esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Relative Dynamic Elastic Modulus of RHA Recycled Aggregate Concrete  
The stiffness of concrete specimens subjected to the freeze-thaw cycles can be characterized by the variation in the relative dynamic elastic modulus [43]. Figure 7 shows the variation in the recycled concrete's relative dynamic elastic modulus with different RHA replacement ratios during the freeze-thaw cycles. As shown in the figure, the relative dynamic elastic modulus of concrete decreases continuously with an increase in the number of freeze-thaw cycles. Specifically, the relative dynamic elastic modulus decreases slowly in the initial stage of the cycle, while it decreases rapidly in the later stage of the cycle, indicating that the internal damage of the concrete gradually increases, subjected to the freeze-thaw cycle. The variation in the relative dynamic elastic modulus is related to the number of cracks and pores in the concrete itself, as there are many unfavorable interfacial transition zones within the recycled concrete aggregate, thus resulting in a lower initial dynamic modulus of elasticity for the concrete. The higher the initial damage degree, the easier it is for the specimens to form new cracks [44]. Under the freeze-thaw cycling, the expansion and icing pressure of pore water inside the specimen will lead to numerous new pores and new microcracks in the unfavorable interfacial transition zone. The presence of these discontinuities in the matrix increases the propagation time of the ultrasonic pulse velocity, which leads to a decrease in the relative dynamic elastic modulus.  
In addition, the decreasing degree in the relative dynamic elastic modulus increased with the increase in the RHA replacement ratio. This is probably because the addition of RHA can enhance the pore structure within the concrete, reducing the freezing point and pore connectivity, retarding the crack generation and water migration between the pores inside the concrete. However, the introduction of the RHA mesoporous structure will lead to the expansion and extension of the pore structure under the action of expansion pressure, resulting in the deterioration of the pore structure, which will adversely affect the concrete's freeze-thaw resistance, thus resulting in the freeze-thaw damage of concrete specimens with 20% RHA after 300 freeze-thaw cycles. In addition, the reduced cement content at a high RHA replacement rate resulted in the poor filling of paste pores, thus leading to the damage of concrete specimens with 30% RHA after only 250 freeze-thaw cycles.  
In conclusion, introducing RHA will reduce the concrete freeze-thaw resistance compared to ordinary concrete (without RHA), and the decreasing degree increases with the increase in the RHA replacement rate. The influence mechanism of the RHA substitution  
Figure 7: The relationship between the relative dynamic elastic modulus of recycled concrete and the number of freeze–thaw cycles with different RHA replacement ratios.  
rate on the freeze-thaw damage deterioration of recycled concrete will be analyzed in detail in Section 3.6.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling
### Safe Ant Colony Algorithm
\tag{18}\]  
Moreover, when returning to the backward ant, security factors should also be considered and the formula should be updated to the backward ant:  
\[\tau_{ij}(t+1)=(1-\rho)*\tau_{ij}(t)+\sum\limits_{k=1}^{n}\Delta\tau_{ij}^{k}(t) \tag{19}\]  
Formula (20) is a security-enhanced version of Formula (19):  
\[\tau_{ij}(t+1)=(1-\rho)*\tau_{ij}(t)+\sum\limits_{k=1}^{n}\Delta\tau_{ij}^{k}( t)*m_{ij} \tag{20}\]  
where \(\rho\) represents the evaporation factor of pheromones in the ant colony algorithm. The calculation formula of \(\Delta\tau_{ij}\) is as follows:  
\[\Delta\tau_{ij}^{k}=\frac{Q}{L_{k}} \tag{21}\]  
where \(Q\) represents the constant and \(L_{k}\) represents the total distance traveled by the ant \(k\). In Formula (19), \(\Delta\tau_{ij}^{k}(t)\) represents the pheromone concentration contributed by the ant \(i\) on the path \(ij\). The formula updates pheromone concentrations without recording safety factor information obtained by other ants. The ants in Formula (16) take security into account when they find the way, but Formula (19) for updating the pheromone does not take security into account, which will lead to deviations when the ants update the pheromone. In order to correspond to the pheromone in Formula (16), Formula (20) combines the pheromone left by \(N\) ants on path \(ij\) with the safety assessment factor. The meaning of this formula is the product result of the pheromone and safety assessment factor left by n ants passing through this path, and the product result and the pheromone volatile factor \(\rho\) jointly determine the concentration of the updated pheromone. In this way, when the ants were renewed to update the pheromone, the security factors were considered to make the results more reasonable. In short, the idea of virtual topology is used to convert the dynamic topology of a satellite network into static topology with time slice changes. The advantage of this is to simplify the interference of many complex factors in the topology changes as much as possible so that the traditional ant colony algorithm can run in different time slice ranges. The operation period of the satellite group of the polar orbit constellation is \(T\), and there are \(N_{s}\) satellite nodes in the same orbit. According to the theorem in [35], when a satellite node enters the polar circle within a certain time and closes the link between different orbits, another satellite node must move out of the polar circle and open the link between its different orbits within this time range. Therefore, we can follow the conclusion above and divide the scope of the time slice into \(T/N_{s}\), and the topology of the satellite network in a time slice can be regarded as unchanged.
## 3 Retrieval  
The retrieval presented in this section may be applied to various passive hyperspectral satellite instruments operating in the NIR or SWIR and may be used to gain information on various gaseous species with suitable absorption bands. However, here we concentrate on the retrieval of XCO\({}_{2}\) (plus XH\({}_{2}\)O and SIF) from an OCO-2 like satellite instrument.  
OCO-2 was launched in July 2014 and is part of the A-train satellite constellation. It flies in a sun-synchronous orbit crossing the equator at 13:36 local time. OCO-2 measures one linear polarization direction of the solar backscattered radiance in three independent wavelength bands: the O\({}_{2}\)-A band at around 760 nm (band 1) with a spectral resolution of about 0.042 nm and a spectral sampling of about 0.015 nm, the weak CO\({}_{2}\) band at around 1610 nm (band 2) with a spectral resolution of about 0.080 nm and a spectral sampling of about 0.031 nm, and the strong CO\({}_{2}\) band at around 2060 nm (band 3) with a spectral resolution of about 0.103 nm and a spectral sampling of about 0.040 nm. OCO-2 is operated in a near-push-broom fashion and has eight footprints across track and an integration time of 0.333 s. The instrument's spatial resolution at ground is 1.29 km across track and 2.25 km along track. More information on the OCO-2 instrument can be obtained from the publications of [PERSON] et al. [21; 22].
# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
## 4 Image Characterization Analysis of Space Target Degradation Caused by Haze  
In order to further verify the research significance of the space target dehazing method and clarify the research target, polarization images of simulated space targets were first used to analyze the characteristics of degraded images.  
To study the effectiveness of different image restoration methods under various conditions from the perspective of quantitative analysis, the degradation model based on atmospheric scattering was used to obtain the degradation image caused by haze. The image degradation model was proposed by [PERSON] [22] in our paper; combined with the Stokes theory, the degradation model applied to polarization images was improved to obtain a more accurate image degradation model. The Stokes component of the improved degenerate model can be expressed as:  
\[\left\{\begin{array}{l}I_{5_{0}}(x,y)=\frac{1}{4}(\frac{{{{\left({{{{\rm{ \it{\it{\it{\it{\it{  
It can be seen from the comparison results that although the polarization detection method can effectively detect some target details from the degraded image when applied under conditions with low ambient brightness, the influence of haze on the polarization image is also serious, resulting in a large amount of target information loss. In the polarization image of real space targets, the detail and contour information of space targets are small; if the image degradation is serious, the target polarization information obtained by direct calculations will be completely lost. Therefore, to avoid the loss of detailed features, the dehazing enhancement method proposed in this paper should be based on the main purpose of recovering the polarization information using the known information to the largest extent while simultaneously enhancing the target image.
# Improving Text Recognition Accuracy for Serbian Legal Documents Using BERT
## 3 A BERT-Augmented Text Recognition Approach
### SrBERTa v2--BERT-Based Language Model for Serbian Legal Texts Written in Cyrillic
Given that the task in question was masked language modeling, the SrBERTa network was implemented as a HuggingFace RobertaForMaskedLM class, which utilizes three types of input tensors: the token ID tensor, the attention mask tensor, and the label tensor. Initially, for the purpose of creating token ID tensors, we applied masks to 15% of the tokens in each input sequence. It is important to note that the loss calculation was performed only on the masked tokens, while the others were ignored. The initial stage of training the SrBERTa v2 network was conducted over 45 epochs and took a total of 31 days on an NVIDIA QUADRO RTX 5000 GPU. For this phase, we utilized 90% of the data from the OSCAR dataset and 90% of the data from the Serbian legal corpus, which we previously created. The rationale was to pre-train the network to understand natural language while also incorporating legal terminology since the vocabulary now included many legal terms as well. By using this approach, we ensured that all embedding layer vectors were trained and not just those for tokens appearing in the OSCAR dataset, which is usually the case during the pre-training process. Figure 3 depicts the previously described training process of the SrBERTa v2 model. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **SrBERTa v1** & **SrBERTa v2** \\ \hline Vocabulary size & 30 K & 50 K \\ Hidden layers & 6 & 12 \\ Attention heads & 12 & 12 \\ Hidden size & 768 & 768 \\ Mini-batch size & 8 & 64 \\ Training epochs & 19 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of SrBERTa versions. During the network evaluation phase, we utilized 10% of the input data previously extracted from the legal dataset, as our focus was solely on assessing the model's domain-specific masked language modeling capabilities. The model's performance was quantified using a modified accuracy metric, implemented as follows:  
* For each masked word, the top_k predictions generated by the network were considered, with k = 10;
* Only those predictions that exactly matched the true label of the masked word were considered correct. After the previously described adapted accuracy metric was applied to the test dataset, the SrBERTa v2 model achieved a score of 90.114%, thereby concluding this phase of development.
###### Abstract
Some linearization works related to compensation of nonlinear systems use a compensation model to update the coefficients of a system, such as nonlinear applications of the global system for mobile communication (GSM) [22]; in addition, models derived from Volterra Series such as MPM to compensate for the characteristics of the short-wave PA are presented, where IMD3 to overcome the nonlinear behavior are reduced [23]. Additionally, a two-step approach was developed for DLA as linearization technique based on convolutional neural network [24]. It should be noted that, in the works developed, they do not use an electrical parameter that can vary to adjust the modeling stage or to compensate the system. Therefore, to address the aggressive multi-carrier LTE-A and WCDMA modulation schemes, it is still an important challenge to fulfill the power, efficiency, and linearity requirements in new architectures of RF-PAs [25]. The main aim of this paper is to offer an adaptive alternative to correct the undesirable effect of spectral regrowth, based on modeling and linearization stages, where the P1 dB of a nonlinear device caused by the memory effects in short time is considered, the obtained results are tested into a development board, the error vector magnitude (EVM) is evaluated, and the developed adaptive linearization scheme is able to adapt to the nonlinear behavior under analysis. The novelty presented in this work is the integration of an adaptive modeling stage for RF-PA linearization for applications in the 2.5 GHz band for highly nonlinear devices related to telecommunications and for worldwide interoperability for microwave transmissions access (WiMax) and long-term evolution (LTE), where the modeling stage considers the P1 dB as a metric for updating coefficients. The remainder of this paper is organized as follows. In Section 2, the modeling description involving the MPM, W-MPM, and ILA linearization methods are depicted. Section 3 describes the experimental setup used to perform the measurements of the RF-PA under test in addition to the simulation system implementation based on the DSP builder tool prior to implementing it on the FPGA development board. Section 4 shows the obtained results and precision of the highly nonlinear device under test, the modeling accuracy, and the linearization result. Finally, in Section 5, a discussion and the main conclusions obtained are presented.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
###### Abstract  
Efficient provisioning of 5G network slices is a major challenge for 5G network slicing technology. Previous slice provisioning methods have only considered network resource attributes and ignored network topology attributes. These methods may result in a decrease in the slice acceptance ratio and the slice provisioning revenue. To address these issues, we propose a two-stage heuristic slice provisioning algorithm, called RT-CSP, for the 5G core network by jointly considering network resource attributes and topology attributes in this paper. The first stage of our method is called the slice node provisioning stage, in which we propose an approach to scoring and ranking nodes using network resource attributes (i.e., CPU capacity and bandwidth) and topology attributes (i.e., degree centrality and closeness centrality). Slice nodes are then provisioned according to the node ranking results. In the second stage, called the slice link provisioning stage, the \(k\)-shortest path algorithm is implemented to provision slice links. To further improve the performance of RT-CSP, we propose RT-CSP+, which uses our designed strategy, called _minMaxBWUtilHops_, to select the best physical path to host the slice link. The strategy minimizes the product of the maximum link bandwidth utilization of the candidate physical path and the number of hops in it to avoid creating bottlenecks in the physical path and reduce the bandwidth cost. Using extensive simulations, we compared our results with those of the state-of-the-art algorithms. The experimental results show that our algorithms increase slice acceptance ratio and improve the provisioning revenue-to-cost ratio.  
5G core network slice; network slicing; resource attributes; slice provisioning; topology attributes +
Footnote †: journal: Applied Sciences
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 4 Discussion
### Experimental Program
It can be inferred that the effective noise reduction region of the ANC system prototype is the fan-shaped region with a forward angle of about 33 degrees of secondary acoustic source. There is a small noise reduction zone at the sector boundary. The possible reasons for this phenomenon is the speaker acoustic radiation has directivity, the positive direction of acoustic waveform is normal, while in the radiation path boundary may occur phase offset or other nonlinear distortion, leading to the location of the secondary sound source sound has become a new source of noise. In addition, as a noise source, the transformer box has many sounding points which radiate non-plane wave. And the reflection and refraction of sound on ground and wall lead to the complex coupling of spatial sound field, which is also the possible cause of this phenomenon. Figure 14 is a contrast analysis of the spectrum of noise signals before and after noise reduction. Figure 14 shows that, the ANC system prototype proposed in this paper for the five noise components were effective. It has the biggest noise reduction effect on 100 Hz and with the increase of frequency, the noise reduction effect is gradually reduced. And there is no obvious noise reduction effect on the 500 Hz noise component. Possible reasons for this phenomenon are: first, the higher the frequency is, the shorter the acoustic wavelength is, and it makes the space radiation more complex. Second, in the control algorithm, it is difficult to achieve high-precision phase compensation in a high frequency control algorithm. Moreover, it has higher requirements for the performance of the algorithm.
## 4 Results and Discussion
### Production Prediction Using Adjacent Well  
Given the proximity of well 1 (MIP-3H) and well 2 (MIP-5H), lying a mere 1700 feet apart, this analysis was undertaken to discern whether the historical production data from well 2 could effectively forecast well 1's production over a 150-day interval. The comparative assessment between well 1's actual production data and the projected outcomes derived from well 2's historical production data is elucidated in Figure 12.  
When comparing the short-term production prediction using well 1's own historical production data with the predictions stemming from well 2's data through the GRU network, the accuracy of forecasting diminishes when leveraging data from the adjacent well. The prediction outcomes drawn from well 2 do not mirror the performance achieved through predictions on well 1's data.  
Figure 11: Comparisons of the final 580 days of production (long-term) for well 1: real data vs. GRU and GRU-MLP predictions.  
Figure 10: Comparisons of the final 387 days of production (medium-term) for well 1: real data vs. GRU and GRU-MLP predictions.  
This distinction is particularly pronounced when one examines the _RMSE_, _MAE_, and _MAPE_ metrics associated with the GRU-based prediction of the neighboring well's production. As outlined in Table 6, the _RMSE_, _MAE_, and _MAPE_ values for the adjacent well's prediction are 156.97, 99.26, and 0.048, respectively. These values significantly surpass the corresponding metrics obtained through the utilization of well 1's own production data.
## 4 Testing
### Results for the Receiver Operation Mode  
It was shown in Section 3.2 that the use of the proposed iterative method along with using the estimated \(\overline{\mathbf{C}}\) and \(\overline{\mathbf{P}}(\Phi_{l},\Lambda_{k})\), leads to accurate estimates of satellite signal steering vectors in the receiver operation mode. This method provides estimates of satellite signal steering vectors based on the received signals and calibration results without any aiding information from the IMU. Estimating the steering vectors of the GNSS signals can be used in different multi-antenna applications such as enhancing SNR, anti-interference, multipath mitigation and attitude determination.  
In this section, the calibrated three-element array is used in two practical applications to highlight the effectiveness of the proposed method in the receiver operation mode. In the first example, the heading angle of the moving vehicle is estimated from the signal of only one satellite and in the second example, using the signal of each satellite, the antenna array beam pattern is shaped to steer the main lobe of the beam pattern toward the direction of that signal and accordingly to enhance its carrier to noise ratio (C/N\({}_{0}\)).
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 3 Experimental Results
### Influencing Factor  
#### 3.2.1 Flow Rate  
To thoroughly analyze the effects of key parameters on cuttings migration, multiple experiments were conducted, allowing for comprehensive data collection and comparative analysis. Figure 9 shows the relationship between the return of cuttings and the maximum height of the cuttings bed at different drilling fluid flow rates. The results indicate that as the flow rate of the drilling fluid increased, the total migration time of the cuttings in the annulus gradually decreased, while the volume of cuttings returned progressively increased. Specifically, at a flow rate of 28 L/s, the return of cuttings was 2.58 times greater than at a flow rate of 12 L/s, which resulted in a 48% reduction in the height of the cuttings bed. These findings suggest that higher flow rates can exceed the critical velocity needed for cuttings migration, leading to more effective transport and a reduction in cuttings bed formation.  
Figure 8: Cuttings bed failure stage. (**a**) The early stage; (**b**) cuttings suspension stage; (**c**) cuttings rolling stage.  
Figure 9: Cuttings bed height and return velocity at different flow rates.
## 2 Materials and Methods
### SLR Observations of BDS Satellites
\begin{table}
\begin{tabular}{c c c c c} \hline
**Detector Type** & **Monument** & **Code** & **Location Name** & **Country** \\ \hline \multirow{9}{*}{PMT} & 1868 & KOML & Komsomsolsk & Russia \\  & 1873 & SIML & Simeiz & Ukraine \\  & 1874 & MDVS & Mendeleevo & Russia \\  & 1879 & ALTL & Atlay & Russia \\  & 1884 & RIGL & Riga & Latvia \\  & 1886 & ARKL & Arkhyz & Russia \\  & 1887 & BAIL & Baikonur & Kazakhstan \\  & 1889 & ZELL & Zelenchukskya & Russia \\  & 1890 & BADL & Baday & Russia \\  & 1891 & IKKL & Krkutsk & Russia \\  & 1893 & KTZL & Katzively & Ukraine \\  & 7407 & BRAL & Brasilia & Brazil \\  & 7501 & HARL & Brasilia & Brazil \\  & 7503 & HRTL & Hartebeesthoek & South Africa \\  & 7811 & BORL & Borowiec & Poland \\ \hline \multirow{9}{*}{MCP} & 7090 & YARL & Yarragadee & Australia \\  & 7105 & GODL & Greenbelt & United States of America \\  & 7110 & MONL & Monument Peak & United States of America \\  & 7124 & THTL & Tahiti & French Polynesia \\  & 7838 & SISL & Simosato & Japan \\  & 7941 & MATM & Matera & Italy \\ \hline \multirow{9}{*}{CSPAD} & 7237 & CHAL & Changchun & China \\  & 7249 & BEIL & Beijing & China \\  & 7396 & JFNL & Wuhan & China \\  & 7810 & ZIML & Hartebeesthoek & South Africa \\  & 7819 & KUN2 & Kunming & China \\  & 7821 & SHA2 & Shanghai & China \\  & 7825 & STL3 & Mt Stromlo & Australia \\  & 7827 & SOSW & Wettzell & Germany \\  & 7839 & GRZL & Graz & Austria \\  & 7840 & HERL & Hertsmonecux & United Kingdom \\  & 7841 & POT3 & Potsdam & Germany \\  & 7845 & GRSM & Grasse & France (LLR) \\  & 8834 & WETL & Wettzell & Germany (WLRS) \\ \hline \end{tabular}
\end{table} 
Table 2: SLR stations with BDS satellite tracking observations.