esa-sceva/satcom-chunk-collection huggingface dataset:
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 4 Discussion  
As smart cities are developing by leaps and bounds, traffic flow management is required to improve prediction accuracy. Therefore, researchers have intensified research into lane-level traffic flow prediction, as it can capture urban road conditions and dynamic changes in the objective environment more accurately than existing prediction methods. We proposed a heterogeneous graph convolution network based on dynamic graph generation, which generally adopts the codec structure. The ACB consists of the internal graph convolution network and external heterogeneous data fusion network, fusing the outputs of both networks. The encoder allocates reasonable weights to each period through a temporal attention mechanism to construct context vectors. Finally, the decoder decodes context vectors and implements the traffic flow prediction. We experimented with two real-world datasets from PeMS and demonstrated the effectiveness of the proposed network. Although the codec structure and attention mechanism improved the efficiency, the network still had defects with respect to real-time computing in changeable traffic conditions. In order to better coordinate the overall framework, we will probe more deeply into edge computing methods, to unload heavy computing tasks to the road-side unit (RSU) and enhance the efficiency of data transmission to improve the quality of service [29,30]. In addition, the idea of model pre-training in transfer learning also provides a new direction for improving real-time learning effectively in the future.  
Conceptualization, methodology, and project administration, J.Z.; experiments and analysis, J.Z., L.W., K.Y. and S.S.; investigation, S.S. and L.W.; resources and data curation, J.Z. and K.Y.; writing--original draft preparation, J.Z., K.Y., L.W. and S.S.; writing--review and editing, X.K., Z.X. and Z.S.; supervision, X.K.; funding acquisition, X.K. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the \"Pioneer\" and \"Leading Goose\" R&D Programs of Zhejiang under Grant 2022C01050, in part by the National Natural Science Foundation of China under Grant 62072409, in part by the Zhejiang Provincial Natural Science Foundation under Grant LR21F020003, and in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant RF-B2020001.  
Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **PeMSD4** & **PeMSD8** \\ \hline ST-AFN & 182.17 s & 158.29 s \\ ASTGCN & 310.65 s & 275.12 s \\ Ours & 276.48 s & 220.14 s \\ T-GCN & 238.26 s & 194.31 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training cost comparison.
# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
## 1 Introduction
With this testbed, accurate performance evaluators are expected to be obtained, allowing us to find a better configuration for WSN applications. Therefore, the contributions of this paper are the following:  
* It explores the challenges of implementing distributed beamforming using sensor nodes operating under strict power constraints. * We demonstrate the impact of inaccurate sensor position estimation on system performance and derive a Cramer-Rao Lower Bound on the number of sensors needed to achieve a target SNR for reliable communication. * We analyse how the drone's position influences received signal strength and illustrate the findings through BER vs. SNR plots for various sensor deployment scenarios. * We show that by increasing the number of sensors, it is possible to lower the power consumption of each sensor while improving BER performance.
# Secure ECDSA SRAM-PUF Based on Universal Single/Double Scalar Multiplication Architecture
## 3 The Proposed Secure SRAM-PUF Scheme Based on Custom ECDSA
### The Proposed Secure SRAM-PUF Scheme
Second, the function directly outputs \(x_{p}\) instead of calculating \(r=x_{p}\pmod{n}\). The purpose is for the subsequent \(x_{p}Z\pmod{p}\) calculation to obtain the \(P\) point \(X\)-coordinate. The purpose of \(r\pmod{n}\) in standard ECDSA is to ensure \(r\) is smaller than the order \(n\). If \(\pmod{n}\) is skipped, it will cause \(r+n,r+2n,r+3n,...,r+xn\) to get the same sign \(u_{2}\) during verification. For custom ECDSA, when \(x_{p}\) serves as the signature, although \(x_{p}+n,...,x_{p}+xn\) can get the same \(u_{2}\) during verification, the final verification compares \(x_{p}Zmodp\). Thus, \(\pmod{p}\) will result in incorrect verification of \(r+xn\), meaning there is no possibility that multiple numbers related to \(x_{p}\) can be verified. Therefore, omitting \(\pmod{n}\) in signatures does not affect security. After completing the custom signature function, execute the custom verification function (Algorithm A7). The custom signature verification function directly inputs the calculated \(s^{-1}\pmod{n}\) and, ultimately, only calculates the projected coordinates \(X\) and \(Z\) without performing the modulo inverse of the coordinate transformation. After completing the signature verification and obtaining \(Z\), calculate \(x_{p}Z\pmod{p}\) in advance in the registration phase, saving computation time on hardware in the recovery phase. For standard ECDSA, \(r\) and \(s\) are safe and reliable in the ROM. For the custom ECDSA, compared with \(r\), \(x_{p}\) lacks the modulo step, and the amount of information in \(s^{-1}\ mod\ n\) is the same as that of \(s\). Thus, \(x_{p}\) and \(s^{-1}\ mod\ n\) are both considered safe in the ROM. In the recovery stage, we use \(x_{p}Z\) to perform the auxiliary data algorithm instead of \(r\). Also, perform bit expansion first, find \(w^{\prime}\) and \(c^{\prime}\) and perform RM code soft-decision to obtain \(c\). While executing the auxiliary data algorithm, we use \(x_{p},x_{p}Z,s^{-1}\pmod{n}\) to execute customized signature verification and calculate the projected coordinate \(X\) of \(P\). As there is no need to perform the modular inverse step and \(x_{p}Z,s^{-1}\pmod{n}\) have already been calculated in the registration stage, the relevant logic for these calculation steps in the circuit of the recovery stage is saved, effectively reducing resource consumption and computation time. Finally, \(X\) is bit-expanded and XORed with \(w^{\prime}\) and \(c\). The final expression of \(y\) is given in Equation (1). \[ \begin{split} y=c\oplus w^{\prime}\oplus X&=c\oplus w \oplus x_{p}Z\oplus X\\ &=c\oplus y\oplus c\oplus x_{p}Z\oplus X\\ &=y\oplus x_{p}Z\oplus X\end{split}  \tag{1}\]
## 4 Construction of the Polynomial System  
We proceed by developing the construction of the polynomial set based on an isomorphism between graphs.  
Consider two isomorphic graphs \(G=(U,D)\) and \(H=(V,E)\) of order \(n\) and size \(e\). Denote by \(K_{U,V}\) the complete bipartite graph on the vertex set \(U\bigcup V\). It is possible to obtain a perfect matching \(M\) in the graph \(K_{U,V}\) by choosing edges \(u_{i}v_{k},u_{j}v_{l}\) if and only if both \(u_{i}u_{j}\) and \(v_{k}v_{l}\) are edges in their respective graphs. In other words:  
(i) If \(u_{i}u_{j}\in D\) and \(v_{k}v_{l}\
ot\in E\), edges \(u_{i}v_{k}\) and \(u_{j}v_{l}\) cannot lie in \(M\) simultaneously.  
(ii) If \(v_{k}v_{l}\in E\) and \(u_{i}u_{j}\
ot\in D\), edges \(u_{i}v_{k}\) and \(u_{j}v_{l}\) cannot lie in \(M\) simultaneously.  
A perfect matching \(M\) gathered in this fashion can also be regarded as a bijection \(\phi\) of the vertices of \(U\) and \(V\), defining an isomorphism between their corresponding graphs. The aforementioned conditions are an equivalent way to assert:  
\[u_{i}u_{j}\in D\iff\phi(u_{i})\phi(u_{j})\in E.\]  
What has been explained can be observed in Figure 2.  
Now, we translate the notion of isomorphism between graphs to a strictly algebraic language. The idea is to perform a proper reduction from GI to \(\mathcal{MQ}\) motivated by conventional reductions of several problems in graphs to Boolean quadratic polynomials [29; 30]. For this, we need to consider a set of \(n^{2}\) variables, denoted \(\{X_{i,k}\}\) for \(i,k=1,\ldots,n\). The first set of polynomials to append, restrict any possible solution to values in the set \(\{0,1\}\). The polynomials are defined as follows:  
\[X_{i,k}^{2}-X_{i,k}\text{ for }i,k\in\{1,\ldots,n\}. \tag{1}\]  
Figure 2: Process of generating the polynomial set associated to graph isomorphism. (**a**) An isomorphism between \(G\) and \(H\) can be seen as a perfect matching in the graph \(K_{U,V}\), preserving adjacencies between \(G\) and \(H\). (**b**) The edges \(u_{2}v_{2}\) and \(u_{3}v_{4}\) cannot belong simultaneously to \(M\) because \(u_{2}u_{3}\in D\), but \(v_{2}v_{4}\
ot\in E\). The polynomial \(X_{2,2}X_{3,4}\) is added to the ideal \(I\).  
These could be discarded if the restriction is made clear by considering only solutions over the binary vector space \(\mathbb{F}_{2}^{n}\). The next batch of polynomials restricts the zero-set to solutions that represent a perfect matching; i.e., exactly one vertex \(u_{i}\) from \(U\) is connected to one vertex of \(V\) and vice versa. This associates the solutions to the existence of a perfect matching \(M\).  
\[\sum_{k=1}^{n}X_{i,k}-1 \qquad\text{for }i=1,\dots n \tag{2}\] \[\sum_{i=1}^{n}X_{i,k}-1 \qquad\text{for }k=1,\dots n.\]  
The last set of polynomials guarantee that the solution is related exclusively to the isomorphism arising from the perfect matching:  
\[X_{i,k}X_{j,l}\text{ for every }i,j,k,l\text{ which satisfy }\] \[\left(u_{i}u_{j}\
otin D\wedge v_{k}v_{l}\in E\right)\vee\] \[\left(u_{i}u_{j}\in D\wedge v_{k}v_{l}\
otin E\right). \tag{3}\]  
The construction of the polynomial set is now complete.
## 5 Model Updating for the Segmental Beam
### Numerical Simulations of Prestress Force Identifications by the Proposed Approach  
To further verify the reliability of the proposed method for identifying the prestress force of the segmental beam, a total of five loading cases are considered from 62 kN to 107 kN. For each case with a different prestress force, the strains at those predefined four locations as shown in Figure 6 are obtained from the updated finite element model, and the corresponding neutral axis locations are calculated based on Equations (1) and (2). The comparison between the prestress force applied to the numerical model and the predicted prestress force by the proposed approach are summarized in Table 11.  
It can be seen from Table 11 that, under these five prestress force levels, the predicted prestress force results obtained from S2 are closer to the applied prestress force to the model, with relative error values less than 3.31%. However, the results calculated from strains on S3 give relatively larger errors. The same observation is also obtained from experimental verifications presented in Section 4. It should be noted that the location of the strain gauges attached to the second segment is close to the middle of the segment, whereas the location of the strain gauges on the third segment is close to the joint. This means that the proposed method can provide more accurate estimations of prestress force when the strains are measured at locations away from the joint. Strain is a local parameter that only measures the influence in the vicinity of the sensor [21].  
The strains measured near the segment joint are affected by the joint and affect the accuracy of prestress force identification of the proposed method in this study. The following investigates the influence of strain measurement locations on the prestress force quantification by using the proposed method.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 2 Light Field Camera and Imaging Modeling  
In this section, the lab-designed light field camera is introduced (Figure 1). Further, the imaging theory is derived by modeling the imaging process of the micro-lens array light field camera based on the ray tracing theory. Table 1 shows the imaging parameters of Raytrix R29, Lytro Illum, and lab-designed cameras. Meanwhile, two imaging structures, Plenoptic 1.0 and 2.0, are shown in Figure 1d,e.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Raytrix R29** & **Lytro Illum** & **Lab-Designed** \\ \hline Imaging structure & Plenoptic 2.0 & Plenoptic 1.0 & **Plenoptic 1.0** \\ Resolution & 29 M & 40 M & 29 M \\ Pixel Size & 5.5 \(\upmu\)m & 1.1 \(\upmu\)m & 5.5 \(\upmu\)m \\ Hardware & Third-party & Self-developed & **Self-developed** \\ Interface & Cam Link & USB3.0 & **USB3.0** \\ Camera Lens & Replaceable & - & **Replaceable** \\ Video & ✓ & - & ✓ \\ Refocusing & ✓ & ✓ & ✓ \\ Depth & ✓ & ✓ & ✓ \\ Online Processing & Slow & - & **Real-time** \\ SDK & Low openness & - & **High openness** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between different light field cameras. “\(\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{\check{ \check{ \checkcheck{ }}}}}}}}}}}}\) represents support. “-” represents not support. SDK denotes the software development kit.  
Figure 1: **(a)** Lytro Illum camera. **(b)** Lab-designed camera. **(c)** Raytrix camera. **(d)** The Plenoptic 1.0 structure of the light field camera [6]. The micro-lens array (MLA) is on the focal plane of the main lens. **(e)** The Plenoptic 2.0 structure of the light field camera [7], which is called the “Focused Plenoptic camera”.
## 3 Composition of PMMA Bone Cement  
Polymethyl methacrylate (PMMA) bone cement is comprised primarily of two main components: a liquid monomer and a powdered polymer. The powder typically contains polymethyl methacrylate or a copolymer of PMMA, an initiator to start the polymerization reaction such as benzoyl peroxide, along with a radiopacifier such as barium sulfate or zirconium dioxide to help visualize the cement on radiographs. The liquid contains a monomer, usually methyl methacrylate (MMA), an accelerator such as N,N-dimethyl-p-toludiine (DMPT), and a stabilizing agent to prevent premature polymerization such as hydroquinone [3; 20] (Figure 2).  
When the two ingredients come into contact with each other, the powder reacts with the accelerating agent in the liquid, leading to the MMA monomer going through polymerization, becoming PMMA, and forming a solid matrix in the process. This process is highly exothermic, it leads to the cement hardening, securing its adherence to the surrounding bone tissue, but could also potentially lead to heat-related complications [3; 20].
## 3 Applications in Earthquake Emergency Response
### 2018 M\({}_{\rm w}\) 7.0 Anchorage Earthquake  
The seismic damage assessment of the 2018 M\({}_{\rm w}\) 7.0 Anchorage earthquake is another typical application case [33]. On 30 November 2018 (local time), a M\({}_{\rm w}\) 7.0 earthquake occurred in Alaska, the United States. The epicenter was at 61.35 N, 150.06 W with a depth of 40 km [34]. Six ground motions of the Anchorage earthquake event were recorded. The ground motions recorded at the 8047 station (61.189 N, 149.802 W, shown in Figure 11) are typical ground motions [35]. The peak ground accelerations (PGAs) of horizontal and vertical components of the 8047-ground motion were 807.162 cm/s\({}^{2}\) and 367.243 cm/s\({}^{2}\), respectively. The ground motions are shown in Figure 11.  
Using the ground motions obtained from the strong motion networks and the city-scale nonlinear time-history analysis, the \"RED-ACT\" system predicted the damage ratio and human uncomfortableness distribution of the buildings near different stations in less than 1 h, as shown in Figures 12 and 13. The post-earthquake investigation showed that this Mw 7.0 earthquake produced less-than-expected damage to buildings, including businesses, homes, and schools in downtown Anchorage, with most damage limited to nonstructural elements and contents [34], which is consistent with the prediction given by the proposed method.  
Figure 11: **(a)** Location of the 8047-ground motion station, and the ground motion recorded by the 8047 station: **(b)** EW direction, **(c)** NS direction, **(d)** UD direction.  
Figure 12: _Cont._
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### Performance Measurement Metrics Used for the Experiments  
To understand the experimental results in detail, it is necessary to first explain the metrics that we used to assess the overall performance of our protocol.  
Figure 7: Flowchart depicting the protocol optimizations.  
Average End-to-End Delay  
The term \"average end-to-end delay\" or \"average one-way delay\" refers to the amount of time it takes a packet to travel across a network from its source to its destination. It was calculated as:  
\[D=1/Number\text{ of Packets Successfully Delivered}\sum_{i=1}^{n}(Tr_{i}-Ts_{i}) \tag{1}\]  
where \(D\) = Average End-to-End Delay  
\(i\) = Packet Identifier  
\(Tr\) = Reception Time  
\(Ts\) = Send Time  
\(n\) = Number of Packets Successfully Delivered  
Average Per-Hop Delay  
When data are sent from a source to a destination, the delay caused by each hop is referred to as the hop delay. It was calculated as:  
\[Average\text{ Per-Hop Delay}=Average\text{ End-to-End Delay/Number of Hops from the Destination Node} \tag{2}\]  
PDR  
This ratio is defined as the proportion of packets that arrive at their destination in relation to the number of packets that were originally sent to the destination (source to destination). The overall performance improved when the packet delivery ratios were high. It was calculated as:  
\[PDR=\sum\text{Packets Received by All Destination Nodes}/\sum\text{Packets Sent by All Source Nodes} \tag{3}\]  
RREQ-RREP Setup Time  
This is the total time taken by the protocol from the time the route request is initiated until it reaches the destination and the destination sends the route reply message to the source and is received by the source. It was calculated as:  
\[RREQ\text{-}RREP\text{ Setup Time}=t\text{\_}{rreq\_}{received}-t\text{\_}{rrep\_}{ sent} \tag{4}\]  
Overhead  
We calculated the overhead by counting the number of packets retransmitted on the mesh network layer level, by adjusting the prompt message and the counter.
# Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures
###### Abstract  
The exploration of remote, unknown, rough environments by autonomous robots strongly depends on the ability of the on-board system to build an accurate predictor of terrain traversability. Terrain traversability prediction can be made more cost efficient by using texture information of 2D images obtained by a monocular camera. In cases where the robot is required to operate on a variety of terrains, it is important to consider that terrains sometimes contain spiky objects that appear as non-uniform in the texture of terrain images. This paper presents an approach to estimate the terrain traversability cost based on terrain non-uniformity detection (TNUD). Terrain images undergo a multiscale analysis to determine whether a terrain is uniform or non-uniform. Terrains are represented using a texture and a motion feature computed from terrain images and acceleration signal, respectively. Both features are then combined to learn independent Gaussian Process (GP) predictors, and consequently, predict vibrations using only image texture features. The proposed approach outperforms conventional methods relying only on image features without utilizing TNUD.  
Keywords:terrain traversability cost prediction; unknown environments; terrain non-uniformity detection; image texture information; vibration information; Gaussian process regression +
Footnote †: journal: Applied Sciences
## 6 Simulated Results
### Data Latency  
In Figure 4, the simulation experiments have been done for the evaluation of data latency between EGCIR protocol and existing works. The data latency is defined as the time taken from source nodes to the destination during sending sensors' information. It can be seen that the EGCIR protocol reduces the network delay in the comparison of other solutions by an average of 28% and 32.4%. This is due the fact that it reduces the length of transmission path over multi-hop points and all the points are reliable in terms of network conditions. In addition, explicit data security and authentication have been imposed by the EGCIR protocol to reduce the chances of malicious activities for data altering and dropping. Unlike other solutions that take a longer time to reselect the alternative route in data forwarding, the EGCIR protocol exploits intelligent methods to evaluate the robust paths with the reduction of route rediscoveries and ultimately it decreases the network end-to-end delay. Furthermore, the cluster members are closer to selected cluster heads, which results in reducing the communication distance among them and leads to improve the fraction of network delay.  
Figure 3: Impact of the nodes and packet drop ratio.
# Multi-UAV Area Coverage Track Planning Based on the Voronoi Graph and Attention Mechanism
## 1 Introduction
Reinforcement learning enables drones to learn autonomously in the environment and dynamically adjust the trajectory planning scheme for different environments and tasks without human intervention. It can also transfer the learned planning strategies to other drones and environments [16]. However, it should be noted that the randomness and exploratory nature of reinforcement learning cannot guarantee that the optimal solution can be obtained every time training is carried out. In addition, learning needs to be carried out in many simulations or actual environments, which will inevitably result in a long training cycle and high requirements for data quality and environmental models. Therefore, in actual application scenarios, staff must comprehensively consider the application scenario and problem characteristics to decide whether to use reinforcement learning for drone path planning. This paper focuses on optimizing multi-UAV trajectory planning for regional coverage to enhance system efficiency and task execution. Our contributions include the following:  
1. Introducing a Voronoi diagram-based regional decomposition method to simplify the multi-UAV coverage problem into manageable single-UAV coverage tasks. 2. Designing a convolutional neural network (CNN) incorporating a CSAM to improve feature fusion and focus on crucial features, thereby optimizing the trajectory planning of individual UAVs. Simulation results demonstrate the effectiveness of our approach.