esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 3 Improved D* Lite
### Adjacent Node Expansion and Steering Angle Limitation
#### 3.2.1 Adjacent Node Expansion  
From Figure 1, we find that there are eight directions for the search if we plan a path based on eight adjacent nodes. When the number of adjacent nodes is expanded to 24 and 48, respectively, the search directions are also increased. This means that USVs may have more steering choices when planning a path. The relationship between the number of adjacent nodes, minimum steering angle and search directions is shown in Table 1.  
Figure 1: Adjacent nodes for search.  
We find from Table 1 that when we increase the number of adjacent nodes from 8 to 24, the search directions are increased from 8 to 16, and the minimum steering angle is significantly decreased from 45\({}^{\circ}\) to 22.5\({}^{\circ}\). This means that the search time is doubled while the minimum steering angle is reduced by two times. When the number of adjacent nodes is increased from 24 to 48, the minimum steering angle is decreased from 22.5 degrees to 11.25 degrees.
## 5 International Roadmap to Mandate Connectivity on Board
### When to Mandate Connected Vehicles in the United States  
On 13 December 2016, in Washington DC, the NHTSA, announced the proposal of the introduction of rules to mandate V2V communication on light vehicles, allowing cars to 'talk' to each other with the aim to avoid crashes. The announcement was then formalized in [46], an action called \"Notice of Proposed Rulemaking (NPRM)\" dated 12 January 2017 in the US Federal Registry, where the NHTSA proposes the establishment of a new Federal Motor Vehicle Safety Standard (FMVSS), to mandate V2V communications for new light vehicles and to standardize the message and format of V2V transmissions. The action put the US on a path as the first in the world to mandate V2V communication on all new light vehicles. In [46], we can read: _without a mandate to require and standardize V2V communications, the NHTSA agency believes that manufacturers will not be able to move forward in an efficient way and that a critical mass of equipped vehicles would take many years to develop, if ever. Implementation of the new standard will enable vehicle manufacturers to develop safety applications that employ V2V communications as an input, two of which are estimated to prevent hundreds of thousands of crashes and prevent over one thousand fatalities annually._ Comments on the action had to be received on or before 12 April 2017 and more than 460 comments were sent, demonstrating high interest in the issue.  
Following this action, it is possible to find a press article dated 1 November 2017, discussing the fact that the White House and the Department of Transportation (DOT) decided not to pursue a final V2V mandate, but the White House declined to comment [47]. However, no official documents have ever been published about this. It is instead official that the V2V Statement Attributable to the DOT, on 8 November 2017, Washington DC, declared that _the DOT and NHTSA have not made any final decision on the proposed rulemaking concerning a V2V mandate. Any reports to the contrary are mistaken and the rule remains regarding DOT's significant rulemaking report [48]_. At the best of our knowledge, this is the last official notice from the US. More recent discussions, dated 28 February 2018, appear in newspapers only and confirm that the proposed federal rule under review by NHTSA has encountered delays, but, if approved in 2019, it would require new vehicles sold in 2023 in the US to have V2V connectivity [49].
# Structural Modal Time Domain Identification Method Based on the Bayesian Uncertain Quantification
## 3 Bayesian Modal Identification Based on Modal State Space Prediction Models
### Decoupling Method for Modal Identification
The MPV of \(\{f_{i},\zeta_{i}\}\) can be obtained by searching for \(\{f_{i},\zeta_{i}\}\), which minimizes the objective function \(f(\{f_{i},\zeta_{i}\})\) through the optimization algorithm, and then \(\mathbf{\Phi}^{*}\) is calculated by Equation (35). The vector \(\mathbf{\psi}_{i}^{*}\) can be extracted from the vector \(\mathbf{\Phi}^{*}\), and the analytical solution of \(\gamma_{i}\) is as follows:  
\[\gamma_{i}^{*}=\parallel\mathbf{\psi}_{i}^{*}\parallel_{2} \tag{37}\]  
The MPV of the mode shape \(\mathbf{\varphi}_{i}\) can be given by the following:  
\[\mathbf{\varphi}_{i}^{*}=\frac{\mathbf{\psi}_{i}^{*}}{\gamma_{i}^{*}} \tag{38}\]  
Since the mode shape \(\mathbf{\varphi}_{i}^{*}\) is a vector subjected to norm constraint, it is common to express the accuracy of the mode shape identification results by using the'modal assurance criterion' (MAC), which is defined as the dot product between two mode shape vectors:  
\[\text{MAC between}\mathbf{\varphi}_{i}^{*}\text{and}\tilde{\mathbf{\varphi}}_{i}= \frac{\big{(}\mathbf{\varphi}_{i}^{*}\big{)}^{\mathrm{T}}\hat{\mathbf{\varphi}}_{i}}{ \big{\|}\mathbf{\varphi}_{i}^{*}\big{\|}_{2}\big{\|}\mathbf{\phi}_{i}\big{\|}_{2}} \tag{39}\]  
where \(\hat{\mathbf{\varphi}}_{i}\) is a theoretical result. The closer the value of MAC is to 1, the closer the identified mode shape is to the theoretical result.
# AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving
## 5 Conclusions  
In this paper, an anchor-free-based object detection method in autonomous driving was proposed. The main contribution is that an aspect-aware prediction head is added at the end of the detector. The accuracy of the detection on BCTSDB can be improved by adding aspect-aware prediction head in RetinaNet and FCOS. The improved sample definition method was used to alleviate the problem of sample imbalance; the comparison results on BCTSDB datasets show that the detection accuracy can be improved by replacing the original sample definition method in YOLO V3, FCOS and Retinanet models. The proposed loss function was added to strengthen the learning weight of the center point, and the validation experiment shows that this can improve the detection accuracy. An overall validation on public datasets demonstrated that the proposed method can achieve a significant improvement in detection accuracy. The AP50 and AP75 of the proposed method are 97.3% and 93.4% on BCTSDB, and the average accuracies of car, pedestrian, and cyclist are 92.7%, 77.4% and 78.2% on KITTI, respectively, which indicates that the proposed method achieves better results compared to other methods. The proposed method improved detection accuracy, but it still encountered many challenges when applied to real traffic scenarios. In autonomous driving scenarios, higher accuracy is required, and this can be further improved in future work. The experiment in this paper is trained on public datasets and real traffic scenes facing challenging with complex lighting and weather factors. This issue can be addressed by considering Transformers and domain adaptation in future work.  
Conceptualization, T.L. and W.P; methodology, H.B.; software, T.L. and W.P.; validation, X.F. and H.L; writing--original draft preparation, T.L.; writing--review and editing, T.L. and W.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Nos. 61802019, 61932012, 61871039) and the Beijing Municipal Education Commission Science and Technology Program (Nos. KM201911417003, KM201911417009, KM201911417001). Beijing Union University Research and Innovation Projects for Postgraduates (No. YZ2020K001). By the Premium Funding Project for Academic Human Resources Development in Beijing Union University under Grant BPHR2020 ZD20.  
Figure 11: Detection results for the BCTSDB dataset.
## 1 Introduction
[22] proposed a rotor model considering radial and tangential unbalanced magnetic pull forces; [PERSON] et al. [23] studied the effect of UMP on radial vibration of hydroelectric generators, and compared and analyzed the simulation results and the field experimental data; [PERSON] et al. [24] established the hydro turbine generating sets dynamics model by using the unbalanced magnetic pull force acting on the generator and the unbalanced force acting on the rotor as the input excitation of the hydro turbine generating sets system; [PERSON] et al. [25] established a hydro-generator dynamics model considering the nonlinear unbalanced magnetic pull force and the linear water unbalance force; [PERSON] et al. [26] studied the response characteristics of unbalanced magnetic pull rotor system considering both static and dynamic eccentricity. [PERSON] et al. [27] analyzed the influence of unbalanced magnetic pull on the nonlinear dynamic characteristics of permanent magnet synchronous motors rotor system; [PERSON] et al. [28] established a two-dimensional hydroelectric generator electromagnetic model, using the finite element method to study the characteristics of the electromagnetic flux and electromagnetic force; [PERSON] et al. [29] analyzed the influence of excitation current on the nonlinear dynamic behaviors of the generator. It should be noted that in all of the above research, most of them only consider one nonlinearity factor. In fact, in the vibration faults of the generator set, the multiple vibration sources are not isolated from each other, but the vibrations caused by different nonlinear factors interact with each other. Therefore, the generator set needs to be studied as a highly nonlinear coupled dynamic system. In this paper, the complex nonlinear dynamic behavior of coupled systems is studied; the influences of the excitation current, radial stiffness, and friction coefficient on the dynamic characteristics was analyzed by numerical simulation, and discussed in detail through Bifurcation diagram, 3-D frequency spectrum, waveform, frequency, axis orbit, and Poincare map. This article consists of four parts. In Section 2, the dynamic equation of a rotor-bearing-runner system (RBRS) is developed, meanwhile, the nonlinearity of electromagnetic force of the rotor and the nonlinearity of rubbing force of the combination bearing are considered; in Section 3, the dynamic behavior of RBRS is analyzed, and the influence of different system parameters on nonlinear systems is investigated. discussed through Bifurcation diagram, 3-D frequency spectrum, etc. Finally, some conclusions are drawn in Section 4.
## 4 Performance and Discussion
### Frequency Response
After that, the bottom graphene sheet grown can be wet-transferred to the hybrid ridge waveguide. The graphene-covered region can be defined by EBL processing to remove the excess graphene. Whereas, the graphene on the other side extends 500 nm to form an electrode contact. A 15 nm-thick SiO\({}_{2}\) isolation layer is deposited on the bottom graphene flake. The second graphene flake is transferred onto the SiO\({}_{2}\) isolation layer and handled with the same process applied on the bottom graphene layer, constructing a complete hybrid waveguide structure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Bandwidth (nm)** & **Re(\(\Delta\)N\({}_{\rm eff}\))** & **MD (dB/\(\upmu\)m)** & **\(\Delta\)MD (dB/\(\upmu\)m)** & **\(f_{\rm 3 dB}\)(GHz)** & **\(E_{\rm bit}\)(f/J/bit)** \\ \hline
[39] & 1500–1600 & - & 0.06 & 0 & 13.4 & - \\ \hline
[40] & 1500–1600 & - & 0.08 & 0 & 80 & - \\ \hline
[41] & 1200–1600 & - & mode A: 1.05, mode B: 1.13, mode C: 0.52 & Min: 0.08 & 95 & 138.8 \\ \hline
[42] & 1530–1565 & \(4.7\times 10^{-3}\) &
\begin{tabular}{c} TM mode: \(\sim\)0.2975, \\ TE mode: \(\sim\)0.2895 \\ \end{tabular} & \(\sim\)\(8\times 10^{-3}\) & 30.2 & 2980 \\ \hline
[43] & 1367–1771 & \(\sim\)0.5 &
\begin{tabular}{c} TM mode: 1.113, \\ TE mode: 1.119 \\ \end{tabular} & \(\sim\)\(6\times 10^{-3}\) & 6.1 & 7800 \\ \hline
[44] & 1450–1650 & \(\sim\)0.1 &
\begin{tabular}{c} TM mode: 1.392, \\ TE mode: 1.347 \\ \end{tabular} & 0.045 & \(\sim\)100 & - \\ \hline
[45] & 1300–1800 & \(1.2\times 10^{-3}\) & - & - & 135.6 & - \\ \hline This work & 1100–1900 & \(6\times 10^{-4}\) &
\begin{tabular}{c} TM mode: 0.474, \\ complex mode: 0.462 \\ \end{tabular} & 0.012 & 101 & 271 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison with reported polarization-insensitive graphene modulators at \(\lambda=1.55~{}\upmu\)m.  
Considering the structural complexity of the proposed DBOSW, the influence of manufacturing errors on the modulator is also evaluated. Firstly, the influence of inequality of the top Ag strip \(\delta\) (the asymmetry of DBOSW) on the modulator performance is investigated, as shown in Figure 14. The geometric parameters used here are the optimal values discussed in above sections. It can be seen that the DBOSW modulator exhibits stable polarization insensitivity in the fluctuation of one side w from 135 to 165 nm (\(-\)15 nm \(>\delta>\) 15 nm). The maximum Re(\(\Delta N_{\text{eff}}\)) at \(\delta\) = 15 nm is 4 \(\times\) 10\({}^{-3}\), which is still lower than the optimal value in Ref. [40]. \(\Delta\)MD remains at a low level, which is still competitive compared with reported works. The undulation of \(\Delta\)MD relates to the difference between the mutative MD of the TM mode and the relatively constant MD of the complex mode, as shown in Figure 14c. PLs exhibit good stability. As discussed in Section 3, though the proposed design has a relatively large fabrication tolerance, \(g\) needs to be treated carefully. A significant negative effect on Re(N\({}_{\text{eff}}\)) may damage the modulation performance at small \(g\).
# Content and Privacy Protection in JPEG Images by Reversible Visual Transformation
## 4 Experimental Results
### Security Analysis  
The security of the proposed algorithm was analyzed by considering a brute force attack [22]. We conducted an experiment on one hundred JPEG images collected from the Images of Groups Dataset [25]. The block number of the protected regions in each JPEG image was calculated and is visualized in Figure 7. It can be seen that the block number ranged from 25 to 1600, while most of them were concentrated between 25 and 400. Besides, the experiments in [13] show that there are 26 nonzero AC coefficients in a block on average. Suppose that the attacker knows the location of the protected regions and the algorithm adopted for visual transformation. An exhaustive search of all the possible combinations of the permuted blocks and the flipped AC coefficients might be conducted. Given that there are at least 25 blocks in a protected region and 26 nonzero AC coefficients in a block, the number of combinations to obtain the permuted blocks is 25!, while there are \(2^{26\times 25}\) combinations to recover the flipped AC coefficients. Thus, the number of combinations to recover the original image is \(25!\times 2^{26\times 25}\), which is close to \(2^{734}\). It can be concluded that the proposed algorithm can resist brute force attack.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Compared** & **JPEG File Size Increment (\%)** \\ \hline Proposed algorithm & 0.39 \\ [PERSON]’s algorithm [6] & 10.80 \\ [PERSON]’s algorithm [7] & 0.51 \\ P3 [11] & \(7.5\pm 2.5\) \\ Scrambling JPEG [12] & 2.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons between five algorithms for content protection in JPEG images.  
Figure 6: JPEG images obtained from the visually transformed ones.
## 4 Cluster-Based Distributed Compressed Sensing for QoS Routing
### Collaborative Node Selection with SCS  
The number of measurements \(m\) required in DCS is preset according to the signal sparsity which is often unknown or even time-varying. If a preset signal sparsity is higher than the actual situation, a redundant measurement sequence is generated. The redundant measurement sequence not only does not further improve CS performance but also results in additional compression measurements and transmission data redundancy. Conversely, the preset signal sparsity may be too low with less measurement sequence, making the detection accuracy requirement challenging to meet. With the support of the SCS technique [32] unaware of signal sparsity, we can determine the number of measurements required to satisfy the reconstruction error, and choose suitable nodes to form clusters to carry out DCS. By minimizing redundant information, such a technique not only saves energy but also helps provide QoS in routing.  
Benefiting from SCS, we can decide whether enough samples have been obtained, with which reconstruction error can be limited to a specified threshold. Assume the sparsity of signal \(x\) is \(k\), SCS first obtains an initial measurement vector \(y^{m}=(y_{1},\cdots,y_{m})^{\prime}\in R^{m}\) based on experience, and then accept \(G\) additional measurements. The distance between the current reconstruction \(x^{m}\) of \(m\) measurements and the affine space \(H_{m+G}\) determined by \(m+G\) measurements is  
\[d(x^{m},H_{m+G})=(\Phi^{m+G})^{+}(\Phi^{m+G}x^{m}-y^{m+G}), \tag{16}\]  
where \(\Phi^{m+G}\in R^{(m+G)\times k}\) represents the measurement matrix, and \((\Phi^{m+G})^{+}\) is a pseudo inverse matrix of \(\Phi^{m+G}\). The reconstruction error of \(m\) measurements with probability at least \(1-\frac{1}{a^{2}}\) is as follows:  
\[||x-x^{m}||_{2}\leq C_{G}^{a}d(x^{m},H_{m+G}). \tag{17}\]  
In Equation (17), the value of \(C_{G}^{a}\) is computed by  
\[\sqrt{\frac{Q-2}{G-2}}+a\sqrt{\frac{Q-2}{G-2}-\frac{Q}{G}},\]  
where \(Q=k-m\); \(C_{G}^{a}d(x^{m},H_{m+G})\) is the estimated value of the reconstruction error. In the following, we stop taking new measurements once the estimated value of reconstruction error in (17) falls below a desired threshold. Otherwise, taking \(G\) as the step and increasing the number of measurements sequentially until the reconstruction error is under a specified reconstruction threshold, we get measurement vector \(y^{m+sG}\), where \(s=0,1,\ldots,S\).  
After \(m\) collaborative nodes are selected, a cluster head compresses their observed images jointly and sends compressed data to sink hop by hop.
## 6 Deficiencies and Prospects  
### Deficiencies  
Firstly, although this study constructed a time series of vegetation growth anomaly data using the total primary productivity of vegetation to represent ecological drought, the factors considered in this construction method are relatively few, and its applicability to other regions is still limited. Secondly, although the run theory can effectively identify drought events on the time scale, there are certain limitations in identifying the same drought event in space. Finally, this study only focused on a typical area in the semi-humid and semi-arid ecotone between agriculture and pastoralism, and the research results have their own particularity.
## 4 Wireless Communication Protocol
### Flow Control and Deadlock Free Routing
For multicast packets, the header maintains a list of all destinations of the packet. If the routing paths for the possible destinations diverge at an intermediate switch, duplicate packets are generated and routed towards the divergent branches of the routing tree. The list of destinations is also split between the packets to prevent subsequent repeated splitting for same destinations. Similar to the multicast messages an explicit list of final destinations can be maintained in the broadcast packet header. This will be split when the packet is split towards divergent destinations according to the adopted tree-based routing policy. This approach will require a large amount of destination information in the header. However, an alternative broadcast messaging protocol may be adopted where an explicit list of all destinations is not required, reducing the control overhead. In this case, the broadcast messages are not forwarded from a particular switch if it has already been received earlier. However, this condition of terminating further downstream flow of broadcast messages will cause a higher volume of packets compared to the earlier case. The same tree-based routing strategy used for multicast/broadcast messages, can be used for unicast messages as well, as it routes messages along shortest paths. In Figure 13 we show the increase in system bandwidth and reduction in packet energy when wireless interconnects are used for a system with 4 chips, each having 16 cores, with increasing percentage of broadcast messages. Each chip is considered to be equipped with one mm-wave WI. The low-latency transmission of broadcast messages over the wireless links compared to the multi-hop wired paths results in the improvement of the bandwidth. The presence of long-distance wireless links over a shared wireless channel will ensure fast delivery of broadcast messages over the multi-chip system thereby dissipating less energy due to the faster data transfer for both broadcast and unicast messages.
## 4 Challenges to Developing a Remote Sensing Based EHA System
### Uncertainties in Ecosystem Health Indicators  
Uncertainties in the estimated ecosystem health indicators are one of the most important factors needed to be taken into account while developing an EHA system. There are still potential uncertainties on the estimation of indicators even if the most appropriate remotely sensed data are used.
# Storing Set Families More Compactly with Top ZDDs
## 4 Top ZDD
### Details of the Data Structure
#### 4.1.1 The Data Structure for the Structure of Top DAG \(\mathcal{T}D\)  
We store top DAG \(\mathcal{T}D\) after converting it to a tree. We make tree \(T^{\prime}\) by adding dummy vertices to \(\mathcal{T}D\). For each vertex \(x\) of \(\mathcal{T}D\) whose in-degree is two or more, we do the following.  
1. Let \(a_{1},\cdots,a_{t}\) be the vertices of \(\mathcal{T}D\) from which there are edges towards \(x\). Note that there may exist identical vertices among them corresponding to different edges. We create \(t-1\) dummy vertices \(d_{1},\cdots,d_{t-1}\).
2. For each \(1\leq i\leq t-1\), remove edge \((a_{i},x)\) and add edge \((a_{i},d_{i})\).
3. For each dummy vertex \(d_{i}\), we store information about a pointer to \(x\). In our implementation, we store the preorder id of \(x\) in \(T^{\prime}\) from which the dummy vertices are removed.  
Then we can represent the structure of the top DAG by the tree \(T^{\prime}\) and the pointers from the dummy vertices.  
Next, we explain how to store \(T^{\prime}\) and the information about the dummy vertices. The structure of \(T^{\prime}\) is represented by the BP sequence [14]. There are two types of leaves in \(T^{\prime}\): those which exist in the original top DAG, and those for the dummy vertices. To distinguish them, we use a bit vector. Let \(m\) be the number of leaves in \(T^{\prime}\). We create a bit vector \(B_{ dummy}\) of length \(m\) whose \(i\)-th bit corresponds to the \(i\)-th leaf of \(T^{\prime}\) in preorder. We set \(B_{ dummy}[i]=1\) if the \(i\)-th leaf is a dummy vertex, and we set \(B_{ dummy}[i]=0\) otherwise.  
We add additional information to dummy vertices to support efficient queries. We define an array _clsize_ of length \(D\) where \(D\) is the number of dummy vertices. For the \(i\)-th dummy vertex in preorder, let \(s_{i}\) be the vertex pointed to by the dummy vertex. We define \(\textit{clsize}[k]=\sum_{i=1}^{k}(\text{the number of vertices in the cluster represented by }s_{i})\). That is, \(\textit{clsize}[k]\) stores the cumulative sum of cluster sizes up to \(k\). This array is used to compute the cluster size for each vertex efficiently.