIT's group is carried out using numerical simulation. In simulation, the material of the resonators is set to be perfect conductor  
Figure 7: Experimental setup and results: (**a**) Simulation model, (**b**) experimental setup, (**c**) WPT efficiency for the \(\pi\) mode, and (**d**) WPT efficiency for the \(\pi+1\) mode.  
(PEC) to eliminate the metal loss. Thus, in the simulation, the efficiency is only impacted by leakage and radiation loss. To compare the WPT systems working at different frequencies, the distance was normalized by the corresponding operational wavelengths. As shown in Figure 8, it is obvious that the WPT system based SR operating at the \(\pi\) mode and the \(\pi+1\) mode shows great advantage towards MIT's systems [3]. In \(0<\mathrm{d}/\lambda<2\), the efficiency of the operation modes of the proposed system is about 5% higher than MIT's system. In \(2<\mathrm{d}/\lambda<8\), the efficiency of the proposed system is still stable above 90%, but the efficiency of MIT's system drops from 82% to 30%.  
The power capacity of the proposed system is also investigated using simulation. Both dielectric resonator (DR) and metal cavity resonator have high power capacity. They can operate and can be applied in high power WPT in microwave regime.  
The maximum electric fields in each system are shown in Figure 9 when the operating frequency (650 MHz) is the same. The electric field of the systems are excited by a signal with an average power of 1W. The maximum electric field in the proposed system is distributed around the input coaxial line and the strapped rings with an amplitude of 16 V/cm, while the maximum electric field in the system based on DR is distributed between the feeding structure and the source resonator with an amplitude of 35 V/cm. To achieve the breakdown voltage of air [18; 19], the input power of the proposed system should be 1800 W, while the input power of the DR system must be higher at 950 W. Therefore, the power capacity of the proposed system is 94% higher than the DR system.  
From the perspective of cost, the SR is made of metal and is inexpensive to manufacture compare to DR. In addition, the metal resonator is easy to package and integrate with high power devices.  
Figure 8: Relationship between radio power transmission efficiency and distance.  
Figure 9: Electric field distribution (**a**) Field distribution in the system based on SR. (**b**) Electric field distribution in the system based on DR.