esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The effect of the thickness of the Au and LiIO\({}_{3}\) layers and the wavelength of the laser influences the reflectance, sensitivity and the distance between the curves of each of the concentrations. The photodetector, which is at the end of the output optical fiber, has a reference signal. This reference is the laser that has passed through all the optical media in the total absence of CO\({}_{2}\). We will therefore have the response of the photodetector at a concentration of 0 ppm. The refractive index of CO\({}_{2}\) increases concomitantly with increasing concentration. As the concentration of CO\({}_{2}\) in the medium increases, the reflected radiation that is injected into the output optical fiber and reaches the photodetector will decrease. When the refractive index increases, the reflectivity decreases and we will get closer to the formation of the SPR [22; 23]. By comparing this photodetector output signal with the 0 ppm, we are able to calculate the reflectivity. Once the reflectivity is known, as we see in the curves in Figure 2, we are able to know the CO\({}_{2}\) concentration at each moment. The sensor of [PERSON] et al. [8] has a better resolution than ours, however, the range is only 0-500 ppm and our sensor has more range than the others [5; 8; 11; 15; 16]. The sensor measures continuously and there is no waiting time between measurements as in the others [5; 9; 10; 12; 14; 16; 34]. The sensitivity of our sensor is greater than sensors which have satellites NOAA and Aqua Terra [4; 12; 34; 35] and the plasmonic sensors [8]. Our sensor is smaller than the UV Strip Resistive Thick GEM (S-RETGEM) [8] and measures concentrations of carbon dioxide instead of measuring presence of this gas [9; 19] and is able to measure with any value of the humidity unlike the sensor of [PERSON] et al. [10]. The sensor of [PERSON] et al. [6] had a very high sensitivity, of around 3000 nm/RIU and a resolution of 3.34 \(\times\) 10\({}^{-6}\) RIU, better results than ours, however, this design works in wavelength interrogation, so we were unable to compare. If we are measuring concentrations of CO\({}_{2}\) in a controlled ambient, it is not necessary to take into account the variations of humidity, temperature or pressure because these are minimum, therefore the results obtained from the sensor can be used without having to take into account the Edlen or modified Birch and Downs equations. Therefore, our sensor could measure concentrations of CO\({}_{2}\) indoors (houses, factories, motors, etc.) without applying the Edlen and Birch and Downs equations. If we are measuring outdoor CO\({}_{2}\) concentrations, as the variations of the variables are not controlled, the changes in the refractive indices will vary with humidity, pressure, temperature, etc. After using the sensor, very simple software modifies the measured refractive indices based on these parameters and will give us the real concentration of the gas based on the Birch and Downs equations.
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 2 Metal Nanoparticle Synthesis
### Physical Methods  
The most important physical synthesis methods are: evaporation-condensation and laser ablation.  
The advantages of using a physical method compared to a chemical one are the absence of solvent contamination and the uniformity of nanoparticles' distribution. Using the evaporation-condensation method, very small nanoparticles (6.2-21.5 nm and 1.23-1.88 nm) can be obtained. However, the process is energy-consuming (energy is needed to raise the operating temperature) and time-consuming [14].  
The laser ablation method is dependent on the wavelength of the laser, the duration of the laser pulses, the laser fluence, the ablation time and the liquid medium. The authors obtained nanospheres (20-50 nm) in water with femtosecond laser pulses at 800 nm [14].
## 2 Frequency Characteristic by Exchange of Frequency Containment Reserves Control
### Communication Interconnection System  
As depicted in Figure 4, Each converter station must establish a remote control interface for connection with the centralized frequency control optimization center, as described in [20]. The author of [16] suggested how to establish each HVDC converter station based on time synchronization (through GPS), which was mentioned in [21]. The main goal is to avoid problems with frequency stability and failure performance brought on by inaccurate local frequency signals. The WAMPAC platform's essential building components are phasor measurement units (PMU) and phasor data concentrators (PDC) [22]. These components can measure voltage/current magnitude, phase angle, frequency, and other quantities at fast speeds and with great precision (30 to 120 time tagged samples per second), which are strategically located in substations. PDCs may connect with other PDCs within the several regions, as well as with nearby utilities, operation centers, and other PDCs to focus on synchro phasor data from various PMUs. In certain instances, mathematical techniques are used to interpolate the missing data. By using its processing power, PDC may carry out a wide range of other activities in addition to data concentration, including data archiving, access control, and data validation. Furthermore, there are three different color which shows the application of communication infrastructure level.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 2 Related Works
In recent years, 3D gaze tracking research has been focused on how to make the gaze point estimation approach or exceed the depth perception of the human vision while the device is also simple and convenient. [PERSON] et al. [2] proposed a 3D PoR estimation method combining scene images. The pupil vector is used as the initial value of gaze direction, then corner feature extraction and Delaunay triangulation are used to analyze scene images then estimate possible PoR. [PERSON] et al. [32] added a multi-camera view geometry in a simple eye model. This method can obtain accurate 3D PoR without scene information, but the calibration process still needs the spatial coordinates of target point which are calculated from scene images. Our previous work also proposed a novel method for the 3D PoR estimation in free space [33]. Pupillary reflex as an important human visual mechanism was introduced in a simplified line-of-sight estimation model. It was an innovative attempt, but it was restricted by illumination conditions. Previous methods were limited to application scenarios or hardware structure, and were not suitable as general gaze estimation methods. Model-based method could significantly improve the accuracy but the system calibration steps became more complex; regression-based methods could adapt to a variety of application scenarios and reduce the complexity of utilization, but the accuracy of was insufficient; the combination methods based on the advantages of both usually had the disadvantages of complex structure or high cost, and they were difficult to be the framework for methods within generality. This paper attempts to propose a general method for 3D PoR estimation. Additionally, the proposed method basically follows the general framework of the geometry-model based method, but some improvements have been made to reduce the operation complexity significantly. This method is suitable for many application scenarios with good accuracy.
# LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction
## 3 Proposed Framework of LSTGINet
### Graph Inference Module with Local Attention Embedding (LAGIM)
It is worth noting that the adjacency matrix \(\mathcal{A}\) in traditional graph convolution is manually produced. It is easily restricted when learning graph structure data, making it difficult for graph nodes to fully model the detailed information of the brain. To learn the association between nodes more effectively, a data-driven strategy is designed to alleviate the error caused by manually producing the adjacency matrix \(\mathcal{A}\). The dynamic association relationship of \(\mathcal{A}\) is shown in Equation (10). \[ \begin{split}\mathcal{A}&=\varphi(\chi,\mathcal{W} _{\varphi})\cdot\phi^{c}(\chi)\cdot\varphi(\chi,\mathcal{W}_{\varphi})^{ \intercal}\\ &+\varphi(\chi,\mathcal{W}_{\varphi})\cdot\varphi(\chi,\mathcal{W }_{\varphi})^{\intercal}\odot\phi^{s}(\chi)\end{split}  \tag{10}\]  
where \(\odot\) indicates the Hadamard product. \(\mathcal{W}_{\varphi}\) indicates the learnable factor. The diagonal matrix \(\phi^{c}(\chi)\in\mathcal{R}^{\mathbb{C}\times\mathbb{C}}\) and spatial constraint matrix \(\phi^{s}(\chi)\in\mathcal{R}^{N\times N}\) are used to construct a data-driven association relationship adjacency matrix \(\mathcal{A}\). These matrix calculations are shown in Equation (11). \[ \begin{cases}\phi^{c}(\chi)=\Lambda(BL(\mathcal{H}^{G}(\chi)))\\ \phi^{s}(\chi)=\Lambda(Conv_{1\times 1}(\mathcal{H}^{A}(\chi)))\end{cases}  \tag{11}\]  
where \(BL(\cdot)\) stands for linear operations with batching normalized. \(\mathcal{H}^{G}(\cdot)\) stands for the global average pooling operation. \(\mathcal{H}^{A}(\cdot)\) stands for the average pooling operation. \(Conv_{1\times 1}(\cdot)\) represents a \(1\times 1\) convolution operation. Finally, to complement different features and establish practical dependencies and interactions, we combine these features using simple feature concatenation and input them into a fully connected layer set for unified mapping. The aggregated output of \(\mathcal{O}\) is shown in Equation (12). \[\mathcal{O}=FC(Conv_{1\times 1}(Cat(\mathcal{F}_{a},\mathcal{F}_{b},\mathcal{F}_{ \mathcal{G}}))) \tag{12}\]  
where \(FC(\cdot)\) stands for the fully connected operation. \(Cat(\cdot)\) stands for the feature concatenation operation. \(Conv_{1\times 1}(\cdot)\) represents a \(1\times 1\) convolution operation.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**MMW Detectors**} & **Modulation Frequency (Hz)** & **Operating Frequency Range (THz)** & **NEP (W/Hz\({}^{1/2}\))** \\ \hline Golay cell & \(\leq\)20 & \(\leq\)20 & 2 \(\times\) 10\({}^{-10}\) \\ \hline Pyroelectric & \(\leq\)30 & \(\leq\)3 & \(>\)3 \(\times\) 10\({}^{-9}\) \\ \hline Microbolometer & \(\leq\)30 & 1.5–100 & 2–3 \(\times\) 10\({}^{-11}\) \\ \hline Thermocouple & \(\leq\)15 & 2.8 & 0.49 \(\times\) 10\({}^{-9}\) \\ \hline \multicolumn{4}{l}{** GDD (N523)} & \(\leq\)10,000 & 0.01 to 0.30 & 3.9 \(\times\) 10\({}^{-10}\) \\ \hline \hline \end{tabular}  
* Based on the settings used in the experimental setup.  
\end{table} 
Table 1: Performance parameters of commercial THz detectors and GDDs. target object are received by the GDD, which captures the frequency difference between the reference and target signals. This frequency difference is processed using Fast Fourier Transform (FFT) analysis in LabVIEW, enabling depth information retrieval crucial for 3D imaging. To generate the 3D image, the system incorporates a 2D grid scan of the object surface using step motors, where each point on the grid corresponds to a surface location with depth information derived from FMCW analysis. The integration of this depth data across the 2D grid enables a full 3D surface map of the object to be visualized. LabVIEW's graphical interface supports image rendering, producing detailed 3D representations for applications in security and industrial quality control. This work highlights the potential of GDDs for low-cost, high-resolution MMW imaging, broadening their application to include 3D imaging capabilities with immediate practical utility in sectors requiring detailed non-destructive testing.
# Real-Time Multidepth Multiphoton Microscopy Using Pixel-to-Pixel Focus-Switching
###### Abstract  
Multiphoton microscopy is a well-established technique for biomedical applications, but real-time multidepth multimodal multiphoton microscopy using non-imaging detection has barely been discussed. We demonstrate a novel label-free imaging system capable of generating multimodal multiphoton signals at different focal planes simultaneously. Two spatially overlapped and temporally interlaced beams are obtained by applying cost-effective electro-optic modulator (EOM)-based fast-switching light paths. The switching beams have different divergence properties, enabling imaging at different depths into samples. The EOM is synchronized to the pixel clock from the microscope, achieving pixel-to-pixel focus-switching. The capability of the imaging system is demonstrated by performing real-time multidepth two-photon fluorescence (TPF) and second-harmonic generation (SHG) imaging of freshly excised mouse lung lobes. TPF and SHG images are acquired at two wavelength ranges. One is between 415 and 455 nm, and the other is between 495 and 635 nm. The microenvironment of pulmonary alveoli is depicted by the distributions of both elastin fibers visualized by TPF and collagen fibers illustrated by SHG. Macrophages residing inside apparent alveolar lumens are also identified by TPF, which shows that the imaging system is capable of localizing biological objects in three dimensions and has the potential of monitoring in vivo cellular dynamics in the axial direction.  
multidepth multiphoton microscopy; label-free microscopy; two-photon fluorescence; second-harmonic generation; mouse pulmonary alveoli Article
## 3 Experiment and Result
### Tree Species Classification
In the test data of ten plots, the tree number of each tree species is shown in Table 2. Using the method in this paper, the tree classification results are listed in Table 5.  
From Table 5, the tree species classification accuracy of shape fitting is better than that of LiDAR metrics method, and the average classification accuracy of shape fitting is 90.9%, while the average accuracy of the LiDAR metrics method is 87.2%. In the classification result of shape fitting, the optimal classification accuracy can reach 95.9%, and for the LiDAR metrics, the optimal classification accuracy can reach 93.8%. The details of tree misclassification are listed in Table 6, which can clearly indicate the number of tree species misclassified into other categories. Table 6 shows that the pines, birches and cedars are often mixed, 12 cedars are classified as pines, and 5 pines are classified as cedars. Meanwhile, 6 birches are classified as pines and 6 pines are classified as birches. However, the Tsubakis and shrubs have good classification results; their overall classification accuracy is 94.03% and 95.95%, respectively, because of their particularity in appearance. Moreover, the Tsubakis and shrubs are obviously different from other tree species in geometry shapes. The kappa coefficient of the test is 0.8935 and the overall classification accuracy is good, reaching the expected results. And The tree species classification results of ten sample plots are shown in the Figure 19. It can be seen from the Figure 19 that the main tree species in the surv \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **01-Pine** & **02-Birch** & **03-Cedar** & **04-Tsubaki** & **05-Shrub** & **06-Others** & **OA (\%)** & **Kappa** \\ \hline
01-Pine & 217 & 6 & 12 & 2 & 2 & 1 & 93.14 \\ \hline
02-Birch & 6 & 98 & 3 & 0 & 0 & 2 & 89.91 \\ \hline
03-Cedar & 5 & 2 & 95 & 0 & 0 & 0 & 84.08 \\ \hline
04-Tsubaki & 2 & 0 & 0 & 63 & 2 & 4 & 94.03 \\ \hline
05-Shrub & 0 & 0 & 0 & 0 & 142 & 5 & 95.95 \\ \hline
06-Others & 3 & 3 & 3 & 2 & 2 & 98 & 89.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The details of the tree species misclassification.
## 3 Study Area and Data Sources
### Modeling Techniques  
Two types of models were used in this research to predict the involvement of people of the target age group, namely, binary logistic regression and classification and regression tree (CART) models. These models are widely used for classification problems in different fields of transportation research, including road traffic crashes. The models were developed to predict the binary response variable if a victim from a certain age category was involved in the traffic crashes or not. More details about the selection of age categories are presented in the next section.  
The logistic regression model works on the principle of maximum likelihood, as shown in Equation (1). A utility function is developed with a linear functional form, as per Equation (2). The utility function is subjected to a logistic function to find the probability of the particular outcome (see Equation (3)).  
\[\textit{Model likelihood}=\textit{Max}[\sqcap P_{i}(n)] \tag{1}\]  
Figure 1: Study area and locations of traffic crashes in the Al-Ahsa region of Saudi Arabia (adopted from [51]).  
Figure 2: Frequency distribution of RTCs with types of crashes in the study area (adopted from [51]).  
\[U_{n}=b+\sum_{j=1}^{I}Y_{j}X_{j} \tag{2}\]  
\[P_{i}(n)=1/(1+e^{-Unt}) \tag{3}\]  
where \(P_{i}\) is the probability of ith sample in the dataset (of \"\(I\)\" values) to have the outcome \"\(n\)\", which is the actual outcome; \(U_{n}\) is the utility function for outcome \(n\) (from \"\(N\)\" possible outcomes); \(b\) is the intercept for the function; \(X\) is an array of \"\(I\)\" independent variables; and \(Y\) is an array of coefficients pertaining to each variable [52]. Logistic regression models have common usage in the prediction of categorical responses, and they have been used in predicting crash severity [53].  
CART is a type of artificial intelligence technique that works on the principle of finding a suitable breakdown of input space to maximize the probability of the correct outcome. This is achieved through minimization of the Gini index, which is shown in Equation (4), at each node to divide it into child nodes. The parent node indicates the most influential variable at a particular level in the tree, while its child node represents the best division or split of that variable [54].  
\[G=\sum(p_{k}(1-p_{k}) \tag{4}\]  
where \(p_{k}\) is the probability for the outcome \(k\). The CART model is a non-parametric technique that is commonly applied for classification problems. It has also been used in predicting crash severity [55].
# Study of Precipitation Forecast Based on Deep Belief Networks
## 4 Results and Discussion
### Algorithm Validation
The python platform has a large number of open source frameworks that can be applied to big data processing, and has a very broad prospect in the field of big data processing. As shown in Figure 8, the blue line indicates the time required for DBN to make predictions. M-SVM is based on mesh optimization, GA-SVM is based on genetic algorithm optimization, and PSO-SVM is based on particle swarm optimization. Because it consumes a lot of time for the model establishment in the previous period, when the model is used for prediction, the time consumed is lower than all the SVM methods used to establish the model. Additionally, it is guaranteed to have a certain accuracy rate. In practice, it is feasible to establish a good model using historical data in advance, and it will not affect the result of predicting precipitation. Therefore, an SVM method with excellent performance can be used when the data set is small, and the high efficiency of the DBN method can be used for large-scale data sets. Data preprocessing can improve the running results of the model. After data preprocessing, the result of the operation is relatively smooth, so it is very necessary to preprocess the source data. According to Figures 4-8, in terms of the test set's training accuracy, the accuracy of the DBN model is more stable than that of the PSO-SVM model. However, because the steps for building the model are different, the PSO-SVM optimizes the parameters (C, g) on the training set. It then uses the value of (C, g) on the test set to recreate the model. DBN establishes and fine-tunes the model on the training set. Then, the model is directly invoked on the test set, which saves the time for building the model. Therefore, in the application of large data volume, the model can be trained in advance and the response speed is faster.
## 4 Experiment and Results
### Comparison with Other Methods  
To further testify the effectiveness and robustness of the DFALS model, two recently proposed universally applicable image segmentation methods (RLSF, SPFLIF-IS) and methods designed specifically for auroral oval segmentation (SIIALSM, LIDLSM), were conducted to compare with our method. The LLSRHT results were adopted as the initial level set contours of the SIIALSM and the LIDLSM to guarantee the fairness of comparison.
# Performance of Polymer Composites Lubricated with Glycerol and Water as Green Lubricants
## 2 Materials and Methods
### Lubricants  
Commercially available redistilled pure glycerol with a purity of more than 99.5% and demineralised water were used as EA lubricants. They were both analysed with an automatic viscometer (SVM 3001, Anton-Paar, GmbH, Graz, Austria). The kinematic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Raw Material** & **Filler [wt\%]** & **Modulus of Elasticity at 25 °C** & **Poisson Coefficient \(\
u\)** & **Water Absorption ISO 62 [\%]** \\ \hline \hline \multicolumn{6}{c}{Polymer—plate} \\ \hline PEEK CF30 & PEEK & 30\% Carbon Fibres & 6.8 & 0.38–0.44 & 0.02 \\ PEEK & & 10\% Carbon Fibres, & & \\ MOD & PEEK & 10\% Graphite and & 5.92 & 0.38–0.44 & 0.3 \\  & & 10\% PTFE Fibres & & \\ POM CF30 & POM & 30\% Carbon Fibres & 3 & 0.35–0.49 & 0.01 \\ PTFE CF25 & PTFE & 25\% Carbon Fibres & 1.1 & 0.46 & 0.05 \\ UHMW PE & UHMW & / & 0.76 & 0.4–0.42 & 0.01 \\ \hline \multicolumn{6}{c}{Counterpart—ball} \\ \hline AISI 440-C & & 16–18\% Cr, & & \\ stainless steel & Carbon & max 1\% Mn, Si, S, & 200 & 0.285 & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of samples used and analysed in polymer–steel tribo-pairs.  
viscosity and density of the lubricants at room temperature, standardised to 40 \({}^{\circ}\)C, and the expected working temperature of 80 \({}^{\circ}\)C are shown in Table 2.  
Measured at room temperature, glycerol has 815.8 times higher kinematic viscosity compared to water. Although the difference is smaller at higher temperatures, glycerol has a kinematic viscosity \(\sim\)71.5 times higher than water at expected working temperatures in hydraulics. Given this difference, glycerol lubrication is expected to have much better tribological properties than water. If we compare glycerol with the standard hydraulic oil ISO VG 46, glycerol also has a much higher kinematic viscosity, from \(\sim\)83 times at room temperature to \(\sim\)2.4 times at higher temperatures, which means that both the coefficient of friction and wear should be very low.  
Based on the polymers and lubricant properties (sample dimensions, radius) as well as the loading conditions (load, speed), the expected minimum lubricant film thickness was calculated and the characteristic film parameter (lambda ratio, \(\lambda\)) was calculated based on the Hamrock-Dowson equation [42; 44]. The lubricants parameters for the calculation of the pressure viscosity index were assumed based on the literature [44]. In the case of glycerol, a mixed lubrication regime is expected, and in the water boundary regime [34; 36; 44].