esa-sceva/satcom-chunk-collection huggingface dataset:
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 5 Application of Infrared and Visible Image Fusion
### Application in Detection and Tracking Field  
In the area of target detection, visible and infrared pictures can work in synergy to detect targets. [PERSON] [32] et al. merged the thermal picture and the visual image of the orange tree canopy scene to overcome the limitations of the two imaging techniques and increase the accuracy of fruit detection. First, the fruit can be seen in the visible picture due to the color difference between the fruit and the tree crown, but since the visible image is sensitive to light fluctuations, the fruit may be misclassified. Following the real test, it is clear that the temperature of the fruit in the evening is obviously greater than that of the tree top, allowing the produced infrared image to effectively detect the fruit. Finally, the precision of fruit detection has improved. For the purpose of enhancing the overall effectiveness of the monitoring system, [PERSON] et al. [97] proposed a target detection method based on the fusion of visible and infrared images.  
In succeeding frames of the video, object tracking locates the object specified in the current frame. In order to locate the target item in a time series, the target tracking algorithm must ascertain the relationship between the frames. Single-mode tracking is the most popular but is less robust. Target tracking performance cannot be assured if it is nighttime or there are poor lighting conditions because the quality of the visible picture is  
Figure 18: Example of infrared and visible face images. (**a**) visible light image; (**b**) infrared image.  
highly tied to the imaging environment. This is similar to how infrared photographs lack texture, have a poor understanding of the scene's three dimensions, and cannot guarantee their performance in specific situations. Therefore, [PERSON] [98] et al. proposed a visual tracking method that integrates color images and infrared images, namely, RGBT tracking, which can fuse complementary information in infrared and visible images to make target tracking more robust.
## 2 Related Work  
In order to address collision problems, readers employ coordination mechanisms based on various communication architectures. These architectures can either be distributed among readers or be centralized under the control of a server. The choice of communication architecture determines the nature of the protocol and is influenced by the constraints of the environment.  
The selection of a specific form of communication architecture is crucial, as it directly impacts the effectiveness of the protocol in mitigating collisions. Factors such as network size, the density of readers, available resources, and system requirements play a significant role in determining the most suitable communication architecture.  
By carefully considering the constraints and requirements of the environment, readers can adopt either a distributed or centralized communication approach to coordinate and ensure efficient operation while minimizing collisions.
## 2 Theoretical Analysis of Energy Spectrum Study
### Ground Motion Propagation of the Soil  
#### 2.2.1 Davidenkov Model of Soil  
For the strong seismic excitations of concern here, the linear elastic solution is not valid, since the soil behavior is inelastic, nonlinear, and strain-dependent. In the condition of increasing shear strain, the shear stiffness decreases rapidly, whereas the load reversal makes the soil experience hysteretic behavior with a percentage of material damping [35]. To deal with this, a nonlinear soil dynamic constitutive model (the Davidenkov model) is recommended [40; 41], and was employed in the nonlinear time history analysis.  
The dynamic shear modulus ratio (\(G/G_{\text{max}}\)) of the Davidenkov model is given by [35]:  
\[G/G_{\text{max}}=1-H(\gamma),\,H(\gamma)=\left\{\frac{\left(\gamma/\gamma_{0} \right)^{2B}}{1+\left(\gamma/\gamma_{0}\right)^{2B}}\right\}^{A}, \tag{1}\]  
where \(G\) is the dynamic secant shear modulus, \(G_{\text{max}}\) is the maximum shear modulus, \(\gamma\) is the dynamic shear strain, and \(A\), \(B\), and \(\gamma_{0}\) are the fitting parameters related to soil properties. Given these, the stress-strain skeleton curve of the Davidenkov model can be expressed as follows:  
\[\tau(\gamma)=G\gamma=G_{\text{max}}\gamma[1-H(\gamma)]=G_{\text{max}}\gamma \left[1-\left[\frac{\left(\gamma/\gamma_{0}\right)^{2B}}{1+\left(\gamma/\gamma _{0}\right)^{2B}}\right]^{A}\right], \tag{2}\]  
Figure 1: Lumped parameter model representation of a horizontally layered soil deposit and the aboveground structure.  
The stress-strain hysteresis curve of the Davidenkov model is given by the following equation:  
\[\tau(\gamma)=\tau_{\rm c}+G_{\rm max}(\gamma-\gamma_{\rm c})\left[1-H\left( \frac{|\gamma-\gamma_{\rm c}|}{2}\right)\right] \tag{3}\]  
where \(\tau_{\rm c}\) and \(\gamma_{\rm c}\) are the values of soil shear stress and shear strain at the last load reversal, respectively. To illustrate this model intuitively, its dynamic shear stress-shear strain curves are plotted in Figure 2, taking \(\gamma_{0}=0.1\%\) as an example.  
By deriving \(\gamma\) in Equations (2) and (3), the tangent shear modulus of the stress-strain curves, \(G^{t+\Delta t}\), can be given as follows:  
\[G^{t+\Delta t}=\frac{\partial\tau^{t+\Delta t}}{\partial\gamma^{t+\Delta t}} =\left\{\begin{array}{ll}G_{\rm max}\left[1-\left[1+\frac{2.A\cdot B}{1+ \left|\frac{\gamma-\gamma_{\rm c}}{2}\right|^{2B}}\right]\cdot H\left(\left| \gamma\right|\right)\right]_{\rm I+\Delta t}&(\mbox{\it for skeleton curve})\\ G_{\rm max}\left[1-\left[1+\frac{2.A\cdot B}{1+\left|\frac{\gamma-\gamma_{ \rm c}}{2\gamma_{0}}\right|^{2B}}\right]\cdot H\left(\left|\frac{\gamma-\gamma _{\rm c}}{2}\right|\right)\right]_{\rm I+\Delta t}&(\mbox{\it for hysteresis curve})\end{array}\right., \tag{4}\]  
where superscript \(t\) and \(t+\Delta t\) represent values at the end of the previous and current increment, respectively.
# Design and Stiffness Optimization of Bionic Docking Mechanism for Space Target Acquisition
## 1 Introduction
(2) An integrated dynamic equation of the bionic docking mechanism in space docking and a stiffness optimization strategy based on the angular momentum conservation are proposed. (3) Facing the optimization of the bionic docking mechanism, the stiffness coefficients of torsion springs and linear spring are considered as particle swarms and the particle swarm optimization (PSO) algorithm is adopted to realize stiffness optimization. Numerical simulation and prototype experiments are carried out to verify the effectiveness. The rest of this paper is organized as follows. In Section 2, we introduce the structure and function of the proposed bionic docking mechanism. Section 3 gives the dynamic equations based on Kane approach. In Section 4, a control scheme employing particle swarm optimization (PSO) is proposed. Sections 5 and 6 can separately provide simulation examples and experiment results to verify the proposed approach and principle. Finally, we conclude all of the work in Section 7.
# MAC Layer Protocols for Internet of Things: A Survey
## 6 Conclusions  
The study performed in this work explores the diverse characteristics of most of the MAC layer protocols used that have been adapted, evolved or created to meet the increasing needs and demands of new emerging IoT applications. The aspects addressed here are mainly related to physical and MAC layers bringing a comparative panorama to the range coverage, data rates, robustness and energy efficiency aspects.  
The discussion illustrates the different operating and performance characteristics of each protocol for different applications, within the same scenario, based on different MAC layer technologies. The lack of interoperability between protocols that challenges their heterogeneity in several aspects leads the paradigm of homogeneity to its rupture. Characteristics of robustness, distance coverage, data transmission rates, and energy efficiency are aspects that contribute to protocol diversity allowing them to serve diverse applications.  
Finally, the MAC layer characteristics necessary to be considered in the development of hybrid or heterogeneous platforms were presented. This interoperability can always be obtained at the application level; however, the interoperability of lower-layers can increase the performance of hybrid or heterogeneous devices, mainly in terms of energy consumption.  
**Author Contributions:** [PERSON] collected and performed a deep analysis and reviewed the related literature on the topic, wrote the first draft of the document, performed the comparison study and identified some open research issues. [PERSON] supervised all the study, consolidated the comparison analysis and open issues, reviewed the structure and the first draft. All the other authors reviewed the text carefully, verified the comparison study, and reviewed the identified open issues. All the authors contributed equally to the scope definition, motivation, and focus of the paper.  
**Funding:** This study was financed by National Funding from the FCT--_Fundacao para a Ciencia e a Tecnologia_ through the UID/EEA/50008/2013 Project; by the Government of the Russian Federation, Grant 08-08; by Finep, with resources from Funtel, Grant No. 01.14.0231.00, under the _Centro de Referencia em Radio communications_-CRR project of the _Instituto Nacional de Telecomunicacoes_ (Inatel), Brazil; by Brazilian National Council for Research and Development (CNPq) via Grant No. 309335/2017-5; and by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Discussion
In addition, each image sequence acquisition time can be improved by pixel-to-pixel binning, a use of objective lenses with higher numerical aperture, an installation of a multiband fluorescence filter set for eliminating time consumption by changing filter sets, and an application of a high-speed camera designed for voltage potential measurements in an animal brain. In this study, DEFINITY(r), which is most widely employed as an ultrasound imaging contrast agent, was applied to the induction of the ultrasound-microbubble cavitation, and the use of functionalized microbubbles is believed to open a possibility of high-performance drug delivery. For instance, several studies of target binders designed for nanoparticles for the attachment of specific, targeted cancer cells have been actively progressed [30; 31], and an implementation of these target binders to the microbubble can further improve selectivity as the functionalized microbubble induces the cavitation and drug delivery to selected cancer cells. Also, it is expected that parametric studies exploring conditions appropriate for ultrasound-microbubble cavitation by applying different conditions of ultrasonic probes will provide meaningful information for the practical use of sonaporation-based drug/gene delivery. The established platform has the appropriate conditions to conduct these comparative studies.
# Research on the Obstacle-Avoidance Steering Control Strategy of Tracked Inspection Robots
## 5 Obstacle-Avoidance Steering Control Strategy and Simulation Analysis
### Principle Analysis of Obstacle Avoidance Steering in Right Angular Obstacles  
The obstacle-avoidance steering motion of the robot in the face of angular obstacles is illustrated in Figure 7. There are various ways for a robot to choose during the steering process. According to the steering mode, the robot can be divided into center- and small-radius steering motions.  
The center steering motion is used to complete angular obstacles; the schemes adopted are ABCHEFG and ABFG, as shown in Figure 7, curve 1 and 2. The former only needs one center steering movement, while the latter needs two center steering movements to complete. When BH and HF are equal, equilateral center steering occurs; when BH and HF are not equal, non-equilateral center steering occurs, and the sum of the two steering angles is 90\({}^{\circ}\). The robot used the small-radius steering mode to complete the angular obstacles, and the ABCHEFG scheme was adopted, as shown in Figure 7.  
Figure 6: Motor contour curve under cement road.  
During the steering process of the robot, the steering radius can be determined by using the following formula:  
\[R=R_{s}+B_{0}+B \tag{18}\]  
where \(R_{s}\) is the distance of the robot from the side obstacles, which is also the safe distance for the robot not to collide with the side obstacles when driving and can be determined according to the size of the driving space. \(B_{0}\) is the distance between the center of the robot's track and its boundary.  
In several steering schemes, the control mode of the robot driving the wheel motor is completely different; for example, the rotation direction and size of the motor are not consistent; therefore, it is necessary to determine the driving parameters of the robot in the face of angular obstacles.
# Effects of Fractional Time Delay as a Low-Power True Time Delay Digital Beamforming Architecture
###### Abstract
The proposed system was implemented in a Field Programmable Gate Array (FPGA) and then compared to the relevant alternative systems available in the literature.
## 6 Conclusions  
A compact and planar dual-notch band antenna with the dimensions of 28 mm \(\times\) 40 mm printed on a low-cost FR4 substrate has been proposed for UWB systems. A dual-band rejection has been investigated by inserting three MTM-SSRRs and a triangular slot on the radiating patch. The measured results show that the fabricated antenna is appropriate for the UWB band operation between the frequency range of 3-12 GHz and a dual band-notched characteristic at 4.17-5.33 GHz and 6.5-8.9 GHz. The first notched-band cover some portion of the C-band and the second notched-band cover the downlink frequency range of the X-band satellite communication systems. Furthermore, omni-directional radiation pattern characteristics have been observed with a gain of \(\sim\)2.5 dBi, \(\sim\)5 dBi, and \(\sim\)5.3 dBi at the center frequency of 3.5 GHz, 6.1 GHz, and 9.25 GHz, respectively. The tunability/controllability of the dual-notch band has been investigated by modifying the width of the triangular and the SSRR slots. The simulated and measured results of the proposed antenna show good agreement with each other, and it is envisioned that the proposed antenna will become an excellent candidate for the 5G mobile BSs, next-generation WiFi-6E IDAS, as well as C-band and X-band applications.  
[PERSON], [PERSON] coined the idea, led, supervised, and evaluated the entire project: edited the write-ups, designed, and simulated the proposed antennas. [PERSON], [PERSON], [PERSON] performed the experimental measurements. [PERSON] completed the initial draft of the paper; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] then analyzed, evaluated, and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"Deanship of Scientific Research, Islamic University of Madinah, Madinah, Saudi Arabia, under the Research Grant No. 657.  
The authors declare no conflict of interest.
# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 4 Experiment, Analysis and Discussion
### Experimental Setup
\(\text{SVM}_{\text{com}}\)\({}^{\text{DTS}}\) and \(\text{SVM}_{\text{all}}\)\({}^{\text{DTS}}\)[19]: [PERSON] constructed the \(\text{SVM}_{\text{com}}\)\({}^{\text{DTS}}\) model to capture the dynamic sequence features that change over time. Additionally, on this basis, [PERSON] proposed a new rumor detection model \(\text{SVM}_{\text{all}}\)\({}^{\text{DTS}}\) with added post popularity, post ambiguity of the spread of rumor events and post spread. This paper compares these two models with our proposed model. GRU-2 [9]: The author uses the RNN model for rumor detection for the first time. In [PERSON]'s work, five models of tanh-RNN, LSTM-1/LSTM-2 and GRU-1/GRU-2 were implemented. This paper selects the best GRU-2 model for comparison and verification. CNN [29]: [PERSON] proposed to use the CNN model to detect rumors. The scholar used Doc2 Vec to make the post text vector and set the dimension of the filter to the length of the text vector to achieve a good detection effect. CallAttRumors [48]: [PERSON] proposed a deep attention model based on a recurrent neural network. This model can selectively learn the time series representation of posts, collect different features with specific focus and mine hidden features. This model is better than the baseline model in performance. CNN-TS and CNN-TS\({}^{\text{SVM}}\)[10]: [PERSON] merged time series features on the basis of the CNN model and proposed the CNN-TS model. The scholar also improved the classification function, enhanced the generalization ability and proposed a new CNN-TS\({}^{\text{SVM}}\) model. This paper selects these two models for comparison. Regarding the setup of the CNN with the attention mechanism, the CNN-TS model proposed by [PERSON] et al. [10] is composed of an input layer, a convolution layer, a pooling layer and a fully connected layer. Additionally, they proved that the model can achieve the best recognition effect. The model proposed in this paper is improved on the basis of [PERSON]. We refer to the settings of the [PERSON] model to set up our model. The details are shown in Table 3.  
The height of the filter is set to 3, the number is 3, the vector dimension \(k\) of the text is set to 50, the size of the filter is also set to 50 and the dropout rate is 0.5. And 10-fold cross validation is used for the experiment.
## 3 Optimum Parameters Selection
### Noise Correlation Effects  
As shown in Section 3.1, at least 20 samples are required per signal period to achieve a good performance (interference rejection \(\geq\) 20 dB) of the RFI mitigation algorithm (or 300-400 samples per period for an interference rejection \(\geq\) 30 dB), while the Nyquist sampling rate requires just two samples per period. It is worth to address the effect of the correlation between consecutive samples introduced by the receiver's frequency response.  
To analyze this effect, the input white noise (zero-mean random Gaussian variable) is convolved with a constant FIR (Finite Impulse Response) filter of variable length (\(C\)) and amplitude such that the variance of the output remains the same (\(\sigma^{2}\)=\(1\))3. The RFI is then added to the \"colored\" (filtered) noise and the wavelet-based RFI mitigation algorithm is applied. Figure 8 shows the average of 10 realizations using different correlation lengths from 1 (no correlation) up to 32. Results show that the impact of the correlation in the interference mitigation is negligible (\(<0.1\) dB). Results appear a bit noisy, but this is due to the small scale in the vertical axis.  
Footnote 3: The practical implementation of the filtering is carried out with the in-phase and quadrature low-pass signals, from which the filtered band-pass signal is then reconstructed.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 5 Case Study: In-Process Melt-Pool Monitoring
### Analyzing Data  
We used a K-means clustering algorithm to find the possible geometric-shape and defect patterns that can arise in the melt pools created during the build on which this study is based. K-means clustering is one of the famous algorithms for finding such patterns. K-means algorithm divides the dataset into predefined \"clusters\", with the property that each data point belongs to one, and only one, cluster. The algorithm attempts to make each cluster have approximately the same number of data points [42]. It allocates the data points to a cluster by minimizing their Sum-of-Square distance (SSD). We chose to minimize SSD based on the idea that, the more similarity among the data points (the lower the SSD, the likelier it is that they belong in the same cluster.  
These clusters become the basis for creating the two previously mentioned patterns: shape or defect. The next step is to identify which cluster belongs to which pattern, but first, we provide the algorithm.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 4 Discussion and Outcomes
### UWB Antenna Results
#### 4.1.4 Time-Domain Characteristics  
The phase response, group delay, and isolation properties of the proposed UWB antenna can only be determined by a time-domain analysis. As can be seen in Figure 13, two identical antennas were placed in HFSS 100 mm apart for these tests. The time-domain study considered both forward- and side-facing scenarios.  
Figure 12: The simulated radiation pattern of the UWB antenna at (**a**) 3.6 GHz, (**b**) 6.8 GHz, and (**c**) 9.7 GHz.  
In Equation (5), GD is the derivative of phase response to angular frequency.  
\[\tau_{g}(\omega)=-\frac{d\varphi(\omega)}{d\omega} \tag{5}\]  
The proposed UWB antenna's group delay is depicted in Figure 14. As can be seen in Figure 14, the group delay of the antenna remains relatively the same, regardless of whether it is operating in a face-to-face or side-to-side configuration.  
Figure 14: Group delay proposed UWB antenna.  
Figure 13: Time-domain analysis with orientations, viz., (**a**) frontal and (**b**) lateral.  
Figure 15 shows the antenna's phase response. Linear phase variation yields desirable time-domain features. Figure 15 demonstrates that the phase fluctuation of the antenna is nearly linear during face-to-face and side-to-side operations.  
Isolation features of the proposed UWB antenna are depicted in Figure 16. Figure 16 shows that the antenna has good isolation characteristics (\(S_{21}<-20\) dB).  
Figure 16: Isolation characteristics of the UWB antenna.  
Figure 15: Phase response \(S_{21}\) of the proposed UWB design.  
Figure 19 shows that a maximum gain of 4.9 dB is achieved for the UWB microstrip antenna.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Lisa  
LISA (Laser Interferometer Space Antenna) [10] was a gravitational wave observatory jointly established by NASA and the ESA. It was introduced in 1998. In 2011, NASA withdrew from the development due to funding reasons. The ESA simplified the mission configuration side length from 5 million km to 1 million km and renamed it the eLISA program. The goal of this project is to detect gravitational waves, with a focus on detecting the mergers between deep blank dwarf binaries and black holes. LISA is composed of three spacecraft with a height of 1.8 m, equipped with laser interferometers to form a triangle. They are positioned in a heliocentric orbit, following the Earth around the sun to detect the characteristics of gravitational waves, as shown in Figure 5, with a measurement accuracy of 1 \(\upmu\)m. The three spacecraft independently conduct relative state measurements and formation maintenance. The current plan establishes a side length of 2.5 million km, with a measurement accuracy of 10 m [11].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Validation Phase** & **Measurement Technology** & **Measurement Range** \\ \hline Autonomous Formation Flying & Phoenix-S GPS Receiver System & 20 m\(\sim\)10 km \\  & Formation Flying Radio Frequency (FFRF) & \\ \hline Homing and Rendezvous & Vision-Based Sensor (VBS) & 3 m\(\sim\)100 km \\ \hline Precision Three-dimensional Proximity & Phoenix-S GPS Receiver System & 3\(\sim\)100 m \\ Operations & Vision-Based Sensor (VBS) & 0\(\sim\)3 m \\ \hline Final Approach and Recede Maneuvers & Vision-Based Sensor (VBS) & 0\(\sim\)3 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: SAFE verification phases.  
Figure 4: Diagram of the virtual telescope, XEUS.