esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Algorithm Design  
The multi-satellite multi-task aggregation problem is a combinatorial optimization problem under multiple constraints, and the solution process is complex and computationally intensive. In this paper, we first construct a Multi-satellite and Multi-task Aggregation Graph(MSMTAG) based on the task aggregation condition better to describe the task-to-task aggregation relationship under multi-satellite resources and design a task aggregation based on dynamic merge priority (DMP-TA) to solve the task aggregation problem based on the MSMTAG model.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 2 Preliminaries
### Discrete Gaussian Distribution  
**Definition 3**.: _(Gaussian-Shaped Function [27]) For any real number \(\sigma>0\), any vector \(\mathbf{c}\in\mathbb{R}^{n}\), and the standard deviation \(\sigma\), where \(\forall\mathbf{x}\in\mathbb{R}^{n}\). Gaussian-shaped function is defined as_  
\[\rho_{\sigma,\mathbf{c}}(\mathbf{x})=\exp\left(\frac{-\pi\parallel\mathbf{x}-\mathbf{c} \parallel^{2}}{\sigma^{2}}\right)\]  
**Definition 4**.: _(Discrete Gaussian Distribution [27]) Let lattice \(\Lambda\in\mathbb{R}^{n\times m}\), for any real number \(\sigma>0\), any vector \(\mathbf{c}\in\mathbb{R}^{n}\), the standard deviation \(\sigma\), where \(\forall\mathbf{x}\in\Lambda\). The discrete Gaussian distribution with distribution center \(\mathbf{c}\) is defined as_  
\[\mathcal{D}_{\Lambda,\sigma,\mathbf{c}}(\mathbf{x})=\frac{\rho_{\sigma,\mathbf{c}}(\mathbf{x} )}{\rho_{\sigma,\mathbf{c}}(\Lambda)}=\frac{\rho_{\sigma,\mathbf{c}}(\mathbf{x})}{\sum_{ \mathbf{v}\in\Lambda}\rho_{\sigma,\mathbf{c}}(\mathbf{v})}\]  
For convenience, we abbreviate \(\rho_{\sigma,0}\) and \(\mathcal{D}_{\Lambda,\sigma,0}\) as \(\rho_{\sigma}\) and \(\mathcal{D}_{\Lambda,\sigma}\). When \(\sigma=0\), we use \(\rho\) to express \(\rho_{1}\). Distribution \(\mathcal{D}_{\Lambda,\sigma,\mathbf{c}}\) is usually defined over the lattice \(\Lambda=\Lambda_{q}^{\perp}(\mathbf{A})\) for a matrix \(\mathbf{A}\in\mathbb{Z}_{q}^{n\times m}\) or over a coset \(\Lambda=\mathbf{t}+\Lambda_{q}^{\perp}(\mathbf{A})\), where \(\mathbf{t}\in\mathbb{Z}^{m}\).
## 4 Conclusions  
A series of flutter tests of spring-suspended sectional models with different leading edges and trailing edges were conducted to explore the influence of aerodynamic configurations on flutter behaviors. In the test, the classical flutter and postflutter LCOs were observed for different aerodynamic configurations. Based on the dataset of self-excited forces and flutter responses, the characteristics of coupling vibration and aerodynamic hysteresis of the two kinds of flutter were analyzed and compared. The following conclusions were obtained:  
The aerodynamic configuration of leading edges played a more important role for flutter behaviors than it did for trailing edges. For a sectional model with an aspect ratio of 1:10 and a rectangular trailing edge, a postflutter LCO occurred when the vertex angle of the leading edge was more than 105\({}^{\circ}\). Nevertheless, the flutter behavior kept a postflutter LCO whenever the aerodynamic configuration of the trailing edge was a leading edge rectangular shape.  
The coupling vibrations were observed for both the classical flutter and postflutter LCOs. Compared with the classical flutter, the participation level of the torsional degree of freedom was higher in the postflutter LCO than for the classical flutter. The vertical degree of freedom played a more important role in both kinds of flutter, as the inflow velocity increased during the flutter process.  
For the two kinds of flutter, the work was done by the first-order harmonic component of self-excited forces, which was the most dominant part of the energy input in the dynamic system. The work done by the higher-order harmonic components of the self-excited forces can be ignored. When the classical flutter occurs, the cumulative work done by the vertical self-excited force was much bigger than that of the torsional self-excited force. By contrast, the torsional degree of freedom was the main effect of the postflutter LCO.  
The phase difference between the displacements and aerodynamic forces had a significant influence on nonlinear flutter behaviors. Different from the classical flutter, there was negative feedback for the postflutter LCO. As the vibration amplitude increased, the phase difference decreased gradually, and the energy input to the dynamic system did not grow rapidly, which limited further vibration divergence and resulted in a stable LCO.  
Conceptualization, W.L. and S.L; methodology, W.L. and S.L; software, W.L.; validation, W.L.; formal analysis, W.L.; investigation, W.L. and S.L.; resources, S.L.; data curation, W.L.; writing--original draft, W.L.; writing--review and editing, S.L.; visualization, W.L.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. Both authors have read and agreed to the published version of the manuscript. This study was financially supported by NSFC under grant No. 51878230, and supported by the Fundamental Research Funds for the Central Universities. The authors declare no conflict of interest.
###### Abstract  
Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.  
Keywords:Industry 4.0; industrial internet of things; wireless sensor networks; manufacturing process; automation; 3D ray launching; wireless channel +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Theory and Methodology
### Sparse Adaptive Leja Interpolation
#### Adaptive Anisotropic Leja Interpolation
We define the set of admissible multi-indices, i.e., those that satisfy the downward-closedness property of Equation (12) if added to \(\Lambda\), as:  
\[\Lambda^{\text{adm}}=\left\{\mathbf{i}:\mathbf{i}\
ot\in\Lambda\text{ and } \Lambda\cup\left\{\mathbf{i}\right\}\text{ is downward-closed}\right\}.\]  
Due to the fact that all univariate Leja rules employ the level-to-nodes function \(m(i)=i+1\), each multi-index \(\mathbf{i}\in\Lambda^{\text{adm}}\) is associated with a single new interpolation node \(\mathbf{y}_{\mathbf{i}}=Z_{\mathbf{i}}\setminus Z_{\Lambda}\)Therefore, should an admissible multi-index \(\mathbf{i}\in\Lambda^{\mathrm{adm}}\) be added to \(\Lambda\), we obtain the hierarchical interpolation scheme:  
\[\mathcal{I}_{\Lambda\cup\{\mathbf{i}\}}\left[g\right]\left(\mathbf{y}\right)= \mathcal{I}_{\Lambda}\left[g\right]\left(\mathbf{y}\right)+s_{\mathbf{i}}L_{ \mathbf{i}}\left(\mathbf{y}\right),\]  
where, similarly to Equation (7), the hierarchical surpluses \(s_{\mathbf{i}}\) are given by:  
\[s_{\mathbf{i}}=g\left(\mathbf{y}_{\mathbf{i}}\right)-\mathcal{I}_{\Lambda} \left[g\right]\left(\mathbf{y}_{\mathbf{i}}\right),\quad\mathbf{i}\in\Lambda^ {\mathrm{adm}},\]  
and \(L_{\mathbf{i}}\) are multivariate Lagrange polynomials given by:  
\[L_{\mathbf{i}}(\mathbf{y})=\prod_{n=1}^{N}l_{n,i_{n}}(y_{n}),\]  
where, due to the use of nested Leja sequences with the level-to-nodes function \(m(i)=i+1\), the univariate Lagrange polynomials can now be defined hierarchically [16], such that,  
\[l_{n,i_{n}}\left(y_{n}\right)=\prod_{k=0}^{i_{n}-1}\frac{y_{n}-y_{n,k}}{y_{n,i _{n}}-y_{n,k}}.\]  
Similar to [47], we expect that coefficients of small magnitudes correspond to terms with relatively small contributions to the available interpolation, which can thus be omitted. Accordingly, terms whose coefficients have large magnitudes are expected to enhance the accuracy of the interpolation and should therefore be added. Based on this argument, we define for each multi-index \(\mathbf{i}\in\Lambda^{\mathrm{adm}}\) the contribution indicator:  
\[\eta_{\mathbf{i}}=\left|s_{\mathbf{i}}\right|.\]  
Naturally, the multi-index set \(\Lambda\) should be enriched with the admissible multi-index:  
\[\mathbf{i}^{*}=\underset{\mathbf{i}\in\Lambda^{\mathrm{adm}}}{\mathrm{argmax}} \eta_{\mathbf{i}},\]  
that is the index corresponding to the maximum contribution. This procedure can be continued iteratively until a simulation budget \(B\) is reached, or until the total contribution of the admissible set is below a tolerance \(\epsilon\). At any given step of the algorithm, the total number of simulations, equivalently, model evaluations or function calls, is equal to \(\#Z_{\Lambda\cup\Lambda^{\mathrm{adm}}}\). After the termination of the iterations, the final interpolation is constructed using all multi-indices in \(\Lambda\cup\Lambda^{\mathrm{adm}}\), since the hierarchical surpluses corresponding to the admissible nodes have already been computed.
## 4 Case Study
### PCA Fault Data Recognition Results  
In the \(T^{2}\) and SPE monitoring diagrams, the dotted line indicates that the confidence level is 99% or the SPE control limit parameter. As can be seen from Figure 8, a large number of data points have been misreported. In the monitoring chart, there have been more serious error alarms in the 100 data points at the beginning of the data, and similar problems occurred near 200. Although noise interference is less affected by the SPE monitoring graph, there is a serious misreporting phenomenon.  
Figure 7: The SPE threshold and contribution graph for matrix X: (**a**) the SPE control limit of X; (**b**) each variable contribution graph of X.  
The fault recognition of the PCA model is caused by the fact that the PCA has no noise interference characteristics, so, if the noise is more intense, the noise information will be treated as the fault information. In addition, traffic flow information is characterized by nonlinear and time-varying characteristics, and the possibility of mutation is relatively large. Therefore, the method of \"one size fits all\" with various control parameters such as PCA will inevitably lead to errors.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 3 Global Path Planning Algorithm Based on the Improved Genetic Algorithm
### Environmental Modeling  
Before path planning of the mobile robot, a reasonable map modeling of its operation environment is required. Because raster maps are simple, effective, and easy to implement, the raster method is used to model the environment of the operating space of AGVs. Then, the size and number of rasters are determined by comparing the size of obstacles and the size of the AGV's running space.  
Figure 3 shows the raster map of the simulation. White rasters indicates the area through which the AGV can pass, whereas the black rasters indicate the location of the obstacle.
###### Abstract  
The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper(r), enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.  
non-terrestrial network (NTN); unmanned aerial vehicle (UAV); unmanned aircraft system (UAS); UAS traffic management (UTM); safety operation; vehicle-to-vehicle (V2V) communications; Drone Mapper(r); autonomous flight coordination control system (AFCCS); collision avoidance; formation flight +
Footnote †: journal: Advances in Neural Information Processing Systems
# Security and Privacy in Physical-Digital Environments: Trends and Opportunities
## 2 The Metaverse and Mixed Reality
### Ethical Issues  
Due to the reliance of these technologies on visual and auditory information, there are concerns about how their interactive nature manages the collection and use of user data. While these technologies are in use, they gather information about users and their actions. This data collection serves various purposes, including advising, informing, tracking, manipulating, and entertaining the end user [11]. Studies have shown that large-scale collection of user data can contribute to privacy risks, particularly when users are unaware of the extent to which their data are being stored or analyzed [12].  
An ethical issue related to these technologies is their persuasive nature, especially regarding novelty, the exploitation of positive reputation, persistence, and control [11; 13]. Because these technologies are relatively new in our daily lives, their novelty can easily obscure the designers' persuasive intentions from users, leading to potential misinformation and overreliance on these systems. Additionally, the widespread enthusiasm for new and innovative technological products enhances their reputation, fostering an unquestioning level of trust from users [14]. This trust can be exploited by designers and developers, leading to ethical concerns regarding user manipulation and autonomy. Another concerning issue is the targeting of vulnerable groups, including children, aged people, individuals with low socio-economic status, those with intellectual challenges, and those with mental illnesses [15]. If persuasive actions seem designed to exploit these groups, it raises significant ethical concerns that warrant further investigation and regulation [16].  
The information collected may include personal details and data, such as how long users stay on certain websites, how frequently they visit them, and their locations through GPS tracking, and also data about bystanders. Then, it raises questions about how this information is managed: whether and where it is stored, who may access it, and how it is used [12]. In many cases, users are unaware of the extent of data retention policies or their implications for privacy [17].  
Moreover, technologies such as facial recognition can collect and store users' biometric data, often without their full awareness of the associated risks [15, 18]. Many users do not recognize the risks associated with storing facial recognition data, as they tend to focus on its future applications rather than its present dangers. Consequently, they may underestimate potential threats, such as the risk of being identified by matching those data with publicly available online photos [15]. This tendency can lead to increased complacency regarding data privacy and security.
# Adaptive Relay Selection Scheme by Using Compound Channel
## 4 The Proposed Scheme
### Relay Selection with Outage
One is a broadcast case in which users require the same data and selections are not necessary. The other case is when all users receive the same data, but they requires different part of the data. In this case, each user disregards the other data. An algorithm of the proposed scheme is summarized in Algorithm 1. ```
Data: CSI of \(\mathbf{H_{k}}\) and \(\mathbf{H_{q}}\), \(\mathbf{B_{x}}\), \(N_{p}\), \(N_{m}\) Result: \(R_{q}:q\in\{1\dots,Q\}\), \(R_{k}:k\in\{1\dots,K\}\), \(R_{s}:s\in\{1\dots,S\}\), \(N_{m}:m\in\{1,\dots,M\}\)
1 initialize \(R_{s}\)while Calculate outage probabilitydo
2 find \(R_{q}\) if\(Q<2\)then
3 case 1, \(R_{s}=1\) for\(s=1\);do
4 perform antenna selection generate antenna combination (10) calculate \(\Gamma\) (11) select \(N_{m}\) antennas with \(\Gamma^{\max}\) (12)
5 end for
6
7 elseif\(Q=2\)then
8 case 2, \(R_{s}=R_{q}\) for\(s=1\) to \(S\); \(s=q\)do
9 perform antenna selection generate antenna combination with two compound channel (14) calculate \(\Gamma\) of compound channel (15) select \(N_{m}\) antennas with \(\Gamma^{\max}\) (18)
10 end for
11 else
12 case 3, \(Q>2\) for\(q=1\) to \(Q\): \(q\
eq s\)do
13 perform antenna selection generate antenna combination (16) calculate \(\Gamma\) of the compound channel (17) select \(N_{m}\) antennas and \(N_{p}\) relays (\(R_{s}\)) with \(\Gamma^{\max}\) (18)
14 end for
15
16 end for
```  
**Algorithm 1**Algorithm for proposed scheme
## 3 Evaluation and Application of PWV Derived from CPRAP
### Performance of CPRAP-Derived PWV
#### 3.1.2 Accuracy Analysis of ERA5-Derived PWV Product  
The comparison was performed using the corresponding GNSS and RS stations in China over the period of 2012 to 2020 to evaluate the performance of ERA5-derived PWV based on the CPRAP. The gridded point was first interpolated into the location of GNSS or RS stations following the steps proposed in Section 2.2. Figure 5 gives the time series of PWV between RS and ERA5 and their differences at two stations (CH20 and CH41) over the period of 2012 to 2020. Those two stations were selected because they were distributed in the south and north of China, respectively. It can be observed that the PWV time series derived from ERA5 showed good consistency with that of RS and their difference approximately follows a normal distribution. In addition, the RMS and Bias distributions of PWV differences between ERA5 and GNSS/RS are also presented in Figure 6. It can be found that the RMS of ERA5-derived PWV is evidently higher in south China than that in north China, whereas Bias does not show evident regional differences. Table 3 gives the statistical results of RMS, Bias and MAE between ERA5 and GNSS/RS over the period of 2012 to 2020 in China. It can be found that the averaged RMS, Bias and MAE between ERA5 and GNSS/RS are 1.86/0.11/1.48 mm and 0.90/\(-\)0.05/1.51 mm, respectively. The above results show the good performance of ERA5-derived PWV in China.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Value** & **Index** & **RMS** & **Bias** & **MAE** \\ \hline Mean & 2.15 & 0.05 & 1.65 \\ Max & 3.24 & 0.61 & 3.54 \\ Min & 0.86 & \(-\)0.69 & 0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical results of RMS, Bias and MAE of PWV differences between GNSS and RS (54) in China over the period 2012 to 2020.  
Figure 5: Time series of hourly PWV derived from ERA5 and RS at two stations and PWV difference statistics over the period from 2012 to 2020.
# Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
## 5 Experiments and Results  
### Implementation Details  
Our experiments are conducted based on the modified Faster R-CNN detector which employs the ResNet-50 model if not specified. The model is initialized by the ImageNet classification model and then fine-turned on our optical remote sensing dataset which contains two-class remote sensing object instances, ship and plane. Some samples from our dataset, which have been resized to 128 \(\times\) 128 pixels for viewing conveniently, are shown in Figure 5. It can be seen from Figure 5 that the images contain scenes of civilian ports, military bases, offshore areas, and far seas. In total, we collected 5922 optical remote sensing images named 'SORSI dataset', 5216 for ship and 706 for plane. It is noteworthy that the numbers of images in different classes are highly imbalanced, which poses great challenges for model training. It can be found from Figure 6 that the areas of most bounding boxes are between \(10^{2}\) and \(100^{2}\) pixels in our dataset. Besides, the areas of bounding boxes falling in the ship category dominate from \(10^{2}\) to \(50^{2}\) pixels while those in the plane category possess from \(50^{2}\) to \(100^{2}\) pixels. Obviously, it is far more difficult to detect ships than to detect planes. To make an evaluation, our dataset is randomly split into 80% for training and 20% for testing. In the training process, we flip all the training images while subtracting the mean value (103.939, 116.779, 123.68).  
In all of the experiments, we trained and tested both RPN and Fast R-CNN on images of a single scale based on the deep learning framework, Caffe [31]. We resize the images such that their shorter side is 608 pixels under the premise of ensuring the longer side less than 1024 pixels. Meanwhile, we apply stochastic gradient descent (SGD) for 20K iterations to train the baseline model and the training rate starts with 0.01 and decreases to 0.0001 after 15K iterations. For anchors, we adopt three scales with box areas of \(16^{2}\), \(40^{2}\), and \(100^{2}\) pixels, and three aspect ratios of 1:1, 1:2, and 2:1, which are adjusted for better coverage of the size distribution of our optical remote sensing dataset.  
Figure 4: The pipeline of balanced sampling.  
The evaluation metric is average precision (AP) of each object instance and mean average precision (mAP) with Interception-of-Union (IoU) threshold as 0.5. To reduce redundancy, non-maximum suppression (NMS) is adopted on the proposal regions based on their box-classification scores. The IoU threshold is fixed for NMS at 0.7. All experiments were performed on Intel i7-6700K CPU and NVIDIA GTX1080 GPU.