esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Waste Express Bag as a Novel Bitumen Modifier
## Discussion
### Modification Mechanism Analysis  
The modification mechanism of WEB-modified bitumen was further evaluated by Fourier transform infrared spectroscopy (FTIR). The FTIR test provides information on the chemical reaction during the modification.  
When an organic compound molecule is irradiated with infrared light, a chemical bond or a functional group in the molecule can be absorbed by vibration. As different chemical bonds or functional groups absorb different frequencies, they will be in different positions in the infrared spectrum. Thus, information about the chemical bonds or functional groups contained in a molecule can be obtained. Figure 5 shows the FTIR spectrum of several typical samples in this research. Figure 5a indicates that WEB additives had two main absorption peaks at 2850 cm\({}^{-1}\) and 2921 cm\({}^{-1}\). Figure 5b shows that virgin bitumen had three main absorption peaks around 750 \({}^{-1}\), 1500 \({}^{-1}\), and 3000 \({}^{-1}\). However, when compared with Figure 5c, it can be seen that almost no obvious new absorption peaks were found in the 6% WEB-modified bitumen while the absorption peak around 1500 cm\({}^{-1}\) became much higher and wider. Also, in Figure 5d, no obvious new absorption peak appeared in 8% WEB-modified bitumen. And the absorption peak around 3500 cm\({}^{-1}\) became much higher and wider. This phenomenon may be caused by the high-speed shearing process, which led to an aging reaction and translated some aromatics and resins into asphaltenes [27; 28]. No evident new absorption peak  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Binder Type**} & \multicolumn{2}{c}{**\(\mathbf{-6\ ^{\circ}C}\)**} & \multicolumn{2}{c}{**\(\mathbf{-12\ ^{\circ}C}\)**} \\ \cline{2-5}  & **Stiffness S (MPa)** & **m-Value** & **Stiffness S (MPa)** & **m-Value** \\ \hline Pen 60/70 & 191.15 & 0.355 & 323 & 0.257 \\
2\% WEB & 154.1 & 0.425 & 340.5 & 0.39 \\
4\% WEB & 141 & 0.309 & 275 & 0.264 \\
6\% WEB & 167 & 0.301 & 343.5 & 0.236 \\
8\% WEB & 154 & 0.325 & 248.5 & 0.250 \\
10\% WEB & 139.5 & 0.337 & 233 & 0.258 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bending beam rheometer (BBR) test results.  
Figure 4: Rotational viscosity (135 \({}^{\circ}\)C).  
indicated no obvious chemical reaction. Since no new harmful chemical substances were produced, the effect of WEB-modified bitumen on soil was similar to that of virgin bitumen, which means that the WEB-modified bitumen can be used at room temperature. Additionally, the WEB-modified bitumen was suitable to the mixture like SMA, which needs high module bitumen. These kinds of structures will protect the land by preventing the rain flow onto the land, therefore the roadbed soil will not be polluted by rainwater.
# Ocular Implications in Patients with Sleep Apnea
## 3 Results
### Dry Eye Syndrome and Damage to the Eye Surface  
A first complication of SAS is the dry eye syndrome, especially in those associated with floppy eye syndrome, highlighted by the Schirmer 1 test and the break-up time test. [PERSON] et al. conducted a cohort study in 2020, in which they confirmed the connection between the dry eye syndrome and other factors, including ODS. High blood pressure and high BMI were strongly associated with the less dry eye or current smoking, while former smokers had drier eyes [14]. The inflammation of the ocular surface specific to the dry eye syndrome develops as a result of increased tear osmolarity, proinflammatory cytokines accumulation secreted by the lacimal gland over the ocular surface, and the delayed clearing by tears [15]. In SAS, the levels of proinflammatory cytokines (TNF-alpha, IL-1, IL-6) are increased based on chronic intermittent hypoxia. As AHI increases, the mechanical stress of tissues, the hypoxia and inflammation of the ocular surface also increase, which in turn lead to the loss of meibomian and goblet cells function, decreased corneal sensitivity, and reduced tear production in response to the stimulation of the tear glands. [PERSON] et al. (2019) showed that the damage to the meibomian glands in the upper eyelids in patients with SAS was much more pronounced compared to healthy subjects, especially those with severe SAS. This rubbing-like effect could also be a cause of meibomian gland distortion in patients with SAS [16]. Figure 1 shows the mechanisms involved in producing ocular changes in patients with SAS.  
Figure 1: Mechanisms involved in SAS that cause eye damage.
## 2 Materials and Methods  
### Study Area and Dataset  
The study area is Keelung City, located in the northeastern part of Taiwan. It has a land area of approximately 132.8 km\({}^{2}\). The majority of the city's land is covered by slopes, accounting for about 94% of the total land area, while flat terrain comprises only around 5%. The remaining areas mainly consist of rivers and lakes, with the Keelung River being one of the most significant rivers in the city, running through the entire area and flowing into Keelung Port. Keelung City is divided into seven districts, namely Renai District, Xinyi District, Zhongzheng District, Zhongshan District, Anle District, Nuannuen District, and Qidu District, as shown in Figure 1a. The city has a total of 157 villages. Keelung City has a total of 156,056 households. The population of Keelung City is nearly 370,000 people [43]. GIS spatial analysis was employed to generate a village unit GIS-based model encompassing 157 villages within the study area. This study utilized ArcGIS 10.8, which is developed by the Environmental Systems Research Institute (ESRI) and renowned as one of the most extensively employed GIS software ([[https://www.esri.com/en-us/home](https://www.esri.com/en-us/home)]([https://www.esri.com/en-us/home](https://www.esri.com/en-us/home)), accessed on 1 March 2023) products globally [44; 45]. ArcGIS provides a broad spectrum of tools for mapping, spatial analysis, data management, and visualization. The comprehensive procedure involves the utilization of the GIS, where geographical information, specifically the Taiwan Datum 1997 (TWD97) coordinate system, pertaining to the study area, is digitized and incorporated into the GIS as layers, along with various attribute data.  
Data regarding the location, frequency, and types of fires occurring in Keelung City over the past decade have been collected from the Keelung City Fire Bureau [16]. By analyzing this historical data, we can identify potential fire hotspots that may be linked to specific geographical conditions or human activities. Following these steps, the fire incident hotspots in Keelung City are depicted in Figure 1b, facilitating the formulation of response measures to minimize losses and safeguard communities.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 2 Datasets and Methods
### Method  
We propose a convolution-Transformer adaptive fusion network (CTAFNet) for pixel-wise HSI classification. CTAFNet uses a novel local-global fusion feature extraction unit, called the convolution-Transformer adaptive fusion kernel, to capture both the local high-frequency features and the sequential low-frequency information. An adaptively feature fusion strategy was designed to obtain a more robust and discriminative representation of the HSI data. Moreover, CTAFNet adopts an encoder-decoder structure to improve the flow of fused local-global information between different stages, thus ensuring the generalization ability of the model.
# Smoothness of Graph Energy in Chemical Graphs
## 1 Introduction
Over a hundred variants of graph energy have been proposed, based on various matrices rather than the adjacency matrix as in our case. Research on these graph energies is very active nowadays and has led to well over a thousand publications. This has led to a rapid increase in the number of published papers, which is now more than two per week. Graph energies have found unexpected applications in fields of science and engineering such as crystallography, air transport, satellite communication, face recognition, protein sequence comparison, spacecraft construction and high-resolution satellite image processing. Some applications have also been tested in medicine. The aim of this paper is to study the smoothness of graph energy of certain acyclic and cyclic hydrocarbons. We calculate the graph energies and give linear relations for their prediction. We also compare the graph energies of acyclic molecules with those of their cyclic versions. We then establish measures of smoothness--the (modified) structure sensitivity and (modified) abruptness for 135 molecular graphs in our dataset. The structure of the paper is the following: In the next section we introduce graph edit distance (GED), structure sensitivity and abruptness, as well as modified structure sensitivity and modified abruptness. The computational details and results are presented in Section 3. First, linear regression analysis of graph energy and correlations is considered and second (modified) structure sensitivity and (modified) abruptness is examined with respect to graph energy. The graph energies of acyclic and cyclic molecules from our dataset are compared finally. A discussion is presented in Section 4 and in Section 5 conclusions are presented. All the calculated data are gathered at the end of the paper.
# Trajectory Tracking Control of a Manipulator Based on an Adaptive Neuro-Fuzzy Inference System
## 3 Kinematic Analysis of the Manipulator
### Trajectory Planning  
Before the manipulator performs the hedge trimming task, it is necessary to plan the trajectory of each joint to facilitate subsequent control. According to the given initial joint  
Figure 4: Setting of the manipulator coordinate system.  
angle and target angle, the quintic polynomial interpolation function is adopted to obtain the joint trajectory. The general form is as follows:  
\[Q(t)=a_{0}+a_{1}t+a_{2}t^{2}+a_{3}t^{3}+a_{4}t^{4}+a_{5}t^{5} \tag{4}\]  
In order to make the manipulator move more smoothly, the constraints are set as:  
\[\left\{\begin{aligned} \theta(0)=\theta_{0},\theta\Big{(}t_{f} \Big{)}&=\theta_{f}\\ \dot{\theta}(0)=0,\dot{\theta}\Big{(}t_{f}\Big{)}&=0\\ \ddot{\theta}(0)=0,\ddot{\theta}\Big{(}t_{f}\Big{)}&=0 \end{aligned}\right. \tag{5}\]  
where \(t_{f}\) is the termination time, \(\theta_{0}\), \(\theta_{f}\) are the initial and target joint angles, and \(\dot{\theta}\) and \(\ddot{\theta}\) are the joint velocity and acceleration, respectively.  
The joint angle corresponding to the target position of the manipulator can be obtained using an inverse kinematics algorithm according to the target pose. The trajectory planning is divided into two working conditions: trimming the top and trimming the side. We can assume that the position coordinates of the target points are (1050, \(-\)300, 1700) and (1050, 480, 500) respectively. Substitute Equation (5) for Equation (4) to obtain the trajectory of each joint as follows.  
Trimming the top:  
\[\left[\begin{aligned} & q_{1}(t)\\ q_{1}(t)\\ q_{1}(t)\\ q_{1}(t)\\ \end{aligned}\right]=\left[\begin{aligned} & -0.000017\\ & -0.000048\\ & -0.000079\\ & -0.000062\end{aligned}\right]x^{5}+\left[\begin{aligned} & 0.00042\\ & -0.0012\\ & -0.0012\\ & 0.0016\end{aligned}\right]x^{4}+\left[\begin{aligned} & -0.0028\\ & -0.0081\\ & 0.013\\ & -0.01\end{aligned}\right]x^{3}+\left[\begin{aligned} & 0 \\ 0\\ 0\\ 0\\ \end{aligned}\right]x^{2}+\left[\begin{aligned} & 0\\ 0\\ 0\\ \end{aligned}\right]x+\left[\begin{aligned} & 0\\ 2.1\\ & -2.1\\ -1.1\end{aligned}\right] \tag{6}\]  
Trimming the side:  
\[\left[\begin{aligned} & q_{1}(t)\\ q_{1}(t)\\ q_{1}(t)\\ q_{1}(t)\\ \end{aligned}\right]=\left[\begin{aligned} & 0.000026\\ & -0.000057\\ & -0.000013\\ & 0.000013\end{aligned}\right]x^{5}+\left[\begin{aligned} & -0.00064\\ & 0.00012\\ & -0.00033\end{aligned}\right]x^{4}+\left[\begin{aligned} & 0.0043\\ & -0.0095\\ & -0.0021\\ & 0.022\end{aligned}\right]x^{3}+\left[\begin{aligned} & 0 \\ 0\\ 0\\ 0\\ \end{aligned}\right]x^{2}+\left[\begin{aligned} & 0\\ 0\\ 0\\ \end{aligned}\right]x+\left[\begin{aligned} & 0\\ 2.1\\ & -2.1\\ & -1.1\end{aligned}\right] \tag{7}\]
## 3 The Main Parameters and Indices of MOT
### Conventional Performance Indices
#### 3.2.4 Atomic Lifetime  
The atomic lifetime is the average duration that atoms remain in the trap, representing the average time it takes for atoms to escape from the MOT as a result of collisions. The atomic lifetime is influenced by the rate of collisions that leads to the escape of atoms per unit time.  
\[\mathcal{L}=\frac{1}{n_{0}\sigma_{\mathrm{e}}\overline{v}} \tag{10}\]  
The lifetime of an atom is usually determined by monitoring the change in its atomic number during the process of atomic capture. The pattern of change in the atomic number during the capture process can be described by Formula (7), which follows an exponential trend. When the atomic number in the trap reaches a 1/e difference from its stable value, the duration of time it takes to reach this point represents the lifetime of the atom in the trap.
## 6 Discussion
### Horizontal and Autonomic M2M  
Based on the review, it is seen that principles for the architecture need to be defined in order to establish a solid basis for multiple stakeholder system for M2M service networks. In addition, architecture principles for solving the heterogeneity of technologies are needed to enable communication between objects and applications, which are not initially been designed to communicate together. An approach may be horizontal approach for the system architecture and application of autonomic computing principles [153]. Such horizontal architecture is based on open standards to enable connectivity and interoperation with multiple M2M domains and heterogeneous devices and to enable reaching wide acceptance as a basis of multiple stakeholder M2M systems. In such a way, several different M2M domains could become capable of deploying the architecture, enabling interoperability, lowering the development cost and boosting the arising M2M markets by contributing towards transferring from vertical towards more horizontal M2M markets.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Datasets
#### 2.2.3 Surface and Subsurface Soil Moisture  
The adopted soil moisture dataset was developed by the Hydrological Science Laboratory (HSL) at NASA's Goddard Space Flight Center in collaboration with USDA Foreign Agricultural Services and USDA Hydrology and Remote Sensing Lab. This dataset was generated by integrating estimates from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach [65]. The model accounts for the daily amount of water withdrawn by \(ET\) and replenished by \(P\). Surface soil moisture (\(SM\)) and subsurface soil moisture (\(SSM\)) data are composed of 3-day composites with a spatial resolution of approximately 28 km (\(0.25^{\circ}\)).
## 1 Introduction
### Background  
Digital image watermarking technology is basically used passively to protect copyright for authentication and identifies the ownership of digital content. This image watermarking technology embeds an additional image (or watermark) into the original cover or host image and produces the watermarked image in such a way that the hidden watermark can be detected or extracted later from the watermarked image to identify the right owner of the digital content [2].  
The general framework of image watermark embedding (or inserting) and extraction are shown in Figure 1[3]. At first, the watermark embedding algorithm takes the host and watermark images and the secret key. The communication channel is used to pass the watermarked image, which was then attacked in numerous ways. The watermark image is extracted from the watermarked image by the watermark extraction algorithm with the same secret key and reverse algorithm of the watermark embedding.  
Inserted watermarking can be visible or indistinguishable to the viewer, but invisible watermarking gains more popularity than visible watermarking in image authentication. Hence, we have designed an invisible image watermarking technique in this work.  
An efficient image watermarking system must satisfy four design requirements like imperceptibility, robustness, security, and embedding capacity, simultaneously [4]. It is really a challenging task for researchers to maintain a proper balance among these requirements because of their limited and contradictory qualities [5]. Also, maintaining a proper balance among these design requirements depends on watermarking applications [6; 7; 8; 9]. Watermarking can be conducted through spatial or transform domain techniques. But, peak-signal-to-noise-ratio (PSNR) values for spatial domain approaches are lower than those for transform domain methods [10]. Also, they are less robust against attacks, whereas transform domain techniques guarantee improved imperceptibility and high robustness, with improved security to the system. Researchers are now concentrating on hybridizing spatial or two or more transform domain methods, as a single transformation cannot satisfy all of the design requirements simultaneously [3].
# Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence
## 3 Experiments
### Discussion
For moderate and severe subsidence patterns (Clusters 2 and 3), spatio-temporal prediction models do not always perform better than the time series prediction model. We found that GCN-Seq2 Seq performs worse than Seq2 Seq. This may be due to the fact that the user-specified spatial proximity relationship cannot effectively capture the spatial autocorrelation [43] due to the datasets exhibiting significant local spatial heterogeneity. In this situation, the diffusion convolution operation [35] and the attention mechanism [36] are more suitable for modeling spatio-temporal autocorrelation. Additionally, this study's results were compared with a previous study on land subsidence prediction in Cangzhou, which accounted for spatial heterogeneity [12]. We found that both of them consider the heterogeneity of land subsidence dataset, so the prediction performance can be improved compared with the global methods. Compared to [12], this study further addresses spatio-temporal heterogeneity in land subsidence prediction. Actually, the model constructed in [12] is similar to the Seq2 Seq model tested in this study. According to the experimental results given in Section 3.5, we can infer that the model developed in this study outperforms that developed in [12]. In addition, the proposed method in this study is different from the related study in numerical performance. The reason is that the related study was proposed to predict differential deformation values, while this study predicts cumulative subsidence values.
## 4 Experiments and Discussion
### Performance Measures  
To evaluate the performance of all ML- and DL-based prediction models for the recognition of wafer map failures, we use the accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) as the performance measures. The equations of the performance measures are shown in Equations (7) to (10) as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Count** \\ \hline Center & 4296 (2.5\%) \\ Donut & 555 (0.3\%) \\ Local & 3597 (2.1\%) \\ Edge-Local & 5199 (3.0\%) \\ Edge-Ring & 9682 (5.6\%) \\ Scratch & 1194 (0.7\%) \\ Random & 866 (0.5\%) \\ Near-Full & 149 (0.1\%) \\ None & 147,431 (85.2\%) \\ Total & 172,950 (100\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data description.  
Figure 5: Typical examples of eight wafer map failure types.  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
\[F1-score=\frac{(2*Recall*Precision)}{Recall+Precision}=\frac{(2*TP)}{2*TP+FP+FN} \tag{10}\]  
where the true positive, true negative, false positive, and false negative in the confusion matrix are shown as TP, TN, FP, and FN in abovementioned equations, respectively.
## 6 Uncertainties Associated with Distances Derived from Point Clouds
### Distance Measurement: Uncertainty Contribution from Spatially Correlated Rotation Effects  
See Section 5.4. We assume the spatial correlation is isotropic, i.e., the same for each axis, and specified by variance \(\sigma_{ER}^{2}\) and length scale parameter \(\lambda_{ER}\). If \(\mathbf{x}_{i}\) is measured using probe offset \(\mathbf{p}_{k(i)}\), etc., \(\mathbf{n}_{ij}\) is the unit vector pointing from \(\mathbf{x}_{i}\) to \(\mathbf{x}_{j}\), and \(G_{i}\) and \(G_{j}\) are the sensitivity matrices associated with \(R(\mathbf{x})\mathbf{p}_{k(i)}\) and \(R(\mathbf{\alpha})\mathbf{p}_{k(j)}\) with respect to \(\mathbf{\alpha}\) evaluated at \(\mathbf{\alpha}=0\), as in Equation (24), then the uncertainty \(u(d_{ij})\) associated with the distance \(d_{ij}\) due to rotation effects is given by  
\[u^{2}(d_{ij})=\sigma_{ER}^{2}\Big{(}\mathbf{m}_{i}^{\top}\mathbf{m}_{i}+\mathbf{m}_{j}^{ \top}\mathbf{m}_{j}-2(\mathbf{m}_{i}^{\top}\mathbf{m}_{j})e^{-d_{ij}^{2}/\lambda_{ER}^{2}} \Big{)},\]  
where \(\mathbf{m}_{i}=G_{i}\mathbf{n}_{ij}\), etc. If the same probe is used for both measurements, then \(\mathbf{p}_{i}=\mathbf{p}_{j}=\mathbf{p}\), say, \(\mathbf{m}_{i}=\mathbf{m}_{j}=\mathbf{m}\), say, and  
\[u^{2}(d_{ij})=2\sigma_{ER}^{2}\mathbf{m}^{\top}\mathbf{m}\Big{(}1-e^{-d_{ij}^{2}/ \lambda_{ER}^{2}}\Big{)}.\]  
For this latter case, the quantity \(\mathbf{m}^{\top}\mathbf{m}\) depends on the relationship between \(\mathbf{n}_{ij}\) and \(\mathbf{p}\). If \(\mathbf{p}\) is in the same direction as \(\mathbf{n}_{ij}\) (unlikely to be so in practice) \(\mathbf{m}=\mathbf{0}\). If \(\mathbf{p}\) is orthogonal to \(\mathbf{n}_{ij}\) (as is often the case), then \(\mathbf{m}^{\top}\mathbf{m}=\mathbf{p}^{\top}\mathbf{p}\), then for the measurement of a distance using the same probe,  
\[u(d_{ij})\leq\sqrt{2}\sigma_{ER}\|\mathbf{p}\|\Big{(}1-e^{-d_{ij}^{2}/\lambda_{ER }^{2}}\Big{)}^{1/2}.\]  
More generally,\[u(d_{ij})\leq\sqrt{2}\sigma_{ER}P\Big{(}1-e^{-d_{ij}^{2}/\lambda_{2,R}^{2}}\Big{)}^ {1/2}\leq\sqrt{2}\sigma_{ER}P,\]  
where \(P\) is the length of the longest probe involved.