esa-sceva/satcom-chunk-collection huggingface dataset:
# Representing Aspectual Meaning in Sentence: Computational Modeling Based on Chinese
## 2 Materials and Methods
### Prediction of Lexical Aspects
#### 2.3.1 Prediction Based on Linguistic Features  
All of the aspectual markers, including ZHE, LE1, LE2, GUO, ZAI and ZHENGZAI, will be used to do the prediction. The SINICA corpus has annotated these aspectual markers as Di or D, which makes it possible to extract their co-occurrence frequencies with the annotated verbs. ASP is used to refer to aspectual markers in the following content.  
In order to improve the prediction performance, other linguistics features, such as verb redulation frequencies and syllable length, are also introduced.  
[PERSON] [6], [PERSON] [9], [PERSON] and [PERSON] [13] have noticed the importance of verb reduplication and treat it as a viewpoint aspect, similar to other aspectual markers. VV is regarded as an abbreviation of verb reduplication in the following content.  
For syllable length, [PERSON] [25] summarizes its relationship with lexical aspects although in a not reliable way. We also include it into the linguistics features for the prediction experiment. SYLL is treated as the abbreviation of syllable length.  
Besides, all the Part-of-Speech(POS) tagging information in the SINICA corpus is also included. POS is used as its abbreviation.  
Three classifiers, including multinomial logistic regression (MNLogit), supporting vector machine (SVM), and artificial neural network (ANN), are chosen to conduct the classification experiment. The classifiers mentioned above were implemented using scikit-learn library (v1.3.0) in Python and their configurations are depicted below.  
Classifier Parameters
* Support Vector Machine (SVM): The penalty value \(C\) was tuned within the range of 0 to 100 and ultimately set to 5. The kernel method was selected as the radial basis function (RBF).
* Artificial Neural Network (ANN): A five-layer sequential neural network model was constructed, with the number of neurons in each layer set to 6, 180, 60, 15, and 3, respectively. All layers were fully connected (dense) layers, and the activation function was a rectified linear unit (ReLU). A default dropout rate of 0.5 was applied to prevent overfitting.
* Multinomial Logistic Regression (MNLogit): The default configuration provided by the sklearn package was utilized for the multinomial logistic regression model.  
In order to raise the reliability, ten-fold cross-validation is implemented for each experiment.
## 5 Simulation Results and Discussions
### Performance of Different Algorithms
In order to further observe the impacts of cluster size on the performance of the proposed algorithm, Figure 4 shows the system mean data rate for different algorithm with various cluster sizes, where the number of ACK-UEs equals to the number of NACK-UEs, _i.e._, \(N_{\text{\tiny{ACK}}}:N_{\text{\tiny{NACK}}}=\)1:1. As observed, all curves go up as the cluster size becomes larger except the OSRS, which indicates the larger cluster size  
Figure 4: System mean data rate for different relay selection algorithms with different size of D2D cluster, where \(N_{\text{\tiny{ACK}}}\):\(N_{\text{\tiny{NACK}}}=1\). is more beneficial to the proposed algorithm. Moreover, we can see that simulation results by the CARS algorithm are closer to the theoretical top bound than the GHRS algorithm, especially when the cluster size increases as Figure 4 shows. The reason for this phenomenon is that better multichannel diversity can be used to improve with the increase in cluster size, and thus the merit of the CARS algorithm over the other two algorithms becomes more evident.
## 3 Proposed Approach
### Experiments  
This section outlines the experiments with three different approaches to predict students at risk of dropout in the database described earlier. The first is the proposed genetic algorithm, the second is a grid search method called GridSearchCV, implemented using the Scikit-learn package. The third and last is the use of classifiers with their default hyperparameters. The machine learning techniques implemented in this study used the Python programming language with the Scikit-learn, Pandas, and Numpy libraries.  
GridsearchCV allowed the testing of different combinations of hyperparameters for classifiers, facilitating choosing the best one. The hyperparameters needed to be explicitly declared and all possible combinations tested. All available combinations in Table 3 were checked with the same algorithms defined in the GA (DT, RF, MLP, LG, and ADA). For each week of the course, we selected the classifier together with its hyperparameters that achieved the best performance for the given week.  
The same machine learning algorithms with their default hyperparameters were also implemented for comparison with the GA and GridsearchCV approaches. All experiments were performed with 10-fold cross-validation, and the number of combinations was approximately 5000 individuals created by the GA. Appendix A shows the quantities tested in each of the classifiers in the Evaluations column.  
An essential task in machine learning is choosing the performance appraisal metric. For this work, we decided to use the area under the ROC curve, also known as AUC and AUROC. AUC is calculated from the size of the area under the plotted curve where the y-axis is represented by true positive rate (TPR) or sensitivity (Equation (1)), and the x-axis is true negative rate (TNR) or specificity (Equation (2)):  
\[Sensitivity=\frac{TP}{TP+FN} \tag{1}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{2}\]  
According to [PERSON] et al. [50], the AUC may be interpreted as follows:  
* AUC \(\leq\) 0.50: Bad discrimination;
* 0.50 < AUC \(\leq\) 0.70: Acceptable discrimination;
* 0.70 < AUC \(\leq\) 0.90: Excellent discrimination; and
* AUC > 0.90: Outstanding discrimination.
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 2 Related Work
### Object Detection  
Traditional object detection methods use handcrafted features. The performance of the algorithm depends on the robustness of the features to a large extent. With the appearance of the huge amount of annotated data and high-performance hardware, the deep learning-based method has been widely adopted in object detection, which can learn semantic, high-level features with good robustness automatically. Deep learning-based object detection methods can be roughly divided into two types. The former is a two-step method, generating region proposals firstly, and then classifying and identifying each proposal by CNN. The typical frameworks of this type include region-based CNN (R-CNN) series, Mask R-CNN, etc. The latter regards object detection as a regression problem, which  
Figure 1: The flowchart of the proposed method. First, the model was pre-trained on the source domain. Second, the model was fine-tuned on the simulation dataset. Third, the model detected UAV in the target domain.  
directly gives the object category and location information simultaneously, for example you only look once (Yolo) series or single shot multibox detectors (SSD) [7]. Compared with the two-step method, the latter can achieve faster detection speed while maintaining comparable detection accuracy. YOLOv5 [22] is the latest version of the YOLO series, which has a significant increment in performance compared to older versions. In this paper, we adopted the smallest YOLOv5s model as the backbone model, which can commendably meet the requirements of our application scenarios.
# A Comprehensive Survey for Deep-Learning-Based
## 3 Abnormal Detection with Images over Short Distances
### Visible Light
#### 3.1.2. Icing Detection and Measurement  
Extreme weather conditions such as snowstorms and hail disasters will harm the power transmission line. The thick icing accumulated on the transmission lines will increase their load and cause power lines to break, resulting in power supply interruption [72]. Therefore, icing detection and measurement is a crucial task in smart grids, which can help grid companies to take measures in advance for icing accident prevention.  
Inspired by the traditional DIP technique, [PERSON] et al. [71] analyzed three kinds of edge detection method (Canny operator, Sobel operator, and adaptive weighted Sobel operator) on the UAV transmission line icing monitoring task. It is shown that the adaptive weighted Sobel operator has higher accuracy in detecting the image's edges than the other two operators. [PERSON] et al. [72] proposed an algorithm to automatically detect icing and estimate the ice thickness of the transmission lines. First, a calibration method calibrates the monitoring camera using just one image. An ice-prior-based scheme is then employed to accurately detect the outlines of power lines and icing. After calculating the pixel-level thickness of the lines and the icing regions based on the extracted outlines, the icing thickness can be derived from the calibration parameters and a region-of-interest (ROI) tracking scheme. In addition to just utilizing images, [PERSON] et al. [73] also measured the distance and level angle of a target using a laser range finder and inertial measurement unit (IMU), and then realized icing parameters measurement with a photogrammetry method.
# Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)
## 7 Recent Advances in the Type of Fiber Material Used in SPME
Ultra-high-performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-MS/MS) in conjunction with the SPME method produced high recovery rates (77.1% to 99.6%), low detection limits (0.002 to 0.020 ug L\({}^{-1}\)), and wide linearity (0.01 to 100 ug L\({}^{-1}\)). The investigation established the adsorption mechanism by revealing strong non-covalent interactions between BDTB-MBD and analytes. It effectively monitored the pharmacokinetics of nicotine in rat brain tissues, demonstrating its quick absorption, widespread distribution, and conversion to cotinine, normicotine, and other metabolites. The researchers underlined that this strategy has uses in drug monitoring and neuroscience and provides a more effective and sensitive substitute for conventional techniques like solid-phase extraction [41]. For sensitive analyte detection, in-tube solid-phase microextraction (IT-SPME), which has become a popular and eco-friendly sample preparation method, is frequently used in conjunction with mass spectrometry or chromatography. This technique minimizes the usage of organic solvents, increases accuracy, and cuts down on analytical time. To improve enrichment efficiency, a variety of sorbent materials and extraction tools have been developed, including fiber-filled tubes, monolithic columns, sorbent-coated tubes, and particle-packed tubes. Although sample preparation is still difficult despite improvements in analytical equipment, solid-phase extraction has essentially supplanted conventional solvent-based techniques because of its ease of use and lower solvent use. To further reduce sample volume, minimize solvent use, and facilitate automation, microextraction techniques such as magnetic bead extraction, stir bar sorptive extraction, micro solid-phase extraction, and dispersive micro solid-phase extraction have been developed. All things considered, IT-SPME is a \"green\" technology that combines eco-friendly practices, online automation, and miniaturization [135; 136].
# Detection of Thermal Strain in Steel Rails with BOTDA
## 3 Results and Discussion
### Thermal-Strain Measurement on the Rail Specim  
Next, the Brillouin frequency shift with temperature variation of the three types of fiber glued on the rail specimen can be found in Figure 6. Since we found that the results from the fiber glued  
Figure 5: (**a**) Brillouin frequency variation with the temperature of Fiber B at different positions of the rail specimen; (**b**) Brillouin coefficient at different positions in the vertical direction of the rail.  
at different parts of the rail were similar, we only chose the results collected from the neutral axis for comparison. The thermal effect of the jacketed fiber that comprised the bare optical fiber and jacket material, strain glue, and thermal strain of the rail may have contributed to the measured results. All of the three fibers had a perfectly linear response, which makes it rather interesting because two types of loose jacketed fiber have a nonlinear Brillouin frequency response, especially in a low temperature range (lower than 0 \({}^{\circ}\)C). Since the optical fibers were glued on the rail and, when the rail underwent extension in high temperatures and extraction in low temperatures, the fiber on the rail would elongate or shrink in the meanwhile. The results clearly show that the thermal strain caused by the rail expansion and contraction played a dominant role on the Brillouin frequency shift of the fiber due to its large thermal strain on the jacketed fiber, and the thermal-strain nonlinearity by the fiber jacket could be neglected. As a result, all the fibers that were glued on the rail had a linear Brillouin frequency shift with a temperature change that verified the applicability of all the fibers for thermal-strain monitoring of the rail specimen.  
Through linear fitting, the Brillouin frequency-shift variations with the temperature of all three types of fiber are summarized in Table 2. Subtracting the Brillouin frequency shift of the bare fiber, the corresponding strain was calculated by taking advantage of the strain coefficient of the normalized Brillouin frequency shift of the single-mode fiber, which was 0.048 MHz/\(\mu\epsilon\)[11]. The difference between the results from the strain gauge with BOTDA from three types of fiber was within measurement accuracy. Nevertheless, different types of fibers have a slightly different response to the same strain from the rail, providing an important instruction to practical applications, that is, before applying a certain kind of fiber to monitor rail thermal strain, calibration should first be done; BOTDA is one promising approach.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 3 SSCM-RT Channel Model
### SSCM-RT Channel Model Simulation
Assuming that the T\(-\)R separation distance is 1000 m, \(d_{2}\) is taken as 20 m, 50 m, 80 m, 100 m, 150 m, and 200 m, corresponding to \(\frac{d_{1}}{d_{2}}\) of 49, 19, 11.5, 9, 5.67, and 4, respectively. The 'RT Distance of SSCM-RT' of 0 and 1000 m correspond to the results of the spatial statistical channel model and ray tracing model, respectively. A comparison of the path loss for each model is shown in Figure 8.  
It can be seen that \(\frac{d_{1}}{d_{2}}\) has an impact on the path loss simulation results, but the error value is not large and does not exceed 2 dB when considering the path loss for all paths. In addition, the type and number of rays remain the same as in Figure 6, which further indicates the validity of the SSCM-RT channel model.
## 4 Case Study  
The proposed model is tested on Sub-network 1 of the CIGRE MV microgrid benchmark, proposed by [22], given in Figure 3. Neglecting the parameters of the transformer 110/20, defined as TR1 in Figure 3, node 1 becomes PCC between the external power grid and the MG. The grid parameters are given in the Appendix A of this paper (Table A3). The grid supplies households as well as industrial load. In addition, it utilizes solar and wind DG, CHP units, fuel cells, and battery storage, as per tables in Appendix A (Table A2). Daily household load, industrial load, as well as wind and solar profiles are given in Figure 4, while the power of each individual resource is defined in Appendix A (Table A1). For the sake of a more realistic approach, each individual load and generation is randomly varied within the range of \(\pm\)10% from the typical curves, using a 5 min resolution.  
The MG has 2 CHP units, which are also capable of providing ASs. Detailed information for these generators is given in Table 2. Nodes 5 and 10 utilize battery and fuel cells, with parameters defined in Table 3. For this case study, only battery storage is considered as fuel cell units, which are of much lower power and are connected in the same nodes as battery storage units. In addition, it is considered that industry load can be controllable to some extent in order to provide ASs, as defined in Table 4. Finally, the hourly market price of electricity for energy exchange between the grid and the microgrid is shown in Figure 5, while the price of delivered balancing energy is 50 EUR/MWh downwards and 250 EUR/MWh upwards, averaged from the ENTSO-E report [41]. These values of the balancing prices are not selected for any specific country but are average values of the prices from the ENTSO-E report.  
The proposed model from Chapter 3 is coded in MATLAB and optimized using Genetic Algorithm (GA) and penalization functions to address the constraints. GA is capable of obtaining the optimal solution in this case within less than 3 min, making it suitable for this type of power-balancing AS provision. Further improvement of GA performance is possible by using powerful real-time simulators that are compatible with MATLAB.  
Figure 4: Daily load and production curves [42].  
Figure 5: Hourly market electricity price.  
The base case simulation is performed just to show how all the microgrid's resources operate under \"normal\" conditions. This provides a baseline understanding of the participation levels of wind, solar, batteries, and CHP in optimal, routine grid operations. The AS provision case is conducted in two scenarios: (a) Detailed grid modeling; (b) Single-bus modeling. The following is a comparative analysis of these two cases, which indicates and quantifyies the significance of detailed grid modeling in MG operation optimization.
## 4 Results
### Ablation Study  
An ablation study was conducted using synthetic data to evaluate the individual contributions of key components within the proposed network. The evaluation used 750 test images in total: 250 each from the speckle noise, Gaussian noise, and combined noise datasets (with speckle variance = 0.1 and Gaussian SNR = 0 dB). The study began by testing the effect of training with only the MSE loss function. Subsequently, we evaluated architectural configurations, including: (i) a model with RDABs but without the DFF module, (ii) a model with RDBs but without attention, and (iii) the full WPD-Net, which includes the SFE module, RDABs, DFF module, and the dynamic hybrid loss function combining MSE and SSIM.  
Quantitative results in Table 4 show that the complete WPD-Net consistently outperformed the other variants in terms of MSE, PSNR, and SSIM. Visual comparisons are also presented in Figure 11 using one representative test sample. The figure includes: Figure 11(a-1) the noisy wrapped-phase input image, Figure 11(a-2) its ground truth, Figure 11(b-1) denoised output using MSE loss, Figure 11(c-1) a model with RDABs but without DFF module, Figure 11(d-1) a model with RDB but without attention, Figure 11(e-1) the final WPD-Net output, and Figure 11(b-2-e-2) the corresponding difference maps.  
Figure 11: Visual results of the ablation study comparing different architectural and training configurations of WPD-Net. (**a-1**) Noisy wrapped-phase input image; (**a-2**) ground truth; (**b-1**) denoised result using only MSE loss; (**c-1**) result with RDABs but without the DFF module; (**d-1**) output from the model with residual dense blocks (RDBs) and no attention; (**e-1**) output from the complete WPD-Net with SFE, RDABs, DFF, and dynamic hybrid loss. Corresponding difference maps with respect to ground truth are shown in (**b-2-e-2**).  
The visual results demonstrate that attention mechanisms in RDABs enable the network to emphasize structurally relevant regions, while the DFF module improves global feature fusion. The combined effect of these components--along with the dynamic hybrid loss--leads to enhanced denoising, sharper phase recovery, and progressive suppression of residual noise and artifacts. Together, these findings provide strong quantitative and qualitative evidence of the importance of attention, feature fusion, and dynamic loss optimization in achieving high-performance wrapped-phase denoising with WPD-Net.
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### The Role of Double Grating  
In the traditional grating spectrometer, coma is eliminated by the symmetry of the system, so the image quality is higher at the image plane symmetrical to the slit. When the spectral coverage is too large, in the traditional grating system, the imaging area will inevitably be far away from the symmetrical area of the slit.  
For example, as mentioned in the introduction, for solar hyperspectral imaging, the wavelengths are H\(\alpha\) line, the H-line and K-line of ionized calcium, the focal length is 900 mm, the number of grating lines is 900, the slit is calculated at 8 microns, and the spectral resolution is up to for each waveband of 0.08 nm, the diffraction angles of the two wavebands are shown in the Figure 3.  
Figure 3: Traditional method to design a spectrometer with H\(\alpha\) line, H-line, and K-line of ionized calcium.  
Figure 2: The E-F structure imaging spectrometer.  
The imaging spectrometer of the traditional structure contains only one dispersive element, so it is constrained by the dispersion angle. As shown in the Figure 3, the H-line and K-line are imaged in the best image quality area, but the H\(\alpha\) line has poor image quality because of the excessive diffraction angle. Therefore, in the traditional method, only two spectrometers can be used to image two bands respectively. However, this will increase the volume of the system.  
The structure in Figure 4 changes the single grating to double grating, and the bands (H\(\alpha\) line, H-line and K-line of ionized calcium) are concentrated in the same area by rotating the gratings. Therefore, the images of the two bands can be concentrated in the best image quality area, so the best image quality can be achieved in the whole waveband. The dual-gratings imaging spectrometer improves the spectral coverage on the basis of the single grating imaging spectrometer, eliminating the need to use two imaging spectrometers, and reducing the volume of the system.