esa-sceva/satcom-chunk-collection huggingface dataset:
# Formation Control of Dual Auto Guided Vehicles Based on Compensation Method in 5G Networks
## 3 Formation Control System Design
### Formation Control and Speed Composition  
As shown in Figure 9, \(\mathbf{P}_{L}\), \(\mathbf{P}_{F}\) and \(\mathbf{P}_{D}\) are the actual position of the leader AGV, the actual position of the follower AGV, and the expected position of the follower AGV. \(L\) is the actual distance between the leader AGV and the follower AGV, \(D\) is the expected distance between the leader AGV and the follower AGV, \(\Theta_{L}\), \(\Theta_{F}\), \(\Theta_{D}\) are the actual angle of the leader AGV, the actual angle of follower AGV, and the expected angle of the follower AGV. In this paper, we set \(\Theta_{L}=\Theta_{D}\). \(\Theta_{0}\) is the formation angle of follower AGV. \(\Theta_{0}\) and \(D\) determine the follower AGV's posture relative to the leader AGV. \(\Theta_{0}\) and \(D\) will be chosen at the start of formation motion and known to leader AGV and follower AGV. In this paper, we focus on the distance error \(error_{d}\) and angle error \(error_{a}\). There is  
\[\begin{cases}error_{d}(k)=|D-L(k)|\\ error_{a}(k)=|\Theta_{d}(k)-\Theta_{L}(k)|\end{cases} \tag{12}\]  
Figure 8: Schematic diagram of AGV kinematics.  
According to the geometric relationship, there is  
\[\begin{cases}X_{D}=X_{L}-D\cos(\Theta_{L}+\Theta_{0})\\ Y_{D}=Y_{L}-D\sin(\Theta_{L}+\Theta_{0})\\ \Theta_{D}=\Theta_{L}\end{cases} \tag{13}\]  
Then the deviation of \(\mathbf{P}_{D}\) relative to \(\mathbf{P}_{F}\) is  
\[\begin{cases}e_{X}=X_{L}-D\cos(\Theta_{L}+\Theta_{0})-X_{F}\\ e_{Y}=Y_{L}-D\sin(\Theta_{L}+\Theta_{0})-Y_{F}\\ e_{\Theta}=\Theta_{L}-\Theta_{F}\end{cases} \tag{14}\]  
With the transfer matrix \(\mathbf{T}\) (9), the error can be represented in co-moving coordinates as  
\[\left[\begin{array}{c}e_{x}\\ e_{y}\\ e_{\theta}\end{array}\right]=\left[\begin{array}{cc}\cos\Theta_{F}&\sin \Theta_{F}&0\\ -\sin\Theta_{F}&\cos\Theta_{F}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}e_{X}\\ e_{Y}\\ e_{\Theta}\end{array}\right] \tag{15}\]  
\([e_{X}\;e_{y}\;e_{\theta}]^{T}\) is the input of formation control, and a proportional control is used to obtain the additional velocity. There is  
\[\left\{\begin{array}{c}\Delta v_{xf}=k_{1}e_{x}\\ \Delta v_{yf}=k_{2}e_{y}\\ \Delta\omega_{f}=k_{3}e_{\theta}\end{array}\right. \tag{16}\]  
The formation additional velocity is \(\Delta V=[\Delta\omega f\;\Delta\omega f\;\Delta\omega f]^{T}\), \(\Delta V\) could be transferred to \(\Delta\mathbf{q}=[\Delta v\;\Delta\omega]^{T}\) according to control system and matrix. The path tracking velocity is \(\mathbf{q}_{r}\) and the actual command velocity is \(\mathbf{q}_{c}=\mathbf{q}_{r}+\Delta\mathbf{q},qc\) can be transferred to the actual angle velocity \(\mathbf{\omega}=[\omega_{i}\;\omega_{r}]^{T}\) of two driving wheels.  
Figure 9: Schematic diagram of leader-follower method.
## 6 Numerical Results and Discussion  
The simulations of the caching scheme are carried out in this section. The performance of P3 DVQC was compared with the baseline schemes. The simulation environment for the mobile video caching system in IoV was programmed in Python. In addition, the TensorFlow platform was used to implement the P3 DVQC caching scheme based on the open-source package convolutional neural networks. The main system parameters used in the simulations are summarized in Table 1. For performance comparison, five benchmark schemes were presented:  
(1) Resolution Optimal Caching Scheme (ROCS): The scheme always priority caches the video copies with the highest bitrate level to realize the rapid delivery of high-quality video content and improve the quality of experience of vehicle users.  
(2) Fluency Optimal Caching Scheme (FOCS): The scheme caches the video copies with the lowest bitrate to cache as many videos as possible and realizes the diversity of the cache to improve the cache hit rate.  
(3) Random Caching Scheme (RCS): The scheme selects the video copies with the random algorithm, which means an equal probability choose each copy cached until the maximum cache capacity of MECS is reached.  
(4) Cost Efficient Scheme (CES): The scheme only depends on the backhaul network delivery of video files, not to use of MECS caching equipment to minimize device cost and energy consumption.  
(5) Brute Force Scheme (BFS): The scheme is optimal, but it is obtained based on ideal conditions where all system information is known. In actual scenarios, system information, such as wireless channel state information, cannot be obtained in advance. Therefore, BFS cannot carry out in real vehicular network scenarios.
## 2 Materials and Methods
### Impact-Echo Technique  
The impact-echo technique utilizes the principle of impact elastic wave propagation for testing. The test setup used in this study is shown in Figure 3. The detection instrument is the SRB-MATS-B multi-function rock anchor tester manufactured by Sichuan Sheng-Tuo (Chengdu, China), which is based on the impact elastic wave principle. The testing procedure involves using an impact hammer to strike the exposed reinforcement end at the center of the sleeve, generating an elastic wave. The sensor then records the elastic wave data, and through the analysis of the properties of the excitation and received signals (wave velocity, amplitude, and dominant frequency), the grout filling density within the sleeve can be qualitatively evaluated.  
The testing procedure for the impact-echo technique is illustrated in Figure 4. The test requires the collaboration of two personnel. First, the sleeve is positioned on the fixture of the test bench to prevent the test component from shaking under external force during the test. A coupling agent is applied to the exposed reinforcement end of the sleeve, ensuring that the entire surface is not covered, leaving a portion exposed for manual excitation. The detection instrument is the SRB-MATS-B multi-function rock anchor tester manufactured by Sichuan Sheng-Tuo, which is based on the impact elastic wave principle. The detection instrument is composed of a signal receiver and a main instrument. The signal receiver (with a magnetic base) is then attached to the coupling agent surface, and the  
Figure 2: Process and schematic diagram of embedded sensor method testing: (**a**) testing flow, (**b**) schematic diagram.  
receiver is connected to the main instrument. One of the personnel then gently strikes the reinforcement end with the impact hammer, while the other personnel monitors the data displayed on the instrument and adjusts the excitation force to obtain the optimal waveform. In this experiment, the optimal waveform is obtained with a specific quantification of the impact force measured at 12.15 N, corresponding to the impact force of a 50 g steel ball falling from a height of 30 cm. The distance between the excitation position and the sensor should be maintained at 0.02-0.03 m, and the instrument's sampling frequency should be set to 250 kHz. To ensure proper adhesion between the sensor and the contact element, the surface of the contact element should be polished prior to testing. The signal receiver is equipped with a magnetic base to prevent experimental errors caused by poor connection with the contact element.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 2 Proposed Methodology
### Deep Feature Extraction
#### 2.1.1 Spatio-Temporal ResNet (ST-ResNet)  
The ST-ResNet [24] is a two-stream convolutional Resnet with a spatial and a flow stream. Both motion and appearance streams use a ResNet-50 model that is pre-trained on the ImageNet dataset. We fine-tuned the model for video datasets, i.e., HMBD51 and UCF101. One of the drawbacks of two-stream architectures is the registration of motion and appearance information. To overcome this issue, spatio-temporal cues are learned by the network at different scales. This is achieved by introducing residual connections inbetween the motion and appearance streams. There are several ways of connecting these streams just like various shortcut connections. The 2048-dimensional features for both streams are extracted after the pooling layer of the last convolutional layer.
## 5 Conclusions
Code S1: Google Earth Engine code for the generation of the NDWI composite, QGIS models and scripts, and R code for images generation, pixel and zonal histogram analysis are available on Github: [[https://github.com/andreatitolo/IraqEmerginSites](https://github.com/andreatitolo/IraqEmerginSites)]([https://github.com/andreatitolo/IraqEmerginSites](https://github.com/andreatitolo/IraqEmerginSites)), and archived on Zenodo (doi:10.5281/zenodo.4446664) NDWI composites, due to their size, are available on figshare (links for the annual and monthly NDWI composites are provided in the Github repository). Code S2: Link to Google Earth Engine repository: [[https://code.eartheinging.google.com/?accept_repo=users/sapienza_at/IraqEmergingSites](https://code.eartheinging.google.com/?accept_repo=users/sapienza_at/IraqEmergingSites)]([https://code.eartheinging.google.com/?accept_repo=users/sapienza_at/IraqEmergingSites](https://code.eartheinging.google.com/?accept_repo=users/sapienza_at/IraqEmergingSites)). Funding Funding for publication costs was covered by the project PRIN 2017 NMK5 FE: Fluid Crescent. Water and Life in the Societies of the Ancient Near East (Sapienza Universita di Roma). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are openly available on Github: [[https://github.com/andreatitolo/IraqEmerginSites](https://github.com/andreatitolo/IraqEmerginSites)]([https://github.com/andreatitolo/IraqEmerginSites](https://github.com/andreatitolo/IraqEmerginSites)), and archived on Zenodo (doi:10.5281/zenodo.4446664) NDWI composites, due to their size, are available on figshare (links for the annual and monthly NDWI composites are provided in the Github repository). Acknowledgments: All Planestscope image data were provided free of charge by the European Space Agency (@TPMO 2020) as part of the Third-Party Mission program ([[https://earth.esa.int/web/guest/data-access](https://earth.esa.int/web/guest/data-access)]([https://earth.esa.int/web/guest/data-access](https://earth.esa.int/web/guest/data-access))). Conflicts of Interest: The author declares no conflict of interest. The project funding the publication had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Gauge-Invariant Formulation of Time-Dependent Configuration Interaction Singles Method
## Theory
### System Hamiltonian and Gauge Transformation
The operators \(\hat{h}\), \(\hat{r}\), and \(\hat{p}\) are defined, respectively, as \(\hat{h}=\sum_{\sigma}^{\uparrow\downarrow}\sum_{pq}h_{pq}\hat{c}_{pv}^{\dagger} \hat{c}_{q\sigma}\), \(\hat{r}=\sum_{\sigma}^{\uparrow\downarrow}\sum_{pq}r_{pq}\hat{c}_{pv}^{ \dagger}\hat{c}_{q\sigma}\), and \(\hat{p}=\sum_{\sigma}^{\uparrow\downarrow}\sum_{pq}p_{pq}\hat{c}_{pv}^{ \dagger}\hat{c}_{q\sigma}\), where \(h_{pq}\), \(\mathbf{r}_{pq}\), and \(\mathbf{p}_{pq}\) are the matrix elements of \(h\), \(\mathbf{r}\), \(\mathbf{p}\), respectively, in terms of \(\{\phi_{p}\}\), and  
\[H_{0}=\sum_{k=1}^{N}h(\mathbf{r}_{k},\mathbf{p}_{k})+\sum_{k=1}^{N}\sum_{l>k}^{N}\frac {1}{|\mathbf{r}_{k}-\mathbf{r}_{l}|}, \tag{4}\]0  
The TDSE of the LG, Equation (3), and VG, Equation (8), read  
\[H_{0}=\sum_{k=1}^{N}h(\mathbf{r}_{k},\mathbf{p}_{k})+\sum_{k=1}^{N}\sum_{l>k}^{N}\frac {1}{|\mathbf{r}_{k}-\mathbf{r}_{l}|}, \tag{4}\]1  
with the transformation  
\[H_{0}=\sum_{k=1}^{N}h(\mathbf{r}_{k},\mathbf{p}_{k})+\sum_{k=1}^{N}\sum_{l>k}^{N}\frac {1}{|\mathbf{r}_{k}-\mathbf{r}_{l}|}, \tag{4}\]2  
\[H_{0}=\sum_{k=1}^{N}h(\mathbf{r}_{k},\mathbf{p}_{k})+\sum_{k=1}^{N}\sum_{l>k}^{N}\frac {1}{|\mathbf{r}_{k}-\mathbf{r}_{l}|}, \tag{4}\]3  
where \(\hat{N}=\sum_{\mu}\sum_{\sigma}^{\uparrow\downarrow}\hat{c}_{\mu\sigma}^{ \dagger}\hat{c}_{\mu\sigma}\) is the number operator. Here and in what follows, we distinguish equivalent operators in the first quantization \(O\) and in the second quantization \(\hat{O}\) by appending hat on the latter. In this work, we consider a closed-shell system with even number of electrons, and choose as \(\{\phi_{p}\}\) the time-independent Hartree-Fock (HF) orbitals satisfying the canonical, restricted HF equation  
\[H_{0}=\sum_{k=1}^{N}h(\mathbf{r}_{k},\mathbf{p}_{k})+\sum_{k=1}^{N}\sum_{l>k}^{N}\frac {1}{|\mathbf{r}_{k}-\mathbf{r}_{l}|}, \tag{4}\]4  
where \(\epsilon_{p}\) is the orbital energy, and \(\hat{W}_{\phi^{\prime}}^{\phi}\) is the electrostatic potential of a product \(\phi^{*}(\mathbf{r})\phi^{\prime}(\mathbf{r})\) of given orbitals, defined in the real space as  
\[H_{0}=\sum_{k=1}^{N}h(\mathbf{r}_{k},\mathbf{p}_{k})+\sum_{k=1}^{N}\sum_{l>k}^{N}\frac {1}{|\mathbf{r}_{k}-\mathbf{r}_{l}|}, \tag{4}\]5  
As usual, we separate the full set of HF orbitals \(\{\phi_{p}\}\) into the occupied orbitals \(\{\phi_{i}\}\) which are occupied in the HF ground-state wavefunction (also referred to as the reference) \(|\Phi\rangle=\prod_{i}\hat{c}_{i\uparrow}^{\dagger}\hat{c}_{i\downarrow}^{ \dagger}|)\) (\(|\rangle\) is the vacuum. ), and the virtual orbitals \(\{\phi_{a}\}\) which are unoccupied in \(|\Phi\rangle\).
###### Abstract  
V2X (Vehicle-to-Everything) communications play a crucial role in enabling the efficient and reliable exchange of information among vehicles, infrastructure, and other entities in smart transportation systems. However, the inherent vulnerabilities and dynamic nature of V2X networks present significant challenges for ensuring secure and trustworthy communication. By enhancing the security of the OLSR (Optimized Link State Routing) protocol through secure MultiPoint Relays (MPRs) Selection, this research aims to provide a robust approach that enhances the overall security posture of V2X networks, enabling safe and secure interactions between vehicles and their environment. The proposed method is based on the Byzantine general's problem, which is the principle used in blockchain. Compared to the classical flooding mechanism, this technique greatly reduces network traffic overhead and improves the efficiency of bandwidth utilization. The results demonstrated that the proposed algorithm performed better than the well-used UM-OLSR implementation. The outcome proved that our MPR election algorithm guarantees a better packet delivery ratio, and it also performs very well in the detection and isolation of malicious nodes, leading to increased security of the OLSR protocol control plane.  
O 2023 by the authors. Licensee MDPI, Basel, Switzerland. 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/))).  
olSR protocol; MPR elections; black hole attack; byzantine fault; trusted topology; scale-free 22 June 2023  
Based on a Byzantine Fault in the OLSR Protocol
# A Patch/Dipole Hybrid-Mode Antenna for Sub-6 GHz Communication
## 3 Results and Discussions  
Figure 4 shows the photograph of the prototype antenna. Nylon screws are used to fix the top and bottom substrates. The reflection coefficients of the prototype are measured by the KEYSIGHT E5071C Vector Network Analyzer (Keysight Technology, New York, USA). The radiation patterns and gains are measured in an anechoic chamber. Figure 5 illustrates the simulated and measured reflection coefficients and broadside gains versus frequency. The simulated 10-dB impedance bandwidth is 55.75%, covering 2.92 GHz to 5.15 GHz. The measured 10-dB impedance bandwidth is 67.50% in the range of 2.75 GHz to 5.45 GHz. Therefore, the impedance bandwidth of the proposed antenna can cover the frequency bands of n77 (3300-4200 MHz), n78 (3300-3800 MHz), and n79 (4400-5000 MHz).  
The simulated radiation efficiency of the proposed antenna varies from 70% to 80% within the operating band, where the minimum value is reached at 5.0 GHz. The simulated gain varies between 8.0 and 9.1 dBi with 1.1-dB variation, while the measured one varies between 7.3 and 9.5 dBi, with 2.2-dB variation. The discrepancies between the simulated and measured results can be attributed to fabrication and assembly errors.  
Figure 4: Photograph of the antenna prototype: (**a**) top view and (**b**) side view.  
Figure 5: The simulated and measured reflection coefficients and realized gains of the proposed antenna.  
The radiation patterns of the prototype antenna at three typical frequencies are displayed in Figure 6. The measured radiation patterns in both _xoz_ and _yoz_ planes agree with the simulated ones. The HPBWs are \(70.4^{\circ}\), \(55.7^{\circ}\), and \(43.2^{\circ}\) in the _xoz_ plane and \(46.3^{\circ}\), \(43.3^{\circ}\), and \(40.5^{\circ}\) in the _yoz_ plane at 3.0 GHz, 4.0 GHz, and 5.0 GHz, respectively. However, the spurious radiation from the asymmetric balun [13] causes a slight shift of the main beam from the broadside direction in the _yoz_ plane. Meanwhile, the polarization purity of the proposed antenna is deteriorated at the _xoz_ plane. The simulated cross polarization in the worst case is less than \(-18.5\) dB over the operating band, while the measured one is around \(-28.5\) dB in the operating frequency band. The difference in the cross polarization may be caused by assembly and measurement errors.  
Figure 6: Simulated and measured radiation patterns of the proposed antenna at (**a**) 3.0 GHz, (**b**) 4.0 GHz, and (**c**) 5.0 GHz.  
A comparison of the proposed antenna and gain-bandwidth enhanced antennas in the literature is summarized in Table 2. Ref. [5] has a lower profile and higher antenna gain, but a narrower bandwidth. The reduction in the profile but increase in the size is obvious when compared with [14]. Though [15] and [16] have a much lower profile, the proposed antenna has a larger bandwidth or compacter size. Moreover, the radiation pattern in [16] has side lobes, which may cause the reduction in the directivity.
# A Novel Digitized Method for the Design and Additive Manufacturing of Orthodontic Space Maintainers
## 2 Materials and Methods
### Additive Manufacturing  
Vat technology additive manufacturing, specifically the DLP technique, was adopted to fabricate the space maintainer. This method uses a DLP or a Liquid Crystal Display (LCD) projector to emit ultraviolet (UV) light, thus curing and solidifying a liquid photosensitive photopolymer. Each digital image produced via the projector or LCD screen determines the all-at-once curing of a complete slice. Therefore, the printing speed is determined by the vertical axis (z-axis) length rather than the dimensions on the x and y planes. DLP was chosen due to its high resolution and accuracy, making it one of the most effective additive technologies for polymers. It easily achieves layer thicknesses of 50 microns, and offers a range of biocompatible, orthodontic, and dental resins [25]. The Anycubic Photon Mono SE 3D printing machine, equipped with a 6-inch monochrome LCD display, was used, and Photon Workshop V2.1.23 software was utilized for setting the printing parameters. Table 2 provides details on the adopted main parameters.  
Figure 5: Finite element model: (**a**) mesh of the rigid pin and the teeth space maintainer and (**b**) surfaces employed for the boundary condition application.  
The space maintainer was printed parallel to the build platform, positioned at a distance of 4 mm, and appropriately supported. The adopted resin, \"OD-Clear MF Bio monomer free\", supplied by 3 Dresyns company, exhibits excellent biocompatibility with minimal risks of residuals or by-products released by saliva. In addition, the manufacturer reports remarkable mechanical properties, such as a flexural strength exceeding 110 MPa, an elastic modulus ranging from 2 to 3 GPa, and a tensile strength exceeding 50 MPa [26]. These properties are crucial for space maintainers, ensuring the prevention of unexpected fractures.  
Figure 6 shows the manufacturing process of the space maintainer, from creating the printing file to its fabrication. Finally, the generated supports are mechanically removed at the end of the procedure by using a standard plier.
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 4 Results and Evaluation
### Qualitative Analysis
#### 4.2.2 Bundling Enhancements  
In this section, we report the results of the two enhancements designed to give the user a set of options to transform the bundling visualization. These transformations are the Adaptive-\(\beta\), and the intermediate vertices filtering.  
Figure 7: A zoomed in part of the visualization produced by the techniques CUBu and SDEB. CUBu bundles all edges close to their source/target vertices, resulting in misleading bundles. SDEB draws edges following a direction perpendicular to the tangent of the radial layout, with the bundling happening at different levels.  
The Adaptive-\(\beta\) enables the individual tension control of each edge. In our method, the tension, that is, the strength with which edges are attracted to the _backbone_ is defined by three parameters: \(\beta\), \(\gamma\), and \(\Delta\) (see Section 3.2.2). \(\Delta\) defines the minimum size of an edge to be bundled, \(\beta\) is the tension for bundled edges, and \(\gamma\) is the tension for the unbundled ones. Figure 9 shows different examples varying these parameters for the same input graph. By properly setting up \(\gamma\) and \(\Delta\) different visualizations can be produced, revealing hidden local information. When \(\Delta=0\) (Figure 9a), short edges are  
Figure 8: A comparison among the results attained by the SDEB, CUBu, FDEB, and MINGLE using the H-Tree layout. Even though the techniques share the same vertices position, the difference in bundling readability is evident. CUBu, FDEB, and MINGLE techniques generate the bundles only considering the spatially-similar edges trajectories, while our technique follows the H-Tree layout, producing more compact bundles and less cluttered layouts.  
hardly identified since they are mixed with long ones. When we increase the value of \(\Delta\) (Figure 9b,c), short edges are separated from the bundles, showing concealed information, such as the groups of similar vertices with more connections. However, the unbundled edges can add noise into the layouts (Figure 9d), and high values of \(\gamma\) and \(\Delta\) might make the visualization worse.  
The second enhancement is the intermediate vertices filtering. In this modification, the user sets the interval \([\phi_{L},\phi_{U}]\) defining the _backbone_ hierarchy levels to be considered on the bundling process, so that the edges are bent only using the intermediate vertices in the interval. Figure 10 presents four variations of intermediate vertices filtering. These examples show the effects of dropping intermediate vertices from the root and the leaves of the _backbone_. Removing intermediate vertices closer to the root creates direct connections between groups on deeper levels, resulting in a more detailed visualization. On the other hand, dropping intermediate vertices closer to the leaves generates bigger and more generic groups.