esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characteristic Wavelength Extraction  
The differences in the liquor NIR spectra are very small, and therefore it is necessary to extract the characteristic wavelength that represents the liquor's characteristic information. The CARS method can effectively select wavelength variables closely related to the characteristics of the measured material, eliminate interference from redundant information, and improve the accuracy of the prediction model. Therefore, in this study, an improved CARS method was used to extract the characteristic wavelengths of the NIR spectrum. The second set of preprocessing parameters shown in Table 1 was used as an example (where the baseline correction penalty term \(\lambda\) = 10 \(\times\) 10\({}^{6}\), the smoothing window was 13, and the polynomial degree was 3). The number of Monte Carlo sampling iterations was set to 50, and the five-fold cross-validation method was used to extract 70 characteristic wavelengths. The results are shown in Figure 4.  
The characteristic wavelength extraction results of the preprocessed data in Table 1 are shown in the \"Characteristic wavelengths of improved CARS\" section in Table 2. The results show that the NIR spectral feature extraction method of the base liquor based on improved CARS effectively reduces the dimension of the spectral data. The multi-class SVM prediction model was used to evaluate the feature wavelength extraction results, and the results showed that the prediction accuracy was improved.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Preprocessing Method** &
\begin{tabular}{c} **Baseline Correction** \\ **Penalty Factor, \(\lambda\)** \\ \end{tabular} & **Smoothing Window** & **Degree of Polynomial** & **Prediction Accuracy (\%)** \\ \hline
1 & \(10\times 10^{6}\) & 9 & 2 & 86.81 \\
2 & \(10\times 10^{6}\) & 13 & 3 & 88.19 \\
3 & \(10\times 10^{6}\) & 13 & 5 & 87.15 \\
4 & \(10\times 10^{7}\) & 11 & 2 & 86.11 \\
5 & \(10\times 10^{7}\) & 11 & 3 & 87.85 \\
6 & \(10\times 10^{7}\) & 13 & 5 & 86.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of different preprocessing parameters based on a multi-classification support vector machine.  
Figure 4: Characteristic wavelength extraction results.
## 4 Experiments
### Experimental Results
#### 4.3.3 Results of a Classroom Multichannel Emotion Recognition Experiment
The analysis of the confusion matrix reveals that the addition of background information significantly improves the prediction accuracy of the model in all emotion categories, particularly in the 'Fatigue' and 'Pleasure' emotions, increasing the recognition accuracy from 64.17% and 72.78% to 93.06% and 95.55%, respectively. Meanwhile, the addition of contextual information significantly reduces the misclassification rate among different emotion categories, which further proves the key role of contextual information in emotion recognition. The background features provide important cues to the context of emotions, significantly enhance the differentiation ability of the model, and are important for improving the accuracy of emotion recognition systems.
## 3 Results and Discussion
### Construction of Datasets
#### 3.3.2 Classification System  
The system is shown in Table 7 below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Precipitation**} & **Mean Air** & **Maximum Air** & **Minimum Air** & **Average Wind** \\  & **(mm)** & **Temperature** & **Temperature** & **Temperature** & **Speed (m/s)** \\  & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **(\({}^{\circ}\)C)** & **Speed (m/s)** \\ \hline
2019 & 313.7 & 5.3 & 32.9 & \(-\)24.1 & 6.3 \\
2024 & 327.2 & 4.6 & 31.5 & \(-\)23.3 & 5.4 \\ August 2019 & 109.8 & 20.7 & 32.6 & 7.2 & 2.4 \\ August 2024 & 128.3 & 19.6 & 30.3 & 6.3 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistics of some meteorological data in 2019 and 2024.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ID** & **Change Type** \\ \hline
1 & Vegetation \\
0 & Sand (Non-vegetation) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Coverage** & **Change Degree** & **Area (km\({}^{2}\))** & **Percentage (\%)** \\ \hline \(-\)8 \(<\) Slope \(\leq-\)5 & Significant Decrease & 38.99 & 8.45 \\ \(-\)5 \(<\) Slope \(\leq-\)2 & Slight Decrease & 118.4 & 25.66 \\ \(-\)2 \(<\) Slope \(\leq 0\) & Basically No Change & 109.44 & 23.72 \\ \(0<\) Slope \(\leq 2\) & Slight Increase & 120.47 & 26.11 \\ \(2<\) Slope \(\leq 8\) & Significant Increase & 74.1 & 16.06 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Trend statistics of vegetation coverage in 2019–2024.
## 3 Results and Discussion
### Method Validation
#### 3.2.3 Analytical Method Linearity and Calibration Curve  
The developed method showed good linearity response for all tested analytes over the tested concentration ranges verified by the correlation coefficients (\(\mathbf{R^{2}}\) close to 1) for all of the constructed calibration curves.  
Over the upper limit (1-100 \(\mathrm{\SIUnitSymbolMicro m}\)/mL), the linear regression equations were (y = 97,631x + 10,949, \(\mathbf{R^{2}}\) = 1) for metformin, (y = 57,676x + 14,145, \(\mathbf{R^{2}}\) = 0.9997) for glipizide and (y = 48,226x \(-\) 15,022, \(\mathbf{R^{2}}\) = 0.9999) for gliclazide.  
The linear regression equations over the lower limit (0.5-10 \(\mathrm{\SIUnitSymbolMicro m}\)/mL) were (y = 94,142x + 4364.1, \(\mathbf{R^{2}}\) = 0.9998) for metformin, (y = 61,174x + 2038.8, \(\mathbf{R^{2}}\) = 0.9996) for glipizide and y = 47,137x \(-\) 12,977, \(\mathbf{R^{2}}\) = 0.9965) for gliclazide. Figure 3A,B exhibit the calibration curves constructed for the three analytes over the upper and lower concentration ranges, respectively.
# A Keyword Detection and Context Filtering Method for Document Level Relation Extraction
## 4 Experiment and Results  
### Dataset  
We evaluated the proposed model on three popular document-level relationship extraction datasets (DocRED [4], CDR [29], and GDA [30]), all of which involve challenging relational inference on multiple entities across multiple sentences. DocRED and CDR provide human-validated data. They use crowdsourcing to manually annotate all possible relationships in the documents, which ensures the quality of the data and better guides the deep learning model. In order to ensure the validity and generality of our approach, we have compared performance primarily on the human-validated part of the DocRED dataset. The statistical information for each dataset is shown in Table 1.  
**DocRED** is a large-scale dataset built with Wikipedia. It provides comprehensive manual annotation of entity mentions, entity types, relationship facts, and corresponding inference-supporting evidence. In addition, DocRED collects remotely supervised data, which uses a fine-tuned BERT model to identify entities and link them to Wiki-data. 101,873 document instances are then scaled by obtaining relationship labels through remote monitoring.  
**CDR** (Chemical-Disease Reactions dataset) is a biomedical dataset constructed with PubMed abstracts. It contains 1500 documents with human annotations, which are equally divided into a training set, a development set, and a test set. CDR is a binary classification task designed to identify induced relations from chemical entities to disease entities, and is of great importance for biomedical research.  
**GDA** (Gene-Disease Associations dataset) is similar to the CDR and is also a binary relationship classification task for identifying interactions between gene and disease concepts but at a much larger scale. Constructed using the remotely supervised MEDLINE summarization technique, it contains 29,192 documents as training set and 1000 documents as a test set.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 3 Results
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles
#### 3.2.4 Transmission Electron Microscopy (TEM) of the Biosynthesisized Zinc Oxide Nanoparticles  
TEM is a powerful method that was used to determine the shape and size of the nanoparticles and their distribution in the colloidal solution. The particle size, as well as the distribution of the ZnO-NPs, were determined based on the TEM images (Figure 5). It is clear that the frequency size ranged from approximately 4.13 to 8.56 nm, with a mean particle size estimated to be 5.6 nm for the ZnO-NPs formed using the _U. lactuca_ extract. Also, the ZnO-NPs formed using the _S. muticum_ extract ranged from 15,66 to 27.15 nm, with a mean particle size of 20.39 nm, while the ZnO-NPs formed by the E. intestinalis extract ranged from 32.16 to 48.25 nm, with a mean particle size of 42.27 nm. Also, based on the TEM images, the ZnO-NPs formed using _U. lactuca_, _U. intestinalis_, and _S. muticum_ were widely distributed.  
Figure 5: Transmission electron micrographs of ZnO-NPs biosynthesized using different seaweeds: _Ulva lactuca_ (**A**), _Ulva intestinalis_ (**B**), and _Sargassum muticum_ (**C**).
# A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications
## 5 Simulations of the Proposed Fully Digital BPSK Demodulator
### Comparison with the State-of-the-Art
& \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ Distance Range (cm) & 5.0 & 0.2 & - & 6.0-5.5 & 10.5 & 320 & 5.5 & 5.0 & 1.0 & \\ Modulation scheme & OOR-FM & ASK & OOK/ASK & OOK & PWM-ASK & OOK & OOK & OOK & **BPSK** \\ Carrier Amplitude & 30-4.5 & 27-1.0 & 1.5 & 1.0 & 4.5- & - & 4.0 & 4.0 & 4.0-5.0 & 2.4-4.5 (18.7) \\ Carrier Range Frequency (MHz) & 1.0 & 13.56 & 1.0 & 1.0 & 1.3 & 40 & 1.85 & 2.0 & 2.0 \\ Data Rate (kbps) & 25.0 & - & 50.0 & 25.0 & 11.0 & 1.0 & - & - & **1333** \\ \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \(\text{Do}_{0}\) (V) & 25-53.3 & 1.9-3.8 & 1.5 & 1.0 & 1.8 & 0.8 & 2.5 & 1.2 & 0.9 \\ Demodulator Power Cons. (\(i\)W) & \(<\)400 & 3.3 & 184 & - & 13.75 & **1.18** & - & \(<\)140 & 3.3 \\ \(E_{b}\) (p/bit
# The Effectiveness of an Authentic E-Learning Environment for Deaf Learners
## 3 Materials and Methods
### Population of the Study  
The study used purposive sampling to acquire data in a reliable position. The sample consisted of eleven deaf adult learners from both the north and south sides of Cyprus who had two different sign languages (as L1): Greek sign language (GSL) and Turkish sign language (TSL). The age range of participants was between 30 and 50 in both genders, seven males and four females. Moreover, Turkish Cypriot participants' education levels varied, with some having completed primary school and secondary school. Greece Cypriotparticipants have high school education diplomas. Socioeconomic status was clarified based on their income, whether from employment or government assistance.
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation  
### Comparison of Methods for Time-to-Go Estimation  
The time-to-go estimation methods proposed in this paper and in [32] can be expressed by Equations (17) and (18), respectively. In order to compare the accuracy of the two methods, the navigation gain is set to \(N=3\); the initial range is set to \(r_{0}=10,000\) m; the constant velocity of the UAV is set to \(V_{M}=330\) m/s. The time-to-go calculated by the two methods is compared with the actual time-to-go, which can be shown in Figure 3.  
Figure 3: Time-to-go with different initial leading angles.  
It can be seen from the figure that when the leading angle is less than 60 degrees (deg.), the time-to-go calculated by the two methods is close to the actual time-to-go; however, when the leading angle is greater than 60 deg., the time-to-go calculated by the method proposed in this paper is almost the same as the actual time-to-go, while the time-to-go calculated by the method in [32] is quite different from the actual time-to-go, so the time-to-go estimation method proposed in this paper is more accurate.
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 3 Proposed Disaster Response Systems for Tunnel Accidents
### Integrated Remote Control System  
To effectively control and monitor the search and rescue equipment, it is necessary to develop a control system that operates in an integrated manner. In addition, a system should also be developed to control the electricity and disaster prevention facilities in the tunnel in real time so that the manager can respond quickly to emergency situations. In general, to monitor and respond rapidly, the data  
Figure 12: Anchor node deployment for localization in the tunnel.  
Figure 13: Test-bed for motor way tunnel in Daegu city, Korea.  
collection period should be shortened to 100 ms. In addition, the data transmission rate should be maintained at 95% or more through adopting of the TCP/IP protocol for reliable data transmission and reception. Therefore, the proposed system is designed to perform web-based monitoring and control so that it can be easily accessed by related organizations (e.g., fire agency, police agency) that need information sharing. The system was designed to provide the commander in charge of a fire and accident scene with as much information as possible regarding conditions inside the tunnel. Thus, the proposed system assists prompt response by providing accurate and abundant situation information to firefighters in the disaster field.  
Figure 14 shows the overall architecture of the proposed control system. Basically, it can monitor the status of disaster prevention equipment (e.g., ventilators, drainage pumps, barriers, generators, transformers, etc.) in a tunnel. In addition it can control accident monitoring equipment, such as CCTV and fire sensors. The communication protocol between various types of equipment maintains compatibility with legacy equipment using MODBUS [41]. The main advantage of using the proposed integrated system is that it can monitor multiple tunnels and various facilities in one computer and can control these facilities in real time. In particular, it is easily accessible from any place where the Internet is connected through the web, and a separate database is maintained to provide operation history and statistics of the equipment.  
Figure 14: Architecture of Integrated Control System.
## 2 Material Growth  
All samples were grown by a Veeco Gen-930 molecular beam epitaxy (MBE) system. Si substrates, with 4-degree offcut towards [110] plane, were used to prevent formation of anti-phase domains. A high-quality GaAs buffer was grown on an Si substrate after desorbing the native oxide at 900 \({}^{\circ}\)C for 10 min under As\({}_{2}\) overpressure. After oxide desorption, the substrate was cooled to 400 \({}^{\circ}\)C for a 100 nm low-temperature GaAs (LT-GaAs) buffer, followed by 400 nm high-temperature GaAs (HT-GaAs) buffer growth at 580 \({}^{\circ}\)C. Then, 3\(\times\) thermal cycle annealing (TCA) was performed at 350-650 \({}^{\circ}\)C to increase the dislocation glide velocity and decrease threading dislocation density (TDD). Afterwards, the substrate temperature was raised to 500 \({}^{\circ}\)C for the growth of a 200 nm In\({}_{0.1}\)Ga\({}_{0.9}\)As single insertion layer (SIL). Subsequently, the 600 nm HT-GaAs was grown at 1 \(\upmu\)m/hr, followed by 3\(\times\) TCA, 200 nm In\({}_{0.1}\)Ga\({}_{0.9}\)As SIL, and 600 nm HT-GaAs layer. After the growth of high-quality GaAs buffer, as shown in Figure 1a, electron channeling contrast imaging (ECCI) was performed by Inspect F50 (FEI) to investigate the TDD. The sample was loaded back to the MBE reactor for the growth of GaAs solar cells, as shown in Figure 2a. Cells consisted of inverted GaAs emitter/base structures with Al\({}_{0.4}\)Ga\({}_{0.6}\)As window/BSF layers. For the epitaxial lift-off (ELO) process, a 50 nm AlAs sacrificial layer was inserted between the p-GaAs contact layer and the 100 nm GaAs buffer.  
Figure 1: (**a**) GaAs buffer structure on 4-degree offcut Si using In\({}_{0.1}\)Ga\({}_{0.9}\)As single insertion layer and thermal cycle annealing. (**b**) An ECCI image of the GaAs buffer surface grown on Si substrate. (**c**) Threading dislocation density benchmark of GaAs/Si materials reported by other groups.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### Nanofluid Stability
#### 2.2.1 Sedimentation and Centrifugation  
The most commonly utilized method for stability assessment is sedimentation. The basis of this method is the settlement of nanoparticles out of the nanofluid under the influence of gravity [61]. The volume or weight of sediment is a good indication of nanofluid stability. If the concentration of supernatant particles remains unchanged over time then the nanofluid is considered stable [62]. The method deals with the observation of nanoparticle settlement from a nanofluid in a container over time. Generally, most papers have considered the stability time frame as the time during which obvious sedimentation does not occur, or at least negligibly occurs, in the nanofluid. Some researchers accepted the  
Figure 2: Methods for improving the stability of nanofluids.  
sedimentation method as a criterion for the assessment of the stability of a nanofluid [63]. But the sedimentation technique is not a time-efficient method [64]. To overcome this problem, researchers use the centrifugation method which is a more time-efficient method for the assessment of nanofluid stability. In this method, sedimentation is accelerated under the influence of centrifugal force which is much stronger than the gravitational force [65].
## 4 Experimental Analysis
### Analysis of Temperature Rise Experiment Results  
A comparison of the nut temperature rise predicted by the thermal network method and that measured in the experimental tests for different ambient temperature fields is shown in Figure 22. The increasing trends of the predicted and measured temperature profiles are similar for the four different ambient temperatures. However, the measured nut temperatures were higher than those calculated by the thermal network method. In the initial phase of operation, the experimentally measured temperature increases rapidly. The rate of increase decreases as the running time increases, but it continues to increase until it reaches a steady-state value. This is evident by the difference between the measured and predicted values, which first increases and then gradually decreases during operation. This is because during the experimental process, the heat generated by the motor, the heat of the bearings, and the heat of the coupling affect the temperature rise of the DPRS. In addition, the thermal network method only considers the axial load. However, radial forces may exist during the experimental operation and may affect the raceway contact on the screw-roller and roller-nut sides, thus increasing the temperature.  
Comparison of the predicted temperature values at 1000 s of DPRS operation cycles at four ambient temperatures with the experimental measurements showed that the predicted  
Figure 22: Comparison of nut temperature rise. (**a**) 20 \({}^{\circ}\)C; (**b**) 80 \({}^{\circ}\)C; (**c**) 110 \({}^{\circ}\)C; (**d**) 130 \({}^{\circ}\)C.  
values were 9.25% below the test values at 20 \({}^{\circ}\)C, 7.78% below the test values at 80 \({}^{\circ}\)C, 6.97% below the test values at 110 \({}^{\circ}\)C, and 5.95% below the test values at 130 \({}^{\circ}\)C. Therefore, the calculated temperature rise was consistent with the experimental results within a 10% error range. This demonstrates the reliability of the thermal network model for the analysis of the thermal characteristics of DPRSs.
## 5 Methodologies
### Uncertainty Estimation-Theoretical Descriptions
#### 5.2.2 Simultaneous CIs for Modelled SVM Power Curve  
Simultaneous CIs are defined as intervals that constitute specific intervals for the independent components of the parameter and are advantageous (in terms of computation and mathematical complexity) for multiple comparisons that include the combination of several single CIs, in contrast to multiple CIs. In this study, simultaneous CIs are constructed for the SVM-based power curve and then compared with pointwise CIs to determine which approach is most robust. Several studies have published novel methods to calculate simultaneous CIs such as [51; 52]. However, the Bonferroni and Sidak corrections techniques were found to be accessible as they are mathematically easy to implement and produce acceptably accurate results, see, for example [53]. Hence, Equation (15) is modified based on the Sidak correction to determine simultaneous CIs, and is expressed by the following equation [50]:  
\[\hat{m}_{c}(x)\pm z_{1-\alpha/2}\sqrt{Var[\hat{m}(x)\big{|}X\ =\ x]} \tag{16}\]  
subject to: \(\hat{m}_{c}(x)\ =\ \hat{m}(x)-\hat{b}[\hat{m}(x)\big{|}X\ =\ x]\). \(\beta\) is assumed as the significance threshold for each test and determined by \(\beta\ =\ 1-(1-\alpha)^{\
icefrac{{1}}{{n}}}\) where \(n\) is the number of test samples. Equation (16) is further modified to include the approximate \(100(1-\alpha)\%\) and is shown below,  
\[\hat{m}_{c}(x)\pm v_{1-\alpha}\sqrt{Var[\hat{m}(x)\big{|}X\ =\ x]} \tag{17}\]where \(v_{1-\alpha}~{}=~{}\sqrt{2\log\left(\frac{k_{0}}{\alpha\pi}\right)}\) and subject to:  
\[k_{0}~{}=~{}\int_{0}^{\chi}\frac{\sqrt{\parallel L(x)\parallel^{2}\parallel L^{ \prime}(x)\parallel^{2}-\left(L(x)^{T}\right)L^{\prime}(x))^{2}}}{\parallel L (x)\parallel^{2}}~{}dx\]  
where \(\chi\) denotes the set of \(x\) values of interest and \(L^{\prime}(x))~{}=~{}\left(\frac{d}{dx}\right)L(x)\) in which elementwise differentiation is applied. The \(k_{0}\) is strongly related to degrees of freedom of the fit and approximates to the following relationship [44]: \(k_{0}\approx\frac{\pi}{2}(trace(L)-1)\). All these values are calculated and used with Equation (17) to determine simultaneous CIs for the SVM model.