esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Communication and Measurement System with BOC-Assisted OFDM
###### Abstract  
For unmanned aerial vehicles (UAVs), high-precision measurement and high-speed communication are necessary to realize flight and operational missions. In this paper, we propose an integrated communication and measurement system in a Doppler frequency offset environment. The system combines orthogonal frequency division multiplexing (OFDM) modulation with binary offset carrier (BOC) modulation to formulate an OFDM+BOC composite signal through power control. High-precision measurement is achieved through BOC modulation, and high data transmission is achieved through OFDM modulation. Furthermore, the high-precision Doppler frequency offset tracked by the BOC signal is adopted to assist in the demodulation of the OFDM signal. This substantially decreases the impact of the Doppler frequency offset on the OFDM signal. Moreover, the ranging error is within \(10^{-2}\), and the maximum Doppler frequency error is within \(2\) Hz.  
BOC; OFDM; accuracy; Doppler frequency offset; demodulation Article
## 3 Experiment
### Evaluating the Performance of DL Inversion Models Using Synthetic Test Dataset
As shown in Figure 15, the deep learning-based model shows superior prediction accuracy compared with the conventional inversion method, as it more accurately predicts the layers and the direction and position of the fault zone. Figure 14: Prediction results of DL-ERT inversion according to the number of boreholes. The NRMSE values obtained with an increase in the number of boreholes are 0.0707, 0.0706, 0.0689, 0.0680, 0.0679, and 0.0669. (**a**) Ground truth. Number of boreholes = (**b,h**) 0; (**c,i**) 1; (**d,j**) 3; (**e,k**) 5; (**f,l**) 7; and (**g,m**) 10.
# Ensembles of Random SHAPs
## 4 Modifications of SHAP
### Ensemble of Random Weighted SHAPs  
The next algorithm is called the ensemble of random weighted SHAPs (ERW-SHAP) algorithm and differs from ER-SHAP in the following parts. A general scheme is shown in Figure 2. First of all, \(N\) points \(\mathbf{h}_{1},\ldots,\mathbf{h}_{N}\) are generated in the neighborhood of explained instance \(\mathbf{x}\). These points do not need to belong to the dataset \(D\). Then, \(t\) features are randomly selected from every \(\mathbf{h}_{k}\), and they produce instances \(\mathbf{z}_{1},\ldots,\mathbf{z}_{N}\). Moreover, the weight \(w_{k}\) of each instance \(\mathbf{h}_{k}\) is defined as a function of the distance \(d_{k}\) between the explained instance \(\mathbf{x}\) and the generated neighbor \(\mathbf{h}_{k}\). The weights are used to implement the weighted average of the Shapley values. The final Shapley values are calculated now as follows:  
\[\phi_{i}=\frac{1}{W_{i}}\sum_{k:i\in J_{k}}w_{k}\phi_{i}^{(k)},\;i=1,\ldots,m, \tag{5}\]  
where \(W_{i}=\sum_{k:i\in J_{k}}w_{k}\).  
On the one hand, using these changes of ER-SHAP, we implement an idea of some kind of diversity of SHAPs to make the randomly selected feature vectors more independent. On the other hand, the approach is similar to the LIME method whe  
Figure 2: A scheme of the ERW-SHAP.  
is perturbed in order to build an approximating linear model around the instance to be explained. The diversity of SHAPs is a very important peculiarity of the proposed ER-SHAP. It prevents SHAP from the situation when a rule for filling the removed features produces features coinciding with the explained instance features. In this case, the Shapley values are incorrectly computed. The use of generated neighbors allows us to avoid this case and to obtain more accurate results.  
Algorithm implementing ERW-SHAP differs from the similar Algorithm 1 implementing ER-SHAP only in two lines. First, after line 1 or before line 2, the line indicating how to generate neighbors has to be inserted. Second, line 5 (combination of the Shapley values) is replaced with expression (5).
# Research Progress and Typical Case of Open-Pit to Underground Mining in China
## 1 Introduction
In recent years, several domestic and international mines have successfully completed the transition from open-pit to underground mining, such as the Chuquicamata copper mine in Chile, the Palabora mine in South Africa, and the Xinqiao iron mine in China. Engineering practices show that designing a scientific development system and selecting appropriate mining methods directly affect resource recovery efficiency and cost control as well as influencing the overall safety and sustainability of the mining system. Furthermore, the spatial overlap, stress disturbance, and hydrogeological coupling within the open-pit system are prone to triggering slope instability, crown pillar failure, and other disasters, becoming key risk factors that limit the success of the transition [9]. Therefore, systematically studying key technologies for the transition from open-pit to underground mining is not only a practical necessity for mining safety but also essential for developing a green mining technology system. Based on the background outlined above, this paper focuses on the core technical issues involved in the transition from open-pit to underground mining. It systematically analyzes the types of development systems and their applicable conditions, reviews the technical pathways and application boundaries of mainstream mining methods, and explores the research progress and engineering strategies for slope stability and crown pillar design. Building on this foundation, the Anhui Xinqiao Iron Mine is selected as a typical case study. This paper provides an in-depth analysis of the technical pathways and engineering experiences related to mining method selection, system coordination layout, and crown pillar safety design, intending to offer systematic theoretical support and practical insights for the green, efficient, and intelligent development of deep resources in China.
## 4 Results and Discussion
### Dynamic Mechanical Properties of Polyurea
#### 4.1.2 Effects of Area  
Dynamic experiments are conducted on polyurea samples with dimensions of \(\phi\)80 mm \(\times\) 20 mm, \(\phi\)60 mm \(\times\) 20 mm, and \(\phi\)40 mm \(\times\) 20 mm to investigate the influences of area on the dynamic stiffness and damping coefficient. In these experiments, the preload is set to 100 N, the excitation frequency is set to 0.5 Hz, and the excitation amplitude is set to 0.5 mm. The obtained force-displacement curves of the three polyurea samples are shown in Figure 8a, with the dynamic stiffness and damping coefficients calculated and presented in Figure 8b,c.  
From Figure 8, it can be observed that as the area of the polyurea sample increases, the area enclosed by the force-displacement curve gradually enlarges, indicating a corresponding increase in energy dissipation capacity. Consequently, the damping coefficient of the polyurea sample increases. As the area of the polyurea sample increases from 1256 mm\({}^{2}\) to 5024 mm\({}^{2}\), the damping coefficient rises from 99.04 N\(\cdot\)s/m to 286.65 N\(\cdot\)s/m. Furthermore, as the area increases, the slope of the force-displacement curve becomes steeper, suggesting that the dynamic stiffness of the polyurea sample also increases. Specifically, as the area of the polyurea sample increases from 1256 mm\({}^{2}\) to 5024 mm\({}^{2}\), its dynamic stiffness increases from 52.64 N/mm to 238.27 N/mm. Although the area of polyurea sample increases four times, the dynamic stiffness increases more than four times while  
Figure 7: Effects of preload on dynamic stiffness and damping coefficient: (a) force–displacement curves; (b) dynamic stiffness; (c) damping coefficients.  
the damping coefficient increases less than three times. Therefore, the changes of dynamic stiffness must be considered when enhancing the damping coefficient using enlarged area.
# Advanced Dense Text Detection in Graded Examinations Leveraging Chinese Character Components
## 3 Results
### Method Comparisons  
To further validate the performance of the handwriting detection model algorithm proposed in this paper, experimental comparisons with other mainstream text detection algorithms, CTPN [5], PixelLink [6], TextSnake [7], Boundary [8], R50_DBU [9] and TextFuse [10] were conducted on the ICDAR2015 dataset. The ICDAR [30] dataset is used for the ICDAR Challenge, in which the ICDAR2015 dataset contains 1500 images, 1000 images are used for the training set and the remaining 500 images are used for the test set.  
CTPN is a single-stage model for text detection, which has significant advantages in dealing with oblique and curved text. PixelLink is a pixel-level-based text detection model, particularly suitable for scene text detection and text regions with great morphological variations. TextSnake is particularly good at dealing with curved and bent text. This study focuses on the segmentation task of Chinese character detection, which usually involves multi-scale target detection, complex background processing, and accurate localization of small targets, so these three models that are good at handling complex text are targeted to be chosen as comparison objects to evaluate the performance of CEE networks in Chinese character detection. The results are shown in Table 3. Boundary is An end-to-end framework utilizing multi-scale boundary point localization and adaptive topology reconstruction to boost accuracy in detecting/recognizing irregularly shaped text within complex scenes, R50_DBU represents a sophisticated segmentation-driven network incorporating U-Net architecture, TextFuse integrates hierarchical feature synthesis with cross-detector consensus mechanisms to resolve single-modality constraints in scene text analysis.  
The EAST model using the combination of mini-component in this paper has higher accuracy, recall, and F-score than the classical EAST algorithm. The values are also improved by one to two percentage points in comparison to other algorithms, and the detection results are improved. The algorithm comparison is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F-Score/\%** \\ \hline CTPN & 74.2 & 51.6 & 60.9 \\ PixelLink & 83.0 & 73.2 & 77.8 \\ TextSnake & 83.2 & 73.9 & 78.3 \\ EAST & 80.5 & 72.8 & 76.4 \\ Boundary & 82.2 & 81.1 & 81.3 \\ R50\_DBU & 85.0 & 77.0 & Null \\ TextFuse & 83.6 & 77.1 & 80.2 \\ CEE & 85.1 & 82.7 & 83.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Algorithm comparison.  
Figure 10: Algorithm comparison.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 3 Data and Methodology
### Deep Learning Models
#### 3.2.8. CA-RegNet Model  
This study introduces CA-RegNet, an innovative model that amalgamates the Coordinate Attention (CA) mechanism with RegNet, aimed at sophisticated wetland image segmentation [16]. By enhancing the prioritization of significant features, both spatially and channel-wise, the CA mechanism enables the efficient discernment of attributes at various hierarchical levels. This refinement optimizes CA-RegNet efficacy in interpreting intricate wetland terrains, thereby elevating classification precision.  
The implementation of the CA-RegNet model in this research initiates with an input layer. The architecture employs an initial convolutional layer for spectral-spatial feature extraction, followed by CA-RegNet blocks. Each block contains a Conv2D layer and a Coordinate Attention layer for dynamic channel-wise feature recalibration. This enhances the model's ability to interpret complex wetland landscapes. Additional layers for convolution, spatial reduction, and tensor flattening are iteratively arranged to increase model complexity. Employing'sparse_categorical_crossentropy' for loss and 'RMSprop' as the optimizer. The model was subjected to 200 training epochs, a parameter established from cross-validation outcomes, designed to critically assess model complexity, learning rate, and potential scenarios of both overfitting and underfitting. Employing dropout layers and L1 regularization served as strategic methodologies to mitigate the risk of overfitting. This novel approach sets a potential benchmark for future studies, amplifying the role of deep learning in environmental and ecological research. Figure 10 depicts the architecture of the CA-RegNet model.  
Figure 10: The architecture of the CA-RegNet model.  
Figure 9: The architecture of the SE-RegNet model.
## 4 Conclusions  
This work has developed a new machine learning (ML) model to estimate the LBV of NH\({}_{3}\)/H\({}_{2}\)/Air, applicable across diverse conditions. Experimental data from various conditions were sourced from existing scholarly works and cleaned of outliers to form a dataset for ML training. The study concludes the following:  
* In terms of prediction accuracy for the LBV of NH\({}_{3}\)/H\({}_{2}\)/Air, the NHLBVNet model proposed in this paper performs better (\(R^{2}\) > 99%) than the improved XGBoost model (\(R^{2}\) > 93%).
* The ablation experiment results of NHLBVNet are all higher than the XGBoost model, proving the effectiveness and necessity of each component of the NHLBVNet model.
* Robustness experiment results indicate that the NHLBVNet model outperforms other baseline models on small-scale datasets, showing better generalization capabilities.  
Given these conclusions, the NHLBVNet model developed in this paper still maintains its superiority on small-sample datasets. It holds promise to replace the laborious experimental data acquisition or numerical simulation calculations for LBV, providing support for the exploration of NH\({}_{3}\)/H\({}_{2}\)/Air combustion mechanisms.  
Conceptualization, W.Y. and B.Z.; methodology, W.Y.; software, W.Y. and B.Z.; validation, W.Y., S.Z. and B.W.; formal analysis, W.Y. and B.W.; data curation, W.Y.; writing--original draft preparation, W.Y.; writing--review and editing, W.Y.; visualization, B.W.; supervision, Y.X.; project administration, Z.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Key Research and Development Program of China [Grant No. 2023 YFB4301700], and the National Natural Science Foundation of China [Grant No. 51306026].  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Figure 10: Data proportions by LBV segmentation.
## 3 UAV Platform for Multi-Sensor Photogrammetry Aerial Mapping
### Remote Sensing Methods for Automatic Detection and Mapping of Weak Structural Parts and Components
#### 3.2.1 Photogrammetric Three-Dimensional (3D) Building Modeling Methods  
Optimal photogrammetric methods need to be developed for an accurate and measurable high-resolution photorealistic 3D building model based on a multi-sensor system and GeoSLAM system [78]. Photogrammetric methods are primarily based on an adequate phototriangulation procedure for all images. During the process of phototriangulation, the self-calibration of cameras control the stability of their internal elements [71]. The development of methods for the rapid creation of 3D models include the test of sensor accuracy for determining the elements of the external orientation of real-time images (GNSS + RTK, IMU) [62; 69]. Markers can be used on the objects to control the phototriangulation process and the final 3D model. Nowadays, indoor spatial data for 3D models are usually acquired with the GeoSLAM system. The final 3D model can be made by the combination of data acquired with the multi-sensor system and GeoSLAM system data. Furthermore, spectralon and other markers to perform radiometric calibration can be placed on the objects [71]. This ensures the accuracy of the spectral signature of the recorded objects. For controlling thermal images, the temperature of the object can be measured by a measuring device and sensors at the time of data collection. This enables obtaining an accurate 3D model in the thermal spectrum, i.e., the accuracy of the temperature obtained by the multi-sensor system. The development and implementation of methods for photogrammetric creation of SfM (Structure-from-motion) point clouds enable the creation of high-resolution, dense 3D point clouds [79; 80; 81]. The dense point clouds are filtered by newly developed outlier detection algorithms for gross error points detection (e.g., glass area) [82; 83; 84]. For quality and accurate point cloud collection on objects with uniform textures, methods for radiometric enhancement of textural features were developed. Radiometric equalization of photogrammetric images based on new methods allow for the creation of seamless photorealistic 3D models [85; 86]. Further, with new 3D modeling methods, the point cloud can be transformed into a high-resolution photorealistic 3D building model (Figure 2).
## 2 Silicon Photonics Micro-Transceiver with Multimode Interface
### Laser Integration, Reliability and Performance
#### 2.2.3 Redundant Laser Scheme for Increased Reliability  
The first-generation IOCore micro-transceiver incorporated a single QD-LD with four CW 1310 nm outputs, each of which was modulated onto each transmitter channel directly (Figure 8a). However, this provided the lowest reliability as any single failure in any one of the outputs or the laser module would have resulted in the failure of the overall system.  
In order to address this, a redundant optical circuit was introduced in the second-generation micro-transceiver (Figure 8b), wherein the original single four-channel QD-LD array was replaced by two independent two-channel QD-LD arrays. One QD-LD array is the primary device, with both its optical outputs passed to 3 dB power couplers which split the light equally into two branches, resulting in four CW streams, each of which is modulated onto a transmitter channel. The second QD-LD array is held on stand-by, only to be activated upon the failure of the primary QD-LD array. An additional Mach-Zehnder switch was designed between the outputs of both the primary and secondary QD-LD arrays and the transmitter channels. As shown in Figure 8b, in the event of failure of the primary QD-LD array, the secondary QD-LD array is activated and its two outputs are switched to the 3 dB couplers, which in turn split them into four transmitter channels.  
Figure 6: Functional block comparison of different laser integration schemes. (**a**) Integrated laser scheme in single wavelength IOCore micro-transceiver with multimode fiber; (**b**) high-end WDM silicon photonics transceiver with external multiwavelength laser source.  
Figure 7: QD-LD performance: (**a**) L-I characteristics of QD-LD at different temperatures; (**b**) RIN for back reflection.  
Using the parameters obtained from reliability testing of the QD-LD, the time within which the chance of failure was 1% was increased from 10,000 to 100,000 h when moving from the non-redundant laser circuit scheme to the redundant scheme.  
The qualification of the redundancy circuit scheme is underway and expected to be deployed in commercial devices by the start of 2022.
## 3 Key Component Design
### Machine Vision System  
The machine vision mechanism is shown in Figure 4. According to the working principle and in combination with the requirements for regulating the verification position, six CCD cameras with resolutions of 1944 \(\times\) 2592 were arranged at each meter line with a length of 5 m, and two CCD cameras were arranged at the 0.5- and 2.5-m lines. During the operation, eight CCD cameras arranged on the verification platform took line pattern images of the standard steel tape and steel tape, in order, through high-speed image switching controlled by the computer. These were converted into digital images by a high-speed real-time image acquisition card and input into the computer for digital image processing in sequence.  
As the design field of view of the CCD camera module is 33 \(\times\) 44 mm, this ensures the measurement requirements of millimeter and centimeter division values and the line width error of the steel tape. The design of the limit mechanism and pressing mechanism ensures the straightness and parallelism of the steel tape.
# The Thermodynamic Change Laws of CO2-Coupled Fractured Rock
## 3 Results
### Simulation of CO\({}_{2}\) Heat Transfer in Pores  
#### 3.4.1 Pore Fracture CO\({}_{2}\) Phase Evolution  
Based on the observations of high-temperature geothermal development, the surrounding rock is susceptible to cracking and subsidence under external loads, leading to the formation of a \"holey\" structure within the rock mass. In this structure, major fracture voids may form at intervals [26]. To facilitate the analysis, the complex fracture morphology was simplified using an equivalent fracture structure model where \"holes\" were used instead of \"slits\".  
The pore flow CO\({}_{2}\) phase change was plotted based on NIST data [27]. It is found that the volume expansion of CO\({}_{2}\) affects temperature and pressure, while its temperature and pressure affect the phase changes. Furthermore, liquid CO\({}_{2}\) evaporates in the fracture hole [28]. It should be indicated that the triple-point temperature and pressure of CO\({}_{2}\) are 238.98 K ( \(-\)58.9 \({}^{\circ}\)C) and 0.34 MPa, respectively. When CO\({}_{2}\) transforms from the liquid state to the supercritical state, its critical temperature and pressure are 304.2 K (31.14 \({}^{\circ}\)C) and 7.38 MPa, respectively. Figure 11 shows that supercritical CO\({}_{2}\) can quickly achieve the goal of energizing phase changes in fracture pores and promoting fissure propagation. However, the process of fracturing rock using liquid CO\({}_{2}\) reduces the temperature surrounding the rock body. Meanwhile, heat transfer takes place between liquid CO\({}_{2}\) and seepage within the rock body. This aligns with the characteristics of heat-fluid-solid coupling.  
Figure 11: Phase change of CO\({}_{2}\) within the pore [24].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **Peak Asperity** \\ **Height \(\xi\)/mm** \\ \end{tabular} & \begin{tabular}{c} **RS of Asperity** \\ **Height \(R_{s}\)/mm** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Asperity** \\ **Height \(R_{n}\)/mm** \\ \end{tabular} &
\begin{tabular}{c} **JRC** \\ **JRC** \\ \end{tabular} \\ \hline Natural sample & 0.89 & 0.207 & 0.172 & 5.7 \\ Coupled rock Sample & 1.17 & 0.359 & 0.329 & 3.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Geometrical parameters and roughness of the fractured samples.