esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 5 Conclusions  
In this paper, a spatial-temporal-semantic neural network algorithm for location prediction is proposed. The algorithm consists of two steps, the spatial-temporal-semantic (STS) feature extraction and the LSTM-based model building. Aiming at solving the problem that traditional methods based on cell partitioning or clustering ignore a large amount of information about points along the road, the STS feature extraction algorithm is proposed. The method transforms the trajectory into location sequences by mapping track points to fixed and discrete points in the road networks. The STS can take advantage of points along the road, which meets the demand of services like delivering systems and transportation systems. After feature extraction, trajectories are transformed into long location sequences. Traditional models cannot perform well with long sequential data; therefore, an LSTM-based model is proposed to overcome the difficulty. The model can better deal with long location sequences to gain better prediction results. Experiments on two real-world datasets show that the proposed STS feature extraction algorithm has lower deviation distance. The location sequences generated by the STS can improve the prediction precision by 27%. Moreover, the proposed LSTM-based model can improve the precision of 25% compared with traditional classification models. The STS-LSTM algorithm has stable and better performance on both datasets.  
There are several potential extensions of this paper. Aiming at solving practical problems, the algorithm should be robust to trajectory of poor quality. More data preprocessing works, such as data compression, noise filtering, and data filling could be done in the future. Moreover, the LSTM-based model could be further modified to accept more features and dimensions, such as temporal features like month, week, and day, and semantic features, like stay points. Moreover, LSTM could be combined and intercepted into other classification models, or replaced by a similar but simpler structure called the gated recurrent unit (GRU) [37].  
**Acknowledgments:** This paper is supported by the National Natural Science Foundation of China (No. 41501485).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 20: Application scenarios of the STS-LSTM algorithms. (**a**) In a food delivery system, given the last location (the red circle) and the current location (the blue point), the next location can be predicted (the red point on the left). (**b**) In a bus system, the location predicted can be used to estimate the arrival time of each bus stop.
## 2 Materials and Methods  
In RF magnetron sputtering, well-cleaned indium tin oxide (ITO) glass substrates were used to deposit Cr-doped NiO thin films. The substrates were cleaned sequentially in an ultrasonic bath by using soapy water, deionized water, methanol, isopropanol and deionized water. Then, a N\({}_{2}\) gas flow was applied to dry the substrates, and then the substrates were rinsed with acetone. Finally, the substrates were UV-irradiated to remove any organic residues. Selective lateral doping of Cr-NiO films was performed using a physical vapor deposition technique under a high-vacuum atmosphere through a co-sputtering technique. ITO substrates with dimensions of 3 cm \(\times\) 3 cm \(\times\) 1.5 mm were used for film deposition. A NiO single sputtering target and a Cr metal target were used as source materials. Preliminary purging of the sputtering chamber, followed by preputtering, was performed at 50 W for 15 min to eliminate unwanted materials from the chamber. The internal pressure of the chamber was reduced to 5 \(\times\) 10\({}^{-6}\) Torr using a turbomolecular pump, and the working pressure inside the chamber was maintained at 18 mTorr throughout deposition by flowing pure argon (99.99 percent) working gas into the chamber. During deposition, the temperature of the substrate was fixed at 100 \({}^{\circ}\)C. The RF guns were set to 50 W and 20 W for NiO and Cr, respectively. All samples were kept inside after deposition and then annealed for 3 h at the deposited temperature.  
The deposition time was fixed at 90 min for the NiO target with the variation in Cr doping from 20 s to 80 s. Every 20 min interval of deposition time, we have performed the doping at one-fourth of the respective doping time. Thickness was monitored and maintained within a range 97 nm to 102 nm. The other parameters were the same for both targets. After deposition, the samples were kept inside the chamber until the substrate temperature dropped to room temperature. This processes the oxidation at the as-grown samples. The schematic and deposition parameters used to optimize the NiO thin film are provided in Table 1, and Figure 1, respectively. The structural and crystallographic orientation of the deposited NiO thin films were measured by using the an XS-D8 Advance Cu-K\(\alpha\) diffractometer (Bruker, Bremen, Germany). X-ray diffraction patterns were recorded at the diffraction angle (20) range of 20\({}^{\circ}\)-70\({}^{\circ}\) by using the Cu K\(\alpha\) radiation wavelength \(\lambda\) of 1.5406 A. A Lambda 950 UV/Vis/NIR spectrophotometer (Perkin Elmer, Massachusetts, United States) was used to measure absorbance and optical transmission in the wavelength range of 300-1000 nm. Finally, the electrical properties of the thin film, such as carrier concentration, carrier mobility, and resistivity, were examined using Hall effect measurements (HMS ECOPIA 3000, Bridge Technology, Queen Creek, AZ, USA; probe current: 10 mA, magnetic field: 0.57 T).
## 3 Establishment of Ship Emission Calculation Model
### Main and Auxiliary Engine Pollution Emission Factor with Correction Factor  
The emission factor is mainly determined by the engine type, sailing conditions, fuel type, etc. The main engine of the regional inland ships is generally a medium-speed engine,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Ship Type** & **Ratio of Auxiliary Engine Power to Main Engine Power** \\ \hline \hline Cargo ship & 0.222 \\ Passenger Ship & 0.278 \\ Oil tanker & 0.225 \\ Special ship & 0.186 \\ Container ship & 0.220 \\ Other type & 0.191 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ship main and auxiliary engine power ratio.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Operating Conditions** & **Main Engine** & **Auxiliary Engine** \\ \hline \hline Cruise & 0.8 & 0.13 \\ Low speed cruise & 0.6 & 0.20 \\ Maneuvering & 0.2 & 0.25 \\ mooring & 0 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Load factor of main and auxiliary engines.  
and the auxiliary engine is a high-speed engine. Theoretically, the fuel used is light diesel with a sulfur content not exceeding 0.1%. This paper refers to the relevant research results at home and abroad and carries out the correction method to comprehensively determine the basic emission coefficient of the main and auxiliary engines of ships in the main urban area of Chongqing [13]. For inland ships in the Yangtze River Basin, the relevant researchers used diesel fuel with a sulfur content of 2.7% for the ship's air pollutant emission factor. The emission factors are shown in Table 3.  
In December 2018, the Ministry of Transport adjusted the scope and control standards of emission control for the navigable waters of the Liuhe Estuary in Jiangsu. The plan requires that from 1 January 2019, ships entering the Yangtze River Basin shall not use fuel oil with a sulfur content exceeding 0.1%. The scope of this study is the main urban area of Chongqing, from September to December 2021. Therefore, based on the basic emission factor (_FCF_), the corrected value of 0.1% of the sulfur content of marine fuel oil is used as the actual emission factor. The correction factor of light diesel with 0.1% sulfur content in ships is shown in Table 4.  
It can be seen from the above table that the emission factors of CO, HC, and CO\({}_{2}\) did not change after the marine fuel oil used light diesel with a sulfur content of 0.1%, while the effect of SO\({}_{\chi}\) was more significant, followed by PM\({}_{10}\), and PM\({}_{2.5}\) has little effect on NO\({}_{\chi}\). The use of light fuel oil with a sulfur content of 0.1% has a greater impact on the calculation of sulfur-containing pollutant emissions and also has a certain degree of impact on PM\({}_{10}\) and PM\({}_{2.5}\).
## 4 ML Techniques
### Learning Approach
#### 4.2.2 Unsupervised Learning  
Contrary to supervised learning, unsupervised ML algorithms do not need an input-output pair to train a model successfully [98]. The most common task performed with unsupervised learning is classification through clustering analysis, where the input data is separated into groups based on their similarity. Unsupervised learning techniques are becoming popular, especially in the context of Big Data [23]. These methods are helpful when expert knowledge is not available or not required; it is also helpful with identifying outliers in manufacturing data [106]. The most common unsupervised learning tasks are clustering, feature learning, dimensionality reduction, and anomaly detection. Two common examples from the literature usually applied for L-PBF are K-means clustering and Gaussian mixture models (GMM) [107].  
Clustering algorithms are among the most common unsupervised learning algorithms and are typically used for anomaly detection in AM applications. This method is usually helpful when used with a large amount of data and has the advantage of not requiring human interaction, and is, therefore, very convenient for defect detection. A clustering approach was applied to acoustic emission data collected during the L-PBF process. The ML model input was a series of AE signals collected during printing laser tracks at different melting conditions, namely under-melting, over-melting, keyholes, and balling conditions. The objective of the model was to classify the AE signals at each melting condition. Visualizing the data was made easier by applying a t-SNE clustering approach, as illustrated in Figure 11[108]. The authors were able to group different melting conditions in specific regions on the 3D map, which can then be used for anomaly detection. A clustering approach using a hierarchical K-means and Gaussian mixture model was also used to classify AE signals related to pores and cracks [74].  
Figure 11: Feature reduction approach applied using t-SNE to visualize acoustic emission signals collected during different melting conditions of an L-PBF process [108].  
Figure 10: Example of a typical convolutional neural network architecture [105]. The input, in this case, is the infrared images acquired (the color map represents the temperature of the layer). The CNN consists of multiple blocks of convolutional layers followed by pooling layers and one or more fully connected layers at the end for classification.
## 3 System Model
### Block Data Transmission Model  
Block data retrieval latency is a critical metric in collaborative storage systems, as it reflects the quality of service in the network. In the proposed collaborative storage scheme, vehicles obtain block data from RSUs through two possible transmission links: RSU-to-RSU (R2R) and RSU-to-Vehicle (R2V). Therefore, it is essential to model the latency of both R2R and R2V transmission links.
# Off-Resonance Control and All-Optical Switching: Expanded Dimensions in Nonlinear Optics
###### Abstract  
The theory of non-resonant optical processes with intrinsic optical nonlinearity, such as harmonic generation, has been widely understood since the advent of the laser. In general, such effects involve multiphoton interactions that change the population of each input optical mode or modes. However, nonlinear effects can also arise through the input of an off-resonant laser beam that itself emerges unchanged. Many such effects have been largely overlooked. Using a quantum electrodynamical framework, this review provides detail on such optically nonlinear mechanisms that allow for a controlled increase or decrease in the intensity of linear absorption and fluorescence and in the efficiency of resonance energy transfer. The rate modifications responsible for these effects were achieved by the simultaneous application of an off-resonant beam with a moderate intensity, acting in a sense as an optical catalyst, conferring a new dimension of optical nonlinearity upon photoactive materials. It is shown that, in certain configurations, these mechanisms provide the basis for all-optical switching, i.e., the control of light-by-light, including an optical transistor scheme. The conclusion outlines other recently proposed all-optical switching systems.  
nonlinear optics; all-optical switch; absorption; fluorescence; resonance energy transfer; FRET; second harmonic generation; laser action; optical transistor; multiphoton process
# Nonparaxial Propagation Properties of Specially Correlated Radially Polarized Beams in Free Space
On the other hand, when a beam has a large divergence angle or a small beam spot that is several orders of its wavelength [39; 40; 41; 42], it will be treated as a nonparaxial beam. A beam emitted from a diode laser, microcavity, or focused by a high numerical aperture is usually nonparaxial [39; 43; 44]. Such nonparaxial beams are commonly encountered in microscopy imaging, beam shaping, optical trapping, and optical data storage [45; 46; 47; 48]. Therefore, several approaches have been developed [49; 50; 51; 52; 53] to describe the propagation of a laser beam in the nonparaxial regime. Until now, the nonparaxial propagation properties of various laser beams have been studied. It was found that the properties are closely related to the initial beam profile [42; 54; 55; 56], phase distribution [57; 58; 59], polarization state [60; 61; 62], and spatial coherence [63; 64; 65]. To our knowledge, no results have been reported until now on nonparaxial propagation of partially coherent radially polarized beams with non-conventional correlation functions. In this paper, we extend the paraxial propagation of the SCRP beam to the nonparaxial region and explore the average intensity, the degree of polarization, and the spectral degree of coherence (SDOC) of a nonparaxial SCRP beam in free space. The nonparaxial propagation of a PCRP beam is also studied for comparison.
## 4 Experimental Validation  
### Experimental Descriptions  
The proposed tension identification algorithm, based on TFR techniques, was verified experimentally using a workshop test. A cable of parallel steel wires was prepared in the test pit as the experimental sample, as shown in Figure 2. The experimental cable consists of 127 steel wires with a diameter of 5.2 mm per each and a double protective layer made of polyethylene, as shown in Figure 3. The length and mass per unit length of the experimental cable are 191.9 m and 22 kg/m, respectively. The detailed parameters of cables are shown in Table 1. The structural system had one fixed end and one end connected with a hydraulic jack. Several rubber rollers were placed under the cable to prevent it from being broken at low-level loads.  
Figure 1: Algorithm flow of identification of time-varying cable tension based on TFR.
## 2 Theoretical Model  
### Geometry  
The two-dimensional shell model considered in this paper is defined by the vector \(\mathbf{r}(\alpha_{1},\alpha_{2})\) over the middle surface of the shell (Figure 1). The parameters \(\alpha_{1},\alpha_{2}\) stand for the orthogonal and principal curvilinear coordinates of the middle surface of the shell. Examples for this notation can be found in the books by [PERSON] et al. [99; 100]. As can be seen in Figure 1, the position vector \(\mathbf{R}(\alpha_{1},\alpha_{2},\zeta)\) defines each point \(P\) of a generic three-dimensional shell element of constant thickness \(h\). Position vector \(\mathbf{R}(\alpha_{1},\alpha_{2},\zeta)\) is defined as  
\[\mathbf{R}(\alpha_{1},\alpha_{2},\zeta)=\mathbf{r}(\alpha_{1},\alpha_{2})+ \zeta\,\mathbf{n}(\alpha_{1},\alpha_{2}) \tag{1}\]where \(\zeta\) is the coordinate that identifies the normal direction along the shell thickness. The outward unit normal vector \(\mathbf{n}(\alpha_{1},\alpha_{2})\), defined along \(\zeta\), can be expressed as  
\[\mathbf{n}=\frac{\mathbf{r}_{1}\wedge\mathbf{r}_{2}}{|\mathbf{r}_{1}\wedge \mathbf{r}_{2}|} \tag{2}\]  
The symbol \"\(\wedge\)\" stands for the vector product, also \(\mathbf{r}_{i}=\partial\mathbf{r}/\partial\alpha_{i}\) for \(i=1,2\) is introduced. The following limitations are included for \(i=1,2\)  
\[\alpha_{i}\in\left[\alpha_{i}^{0},\alpha_{i}^{1}\right] \tag{3}\]  
where \(\alpha_{i}^{0},\alpha_{i}^{1}\) denote respectively the minimum and the maximum values of the coordinates at issue. The third coordinate \(\zeta\) must be defined within the limits of the shell thickness; therefore, it is bounded as  
\[\zeta\in\left[-h/2,h/2\right] \tag{4}\]  
For a laminated composite structure made of \(l\) plies (Figure 1), the total thickness of the shell \(h\) can be expressed as  
\[h=\sum_{k=1}^{l}h_{k} \tag{5}\]  
where \(h_{k}\) denotes the thickness of the \(k\)-th ply.  
With the defined position vector \(\mathbf{r}(\alpha_{1},\alpha_{2})\), the Lame parameters \(A_{1}(\alpha_{1},\alpha_{2})\) and \(A_{2}(\alpha_{1},\alpha_{2})\) of the surface can be computed as  
\[A_{1}=\sqrt{\mathbf{r}_{1}\cdot\mathbf{r}_{1}},\quad A_{2}=\sqrt{\mathbf{r}_ {2}\cdot\mathbf{r}_{2}} \tag{6}\]  
where the symbol \"\(\cdot\)\" denotes the scalar product. Radii of curvature \(R_{1}(\alpha_{1},\alpha_{2})\) and \(R_{2}(\alpha_{1},\alpha_{2})\) of the doubly-curved reference surface, due to the hypothesis of orthogonal and principal coordinates, can be evaluated as follows  
\[R_{1}=-\frac{\mathbf{r}_{1}\cdot\mathbf{r}_{1}}{\mathbf{r}_{11}\cdot\mathbf{ n}},\quad R_{2}=-\frac{\mathbf{r}_{2}\cdot\mathbf{r}_{2}}{\mathbf{r}_{22} \cdot\mathbf{n}} \tag{7}\]  
Figure 1: Doubly-curved panel representation and lamination scheme.  
with the notation \(\mathbf{r}_{ji}=\partial^{2}\mathbf{r}/\partial\alpha_{i}^{2}\), for \(i=1,2\). The parameters \(H_{1}(\alpha_{1},\alpha_{2},\zeta)\) and \(H_{2}(\alpha_{1},\alpha_{2},\zeta)\), which must be introduced to consider the three-dimensional size effect of the structure, are evaluated by  
\[H_{1}=1+\frac{\zeta}{R_{1}},\quad H_{2}=1+\frac{\zeta}{R_{2}} \tag{8}\]
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 3 Methods
### Construction of the Target Observation Model
The PigNet model segments and labels the targets, and the target observation model determines how similar the targets are to the true state and modifies the particle weights to estimate the target location and state. Finally, the motion of the respective pixel positions is determined by the improved optical flow estimation algorithm for the temporal variation and correlation of the pixel intensity data in the image sequence to achieve high-accuracy tracking of multiple targets.
# Space Robot Sensor Noise Amelioration Using Trajectory Shaping
## 2 Materials and Methods
### Natural Frequencies  
After reformulating Equation (7) into canonical form elaborated in Equations (19)-(23) in reference [10], the natural frequencies of the flexible spacecraft system were derived using the finite element method and by solving the eigenvalue problem using the stiffness and mass matrices. It was assumed that all displacements are normal, and the system is constrained in Nastran. The stiffness (\(k\)) and mass (\(m\)) matrices are constructed for each beam element. The individual stiffness and mass matrices are shown for a given beam element i in Equations (8) and (9).  
\[\left[k^{i}\right]=\left[\begin{array}{cccc}12&6L&-12&6L\\ 6L&4L^{2}&-6L&2L^{2}\\ -12&-6L&12&-6L\\ 6L&2L^{2}&-6L&4L^{2}\end{array}\right] \tag{8}\]  
\[\left[m^{i}\right]=\left[\begin{array}{cccc}156&22L&54&-13L\\ 22L&4L^{2}&13L&-3L^{2}\\ 54&13L&156&-22L\\ -13L&-3L^{2}&-22L&4L^{2}\end{array}\right] \tag{9}\]  
The individual stiffness and mass matrices are added by superposition to form the total stiffness and mass matrices respectively, which are presented in the Appendix A. The solution to the eigenvalue problem, presented in Equation (10), provides the natural frequencies and mode shapes of the flexible spacecraft system. Recall the expressions for the rigid-elastic coupling using modal coordinates: in accordance with Equation (2), \(\varphi\)'s are mode shapes from finite element analysis using the eigenvalues of \(K/M\) (stiffness/mass). The system stiffness matrix is included in Equation (8) and mass matrix in Equation (9) and result in the natural frequencies and mode shapes for the flexible system. The resulting natural frequencies are listed in Table 2, and their corresponding mode shapes can be found in the Appendix A.  
\begin{table}
\begin{tabular}{c c c c} \hline
1809.46 & 596.81 & 43.72 & 10.22 \\
1415.52 & 478.77 & 30.89 & 2.07 \\
1042.16 & 419.02 & 15.77 & 0.69 \\
774.31 & 54.87 & & \\ \hline \end{tabular}
\end{table}
Table 2: Natural Frequencies, \(\omega_{n}\) for the Flexible Spacecraft System, in rad/s.
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 1 Introduction
Furthermore, this connection can fully exploit hierarchical features and fuse complementary information from auxiliary views. Among these DMFMs, a collect-and-distribute strategy is designed to aggregate informative features from prior DMFMs, and distribute them to the next DMFM. Finally, a feature enhancement block (FEB) is designed to obtain robust SR features. The experimental results on real-world and synthetic datasets demonstrate that our PMFN achieves both higher quantitative and better qualitative performance compared with the state-of-the-art methods. The main contributions of this paper are listed as follows:  
Figure 1: Comparison of image content in different SAIs. The location of the two **(a)** SAII and **(b)** SAII are \((1,1)\) and \((9,9)\) of the \(9\times 9\) HCI_new named “Origami”. The area indicated by the black arrow shows that the different views contain distinctive information near occlusion edges. * We design the DMFM using a dual-branch structure to implicitly cover the influence of disparities and incorporate informative information from auxiliary views. * The core PFFB of our network is mainly constructed by three DMFMs with a dense connection. This block can fully exploit multi-level features and the multi-scale fusion information can be preserved among complementary views. It is demonstrated that complementary features are effectively fused by this block to improve SR performance. * The performance of our PMFN has achieved improvements compared with the state-of-the-art methods developed in recent years. The rest of this paper is organised into the following sections. Section 2 introduces a brief overview of the related work. Section 3 mainly describes the architecture of our PMFN. In Section 4, we provide extensive analysis, comparative experiments and ablation studies by using synthetic and real-world datasets. Finally, Section 5 summarises the conclusion of this paper.