esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Relation extraction is a task that involves recognizing semantic relations (i.e., tuple structures; {subject, relation, object triples}) among entities in a sentence [1]. [PERSON] et al. [2] divided sentences into three types according to the triplet overlap degree, i.e., normal, entity pair overlap (EPO), and single entity overlap (SEO). In the normal type, the triples do not have overlapped entities; in the EPO type, some triples have an overlapped entity pair; and in the SEO type, some triplets have an overlapped entity, but these triplets do not have overlapped entity pairs. In this study, we focus on promptly extracting both the normal and SEO types because most relations are included in these types, as shown in Figure 1.  
In Figure 1, [[PERSON], employed, ABC Mart], [[PERSON], Family, his Father] and [his Father, Owner, ABC Mart] are SEO types. To promptly extract these relations, we adopt the concept of dependency parsing  
Figure 1: Subject-relation-object triples in a sentence. PER: person; ORG: organization.  
in which dependent words point to the head words by scanning each word in a sentence. We propose a dual pointer network model to efficiently extract multiple relations from a sentence through forward scanning (i.e., scanning from the first word to the last) and backward scanning (i.e., scanning from the last word to the first). The proposed model discovers an object of the current subject during forward scanning. Through forward scanning, all normal type relations can be found. However, SEO type relations are only partially found because a subject should point to only one object in the pointer network architecture. To address this limitation, the proposed model performs backward scanning to identify the subject of the current object.  
The remainder of this paper is organized as follows. In Section 2, we review previous studies on relation extraction. Section 3 describes the proposed dual pointer network model. In Section 4, we elaborate on the experimental setup and results. Finally, we conclude the study in Section 5.
## 2 Materials and Methods
### FLUKA Simulations  
A simulation of the LaGEMPix was carried out using the FLUKA Monte Carlo code version 4-0.1 [45; 46; 47] with the default settings PRECISIO. The goal of the simulation was to study the energy deposition in the active area of the detector, and to evaluate its spatial resolution using the ESF and LSF methods. Transport and production energy cuts for electrons, positrons and photons were set to 1 keV in all regions. To overcome most of the limitations arising from low energy primary photons and enhance the transport precision for the energy deposition in the very thin layers of both gaseous and heavy materials present in the LaGEMPix geometry, the single-scattering algorithm was activated everywhere for all charged particles by adding the MULSOPT card.  
The simulation comprises a realistic implementation of the detector geometry, which includes the triple-GEM stack, the image sensor and peripheral electronics, as shown in Section 3.5. Due to the lack of information on the X-ray tube head, we implemented a user-defined source routine, using the reference radiation fields for the ISO N-40 for the simulation of the spectral distribution of the X-ray beam. Other contributions due to scattered photons in the target or in any surrounding material that are inherently present in the beam were not simulated, due to the lack of information.  
It should be noted that the simulation did not intend to reproduce the experimental results, but rather help in evaluating the contributions to the spatial resolution associated with the detector and the experimental conditions. Therefore, neither the electron diffusion in the gas volume nor the electron recombination with positive ions, impurity absorption, electron amplification in the GEM holes and scintillation light emission were taken into consideration. Nonetheless, as explained in Section 2.1, the number of photons detected by the LaGEMPix matrix of OPDs is proportional to the number of secondary electrons produced in the GEMs, which is in turn proportional to the energy deposited in the gas during the primary ionization process. Therefore, we can assume that the energy deposited by the X-ray photons in the gas volume of the detector is a rough approximation of the image produced by the scintillation photons detected by the image sensor. To account for the contribution of primary ionizations in the different gas regions and the effective gain per GEM foil to the image, we used the image obtained as the average deposited energy in the drift and transfer gas layers weighted by the corresponding effective gains. Interactions in the induction gap will not generate secondary scintillation photons and therefore, will not contribute to the final image. A FLUKA USRBIN scoring was superimposed in the entire detector volume. The statistical uncertainties were estimated by calculating the standard deviation of the results from independent runs.
# A New Method of Geological Modeling for the Hydrocarbon Secondary Migration Research
## 4 Reservoir Modeling of the Sangonghe Formation in the Central Junggar Basin  
In this study, the sandstone reservoir of the Sangonghe Formation in the central area of the Junggar Basin, which represents a braided river delta sedimentary system, was selected as a case study to apply the new reservoir modeling method. The main objective was to establish a geological model and accurately characterize the reservoir's heterogeneity. To assess the reliability of the modeling results, a comparison was made between the drilled sand thickness and the modeled sand thickness. This analysis showcased the flexibility of the new method and highlighted its ability to effectively capture the multi-scale heterogeneity of the reservoir.
## 5 Discussion  
A WGAN-GP-based color correction technique is proposed in this study to address the color correction problem of remote sensing images. To overcome the limitations of traditional methods, an approach using WGAN-GP is introduced that performs natural color correction while maintaining color consistency and the structural characteristics of the image.  
The results show that the proposed WGAN-GP model can more consistently correct irregular color distributions and reproduce the texture and structure of images more precisely compared to existing methods. In particular, it performs well in preserving the structural elements of the target image while reflecting the style of the reference image during training.  
However, as training progresses to the later stages, a slight decrease in color consistency is observed despite improvements in texture reproduction. This may be due to the model's increased focus on optimizing texture details over color accuracy, leading to a trade-off between these two aspects. Further adjustments in the loss function or model architecture may be necessary to achieve a more balanced performance in both color and texture preservation.  
However, the purpose of this study is to enhance the visual quality of aviation simulators; therefore, additional research is needed regarding extreme weather and seasonal conditions. As the BigEarthNet-S2 dataset is post-processed to remove cloud cover, future studies will be conducted using Landsat or Sentinel tiles to explore these conditions further.  
However, the proposed WGAN-GP-based approach has some limitations. It may be challenging to perfectly transfer the style of the reference image to the target image  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **PSNR/dB** & **SSIM** & **CIEDE2000/Mean \(\Delta\)E** & **LPIPS** \\ \hline Histogram Matching & 27.5229 & 0.6949 & 9.4066 & 0.3194 \\ Color Transfer & 27.495 & 0.5585 & 9.8801 & 0.1933 \\ Proposed Method (Early-Stage Model) & 27.6841 & 0.4559 & 26.0101 & 0.6318 \\ Proposed Method (Mid-Stage Model) & 28.9298 & 0.7116 & 7.3487 & 0.3978 \\ Proposed Method (Fully Trained Model) & 28.2966 & 0.6459 & 9.6674 & 0.4005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of Histogram, Color Transfer, and Proposed Method.  
by simply combining generators. To address this issue, it may be necessary to extend the research by incorporating additional models such as StyleGAN [31]. For example, StyleGAN's style transfer feature could be utilized to more precisely reflect the detailed style of the reference image in the target image. Additionally, it may be necessary to extend the critic's structure to include a two-image comparison input that can more clearly identify differences between the reference image and the generated image, thereby enhancing the critic's discrimination ability.
## 4 Results
### Results and Comparison
#### 4.4.3 GF-2 Data
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Image 1**} & \multicolumn{5}{c}{**Image 2**} \\ \cline{2-10}  & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline U-Net & 93.08 & 93.11 & 99.66 & 96.28 & 92.83 & 94.32 & 93.31 & 99.67 & 96.39 & 93.03 \\ DeepLabv3+ & 95.77 & 96.01 & 99.57 & 97.76 & 95.62 & 89.45 & 87.09 & 99.82 & 93.02 & 86.96 \\ Swin-UNet & 98.64 & 99.52 & 99.22 & 99.36 & 98.61 & 96.71 & 98.48 & 97.65 & 98.05 & 96.11 \\ TransFuse & 98.55 & 99.40 & 99.09 & 99.23 & 98.50 & 96.67 & 97.33 & 98.57 & 97.94 & 96.01 \\ DPBFN & 98.50 & 99.16 & 99.25 & 99.21 & 98.42 & 96.60 & 97.10 & 98.65 & 97.87 & 95.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantitative evaluation results in Figure 10.  
Figure 11: Local amplification results for Figure 10. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Methods** & **OA (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **IoU (\%)** \\ \hline \multirow{4}{*}{GF-2} & U-Net & 90.52 & 56.52 & 75.11 & 64.50 & 56.24 \\  & Deeplabv3+ & 90.69 & 54.87 & 78.38 & 64.55 & 58.16 \\ \cline{1-1}  & Swin-UNet & 93.87 & 72.30 & 63.23 & 67.46 & 72.46 \\ \cline{1-1}  & TransFuse & 95.71 & 76.01 & 65.03 & 70.12 & 74.27 \\ \cline{1-1}  & DPBFN & 95.66 & 75.34 & 65.05 & 69.84 & 74.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Various compared results on the GF-2 road test dataset.
### Benefits of DSM  
DSM comes into play to solve such difficulties concerning the current situation on the load end and to enable greater flexibility and the robust scheduling of specific devices and gadgets at an autonomous stage through intelligent control mechanisms. DSM can provide several advantages, such as:  
* To help minimize voltage fluctuations on a poor distribution feeder by providing grid support [6].
* To resist environmental concerns by lowering peak demand, which decreases the need for new traditional generating plants.
* To allow the principles of DSM to be successfully implemented, where it can benefit both customers and the utilities economically.
* To guarantee steady and sustainable power delivery within the system, thereby avoiding shortfalls.
* To provide cost savings in energy usage while also assisting in achieving positive environmental goals.
* To decrease load profiles by intelligently managing loads [7].
# Dynamic Cooperative Spectrum Sensing Based on Deep Multi-User Reinforcement Learning
## 3 The Proposed Deep Multi-User Reinforcement Learning
### Simulation Results
Figure 6: When \(K=2\) and \(N=3\), the total reward, cumulative collision, and cumulative reward for the last 5000 time slots. To verify the performance of cooperative strategy, the proposed DMRL is compared with two popular methods: MARL and MADRL. MARL is a multi-agent reinforcement learning (MARL) method based on Q learning, which is proposed to avoid sweeping interference signals and accidental interference from other wideband autonomous cognitive radios (WACRs). For MADRL, it allows multiple WACRs to simultaneously operate over the same wide spectrum band. During the learning process, it is easier to achieve faster convergence speed and better reward performance by balancing exploration and utilization. The same data are applied to train the MARL and MADRL and the average collision and average reward is computed. The specific data is shown in Table 3.  
Figure 7: When \(K=6\) and \(N=10\), the total reward, cumulative collision and cumulative reward for (**a**) the first 5000 time slots (**b**) 20,000 to 25,000 time slots (**c**) 65,000 to 70,000 time slots (**d**) 95,000 to 100,000 time slots. According to Table 3, the average collision from each policy is listed in descending order: 0.37 (MARL), 0.24 (MADRL), 0.06 (DMRL). It can be seen that DMRL performs best in the complicated real scenario. It can also be seen from the indicator of average reward that our method can achieve a higher average reward. That's because the dynamic multi-channel cooperative sensing method based on DMRL adaptively learns multi-user sensing signals, and allocates multiple channel resources equally and reasonably while maximizing the objective function. Although MARL and MADRL can learn the sensing strategy through user history information, users can easily choose a fixed channel in the multi-channel case. Therefore, when two users are transmitting on the same channel, it is easy to fall into a persistent conflict.
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 3 Overview of Proposed Method
### Tightening Judgment Module  
The tightening judgment module judges whether the target bolt is correctly tightened according to the pattern information of the monochrome image of the extracted marker. It solves the classification problem of judging whether a monochrome image represents a correct tightening pattern via a classification model using a CNN. The classification model learns the two classes of successful tightening images and unsuccessful tightening images as training data. Since there are only a few cases of failed fastening, this paper uses pseudo-tightening failure marking images generated by creating bolt fastening points using a 3D modeling tool and coloring only the marked points. For each extracted image of a bolt marker, a score of 0 to 1 is output to indicate whether the bolt was successfully tightened or not. The higher the score, the higher the probability of successful tightening.  
Figure 3: Example of input image and output result of marker extraction; (**a**) an input bolt image; (**b**) an output image.  
Figure 2: Example bolt detection results obtained using YOLOv3; (**a**) an input image; (**b**) a result of detection image; (**c**) an example of extracted bolt image.  
In this experiment, 7196 black-and-white images of successful tightening and 3600 images of pseudo-tightening failure were used to train the model (Figure 4). As described in (1), a single bolt may exist in multiple frames in the proposed method. The proposed method performs processes (2) and (3) on all the bolt extraction images, makes a tightening decision, and stores the result. Since the final output of the proposed method is the success or failure of tightening each bolt, the bolt is considered to have been successfully tightened if any of the identification results in all frames of the bolt are successful, rather than the results of individual frames. This is because bolts are shot at various angles in a single video, and depending on the shooting angle, there are cases where the markings are completely hidden.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 2 Problem Formulation and Objective Function
### Analysis of SDM  
Figure 1 explains the SDM equivalent circuit of the PV solar cell. The mathematical equations to calculate the output current of the SDM can be formulated as follows:  
\[I=I_{ph}-I_{d1}-I_{sh} \tag{1}\]  
\[I=I_{ph}-I_{s1}\Big{[}e^{\frac{\text{d}(V+IR_{s})}{a_{1}K_{c}}}-1\Big{]}-\frac {V+IR_{s}}{R_{sh}} \tag{2}\]  
where I is the current output from the solar cell SDM, \(I_{ph}\) is the photogenerated current, \(I_{sh}\) is the current due to leakage in the PN junction, \(I_{d1}\) is the dark saturation current of the SDM, \(R_{sh}\) is the shunt resistance, \(R_{s}\) is the series resistance, \(a_{1}\) is the diode ideality factor, K is Boltzmann's constant, q is the charge of the electron, and \(T_{c}\) is the cell temperature.  
According to the previous mathematical formula, the five unknown parameters required to estimate the SDM are \(\Big{(}I_{ph},\;\;I_{s1},\;a_{1},\;R_{s},\;R_{sh}\Big{)}\).
## 2 Landslide Experiment Equipment
### Introduction to the Principle of Deep Displacement Monitor Sensor  
The principle of the deep displacement monitor sensor based on the double mutual inductance voltage contour method is shown in Figure 5. The whole instrument is composed of several identical sensing units. Each sensing unit is 10 cm high, and the number of sensors in a sensing array is determined by the depth of the bedrock. Each sensing unit consists of an external air-core coil, an internal coil with a magnetic core, associated circuitry, and structure. The lower sensing unit is referred to as the excitation end while the upper sensing unit is called the measurement end. The upper and lower sensing units form a group of measuring units. Therefore, N sensing units form N \(-\) 1 measuring units from bottom to top. When the air-core coil and the core coil at the excitation end are connected to the same sinusoidal signal, due to the electromagnetic induction, two mutual inductance voltages with the same frequency and different amplitude will be generated on the air-core coil at the measurement end.  
Since the sensing array is located in the same environment and each unit has the same structure, the mutual voltage is only related to the relative tilt angle and position between adjacent sensing units. Therefore, when any of the horizontal resultant displacement \(R\), vertical displacement \(Z\) and tilt angle \(\theta\) in Figure 5 change, the mutual inductance voltage collected in the measuring unit will change. Since any vector on the \(XOY\) plane consists of two components, the horizontal resultant displacement can be divided into two vectors, \(X\) and \(Y\).  
Figure 4: Details of the deep displacement monitor sensor.  
Figure 5: Schematic diagram of double mutual inductance voltage method.  
For any two adjacent sensing units, the measurement model for describing the relationship between the relative displacement and the double mutual inductance voltage can be established by the following methods. (1) Collecting the data of the mutual inductance voltage at different relative inclination angles \(\theta\), horizontal displacement \(R\), and vertical displacement \(Z\). (2) Quantify this data by theoretical analysis and experimental testing. (3) Using the model established in step 2, the change of the relative position between adjacent sensing units can be calculated by collecting the change of the double mutual inductance voltage.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results
### Implementation Methodology  
The MATLAB implementation provides straightforward visualization and analysis capabilities for both modulation techniques without requiring complex coding structures or extensive signal processing operations. This approach enables clear observation of the modulation processes, facilitating both educational understanding and practical system development. The simulation results confirm that both PWM and PPM techniques offer reliable methods for information encoding, transmission, and recovery, with each method providing distinct advantages depending on the specific application requirements and system constraints.  
Figure 14: PPM signal synthesis through mathematical manipulation of PWM signals. (**a**) Original information signal serving as the modulation reference; (**b**) Delayed PWM signal (dashed) and non-delayed PWM signal (solid) used for differential processing; (**c**) Resulting PPM signal obtained by subtracting the delayed PWM from the non-delayed PWM, effectively converting pulse width information to pulse position information.
## 4 Simulation Results
### Results on the Benchmark Test-Suite  
The benchmark suite (Table 1) [4; 19] includes functions of a wide variety of features and levels of problem difficulty, including unimodal/multimodal, separable/non-separable, continuous/discontinuous, differentiable/non-differentiable, and convex/non-convex functions. For each test function, the population size and the number of generations were chosen based loosely on the problem size (number of variables) and the problem difficulty. No systematic tuning of the population size (PopSize) or the number of generations (Gens) was attempted; the values used in this study were found to be reasonably good across a majority of the problems after a few initial trials. Two PopSize-Gens combinations were used for each function (see Table 2). For \(d=30\), population sizes of 100 and 150 were used, with the corresponding number of generations being 3000 and 5000. For \(d=2\), the population sizes were 15 and 20, with 5000 generations used for both. Thirty independent runs of each of the two algorithms were executed for each PopSize-Gens combination on each of the test functions. A run is considered a success if it manages to produce at least one solution with a fitness within a distance of \(\pm 1.0\times 10^{-6}\) from the true (known) global optimum, and the number of fitness evaluations corresponding to the first appearance of such a solution is recorded as the FirstHitEvals of that run. Tables 2 and 3 show the results of [PERSON] and [PERSON], respectively, on the 12-function test-suite. In all the tables in this paper, results are rounded at the fourth decimal place. From Tables 2 and 3 we see that [PERSON] produces superior results than [PERSON] on all the metrics. Specifically,  
* On the best of best-of-runs metric, out of 24 cases, [PERSON] outperforms [PERSON] in 12 cases and is outperformed by [PERSON] in 2 cases, with 10 cases resulting in ties. In a few cases (such as the values of 3.0000 of the best of best-of-run fitnesses and of the mean of best-of-run fitnesses corresponding to the Goldstein-Price function for both [PERSON] and [PERSON]), differences exist at the fifth or a later decimal position but do not show in Tables 2 and 3. * On the mean of best-of-runs metric, [PERSON] is the winner with win-loss-tie figures of 18-1-5. * The success counts are higher (5-1-18) for [PERSON]. * [PERSON] outperforms [PERSON] 19-1-4 on the best FirstHitEvals metric. * On the mean FirstHitEvals metric, [PERSON] outperforms [PERSON] 19-1-4.
## 2 Materials and Methods
### Convolutional Neural Network Backbones Used in This Work
#### 2.3.2 VGGNet  
In 2014, VGG [27], a name derived from the Visual Geometry Group from Oxford, was released. Compared to AlexNet, the VGG model uses smaller convolutional filters of sizes 2 \(\times\) 2 and 3 \(\times\) 3, allowing it to reduce the complexity of the computation but achieve similar performance as larger filters. The authors released a series of VGG models with different numbers of layers from 11 to 19. Since some previous studies [31] have reported higher performance achieved by VGG with 16 layers, this paper adopts the VGG-16 model.