esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems
## 5 Conclusions  
In this paper, we focus on the design of the hybrid precoding codebook in FDD-based millimeter-wave massive MIMO systems. By improving the reconstruction algorithm and combining it with the dictionary learning algorithm, we propose a new hybrid precoding codebook construction algorithm to improve the spectral efficiency. Firstly, we reconstructed the digital precoding matrix according to the idea of CoSaMP algorithm, then combine the dictionary learning algorithm to update the analog codebook and digital precoding matrix, and then obtain the final digital precoding codebook through iterative optimization; finally, the qualified size digital precoding codebook is generated by using the clustering algorithm. The simulation results clearly demonstrate that the spectral efficiency of the proposed algorithm is higher than that of the OMP-based joint codebook [4] algorithm and the hybrid precoding with quantization [1] algorithm under low SNR and different numbers of RF chains. Particularly, when SNR is lower than 0 dB, the proposed algorithm is very close to the optimal unconstrained precoding algorithm because it carefully avoids the error accumulation effect by selecting multiple columns at each iteration. However, in the high-SNR region, compared with the other two algorithms, our algorithm has no great advantages and sometimes even equal spectral efficiency performance. This is because the algorithms in this paper have little error effect in iteration, which is caused by high SNR.  
Conceptualization, G.L. and K.Y.; methodology, G.L., H.D. (Honggui Deng) and G.L.; software, G.L. and K.Y.; formal analysis, G.L. and J.L.; investigation, G.L. and Z.Z.; data curation, G.L. and H.D. ([PERSON]); writing--original draft preparation, G.L. and K.Y.; writing--review and editing, G.L., K.Y. and H.D. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 4 Experimental Results and Discussions
### Differential Accumulation Algorithm
It can be demonstrated that the method of combining the GPU and CPU is faster than serial computing architecture based on the CPU in relation to the differential accumulation algorithm for a \(\Phi\)-OTDR vibration sensing system. Therefore, GPU can speed up the data processing of a differential accumulation algorithm and improve the real-time performance of a \(\Phi\)-OTDR sensing system.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 2 Related Work
### Sentiment Information and Stock Forecasting  
Amid the rapid advancement and pervasive integration of network technologies, big data has emerged as a pivotal resource in the modern era. An increasing number of researchers [28; 29; 30] have focused on enhancing stock price prediction accuracy through sentiment analysis of news texts, incorporating sentiment indicators with traditional stock market data.  
[PERSON] et al. [31] demonstrated that combining sentiment data extracted from news headlines with daily stock data significantly improves the prediction of stock trends and price movements. Similarly, [PERSON] et al. [32] utilized financial news headlines from the Financial Times website for stock prediction. Their approach highlights the ability of newsheadline analysis to capture market sentiment and underlying macroeconomic factors that are often difficult to discern from historical price data alone.  
[PERSON] et al. [33] introduced text mining techniques into stock price forecasting, showing that their proposed algorithm achieves superior performance in extracting sentiment values from news texts. [PERSON] et al. [34] employed historical stock market data and sentiment data from diverse textual sources, using the XGBoost model for stock price prediction. Their work explores the intersection of machine learning and finance, demonstrating the value of integrating sentiment analysis. [PERSON] et al. [35] proposed a multi-channel collaborative network model that combines candlestick charts and social media data for stock trend prediction, achieving a prediction accuracy of 75.38% for Apple stock. These studies underscore the potential of analyzing sentiment in news and other textual sources to capture subtle shifts in market sentiment that may influence stock prices.  
The aforementioned literature explores various methods for constructing sentiment indicators, such as investor sentiment and social media sentiment, and applies them to stock market forecasting. It has been established that sentiment indicators can significantly impact stock market volatility and returns. Incorporating sentiment indicators into time series models has been shown to enhance the accuracy of stock predictions. By integrating sentiment data with other market indicators, investors and researchers can gain deeper insights into market sentiment and achieve more accurate stock prediction results.
## 5 Research on Mineral Prospecting Prediction
### Abnormal Characteristics of Soil Ionic Conductivity and Thermally Released Soil Mercury  
The anomalies in soil ionic conductivity and thermally released soil mercury were distributed across the study area from the north to the south. The anomaly's zoning was distinct, exhibiting single-point, beaded, band-like, and irregular planar distributions (Figure 10). The anomalous area of soil ionic conductivity measured 0.16 km\({}^{2}\), with a peak abnormal value of 5.22 uS/cm. These findings exhibited certain discrepancies compared to the anomalies extracted using in situ electrochemistry. At the convergence sites of each element's anomalies, the conductivity anomalies were not pronounced. This could potentially be attributed to the uranium ore being situated near the geochemical barrier within the oxidation-reduction transition zone [49]. Additionally, there is an oxidation-reduction environmental gradient extending from the north to the south in the study area. In the deep oxidation zone, the low mineralization degree of interlayer water results in high soil ionic conductivity, thereby causing the observed conductivity anomalies. Conversely, in the deep reduction zone, the interlayer water exhibits a high mineralization degree while the soil ionic conductivity remains low, resulting in no apparent anomalies. This phenomenon arises due to variations in the types of ions contributing to conductivity between the deep interlayer water and surface soil water [29].  
The anomalous area of thermally released soil mercury is 0.11 km\({}^{2}\), with a peak value of 49.39 \(\times\) 10\({}^{-9}\). The anomaly is relatively large at the intersection of the L11 line's  
Figure 10: Anomaly map of soil ionic conductivity and thermally released mercury in the study area. 1—Quaternary; 2—Yingtang Formation, Lower Carboniferous; 3—Rongavian Formation, Upper Devonian; 4—Dongcun Formation, Upper Devonian; 5—Guilin Formation, Upper Devonian; 6—Tangjiawan Formation, Middle Devonian; 7—Xindu Formation, Middle Devonian; 8—Caledonian granite; 9—stratigraphic boundary; 10—fault; 11—interred fault; 12—sampling location and exploration line number; 13—outer anomaly zone; 14—middle anomaly zone; 15—inner anomaly zone.  
structure, and it matches well with the anomalies of various elements extracted via geo-electrochemistry. Due to the strong penetrating power of mercury, the tendency of mercury vapor to rise along the structural fracture zone, and the control of uranium deposits by interlayer fractures zones [34; 50], it is inferred that the two open anomalies located at L05 and L16 lines, as well as the string-of-beads anomalies distributed along the southeast direction at L13-21 lines, may indicate the existence of concealed fault fracture zones, which also have good potential for mineral exploration.
## 5 Conclusions  
In this paper, we proposed a novel attention gate in order to overcome the limitations of the encoder-decoder U-Net architecture. Our spatial-channel attention gate can be easily incorporated into popular U-Net architectures while minimizing the computational cost and remarkably increasing the power of the model. By taking advantage of the information on the encoder and decoder features, our proposed attention gate can capture meaningful information of the \"where\" and \"what\" for segmentation. Comprehensive experiments show that the proposed method significantly improves the segmentation results by adding very few parameters. We also expect that the proposed spatial-channel attention gate will be extensively used in deep learning, which is a vital approach for medical image analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method:Method2** & **Metric** & **CVC-ClinicDB** & **TCGA** & **VIP-CUP18** \\ \hline \multirow{3}{*}{Deep U-Net-scAG:U-Net} & Dice score & 0.1251 & 0.0622 & 0.1822 \\  & Precision & 0.0646 & 0.0082 & 0.3779 \\  & Recall & 0.0963 & 0.8083 & 0.3072 \\ \hline \multirow{3}{*}{Deep U-Net-scAG:Attention U-Net} & Dice score & 0.2243 & 0.1168 & 0.2272 \\  & Precision & 0.0728 & 0.0185 & 0.0039 \\ \cline{1-1}  & Recall & 0.8755 & 0.3836 & 0.3507 \\ \hline \multirow{3}{*}{Deep U-Net-scAG:U-Net++} & Dice score & 0.3551 & 0.2639 & 0.5512 \\ \cline{1-1}  & Precision & 0.2591 & 0.0234 & 0.6049 \\ \cline{1-1}  & Recall & 0.8626 & 0.4368 & 0.1372 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of \(p\)-value between Deep UNet-scAG versus UNet, Attention UNet, and UNet++ based on Dice score, Precision, and Recall on the three dataset CVC-ClinicDC, TCGA and VIP-CUP18.  
Author Contributions:Conceptualization, T.L.B.K., D.-P.D., N.-H.H., H.-J.Y. and E.-T.B.; Funding acquisition, S.-H.K.; Investigation, T.L.B.K.; Methodology, T.L.B.K.; Project administration, S.-H.K. and G.L.; Supervision, H.-J.Y.; Validation, T.L.B.K.; Writing--original draft, T.L.B.K.; Writing--review and editing, T.L.B.K., D.-P.D., N.-H.H., H.-J.Y., E.-T.B. and S.B.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2020R1A2B5B01002085) and the Bio and Medical Technology Development Program of the National Research Foundation (NRF) grant funded by the Korean government (MSIT) (NRF-2019M3E5D1A02067961). The authors declare no conflict of interest.
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 2 Related Works
They also introduced a weighted binary cross-entropy loss function to emphasize foreground lesion regions and achieved state-of-the-art performance in the ISIC2017 challenge. [PERSON] et al. [31] proposed an efficient channel attention (ECA) module specifically designed for deep CNNs, which avoids dimension reduction and effectively captures inter-channel interactions. [PERSON] [32] proposed a multi-scale attention U-shaped network (MSAU-Net) that addresses the challenges of texture and shape variations by improving the traditional U-shaped network with attention mechanisms and incorporating bidirectional convolutional long short-term memory (BDC-LSTM) structures to capture shared discriminative features and suppress less informative features. These methods have achieved promising performance in skin lesion segmentation tasks. Medical image lesions have complex features such as low contrast, blurry borders, and variable positions, shapes, and sizes. Current network models have difficulty accurately segmenting lesion areas in complex medical image segmentation tasks. Therefore, further research is needed to improve the segmentation performance of network models in such complex segmentation scenarios.
# Investigation of Rock-Breaking Mechanisms Based on the Adaptive Matching Method for Drilling Loads
## 1 Introduction  
During drilling in strongly heterogeneous formations or soft-hard interbedded formations, the drilling loads suffered by PDC bits while in contact with the high-strength components of bottomhole rock could increase rapidly, resulting in stick-slip vibration and impact loads on the bit, thereby reducing the rock-breaking efficiency and its service life. Therefore, lots of researchers have conducted research on adaptive vibration reduction drill bits and corresponding downhole tools [1, 2]. The adaptive drill bit proposed by Baker Hughes is an innovative technology [3, 4, 5] aimed at addressing the problem of stick-slip in drill bits, which can automatically adjust the cutting depth of the bit as the drilling environment changes. By using a cutting depth control device to offset some of the impact, it avoids excessive biting of the drill bit into the formation, improving drilling efficiency and reducing drilling costs. [PERSON] [6, 7] reviewed the new progress of PDC bits used in oil and gas wells and put forward a kind of hybrid bit to release the stress effect of downhole rock to reduce its strength and convert compressive stress into tensile stressand reduce the difficulty of rock breaking [8; 9]. [PERSON] [10] introduced a kind of self-adjusting depth-of-cut control technology to help mitigate drilling vibrations on downhole drill bits, pushing the upper limit of performance when drilling long sections in a single bit run, and further details of its working principle and its beneficial performance under different drilling conditions were explained. [PERSON] [11] developed one type of adaptive matching tool to release downhole energy during drilling in gravel layers or heterogeneous formations to help with rock breaking, using a planetary gear and torsion spring in the tool to store the drilling loads in advance when drilling in homogeneous formations. [PERSON] [12; 13] introduced an anti-stall tool developed by Tomax AS for coiled tubing applications in order to adjust drilling torque automatically, which was proven to possess the ability to reduce the vibration in the drill string and increase ROP and run length. The adaptive matching method for downhole drilling load is presented in this paper to adjust the applied axial load acting on the drill bit automatically, so that the greater contacting force can be reduced. Based on the proposed method, a kind of downhole tool was designed. To clarify the rock-breaking mechanisms using this method, a discrete element simulation model was established with a PDC cutter cutting heterogeneous rock or into a soft-hard interbedded formation. Based on the established simulation model, the effect of the load factor, the applied initial axial force, and the driven force on the variation in the axial force and the moving displacement of the PDC cutter, as well as on the rock-breaking characteristic parameters, was studied, which provides a feasible method for rapidly drilling into highly heterogeneous formations or soft-hard interbedded formations.
## 3 Performance of IWP with Steel Anchor Box
### Parametric Study
#### 3.2.1 Effect of Perforated Side Plate Thickness  
Figure 25 shows the influence of the thickness of the perforated side plate on the shear force of the connectors and the vertical force distribution ratio. When the thickness of the perforated side plates increases from 15 mm to 30 mm, the maximum shear force on the connectors of the perforated side plates, the average shear force on the connectors of the end plate, and the maximum shear force all decrease by more than 24 kN. The proportion of vertical force borne by the end plate decreases from 27.5% to 21.1%, while that borne by the surface plate increases from 1.3% to 5.1%. This indicates that the increase in the thickness of the perforated side plates leads to an increase in their stiffness, enhancing the overall interaction between the surface plate and side plates. As a result, the force on the C-1 row perforations of the side plates decreases, while that on the C-5 row increases, and the proportion of vertical force transferred by the surface plate also rises. In addition, the principal tensile stress of the concrete on the outer side of the pylon wall ranges between 5.57 MPa and 5.48 MPa, and the utilization rate of the steel anchor box remains at 83%, indicating that the stress on the pylon wall and the utilization rate of the steel anchor box are essentially unaffected by the thickness of the side plates.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Section Number**} & **Internal** & **Exposed** & \multicolumn{2}{c}{**IWP**} \\ \cline{2-6}  & **End Plate** & **Side Plate** & **Side Plate** & **End Plate** & **Surface Plate** \\ \hline
1 & 100 & 100 & 68 & 23 & 9 \\
2 & 100 & 100 & 71 & 25 & 4 \\
3 & 100 & 100 & 72 & 25 & 3 \\
4 & 100 & 100 & 72 & 24 & 4 \\
5 & 100 & 100 & 71 & 24 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Proportion of vertical cable force carried by different plates of steel anchor box.
## 3 Comparison between Flow-3D, MIKE 3 FM, and 1D Exact Riemann Solver Predictions
### Velocity
#### 3.3.2 Vertical Velocity Profiles  
Figure 13 shows the streamwise (\(u\)) velocity profiles in Ozmen-Cagatay and Kocaman [39] for locations P1, P3, P5, and P6 at \(t\) = 0.8, 2, and 5 s. The magnitude of velocity simulated by the Flow-3D and MIKE 3 FM models shows a similar trend. The difference in the predicted velocity profiles highlights the computational cost of predicting the highly transient flow. It was clear that strong vertical velocity components were present in the front and on the top of the wave, resulting from the sudden change in the boundary condition. The reason for the difference in the magnitude of the velocity near the bottom of the channel was likely the different vertical spatial resolution used in the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Water Depth Ratios**} & \multirow{2}{*}{**Probe**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{3-4}  & & **Flow 3D** & **MIKE 3 FM** \\ \hline \multirow{2}{*}{0} & G4 & 0.26 & 0.23 \\  & G6 & 0.29 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE values for the averaged velocity evolution at G4 and G6 based on experimental data by [PERSON] et al. [39].  
Figure 12: Average velocity for the dry-bed at G4 (0.8 m) and G6 (1.2 m) by [PERSON] et al. [39].  
MIKE 3 FM and Flow 3D; when the water depth is larger than 0.02 m, the velocity reaches its maximum value and then does not change with the increased water depth. The results show different magnitudes of the velocities simulated by the MIKE 3 FM and Flow-3D models at both locations in the initial stage; however, in the later stage, their difference decreased, meaning that the longwave approximation became more reasonable. Further upstream and downstream of the gate, the water surface slopes were small (i.e., the \(u\) is small). At position P1, this velocity component reached a value of \(\sim\)0.2 m s\({}^{-1}\) at \(t\) = 0.8 s, indicating that the slope of the water surface sharply changed with a large \(u\) near the gate during the initial stage. The results clearly indicate that 3D effects are important in dam-break flows, and the comparisons demonstrate that the Flow-3D and MIKE 3 FM models could provide more detailed information, such as vertical velocity variations, than the 1D and 2D shallow-water models.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 2 Submarine Optical Networks Basics and Demonstrations Technologies on Submarine Cable Systems
### Basic Segments (\"Plants\") of a Submarine System  
Submarine networks consist of the dry plant (the coastal supporting equipment) and the wet plant (the submerged part).  
Figure 8 shows the basic components of both plants, focusing on SDM technology.  
The dry part includes the transponders (which transmit and receive the optical data carried by the submerged FPs), the PFE, which as its name suggests supplies power (in the range of 15-20 kV) to the wet plant, and the monitor and control equipment which perform network management tasks.  
The wet plant includes the cable (containing the actual optical data carriers, the FPs), the conductor (which manages the high-voltage supplied by the PFE), and the necessary repeaters (installed every 75-100 km), which provide the necessary amplification for optical signals.  
Figure 9 shows all types of a wet plant submarine cable system, with time and technology-based classification. The upper left part of Figure 9 depicts the dispersion-managed cable used until 2010, and the lower left part depicts uncompensated schemes launched with positive dispersion after 2014, which resulted in SDM technology technique cable systems that are used up to the present and seen at lower right. The festoon and unrepeated cable systems (upper right part of Figure 9) form an alternative form of the dry plant which is used only in short-distance submarine cable systems. Festoon networks connect coastline locations by using unrepeated (passive) submarine cables in order to avoid terrestrial routes for many reasons, such as the presence of challenging terrain (mountains), dense cities, etc.  
Figure 8: A typical submarine cable system takes into account all network elements and state-of-the-art SDM repeaters (analytically described below).
## Case Study: Data Analysis and Results
### Bayesian Statistics and Hierarchical Model Fitting
#### 4.3.3 Posterior Simulation  
The hierarchical model developed in the previous section is now solved iteratively with the numerical Markov chain Monte Carlo method (MCMC) [12, 13]. The goal is to find the most probable set of parameters (e.g., \(r_{d}\), \(\mu_{30}\), _etc._) which is most likely in describing the given data.  
If the model is well posed, then the iterative MCMC will converge to the true distribution of the parameters. This also means that the first draws/simulation runs need to be discarded, and only values after this _burn in_ period should be considered. To improve the (required) independence between successive draws, only every \(n\)th simulation draw is considered, _i.e._, _thinning_ is applied.  
In order to compute the parameters of the hierarchical model, which was presented in the previous chapter, \(2\times 10^{5}\) simulation runs were conducted, allowing for a burn in of \(1\times 10^{4}\) and a thinning of 20. These parameters were determined empirically by observing the traces and autocorrelations of the model parameter draws.