esa-sceva/satcom-chunk-collection huggingface dataset:
# DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths
## 5 Experiments and Analysis
### Ablation Study  
In this section, we describe the ablation tests performed to investigate the effectiveness of the critical designs of DEEDP. To address this issue, we constructed the following variants of DEEDP: (1) w/o RicFea, the input embedding at the sentence-level encoding stage without using the document topic, POS, and entity type features; (2) w/o DocEnc, the input embedding at the sentence-document feature fusion stage without using document-aware sentence representations; (3) w/o SF-ATT, removing Syntactic Feature Attention from the Transformer; and (4) w/o TF-M, removing _TF-M_ encoder. Table 5 presents the result of ablation tests on DEEDP, and we have the following observations: (1) compared with only use token embedding and positional embedding, richer input features inject more useful external features to enhance the semantic information for each token of the input sentences, benefiting the following tasks accordingly, and a +0.7 F1-score on average contribution is obtained by using the information of these rich features; (2) the use of document-aware sentence representations contributes a +1.4 F1-score on average compared to when they are not used; (3) the _SF-ATT_ strategy that explicitly models the syntactic information of input sentences facilitates enhancing the semantic information for each document-aware representations of each entity, hence making great contributions, improving by 2.4 F1-score points on average; (4) the _TF-M_ is a pivotal design for DEE that helps to achieve a +2.7 F1-score improvement on average, indicating that the _TF-M_ can aggregate sentence-aware and document-aware features to improve the performance of DEE.
# Robust Full-Screen Wavelength Calibration Algorithm
## Appendix A
### Functional Relation at Non-Central Pixels
#### a.2.2 Two Loss Functions of Linear Regression  
Linear regression is a widely used model to explore the linear relationship between multiple variables, and its regression parameters are usually obtained by the least square method (LS). However, the LS method is sensitive to outliers. In the situation of the observed data involving outliers, a robust estimation can reduce their influence on the estimated parameters. Because previous analysis has shown that there exists a linear relationship between \(\Delta x\) and \(\Delta p\), we will study the LS method and robust regression methods including Huber and Turkey regression models.  
In conjunction with the model we need to solve, let's take the linear parametric model as an example  
\[f_{\beta}(x)=\sum_{j=1}^{m}\beta_{j}x_{j}=\beta^{\top}x, \tag{10}\]  
where \(x\) is the predictor variable, \(m\) is the dimension of \(x\) and is set to 2 in Equation (10), \(y\) is the response variable, \(\beta\) is the unknown regression vector of the function.  
Provided some observed data \((x_{i},y_{i})(i=1,2,\cdots,n)\), denote the residuals by \(r_{i}=f_{\beta}(x_{i})-y_{i}\) in which \(f_{\beta}(x_{i})\) denotes the value fitted by a linear regression model with fixed \(\beta\). In the LS regression algorithm, we use the loss of mean squared error (MSE) to ensure that we get a suitable result.  
\[\hat{\beta}=\underset{\beta}{\text{argmin}}L(\beta),L(\beta)=\frac{1}{2}\sum_ {i=1}^{n}{r_{i}}^{2}. \tag{11}\]  
We note that the gradient of MSE is \(\sum_{i=1}^{n}r_{i}x_{i}\). The advantage of MSE is that as the error decreases, the gradient also decreases, which is conducive to the convergence of the loss. If some outliers exist in the observed data, they will make MSE unsurprisingly large. As a result, the attained function will greatly deviate from the true function. We define the influence function as \(\phi(r_{i})=r_{i}\), which reflects the influence of the observed data \(x_{i}\) on the gradient. The weight function is defined as \(\psi(r_{i})=\frac{\phi(r_{i})}{r_{i}}\). Notice that the weight function reflects the importance of each \(x_{i}\) to the loss function. In LS method, \(\psi(r_{i})=1\) which means that each \(x_{i}(i=1,2,\cdots,n)\) are treated equally when minimizing MSE.  
From the point of view of pursuing robustness, it is always better to use mean absolute error(MAE) as the primary criterion.  
\[\hat{\beta}=\underset{\beta}{\text{argmin}}L(\beta),L(\beta)=\sum_{i=1}^{n}|r _{i}|. \tag{12}\]Obviously, MAE is less sensitive to outliers than MSE. Nevertheless, it achieves robustness to outliers by losing some accuracy. We also note that the gradient of MAE is \(\sum_{i=1}^{n}sgn(r_{i})x_{i}\) in which \(\text{sgn}(\cdot)\) is the sign function. However, the influence scale of \(x_{i}\) is always \(1\) when their residuals \(r_{i}\) are small, which is not helpful to the convergence of the model.
## 3 Results
### Equation Fitting of Ammonia Volatilization Fluxes  
The ammonia volatilization fluxes (Y, mg) and ammonia nitrogen concentration (C, mg\(\cdot\)L\({}^{-1}\)) in the solution were simulated with an equation of Y = _a_C + \(b\); namely, the rela  
Figure 3: The fluxes of ammonia volatilization of SDP under different temperature and pH levels. Figure 2: The fluxes of ammonia volatilization of SAB under different temperature and pH levels. tionship between the ammonia volatilization fluxes and ammonia nitrogen concentration and the coefficients of \(a\), \(b\) and correlation coefficient \(R^{2}\) are listed in Table 1.  
In the simulated rate equation, \(a\) represents the slope of the rate equation; namely, the concentration coefficient of the ammonia volatilization fluxes in the given pH, temperature and concentration, which is called the concentration influence factor (CIF); \(b\) represents the intercept of the rate equation, namely the ammonia volatilization quantity when the ammonia nitrogen concentration is 0 mg\(\cdot\)L\({}^{-1}\), which is called the initial ammonia volatilization fluxes (IAVFs). From the data in Table 1, we can see that in the same pH and temperature conditions, the value of the CIF of ammonium bicarbonate as the nitrogen source is higher than that of SDP as the nitrogen source; a greater slope value shows SAB volatile ammonia more easily. The double hydrolytic reaction plays an important role in the dissolution process of SAB and SDP, because the ammonium group, bicarbonate radical, and hydrogen phosphate are weak bases or weak acids. The aqueous solution of these two fertilizers is alkaline, but carbonic acid is weaker than phosphoric acid, which are all produced by the hydrolytic reaction. The alkaline of SAB is always stronger than that of SDP, which is more conducive to the volatilization process. This implies that different fertilizers show different characteristics of ammonia volatilization. Theoretically, there should be no ammonia being volatilized when the ammonia nitrogen concentration is 0 mg\(\cdot\)L\({}^{-1}\), because ammonia nitrogen is the nitrogen source of the ammonia volatilization process. However, in our simulations, a special phenomenon was found; that is, the values of the IAVFs (\(b\)) greatly increased with increasing temperature and pH. This phenomenon can be considered: that the value of b represents the trend of ammonia volatilization under the specified temperature and pH; in other words, the concentration of ammonia nitrogen increases by the same infinitesimal amount, and the higher the temperature and the higher the pH, the more ammonia is being volatilized. In addition, the experiment took different nitrogen fertilizers as the ammonia nitrogen source; the correlation coefficients of the ammonia volatilization fluxes and ammonia nitrogen concentration were higher than 0.9, except for SDP with a pH of 7.0 and a temperature of 15 \({}^{\circ}\)C. Therefore, ammonia volatilization fluxes and ammonia nitrogen concentration show a positive relation, which belongs to the zero-order reaction in kinetics.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method
### Ore Rock Fragmentation Calculation Based on Multi-Modal Calibration Mapping
#### 3.6.2 Point Cloud Segmentation and High-Density Ore Rock Fragments Screening  
Once the calibration establishes the mapping relationship between point clouds and images, the 3D rock point clouds can be projected onto the segmented ore and rock images. This projection creates a direct correspondence between individual points and their respective pixels. Following the watershed image segmentation, different minerals in the ore image are distinguished by unique colors. By assigning the RGB values of the corresponding mineral pixels to the mapped points, the segmentation of mineral blocks in the ore point cloud can be obtained.  
In the subsequent step, the Random Sample Consensus (RANSAC) algorithm is employed to fit planes to the segmented point cloud of mineral blocks. The point cloud is then projected onto these fitted planes, as depicted in Figure 9. The black points represent the background, while points of different colors correspond to different rock formations.  
In order to mitigate the impact of point cloud sparsity on rock block volume calculations, it is essential to establish a suitable metric for evaluating point cloud sparsity. Furthermore, high-density rock block point clouds are filtered as reference objects for size measurements. This study evaluates the sparsity of rock block point clouds by comparing the number of points in the point cloud modality with the number of pixels in the image modality. The ratio between the point cloud point count and the image pixel count serves as an indicator for assessing the sparsity of the rock block point cloud. Sparsity analysis is conducted on all rock blocks, and they are sorted based on their sparsity levels. Finally, the three rock block point clouds with the lowest sparsity are selected as the reference objects for size measurements.
## 2 Materials and Methods
### Sensitizer Load and Entrapment Efficiency  
Loaded sensitizer concentration determinations were performed by preparing a standard curve of free ZnPc-BCH\({}_{3}\) and measuring the absorption in THF at 677 nm. To determine the amount of nanoparticles formed, fixed volumes of nanoparticle solution were lyophilized and the dry weight measured. Sensitizer encapsulation was measured by dissolving the lyophilized nanoparticles in THF and measuring the absorbance at 677 nm. Nanoparticle yield, sensitizer load and entrapment efficiency were calculated using standard equations (cf. Equations (3)-(5)) obtained from the literature [23].  
\[\text{Nanoparticle yields}\ (\%)=\frac{\text{{Weight}\ of\ nanoparticles}}{\text{{Weight}\ of\ polymer and\ sensitizer\ fed\ initially}}\times 100\%. \tag{3}\]  
\[\text{Sensitizer loading}\ (\%)=\frac{\text{{wight}\ of\ sensitizer\ in\ nanoparticles}}{\text{{Weight}\ of\ nanoparticles}}\ \times\ 100\% \tag{4}\]  
\[\text{Entrapment Efficiency}\ (\%)=\frac{\text{{Weight}\ of\ the\ sensitizer\ in\ nanoparticle}}{\text{{Weight}\ of\ sensitized\ fed\ initially}}\times 100\%. \tag{5}\]
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 5 Experimental Verification of Adsorption Properties of GO
### Results and Discussions  
#### 5.2.1 Characterization  
(1) SEM  
As is shown in Figure 7, the SEM images of GO are presented at different measurement scales. It can be seen that GO exists in sheet form, the surface has certain folds, and the specific surface area is large.  
(2) FTIR analysis  
As is shown in Figure 8, the GO only containing hydroxyl and carboxyl groups was constructed using Gaussian16 software, and the infrared spectra in its stable configuration were calculated. By examining the vibration modes of oxygen-containing functional groups on GO, it can be observed that the absorption peaks at 1295.59 cm\({}^{-1}\) and 1689.70 cm\({}^{-1}\) correspond to the deformation vibration of -OH. The absorption peak at 1750.79 cm\({}^{-1}\) corresponds to both the deformation vibration of -OH and the stretching vibration of -C=O in the carboxyl groups. Additionally, at 3048.38 cm\({}^{-1}\), an absorption peak arises from superimposed stretching and deformation vibrations of -OH.  
The infrared absorption spectra of GO measured by an infrared spectrometer are presented in Figure 9. However, because the Gaussian-constructed GO model is relatively simplified compared to the actual molecular structure, certain deviations in position and intensity of spectral peaks of the model GO and the actual model exist.  
Figure 7: SEM images of GO at 10 μm scale (a) and 30 μm scale (b).
## 3 Results
### Accuracy Metrics
Above each example, the pVF estimations are displayed (TF DNN \(\rightarrow\) quantized LiteRT DNN), revealing that the IntQ model's pVF output shows differences within the range of 0.03-0.05, corresponding to up to 10% of the pVF threshold. In contrast, the Full-IntQ model demonstrates larger deviations, ranging from 0.06 to 0.41, reaching up to 80% of the threshold. Despite these deviations, the cases with inversed classification outcomes are negligible for the test dataset--0.1% (7/7482 for IntQ and 11/7482 for Full-IntQ). Figure 10: Observations of pVF output of TF DNN (reference) vs. quantized LiteRT DNN (IntQ on top and Full-IntQ on bottom) in the test dataset, illustrating the performance results in Table 4 for pVF threshold = 0.5. Observation points are presented as scatter plots and histograms for four rhythms: normal sinus rhythm, other non-shockable rhythms, asystole, and ventricular fibrillation.
## 4 Model Construction
### Least Square Contribution of Trapezoidal Fuzzy Numbers Considering Contribution Excess of Players
: _(Additivity): For any trapezoidal fuzzy number cooperative game \((N,\widetilde{v})\) and \((N,\widetilde{w})\), there exists \({SH{L_{i}^{*}}_{l}}^{\prime\prime}(\widetilde{v}+\widetilde{w})={SH{L_{i}^{*}} _{l}}^{\prime\prime}(\widetilde{v})+{SH{L_{i}^{*}}_{l}}^{\prime}(\widetilde{w})\). That is, regardless of which form of cooperation is employed, the profit allocation method for each form of cooperation is mutually independent and does not rely on other cooperation outcomes._  
**Proof of Theorem 3. **  
\[ \begin{split} SHL_{i}^{*\prime}(\widetilde{v}+\widetilde{w})& =\sum\limits_{S\subseteq N:i\in S}\frac{(n-s)!(s-1)!}{n!}(\overline{x}_{i}^{*} (S)-\widetilde{v}(S)\,\frac{\widetilde{v}(i)}{\sum\limits_{i\in N} \widetilde{v}(i)}+\overline{x}_{i}^{*}(S)-\widetilde{w}(S)\,\frac{\widetilde{w }(i)}{\sum\limits_{i\in N}\widetilde{w}(i)}\\ &+\widetilde{v}(N)\,\frac{\widetilde{v}(i)}{\sum\limits_{i\in N }\widetilde{v}(i)}+\widetilde{w}(N)\frac{\widetilde{w}(i)}{\sum\limits_{i\in N }\widetilde{w}(i)}\\ &=SHL_{i}^{*\prime}(\widetilde{v})+SHL_{i}^{*\prime}(\widetilde{ w})\\ &\quad\Box\end{split}  \tag{30}\]  
**Theorem 4. ** (_Symmetry_): \(SHL_{i}^{*\prime}(\widetilde{v})=SHL_{i}^{*\prime}(\widetilde{v})\)\((i,j\in N)\), where the player \(i\) and the player \(j\) are symmetrical. That is, the profit allocated to each individual in the cooperation does not vary with the sequence of each individual within the cooperative alliance._  
From the above Theorems 1-4, it can be seen that the improved Shapley value with trapezoidal fuzzy numbers considering the contribution excess of players proposed in this paper also satisfies the important properties of classical Shapley value, such as efficiency, efficiency, additivity, and symmetry.
# A REM Update Methodology Based on Clustering and Random Forest
## 2 Measurement Methodology  
The scenario considered for indoor REM analysis was Room 302 in the Engineering Building at the University of Ulsan. The equipment used for the analysis included the TurtleBot3 [16] mobile robot, which is equipped with sensors, navigation systems, and decision-making algorithms that enable it to operate and complete tasks without human intervention. In particular, TurtleBot3 is composed of several parts, including an embedded controller, a light detection and ranging (LiDAR) sensor (laser distance sensor LDS-02), an inertial measurement unit (IMU) sensor, an encoder, a single board computer (SBC), and the robot operating system (ROS). A Raspberry Pi powers the SBC, which configures algorithms in a Linux environment and relies on ROS to enable communication between different processes. Meanwhile, the embedded controller, which utilizes OpenCR, is responsible for controlling the movement of the mobile robot through various sensors. The LiDAR sensor utilizes laser beams to calculate the distance to objects in its surroundings. By scanning the laser beams in a 360-degree horizontal field of view, LiDAR can generate a 2D point cloud map of the robot's surroundings. This map can be utilized for obstacle detection, mapping, and localization. Moreover, TurtleBot3 utilizes the odometry technique that employs sensor data from the encoders and the IMU to estimate the position and orientation of the robot in relation to its starting position. The odometry data are then combined with the LiDAR data to provide a more accurate and robust localization system, where the coordinates of the location points are presented in a 2D Cartesian coordinate system [17].  
RSSI was utilized to estimate the link quality between the transmitter and receiver. RSSI is a measurement of the power present in a received radio signal, and it is commonly used in wireless communication systems like Wi-Fi, Bluetooth, and cellular networks to determine the signal strength received from a transmitter. The RSSI value is typically expressed in dBm (decibel-milliwatts) and reflects the power of the signal received by the receiver's antenna. A higher RSSI value indicates a stronger signal, while a lower RSSI value indicates a weaker signal. In our experiments, we used the built-in Wi-Fi module of the Raspberry Pi to collect RSSI data, along with the SSID, signal frequency, and link quality. The RSSI and location data were assembled in Ubuntu 22.04 LTS through a shell script and synchronized based on timestamps.  
The access point (AP) for the experiments was the IPTime N704M at 2.4 GHz. Figure 1 is a floor plan of the room used for the experiments, indicating the location of the AP. The floor plan presented in Figure 1 was obtained with the Hovermap HF1 [18], which is a mobile LiDAR 3D scanner, to map GPS-denied environments. Hovermap uses innovative simultaneous localization and mapping (SLAM) algorithms along with LiDAR data to produce 3D point clouds of the scanned area.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 2 Complete Machine Structure and Working Principle
### Platform Structure and Details
#### 2.1.4 Motion Control Module  
The hull motion control module is the basis for unmanned boats to realize automatic cruising and accurate feeding. Traditional aquaculture equipment usually relies on fixed-position mechanical operation with low flexibility and automation. Through precise motion control, the unmanned boat can navigate autonomously in complex water environments. It follows a predetermined path for water quality testing and feeding, significantly improving work efficiency and accuracy. The boat motion control module mainly consists of an STM32 development board, a BeiDou positioning antenna, a servo, and a motor (Figure 6).  
(1) STM32  
The STM32 development board is a microcontroller platform based on ARM Cortex-M cores, widely recognized for its versatility, cost-effectiveness, and reliability. Its low power consumption and high processing efficiency make it ideal for embedded systems, while its extensive community support and comprehensive documentation simplify development. These features make the STM32 development board a common, economical, and reliable choice for microcontroller-based projects (Figure 6a).  
The hull motion control module is developed based on the STM32 development board, which controls the servo and motor through PWM signals [22; 23]. The main component of the servo is the servo motor, and the so-called servo is to obey the signals and move. Before the signal comes, the rotor stops moving; after the signal comes, the rotor moves immediately. Therefore, we can input different signals to the servo to control its rotation to different angles. The servo receives a PWM signal, when the signal enters the internal circuit to generate a bias voltage, the contactor generator drives the potentiometer through the reduction gear to move, so that when the voltage difference is zero, the motor stops, so as to achieve the effect of servo. Simply put, to give the servo a specific PWM signal, the servo can rotate to the specified position.  
Figure 6: Motion control module. (**a**) STM32; (**b**) Beidou antenna.  
2. BeiDou Antenna The BeiDou antenna is a positioning and navigation module designed for development boards, which can receive signals from the BeiDou satellite system and realize a high-precision positioning function (Figure 6b). Compared to other mainstream positioning systems, like GPS and GLONASS, the BeiDou system offers unique advantages, such as higher accuracy in the Asia-Pacific region and enhanced security due to its independent design. This makes it particularly suitable for applications requiring reliable and precise navigation. It supports a variety of communication interfaces, such as UART and SPI. This makes it easy to quickly integrate into various embedded systems, enabling accurate navigation and positioning.
## 2 Materials and Methods
### Methods
#### 2.3.1 Data Gap-Filling
Model Performance and Accuracy Assessment of Predicting Land Surface Temperature  
A comprehensive set of statistical measures is computed on the test set to assess the accuracy and performance of the predictive model for LST. These measures include important metrics like the coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), minimum and maximum confidence intervals, mean absolute error (\(MAE\)), and mean bias error (\(MBE\)) as  
\[R^{2}=1-\frac{\sum_{i=1}^{n}{(\hat{y}_{i}-\underline{y})^{2}}}{\sum_{i=1}^{n} (y_{i}-\underline{y})^{2}}, \tag{3}\]  
\[RMSE=\left[\sum_{i=1}^{n}{\frac{(y_{i}-\hat{y}_{i})^{2}}{n}}\right]^{1/2}, \tag{4}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}, \tag{5}\]  
\[MBE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i}), \tag{6}\]  
where \(N\) is the number of records in validation datasets used in this study, \(\hat{y}_{i}\) is the estimated variable, \(y_{i}\) is the observed variable, \(\underline{y}\) is the mean of all the values, and the confidence level is set to 0.95. For the assessment, the calibration phase utilizes variable data spanning from 2000 to 2017, while the subsequent validation phase involves data from 2017 to 2019, following an 80% train and 20% test split. The \(R^{2}\) value, ranging from 0 to 1.0, provides a valuable indication of the predictive model's accuracy in estimating outcomes. The RMSE is effectively utilized to evaluate biases in both mean and spatial variance, whereas the MAE serves as a reliable measure of error magnitude, with lower values demonstrating superior performance. Additionally, the MBE offers insightful observations regarding the direction of error bias, with a value of zero denoting an unbiased estimation by the model [92; 93].