esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV-Cooperative Penetration Dynamic-Tracking Interceptor Method Based on DDPG
## 2 Problem Statement
### Dynamic-Interceptor Design  
Compared with the static environment, the position of the dynamic-interceptor changes in real time in the environment, so the UAV is required to be able to perform real-time planning. In this paper, the dynamic interceptor is defined as a tracking interceptor according to the proportional-guided pursuit law. Compared with most common dynamic interceptors with simple motion rules, such as interceptors that cycle in a uniform linear motion or circular motion, the tracking interceptor has stronger uncertainty, and it is difficult to evade by predicting the motion. The movement requirements of the multi-UAV system are much higher. In this paper, it is assumed that the linear velocity of the tracking interceptor is constant, and the angular velocity is calculated by proportional guidance. The schematic diagram of its movement is shown in Figure 3.  
The basic principle of the proportional-guidance method is to make the interceptor's rotational angular velocity proportional to the line-of-sight angular velocity. Next, the proportional-guidance mathematical model of the interceptor is introduced.  
Assuming that the position of the interceptor is \(x_{y},y_{y}\), the speed is \(v_{x_{y}},v_{y_{y}}\), the position of the UAV is \(x,y\), and the speed is \(v_{x},v_{y}\), and the relative position and speed of the interceptor to the UAV are shown in (2):  
\[\left\{\begin{array}{c}x_{r}=x_{b}-x\\ y_{r}=y_{b}-x\\ v_{x_{r}}=v_{x_{b}}-v_{x}\\ y_{y}=v_{y_{b}}-v_{y}\\ \end{array}\right. \tag{2}\]  
The interceptor's line of sight angle to the UAV can be obtained as:  
\[q=\arctan\left(\frac{x_{r}}{y_{r}}\right) \tag{3}\]  
Figure 3: Schematic diagram of interceptor’s movement.  
To obtain the line-of-sight angular velocity, (3) is derived, as shown in (4):  
\[\dot{q}=\frac{v_{y_{r}}x_{r}-v_{x},y_{r}}{x_{r}^{2}+y_{r}^{2}} \tag{4}\]  
The rotational angular velocity of the dynamic-tracking interceptor can be obtained by (5):  
\[\omega_{b}=K\dot{q} \tag{5}\]  
\(K\) is the proportional guidance coefficient, taking \(K=2\).  
Based on the angular velocity of rotation calculated by the proportional guidance, the interceptor performs a two-dimensional movement according to the angular velocity of the rotation obtained by the proportional guidance. The movement model is similar to that of a UAV:  
\[\left\{\begin{array}{c}\theta_{b}=\theta_{00}+\int_{0}^{t}\omega_{b}dt\\ v_{bx}=v_{b}\sin\theta_{b}\\ v_{by}=v_{b}\cos\theta_{b}\\ x_{b}=x_{b0}+\int_{0}^{t}v_{bx}dt\\ y_{b}=y_{b0}+\int_{0}^{t}v_{by}dt\end{array}\right. \tag{6}\]
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Dataset Construction  
Different features of AIS data have different scales, and the variability of scales will inevitably have an impact on the final restoration results of the model. Moreover, AIS data contains temporal and spatial information, and this spatial-temporal connectivity lends higher dimensionality and complexity to the data. Therefore, it is necessary to normalize the data before restoration to balance the differences in scale between different features, based on which the sliding window method is used to construct the model dataset to improve the efficiency of data utilization and the accuracy of the model.
# A Two-Stage Automatic Container Code Recognition Method Considering Environmental Interference
## 3 Methodology
### Step 2: The Framework of CCR: C-DeepLabv3
#### 3.3.3 Improved Decoder Module  
The task of the Deeplabv3+ decoder is to classify each pixel in the feature map transmitted and finally complete the segmentation task. And the upsampling structure in the decoder reduces the computational complexity by interpolating between the original feature maps. But these interpolation methods need more manual operation, which makes the network not have strong plasticity. In addition, traditionally, sampling needs to prefill 0 in the middle or around the low-resolution image, and this information is invalid, which will lead to difficult convergence of the model in the process of gradient optimization. Therefore, in this paper, we use, respectively, upsampling based on transpose convolution and upsampling based on sub-pixel convolution to replace the two upsampling structures.  
The upsampling with transposed convolution has learnable parameters, which makes the decoding network have learning ability, and it is convenient to train the model. Upsampling with sub-pixel convolution directly obtains pixel information from the feature map and performs Pixel Shuffle, without the need to complement the 0 operation, which can effectively reduce the gradient optimization problem caused by invalid information transmission. In addition, the receptive field of upsampling with sub-pixel convolutional is larger, which can collect more contextual semantic information to generate richer pixel details, shorten the image reconstruction time, and improve the segmentation accuracy.
# Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
## 2 Smoothing Method Based on Angle and Deep Q-Network  
### Unconstrained Deep Q-Network Smoothing Method  
The unconstrained DQN smoothing method [27] trains polygon datasets with different shapes by exploiting the self-learning ability of the DQN so that agents can find the optimal policy to maximize cumulative rewards. In this method, the central node in the polygon is regarded as the agent, and the polygon is regarded as the moving environment of the agent. The agent takes actions in the environment area, and the action space is selected as four actions on the plane: up, down, left, and right. Each time the agent chooses an action, the state of the environment will change. The state space is the coordinate set of the polygon and central nodes. The node quality is specified as the lowest quality of the triangle element in the polygon. The reward function is set according to three conditions: the change in the quality after the action is taken, the agent moving out of the polygon, and whether the node quality meets the threshold.  
The smoothing process of the unconstrained DQN is shown in Figure 1. Before optimization, the data are normalized and smoothed using the trained DQN model. After the DQN model smooths the mesh, the data need to be denormalized, the coordinates are updated, and finally, the optimized mesh is output.  
The unconstrained DQN smoothing method does not constrain the moving range of the agent during training. As shown in Figure 2a,b, when the central node moves near the polygon's boundary, the poor element's shape is not improved. Because of the exploration rate, the center will try to move to the boundary during training. Such actions do not help improve the quality of nodes. Instead, they consume considerable time for trial and error during training and even make the quality of nodes fall into local optima, leading to difficulties in improving the quality of elements. As shown in Figure 2c,d, when the central node moves in the central area of the polygon, it is helpful to further improve the node quality. Therefore, this paper proposes adding an inner polygon constraint to the polygon's interior and limiting the central node's moving range through the iteration during training.  
Figure 1: Flow chart of the unconstrained DQN smoothing method.
## 2 Materials and Methods
### Aroma Production Capability in Two Synthetic Media
#### 2.7.2 Gas Chromatography (GC) Analysis  
Volatile compounds were identified and quantified by the headspace/solid phase microextraction (HS-SPME) and gas chromatography coupled to mass spectrometry (GC-MS). A triple SPME fiber of DVB/CAR/PDMS 50/30 \(\upmu\)m (conditioned at 270 \({}^{\circ}\)C/30 min), was exposed for 1 h and 30 min at 40 \({}^{\circ}\)C. In a capped vial, 50 \(\upmu\)L of each sample were dispensed. The volatile compounds were transferred to the GC injector and desorbed (260 \({}^{\circ}\)C/5 min). The analysis was performed in a spitless mode (50 mL/min during 2 min) on a 6890 N Agilent gas chromatograph coupled to a 5973 N Agilent Mass Detector. The selected chromatography conditions were: A polar column (DB-WAX, 60 m x 0.25 mm id; 0.25 \(\upmu\)m film thickness), helium as the carrier gas (1 mL/min), and oven temperature started at 40 \({}^{\circ}\)C/3 min, ramped at 3 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C and up to 220 \({}^{\circ}\)C (7 \({}^{\circ}\)C/min for 5 min). The MS operated with an electron energy of 70 eV using the electron impact mode, the ion source temperature was 230 \({}^{\circ}\)C, and the scanning was carried out from 45 to 550 amu. Compounds were identified by comparison with mass spectra and the linear retention index (RI) with spectral data from NBS75K and Wiley G 1035 libraries, and with the Kovats index calculated using a series of standard n-alkanes. The semi-quantitative analysis by MS was performed assuming a response factor equal to 1. The results were expressed as \(\upmu\)g/mL of the sample. Two controls that contained ethanol or methanol without cells were also studied.
## 2 Numerical Aspects and Experimental Designs
### The DVI Method  
A description of the DVI method developed for MPAS can be found in [PERSON] et al. [13]. The DVI uses the integration of the complete forecast model in cycle runs of one-hour forecasts from the initial time. The vortex in a radius of 600 km of the vortex center is then relocated to the best track position after each cycle to replace the departure vortex before this forecast. The cycling in DVI is stopped when the central sea-level pressure or maximum wind speed of the simulated vortex reaches or exceeds the best track intensity, called P-match or V-match, respectively. The performances of the DVI with both matches on the typhoon track and intensity predictions are investigated for 16 typhoons in 2015-2020 [13]. The DVI application can improve the typhoon track prediction with statistical significance. This study aims to illustrate the impacts of RO data on typhoon prediction with and without the DVI application.
# Space-Efficient Fully Dynamic DFS
## 7 More Space-Efficient Dynamic DFS
### Worst-Case Update Time Dynamic DFS  
Finally, we consider the worst-case update time algorithm for the dynamic DFS, following the \"worst-case time\" algorithm described in Section 3.2. To implement this space-efficiently, again we must consider (a), (b) and (c) described in Section 7.2, but two of them are almost the same argument. In the worst-case time algorithm, we should solve the fault tolerant DFS problem with at most \(c_{j-1}+c_{j}\) updates and thus store information of last \(c_{j-1}+c_{j}\) updates. Therefore, the required space for the reduced adjacency list and the information of updates are multiplied by some constant, but these are absorbed in the big \(O\) notation.  
Therefore, we have only to consider (c). Let \(\mathcal{D}_{j}\) be the pair of the bit vector \(B_{j}\) and the wavelet tree \(\mathcal{W}_{j}\). Then during phase \(0\), \(\mathcal{D}_{0}\) is used to perform fault tolerant DFS and rebuilding of data structures is not needed. During phase \(j(\geq 1)\), \(\mathcal{D}_{j-1}\) is used and the following processes are done gradually: first destroy \(\mathcal{D}_{j-2}\) (this is not needed for phase \(1\)), and then build \(M_{j}\), \(S_{j}\) and \(\mathcal{D}_{j}\) from \(M_{j-1}\), \(S_{j-1}\) in the same way as Section 7.2. At the end of phase \(j\) there exist \(\mathcal{D}_{j-1}\), \(M_{j}\), \(S_{j}\) and \(\mathcal{D}_{j}\), and we can continue to the next phase \((j+1)\).  
Finally, we consider how much space is needed to implement this algorithm. In phase \(j\), \(\mathcal{D}_{j-1}\) takes \((m+o(m))\log n\) bits, and rebuilding the data structures requires at most \((2m+o(m))\log n\) bits as described in Section 7.2. Therefore, the total required space is \((3m+o(m))\log n\) bits. These results can be summarized in the following theorems.  
Theorem 4: _There exists an algorithm such that given an undirected graph \(G\) and its DFS tree \(T\), for any on-line sequence of edge insertions on \(G\), a new DFS tree after each insertion can be built in worst-case \(O(n\log n)\) time per update, with \(O(m\sqrt{\log n})\) preprocessing time. This algorithm requires only \((3m+o(m))\log n\) bits once data structures for the original graph are built._  
Theorem 5: _There exists an algorithm such that given an undirected graph \(G\) and its DFS tree \(T\), for any on-line sequence of graph updates on \(G\), a new DFS tree after each update can be built in worst-case \(O(\sqrt{mn}\log^{1.25}n)\) time per update under fully dynamic setting, with \(O(m\sqrt{\log n})\) preprocessing time. This algorithm requires only \((3m+o(m))\log n\) bits under \(m=\omega(n\log^{0.5}n)\) once data structures for the original graph are built._
# Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm
## 5 Method and Simulation  
### Method Summary  
First, the original point cloud data are simplified according to the method described in Section 2. During the iteration, whenever the ants in the ant colony algorithm complete a tour, the generated path must be evaluated. First, the path points are converted into the joint angles of each joint according to the inverse kinematics results. Then, the minimum value of the optimisation objective function is calculated.  
In Section 3, the optimisation objective was converted into a function of the running time of the manipulator. When the number of points is relatively small, the minimum analytical solution can be obtained easily. However, when the number of points is large, this process becomes extremely challenging.  
Therefore, the greedy principle is used to solve the problem, based on the principle that when moving from one point to the next, only the solution that minimises the optimisation objective function between these two points is calculated.  
On this basis, Figure 5 illustrates the overall algorithm flowchart.  
The proposed method was compared with the previous methods to demonstrate the proposed method's superiority. The proposed method differs from previous methods in the following two aspects:  
(1) The proposed method uses a point cloud simplification method based on a continuous grid partition. Laser processing is characterised by a variable laser spot size. The grid sizes are computed using a recursive method, which can work well with this characteristic. Meanwhile, conventional methods use point cloud slicing technology to obtain regular routes, which means the distance between path points is constant. The proposed method has higher efficiency because a larger spot size can improve efficiency at a position with a small curvature change. (2) The conventional methods' core idea is to use regular curves or broken lines to traverse the fixed area without considering many optimisation conditions. The proposed method uses an improved ant colony algorithm for path planning so that different optimisation conditions can be considered and better solutions can be obtained than traditional methods.
## 4 Inference Rules for Matching Geographic Ontologies in Different Languages
### Conceptual Framework
#### 4.1.9 Geographic Knowledge Base  
To sum up what was previously said, a geographic knowledge base, \(GKB\), will be defined as follows:  
\[\begin{split} GKB=(\textit{Terr},\,\lambda,\,\Omega,\,OG,\, \Gamma,\,R)\\ \text{with}\,\,OG=\{Og_{1},\,Og_{2},\,...\,\,Og_{m}\text{: }m\in \text{N}\}\end{split}\]  
in which _Terr_ defines a territory, such as _Terr INSIDE Earth_, \(O_{G}\) is a set of geographic objects (see Section 4.1.2), a \(\Gamma\) gazetteer, \(\Omega\) an ontology and \(R\) a set of relationships between geographic objects. Remember that when considering _Terr_ as the whole Earth, several Egenhofer planar relations do not hold anymore due to its rotundity [29]. In this paper, a strong assumption is that a gazetteer and an ontology are designed in only one language (\(\lambda\)). The gazetteer will consist of two things, a list of toponyms and a list \(R_{\Gamma}\) of relationships between them:  
\[\begin{split}\Gamma=(\lambda,\,To,\,R_{\Gamma})\\ To=\{Toponym_{1},\,Toponym_{2},\,Toponym_{3},\,....\,\,Toponym_{i }\text{: }t\in N\}\end{split}\]  
in which \(\lambda\) is a language, \(Toponym_{1}\) and \(Toponym_{2}\) are \(Toponyms\) and \(R_{\Gamma}\) a set of relationships among toponyms:  
Concerning the ontology, it has a similar structure:  
\[\Omega=(\lambda,\,Ty,\,R_{\Omega})\]  
\[Ty=\{Type_{1},\,Type_{2},\,Types,\,....\,\,Types\text{: }s\in N\}\]in which _Type\({}_{1}\)_ and _Type\({}_{2}\)_ are _Types_ and \(R_{\Omega}\) a set of relationships among types, including the following relations, more precisely in the \(\lambda\) language:  
* the three classical ontological relations, _is_\(a\), _i.e._, the \"is-a\" relation, _has_\(a\)_, _i.e._, the \"has-a\" relation and _part_of_, _i.e._, the \"part-of\" relation;
* the eight Egenhofer topological relations (see Figure 2) are, respectively, _Disjoint_, _Contains_, _Inside_, _Overlaps_, _Touches_, _Equals_, _Covers_ and _CoveredBy_;
* and possibly other additional relations.
## 4 Simulation and Analysis  
To evaluate the performance of our proposed algorithm, the three commonly used multi-\(h\) quaternary CPM schemes with \(h=\{\frac{4}{16},\frac{5}{16}\}\), \(h=\{\frac{5}{16},\frac{6}{16}\}\), and \(h=\{\frac{9}{16},\frac{10}{16}\}\) are considered in the following tests. We first analyze the spectrum of the mentioned three CPM schemes. Then, we compare the mean square error (MSE) and BER performances for the CPM with \(h=\{\frac{4}{16},\frac{5}{16}\}\) between the proposed LPA-based method and the conventional EL-based method through numerical simulation, denoted as the floating-point simulation. The specifications of the simulation model are based on the implementation requirements as follows:  
* Quaternary 3 RC CPM, \(h=\{\frac{4}{16},\frac{5}{16}\}\);
* IF: 70 MHz;
* Bit rate: 50 Mbps;
* Samples per symbol: 8;
* Carrier offsets: 125 kHz;
* Timing error: 100 ppm.  
After performing a floating-point simulation, the receivers with the proposed synchronization algorithm for the three multi-\(h\) CPM schemes are implemented on a target platform equipped with a Xilinx FPGA Kintex-7 xc7k325 ffg900-2. The design is synthesized by the Xilinx synthesis tool (XST). The implementation details are discussed in Section 3. The tested results are presented as the fixed-point simulation.
## 2 Circuit Design and Performance
\begin{array}{cc}\frac{1}{OP_{\text{LNA}}G_{ \text{MIX}}G_{\text{AMPL}}G_{\text{FILT}}G_{\text{MIX}}G_{\text{FILT}}}+\frac{ 1}{OP_{\text{MIX}}G_{\text{AMPL}}G_{\text{FILT}}G_{\text{MIX}}G_{\text{FILT}}}+ \\ +\frac{1}{OP_{\text{AMPL}}G_{\text{FILT}}G_{\text{MIX}}G_{\text{FILT}}}+\frac{ 1}{OP_{\text{FILT}}G_{\text{MIX}}G_{\text{FILT}}}+\frac{ 1}{OP_{\text{MIX}}G_{\text{FILT}}}+\frac{ 1}{OP_{\text{FILT}}2}\end{array} \right)^{-1} \tag{7}\]  
According to Equations (5) and (6), the computed values of SFDR and LDR are 40.2 dB and 52.6 dB, respectively. A two-tone analysis was performed to graphically evaluate the intermodulation distortion (IMD) contributions vs. frequency and the \(OIP_{3\,\,\text{c}ss}\), as reported in Figure 7.  
Although both the topics of satellite/nanosatellite technologies and electronic warfare systems separately achieved resounding interest in the literature, the possibility to integrate EW systems in CubeSats has not been investigated in depth. Moreover, a very limited effort has been devoted to the development of advanced down converter sections for this specific application and such a wide operating bandwidth. Filling the gap in research on very high operating bandwidth receivers in the literature represents one of the main purposes of the present article. As a result of a scientific literature investigation, the system performance was compared with recent similar results, as reported in Table 2. From Table 2, it is possible to note the superior performance of the proposed system in terms of noise figure, linear dynamic range and conversion gain, whereas in [30; 31], the premium performance in terms of spurious free dynamic range was reported. It is worth noting that previous works do not always share the same metric, due to different technologies and operating frequencies, thus making an accurate comparison quite challenging. The system exhibits premium performance in terms of NF, LDR and conversion gain, whereas the next effort should be directed towards improving the SFDR. Figure 7: (a) IMD vs. frequency and (b) \(OIP_{3\,\,\text{c}ss}\).
## 1 Introduction  
By the end of 2016, the world solar photovoltaic (PV) installed capacity was around 78 GW, which is more than double compared with 2014 and 32 times more than in 2000 [1]. Because of this growth in the photovoltaic market, the cumulative global PV waste reached 250,000 tonnes by the end of 2016, while predictions show that, by 2050, the amount will increase to 5.5-6 million tonnes per year [2]. The increasing waste from PV panels is an environmental obstacle to be overcome, but it also opens a range of opportunities to create processes that can transform this discarded material into an economic and ecological solution. For this to happen, there must be adequate end-of-life (EoL) management technologies and policies for PV systems and, particularly, PV modules [2].  
It is well known that today most of the PV modules go to landfill sites [2], because PV recycling processes are not economically feasible yet and regulation in most countries has not been established for this waste stream [2]. The late or non-inclusion of PV waste within countries' waste legislation is usually related to the thus-far low quantities of EoL PV modules, due to their long lifetime (up to 25-30 years or more) [3]. Regarding the financial aspects, the number of waste PV modules being taken to recycling facilities is currently small compared to the amount of other electronic wastes [4], thus the technology developed for recycling PV waste is still unable to generate significant profits. Furthermore, the lack of a substantial body of knowledge on the potential environmental and economic impacts of c-Si PV waste treatments has not encouraged policymakers to get involved with this issue yet.  
The majority of Life cycle assessment (LCA) studies of solar modules focus on the production and operation phases, with an emphasis on the energy requirements of these processes, which have been shown to have an important contribution to environmental impacts [5]. Lately, there has been increasing interest and research on the impact of recycling processes of PV modules. Most of the studies in c-Si have focused on the impacts of a specific recycling process [6; 7], while some of them compare recycling process with other scenarios such as landfill [8] and incineration [9]. This report compares the environmental impacts of landfill, incineration, reuse and three potential recycling options (mechanical, thermal and chemical routes) of EoL c-Si solar modules, considering results already described in the literature. Therefore, this paper aims to present an overview of options for PV waste management based on the environmental benefits or disadvantages produced by each EoL possibility. The results presented here can inform on the best route waste management to be taken from an ecological perspective, including the possible recovery of materials for their reuse during the initial steps of solar cells and modules production.