esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Modern research in civil robotics includes at least such fields (see Figure 1) as unmanned logistics and Internet of Vehicles (IoV) [1], power grids with smart metering [2], \"smart\" city [3], autonomous robotic manufacturing [4], Internet of Things (IoT) [5], and medical nursing [6]. The general goal of these investigations, which are based on artificial intelligence (AI) concepts of agents and multi-agent systems (MASs), is to imitate human abilities and to design autonomous and self-sustaining large-scale robotic systems which can operate in wide space-and-time bands without permanent human control. However, the design of autonomous robotic agents is a complicated task, and the development of such systems involves the whole spectrum of conjugated technologies, including the research of modern mathematics [7, 8], 5/6 G Internet [9], microprocessors [10], neural networks (NNs) [11], fuzzy logic and controllers [12], computer vision [13, 14] and speech process [15], big data process [16], traditional and post-quantum cryptography [17], quantum key distribution [18], and quantum computing [19]. The design, debugging, and modification of autonomous agents include autonomous data verification and correction of errors, caused by natural factors or cheaters [18].  
As the number of autonomous mobile robots unavoidably grows larger, the designers of IoV systems already propose special network tools and devices to control the growing data traffic and to provide data security [17; 18; 19; 20; 21; 22]. However, the number of service, industrial, and IoT robots is also expected to enlarge the overall data traffic, and the resulting \"burst\" of the volume of information and involved control parameters [23] creates new control and verification tasks.  
The final goal of any type of verification procedures in agents is to exclude possible incidents and crashes caused by errors, faults, attacks, and illegal data modifications [24; 25; 26]. An ideal verification method should take into consideration all possible vulnerabilities of agent's architecture, hardware, and software. It also should take into account current tasks, situation in the scene of action, the trust level of data sources, processed materials, characteristics of objects, and the workload. However, the problem is also to create and support the reliable referent (or template) model for data verification, as attacks can be aimed not only at agents but also at collective resources. One of the possible schemes here is to use verification schemes with the selective extraction of reference content from the distributed structure of local data storages, using free memory resources in loyal robots and network nodes. A holistic formal model for such procedures should additionally integrate data taken from distributed ledgers, partially reproducing the linked list (LL) scheme known in blockchain [27; 28] for data protection from illegal modifications. These new options are to complement the known data verification methods reviewed further.
## 2 Theory and Methodology
### Stochastic Parametric Models
Similar to the interpolation, nodes tailored to the PDF are crucial for the convergence of the quadrature. Finally, in the case of a high-dimensional random vector \(\mathbf{Y}\), sparse quadrature and interpolation schemes must be used in order to mitigate the effect of the curse of dimensionality. Such sparse schemes most commonly rely on nested univariate node sequences (see Section 2.2). As pointed out in Section 1, there exist interpolation nodes that meet the aforementioned requirements for the well studied cases of uniform and normal PDFs, e.g., Clenshaw-Curtis andGenz-Keister nodes, respectively [20; 21]. We note that only a finite number of Genz-Keister nodes are available, thus imposing an additional limitation on its achievable accuracy. However, constructing suitable interpolation and quadrature rules for more exotic or even arbitrary PDFs, while in principle possible [22; 23], remains a computational challenge. This problem is here circumvented with the use of weighted Leja nodes, discussed in Section 2.3, which are easy to compute, by definition nested, and tailored to a continuous PDF.
## 2 S-L Analysis Method
### Overview of Power System Frequency Regulation
which is shown in Figure 2. As the speed of the unit decreases, the speed governor allows more steam from the boiler through to the turbine to arrest the decline in speed. Equilibrium between input and output power occurs at the new frequency \(f~{}=~{}f_{0}+\Delta f\) as shown. According to the slope of the power-frequency characteristic given by Equation (5), the frequency change (in Hz) is shown in the equation below. \[\Delta f~{}=~{}-\Delta P_{\rm G}\cdot R~{}=~{}-\Delta P_{\rm G}\cdot\left(r_{ \rm u}\frac{f_{\rm R}}{S_{\rm R}}\right) \tag{6}\]  
when \(m\) generating units are operating in parallel on the system, their power-frequency characteristic determine how load changes are apportioned among them in the steady state. In this case, there is  
\[-\Delta P_{\rm G1}\cdot R_{\rm G1}~{}=~{}\cdots~{}=~{}-\Delta P_{\rm Gi}\cdot R _{\rm Gi}~{}=~{}\cdots~{}=~{}-\Delta P_{\rm Gm}\cdot R_{\rm Gm}~{}=~{}\Delta f \tag{7}\]  
where \(R_{\rm Gi}\) is the magnitude of the slope of the power-frequency characteristic of generating unit \(i\). Additionally, Equation (7) is the other basic relation of frequency regulation process. Equation (3) characterizes the \"participants\" of the frequency regulation process. Equation (7) characterizes the \"degree of participation\" of \"participants.\" The two basic relationships represented by Equations (3) and (7) are the essence of the frequency regulation process. The isolated unit of Figure 1 would continue to operate at the reduced frequency \(f\) except for the supplementary control action of the speed changer. The regulation is characteristic to the new position shown by the dashed line of \[\sum_{i\ =\ 1}^{m}{P_{\text{G}i}}^{1}=\sum_{j\ =\ 1}^{n}{P_{\text{L}j}}^{1} \tag{2}\]0 Effectively, the speed changer supplements the action of the governor by changing the speed setting to allow more prime-mover energy to increase the kinetic energy of the generating unit so that it can again operate at the desired frequency \(f_{0}\) while providing the new output \(P_{\rm G}\)[20]. From the above, the frequency regulation according to the natural regulation characteristic line is generally called primary frequency regulation. The frequency regulation by supplementary control is generally called secondary frequency regulation.
# Magnetic Self-Healing Composites: Synthesis and Applications
## 3 State-of-the-Art Fabrication Methods of Magnetic Self-Healing Composites
### Alternative Fabrication Methods  
#### 3.4.1 Polymerization Induced by an Alternative Magnetic Field  
In addition to inducing self-healing, magnetic hyperthermia has been shown to be an effective manner of inducing polymerization in magnetic self-healing gels. This is because magnetic (nano)particles act as hot spots under alternative magnetic fields, igniting the polymerization process. The fabrication of a magnetic gel from \(\beta\)-cyclodextrinrin and N-vinylimidazole monomers in the presence of SPIONs under an external stimulus of 450 kHz for 30 s was evaluated by [PERSON] et al. [54]. The fast polymerization process was induced by a frontal polymerization based on the propagation of a localized reaction zone through the whole system. The resultant supramolecular gel showed an autonomous  
Figure 5: The geometries made by the 3D printing of the AAD-CS-Fe DN hydrogel containing 8 mg\(\cdot\)mL\({}^{-1}\) nano-Fe\({}_{3}\)O\({}_{4}\). (**a**) A 3D model of the DN hydrogel in the pre-solution just after printing. (**b**) The 3D geometries made by the 3D printing. From left to right: Quadrangular pyramid, left ear, hexagonal prism, cuboid, nose, letters. The height of the corresponding models is shown in the red box. Adapted with permission from [30]. Copyright 2021, [PERSON].  
self-healing efficiency of 95%. Interestingly, the healing kinetics could be increased up to 6 times when samples were treated under the magnetic stimulus. The supramolecular gel possessed high mechanical strength, with a stress intensity of 0.12 MPa and an elongation at break over 826%. In addition to network polymerization and self-healing enhancement, magnetic hyperthermia treatments can depolymerize thermoreversible networks by heating above the gel transition temperature (T\({}_{\text{gel}}\)), thus creating a low-viscous, reprocessable melt [45]. To the best of the authors' knowledge, although it has been proven that this approach is feasible, no detailed evaluations on the reprocessing effectiveness are available in the literature.
## 4 Conclusions  
Every year, there are aircraft faults caused by crack defects. Thus, the research on crack inspection in aircraft structures is of far-reaching significance. However, The current related methods either depend on specialized hardware devices or adopt edge detection methods, which can not effectively resist the influence of background noise. We proposed the YOLOv3-Lite method to address these problems:  
* We use deep separable convolution to design a feature extraction network. Using depthwise convolution and \(1\times 1\) pointwise convolution, instead of standard convolution, reduced lots of parameters.
* We adopt the idea of a feature pyramid network which combines low- and high-resolution information. This feature pyramid has rich semantic information at all levels and can be built quickly from a single input image scale.
* We use YOLOv3 for bounding box regression. The results show that the offline detection speed of YOLOv3-Lite is 50% faster than YOLOv3, and the detection accuracy and speed are better than SSD-MobileNet and YOLO-Tiny.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **AP** & **Time (s)** & **The Number of Parameters (Million)** \\ \hline YOLOv3-Lite & 38.7\% & 0.125 & 31 \\ \hline SSD-Mobilenet & 17.1\% & 0.128 & 31 \\ \hline YOLOv3 & 43.1\% & 0.225 & 61 \\ \hline YOLO-Tiny & 2.5\% & 0.09 & 9 \\ \hline \end{tabular}
\end{table}
Table 4: Average precision, offline detection time and the number of parameters of each network.  
Figure 8: The first line is pictures of the original aircraft structures with cracks. The second line is the detection results of SSD-Mobilenet. The last line is the detection results of YOLOv3-Lite.  
Therefore, YOLOv3-Lite is a light-weight, fast (50% faster), and accurate (38.7%) crack detection network, which shows that it can reach state-of-the-art performance. In order to extend YOLOv3-Lite to a general algorithm, our future work will aim optimize YOLOv3-Lite and apply it to more scenarios.  
Project administration, Y.L.; Validation, Y.L.; investigation, Y.L., Z.H. and X.L.; resources, Y.L., K.Z., L.L. and H.X.; visualization, Y.L.  
This research was funded by Science and Technology Commission of Shanghai Municipality grant number 17511106400,17511106402 and Aerospace System Department grant number 30508020301.  
Thanks for part of data collecting and labeling works by [PERSON] of our research group.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Designing a Novel High-Performance FBG-OADM Based on Finite Element and Eigenmode Expansion Methods
###### Abstract  
This study designed a novel high-performance fiber Bragg grating (FBG) optical add/drop multiplexers (OADMs) by referring to current numerical simulation methods. The proposed FBG-OADM comprises two single-mode fibers placed side by side. Both optical fibers contained an FBG featuring identical parameters and the same geometric structure. Furthermore, it fulfills the full width at half maximum (FWHM) requirement for dense wavelength-division multiplexers (DWDMs) according to the International Telecommunication Union (i.e., FWHM \(<\) 0.4 nm). Of all related numerical calculation methods, the combination of the finite element method (FEM) and eigenmode expansion method (EEM), as a focus in this study, is the only one suitable for researching and designing large-scale components. To enhance the accuracy and computational performance, this study used numerical methods--namely, the object meshing method, the boundary meshing method, the perfectly matched layer, and the perfectly reflecting boundary--to simulate the proposed FBG-OADM. The simulation results showed that the novel FBG-OADM exhibited a \(-3\) dB bandwidth of 0.0375 nm. In addition, analysis of the spectrum revealed that the drop port achieved the power output of 0 dB at an operating wavelength of 1550 nm.  
fiber bragg grating; optical add-drop multiplexer; finite element method; eigenmode expansion method; perfectly matched layer; perfectly reflection boundary; object meshing method; boundary meshing method +
Footnote †: journal: Applied Physics Letters
## 5 Results
### Construction of the Combined Drought Index (GRACE/Swarm-DSI)  
Figure 6 shows the time series of drought indices from GRACE and Swarm models in the ARB from 2014 to 2019. From Figure 6, we found that two drought indices have the similar trend. The correlation coefficient between GRACE-DSI and Swarm-DSI is 0.6711, and the result passed the 99% confidence level. It explains that GRACE-DSI has a strong correlation with Swarm-DSI.  
To further evaluate the Swarm-DSI, we plotted the spatial distribution of the uncertainties of GRACE-DSI and Swarm-DSI in the ARB (Figure 7). From the two figures, we can see that the uncertainties of GRACE-DSI are much smaller than those of Swarm-DSI.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Correlation Coefficient** & **CSR-SH** & **GFZ-SH** & **JPL-SH** & **ITSG-SH** & **CSR-M** & **JPL-M** \\ \hline Fused results & 0.9962 & 0.9955 & 0.9957 & 0.9950 & 0.8610 & 0.8363 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The correlation coefficients between the fused results and TWSC results from six GRACE solutions (These results have passed the 99% confidence level).  
Figure 5: The time series of TWSC results from six GRACE solutions and fused result.  
Figure 6: The time series of drought indices form GRACE and Swarm models in the ARB from 2014 to 2019.  
Most of the uncertainties of GRACE-DSI are less than 0.6, while those of Swarm-DSI are larger than 0.8. However, GRACE-DSI has a similar distribution of uncertainties with Swarm-DSI, that is, the larger uncertainties are concentrated in the western and southern ARB. We selected the median of all uncertainties gridded values of GRACE-DSI (0.4299) and Swarm-DSI (0.9880) to represent the accuracy of two drought indices. Subsequently, we used Swarm-DSI to fill the gap in the middle of GRACE-DSI to form a continuous long time series of TWSC-based drought index (GRACE/Swarm-DSI, see Figure 8).
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 4 Experimental Results and Discussion
### Total Efficiency  
The overall efficiency of an antenna typically indicates its capability to convert input power into radiated power, directly impacting the transmission range and reliability of the wireless communication system. Higher antenna efficiency results in more effective conversion of transmitted power into electromagnetic waves, thereby enhancing signal propagation range and coverage area. The measurement of an antenna is typically categorized into active and passive testing. In active testing, the emphasis lies on assessing factors such as the antenna's radiation pattern, polarization, and impedance characteristics as it operates in real time. Passive testing is primarily utilized to assess the radiation performance of the antenna, and it focuses on measuring parameters such as antenna efficiency, gain, and radiating pattern. The process of measuring antenna efficiency involves several steps. The proposed antenna was positioned in a microwave anechoic chamber, to minimize external interference. A signal was then transmitted to the antenna using a vector network analyzer. The received power and the output power of the signal source were meticulously measured and compared. Antenna efficiency was subsequently calculated based on these measurements. It was crucial to maintain a reliable connection between the signal source and the antenna to minimize signal loss and ensure precise measurements. Additionally, the process required equipment calibration and careful consideration of potential sources of error. Overall, measuring antenna efficiency demands precision and meticulous attention to detail. As depicted in Figure 11, the total efficiency of both simulated and tested antennas ranged from 41.5% to 86%. Notably, the total efficiency measured during testing was lower  
Figure 10: Simulated and measured envelope correlation coefficient.  
than that of the simulated antenna, possibly attributed to errors in the microwave anechoic chamber and human operational inaccuracies.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 7 Challenges and Future Research Directions
### Advanced Antenna Technologies  
The deployment of single omnidirectional antennas in UAVs is not an optimal case, especially in terms of power management. A directional antenna, on the other hand, may meet this problem with greater power efficiency by beamforming a signal solely towards a conic zone range rather than across all-around areas. It results in a significant reduction in power consumption up to \((2\pi\Phi-1)\) times, where \(\Psi\leq\pi\) is the half-power beamwidth [166]. However, since there is high mobility in a FANET topology, real-time and high-performing beam switching is a crucial task to make sure the data traffic is exchanged between mobile nodes effectively. Directional beamforming and beam switching are applicable by technologies such as MIMO [167; 168; 169] or steerable antennas [170]. Furthermore, the location information of the network should be accessible by the intended UAVs. This requires further research and studies to use narrow beam operations' beam sweeping characteristics to increase coverage and capacity while decreasing interference [58].  
Figure 16: FANET can be equipped with RIS for reducing the number of backhaulings and/or overcoming the blockage in dense urban areas.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results
### Network Configuration Environment  
The network topologies are mostly based on spanning tree concept to allow nodes to be evenly distributed and to keep control of path length. Each node is included in all instances to provide more route options and thus making more complete use of alternative routes available on the network in order to meet the different requirements of each application. In addition, no one node is isolated from the network infrastructure, i.e., all nodes have at least one link with one or more adjacent nodes.  
All the simulation' parameters employed for the configurations of the sink and other nodes are shown in Table 2. They are based on the work provided by [21; 22; 33], where ZigBee Second Generation (IEEE 802.15.4) configurations are presented using CC2520 transceiver emulation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Initial Residual Energy & 80.000 mA \\ Routing Metric & ETX \\ Control Messages Length (DIO e DAO) & 4 bytes \\ Application Message Payload & 40 bytes \\ Sensor Actuator & WiSMote \\ Simulation Time & 24 h \\ PHY layer & IEEE 802.15.4 \\ MAC layer & ContitikiMac \\ Microcontroller & MSP430 \\ Transceiver & CC2520 \\ RAM memory & 768 bytes \\ Processor Clock & 1–16 MHz \\ Maximum transmission rate & 250 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: RPL parameters and nodes’ configuration on Cooja simulator.
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
## Appendix B The \(I\!f\!f\!f\) Attack and Quantum Channel Substitution  
The eavesdropper would try to adjust the two gains, from single and double detection events applying a quantum channel substitution and adjusting it to a specific transmittance. We define the Quantum Photon Error Gain (\(QPEG\) or simply \(\Delta Q\)) as the deviation from the reference gain that is caused by [PERSON]'s apparatus at [PERSON]'s receiver station when she performs the \(1\!f\!f\!f\) attack. In normal conditions, it is expected that \(\Delta Q\sim 0\), ideally for the single and the double detection events.  
We represent the \(QPEG\) of double \((+,+)\) detection events as \(\Delta Q_{(+,+)}\), while the \(QPEG\) of single \((\pm,\mp)\) detection events is written as \(\Delta Q_{(\pm,\mp)}\). \(\Delta Q_{(+,+)}\) is computed as the difference \(Q_{(+,+)_{AB}}-Q_{(+,+)_{EB}}\) where the symbol \((+,+)_{AB}\) defines the reference gain of the double detection events and \((+,+)_{EB}\) denotes the gain of the double detection events at [PERSON]'s side, but in the presence of [PERSON]. Similarly, \(\Delta Q_{(\pm,\mp)}\) as \(Q_{(\pm,\mp)_{AB}}-Q_{(\pm,\mp)_{EB}}\), where we use the sub-index of \((\pm,\mp)_{AB}\) and \((\pm,\mp)_{EB}\) with the same purpose.  
Using the relations of Table 1, it is possible to establish if the eavesdropper can fulfill simultaneously the conditions \(\Delta Q_{(+,+)}=0\) and \(\Delta Q_{(\pm,\mp)}=0\). Let the eavesdropper adjust freely \(\eta_{BT}\) and \(\eta_{ET}\). Thus, [PERSON]'s goal is to make \(\Delta Q_{(+,+)_{AB}}=\Delta Q_{(+,+)_{EB}}\) and \(\Delta Q_{(\pm,\mp)_{AB}}=\Delta Q_{(\pm,\mp)_{EB}}\). We get the following equation system:  
\[2(e^{-\mu\eta_{BT}}-Y_{0})(Y_{0}+1-e^{-\mu\eta_{BT}})=(e^{-\mu\eta_{ET}}-Y_{0} )(Y_{0}+1-e^{-\mu\eta_{ET}}) \tag{1}\]  
\[(Y_{0}+1-e^{-\mu\eta_{BT}})^{2}=\frac{1}{2}(Y_{0}+1-e^{-\mu\eta_{ET}})^{2} \tag{2}\]  
Solving the system for \(\eta_{ET}\), we obtain \(\frac{\ln Y_{0}}{-\mu}\) and \(\frac{\ln(1+Y_{0})}{-\mu}\), which cannot be satisfied in practice. This is true because the second relation yields \(\eta_{ET}\) negative, and the first relation cannot be fulfilled for typical parameters, e.g., \(Y_{0}=10^{-5}\), \(\mu=0.1\) produces \(\eta_{ET}=1.15\). Consider also the cases depicted in Figure 1.
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 5 Conclusions  
In this article, we have presented a novel framework--SatEC--for providing services using 5G satellites. In this framework, all resource nodes are embedded, and they are virtualized into a resource platform for deploying services. All functions of this framework are decomposed into services. There are three kinds of services: system services, basic services, and user services, and they play different roles in the framework. Basic services and user services can be deployed to resource platforms by the system service. Compared to other frameworks, the SatEC framework has the characteristics of broader coverage, lower user latency, a lower packet loss rate, and lower bandwidth consumption.  
In the future, we will build a prototype system with eight embedded resource nodes. Lightweight platform software and a specially designed resource allocation algorithm will be developed to improve service performance. We will also explore the possibility of using more kinds of high-performance embedded platforms in the resource nodes. Finally, more services for 5G satellite users will be deployed on this platform.  
**Author Contributions:** Conceptualization, L.Y. and S.C.; Methodology, L.Y.; Validation, H.H. and Y.Z.; Investigation, S.Y. and J.W.; Writing--Original Draft Preparation, L.Y.; Writing--Review and Editing, H.H., Y.Z., S.Y., and J.W.; Visualization, H.H. and J.W.; Supervision, L.Y., Y.G., and S.C.; Project Administration, L.Y.  
**Funding:** The project was supported by the National Natural Science Foundation of China (No. 617011484), the Open Fund of IROC (BUPT), and the National Defense Science and Technology Innovation Zone Project of China.  
**Conflicts of Interest:** The authors declare no conflicts of interest.