esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Sensor Types and Specifications  
Our SNN-based mobile robot navigation and obstacle avoidance system utilizes two main types of sensors: ultrasonic sensors and color detection sensors.  
Ultrasonic sensors: These sensors emit sound waves and calculate the time it takes for the waves to bounce back, enabling them to detect obstacles and measure distances. The signals from these sensors are encoded in the sensory neurons of our SNN-based system, which control the movements of the robot. Ultrasonic sensors have been proven effective in obstacle detection and avoidance through previous studies [31].  
The main specifications of ultrasonic sensors include the frequency at which they operate, the detection range they can cover, the beam angle that determines the scanning area, the accuracy of distance measurements, the response time for detection, the output format of distance measurements, the power requirements, environmental considerations, and integration and connectivity options. These specifications can vary depending on the specific model and manufacturer of the ultrasonic sensor.  
Color detection sensors: These sensors analyze the wavelengths of light reflected from objects to identify targets and obstacles. The main specifications of this sensor include the wavelength range they operate within, their resolution for distinguishing colors, accuracy in color identification, detection speed, output formats for color information, integration, connectivity options, considerations for ambient lighting and environmental factors, and power requirements. Like ultrasonic sensors, the signals from these sensors are encoded in the sensory neurons of our network, guiding the movements of the robot. Research has also demonstrated the effectiveness of color detection sensors in obstacle detection and avoidance [32]. Together, these sensors and our SNN model form the basis of our  
Figure 1: A schematic model of a two-wheeled mobile robot.  
methodology. Our system can make informed decisions by processing data from these sensors, enabling effective navigation and obstacle avoidance in real-world environments.  
Additionally, our system includes a mapping component that allows the robot to build a representation of its environment. By using the data gathered from the sensors, the robot can create a map that includes information about obstacles, landmarks, and other relevant features. This map is a reference for the robot's navigation, enabling it to plan efficient paths and avoid potential obstacles. To ensure real-time operation, our SNN-based mobile robot navigation system is implemented on a powerful embedded platform capable of handling the computational demands of the neural network and sensor data processing. This platform also provides the necessary interfaces to control the robot's actuators and communicate with external devices.
# Tunable 60 GHz Multiwavelength Brillouin Erbium Fiber Laser
## 1 Introduction
However, a fiber Bragg grating (FBG) was used to extract the first-order quadruple Stokes signal, which added more cost and complexity to the cavity. A wide quadruple MBEFL cavity was suggested by [32]. Four quadruple Stokes lines at many output ports were generated. The generated Stokes lines had a a high Stokes power of 10 dBm and were tuned over 60 nm. Brillouin Stokes lines with a quintuple frequency shift (50 GHz) were proposed by [33]. Four quintuple channels with a maximum power of 10 dBm and a large optical signal-to-noise ratio (OSNR) of more than 50 dB were achieved. In the absence of the self-lasing cavity mode, the generated Stokes lines were tuned over a 40 nm from 1540-1580 nm. In terms of real-time applications of optical communication systems, a quintuple Brillouin frequency shift (50 GHz) can achieve a data rate of about 200 Gb/s per single channel. Increasing the channel spacing to 60 GHz is required for a high data bit rate of 300 Gb/s per channel. To the best of our knowledge, no multiwavelength Brillouin erbium fiber laser with 60 GHz spacing has been produced. In this paper, experimental results on sextuple Stokes lines (60 GHz) are proposed. Four Brillouin Stokes lines with a sextuple frequency shift (0.48 nm) with a large OSNR of more than 50 dB and a high Stokes power of 10 dBm are achieved. The achieved signal can be tuned over 30 nm from 1560 to 1590 nm. Up to 62 channels with 60 GHz spacing can be implemented within the achieved 30 nm tuning range. In addition, most of the useful sextuple signals lie between 1570 and 1590 nm in the L-band region of DWDM communication systems. The wide tuning range was obtained due to two parameters: a high Brillouin pump (Bp) power and pre-amplification cavities that provide two factors, i.e., an increase in Brillouin gain and a reduction in the self-lasing cavity modes' competition.
## 3 Reconstruction Method and Protection Algorithm
### Theoretical Interpretation of Current Trajectory Image Features  
To further explain the underlying mechanism of current trajectory-based fault identification, this section provides a theoretical interpretation of how internal and external faults produce distinguishable patterns in two-dimensional current trajectory images.  
The trajectory image is constructed using the terminal current measurements of the PV and grid sides, defined in Formulas (4) and (5). Each image point corresponds to a pair of current values (\(i_{\text{p}}\)(t),\(j_{\text{g}}\)(t)) over time. These images are formed by mapping these current pairs into a 2D coordinate space.  
Under external fault conditions, the PV and grid-side currents are nearly equal in magnitude but opposite in direction due to the symmetrical response to a remote fault. This results in trajectory images that are approximately symmetric with the line y = \(-\)x. In contrast, internal faults lead to unequal current contributions from each terminal: the power grid often injects a higher fault current than the PV source, which is limited by inverter constraints. This imbalance causes the image trajectory to shift away from the origin, producing a distinct geometric pattern, typically eccentric or asymmetric.  
This geometric distinction can be quantified by:  
(1) Centroid deviation: Internal fault images tend to have a centroid that shifts away from the origin, indicating asymmetric current flow.  
(2) Radial variance: The spread of trajectory points in polar coordinates shows higher fluctuation in internal faults.  
These properties enable effective fault classification. Figures 9a,b and 10a,b illustrate the differences in trajectory images between external and internal faults. These physical and geometric properties allow convolutional neural networks (CNNs) to extract discriminative features from trajectory images. In our A-CNN, shallow layers capture local curvature patterns, while deeper layers summarize global spatial characteristics. The model, therefore, learns a data-driven decision boundary between internal and external fault image classes.  
This mechanism is supported by recent studies [18], which theoretically analyze fault-induced trajectory distortions and demonstrate how spatial patterns reflect fault location and system asymmetry. Our method builds upon this foundation with a lightweight CNN design and simplified fault modeling to balance real-time applicability and classification accuracy.
## 3 Fast Handover Algorithm
### Process of the Fast Handover Algorithm  
The process of the fast handover algorithm includes the speed decision and location recognition of UE, and the weight calculation of the handover.
## 3 Results
### Training Performance of SC-MADDPG  
In this section, we analyze the training performance of our proposed SC-MADDPG optimization method. In the multi-UAV-assisted MEC system, we propose optimal path planning for UAVs and computation task assignment. The training curve of the SC-MADDPG optimization method is shown in Figure 5. The reward value is unstable during the 0-1000 rounds of training, and it slowly rises during the 1000-2000 training rounds. After 2000 rounds of training, the reward value of the SC-MADDPG algorithm is in a relatively stable state. This demonstrates that the SC-MADDPG algorithm has a certain convergence.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of mobile devices & 80 \\ \hline Number of UAVs & 4 \\ \hline Number of edge servers & 2 \\ \hline Task size & [1; 5] Mbits \\ \hline Number of CPU cycles required topprocess 1-bit data & \([100,200]\) cycles/bit \\ \hline Task achievement rate & 1 task/s \\ \hline Maximum flying altitude of unmanned aerial vehicle & 100 m \\ \hline Minimum flying altitude of unmanned aerial vehicle & 50 m \\ \hline Maximum horizontal flight distance of unmanned aerial vehicle & 49 m \\ \hline Vertical maximum flight distance of unmanned aerial vehicle & 12 m \\ \hline Unit path loss & \(-50\) dB \\ \hline Channel bandwidth from device to UAV & 10 MHz \\ \hline Channel bandwidth from UAVs to edge servers & 0.5 MHz \\ \hline Maximum transmission power of UAVs & 5 W \\ \hline Transmission power of mobile devices & 0.1 W \\ \hline Received power of UAVs & 0.1 W \\ \hline Computing resources of UAVs & 3 GHz \\ \hline Computing resources of edge servers & [6; 9] GHz \\ \hline Effective Switched capacitor & \(10^{-28}\) \\ \hline Gaussian noise at UAV & \(-100\) dBm \\ \hline Gaussian noise at the edge server & \(-100\) dBm \\ \hline Weight \(\omega_{1},\omega_{2}\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation environment parameters.  
Figure 5: Training Set Rewards.  
The flight trajectory of the UAVs is shown in Figure 6. The black pentagram represents the mobile user devices, and the four different colored dots represent the four UAVs. As can be seen from the experimental results, the flight trajectories of each UAV are relatively concentrated, with the flight range not exceeding \(1/4\) or \(1/2\) of the entire specified range. This indicates that the four UAVs can cover all mobile user devices by communicating with each other, and only one UAV provides service to user devices without any overlapping coverage.
## 5 Experiment Result
### System Linear TF Estimation and System TFs  
As shown in Figure 14, the magnitude of the linear TF of the system has an extreme point at 10 Hz, so the corresponding extreme point also has an extreme point at 10 Hz in NSRE.  
Figure 14: System linear TF estimation.  
Figure 13: Iteration coefficient related data in the second, sixth and tenth iterations with a modified method. The x axis of each plot represents the real portion, the y axis represents the imaginary portion, and the color varying from black to white represents the frequency. From left to right, the plots in the first row are \(P_{k}\), \(\mathcal{M}_{k}\), \(\mathcal{L}_{k}\), and the second row are \(\mathcal{M}_{k}/\mathcal{L}_{k}\), \(\Psi_{k}\), \(\Psi_{k}^{s}\).  
Under the control of strategy 1, the system TF estimation of the iterative process is shown in Figure 15. Although the estimated iterative TF of the system is similar to the linear transfer function in general shape, the iterative TF of the system changes after each iteration, so it can be judged that the system is not completely linear.  
The system linear TF estimation \(H_{L}\) is shown in Figure as an initial estimation before the iteration. It is calculated with the standard road drive and the test response on the bench test. The system linear TF estimation \(H_{L}\) used in iteration remains the same, while the system TF estimation in iterative process \(H_{k}\) is variable and shown in Figure. According to the traditional RPC, the system linear TF estimation \(H_{L}\) is used to calculate the drive correction with response error. The system TF estimation in iterative process \(H_{k}\) is used to calculate the iteration coefficient based on the strategies proposed in this paper.  
The system under test in this paper is the front part of the motorcycle, including wheel, suspension and frame; these components are nonlinear. The nonlinear characteristics of the vehicle under test will make the TF of the system under test vary with the input. In the iteration process, as the iteration progresses, the iterative input gradually approaches the real road. Therefore, when the iterative input keeps changing, the TF of the system under test will also change.
## 3 Comparative Analysis of Existing Evaluation Methods
### Existing Evaluation Methods
#### 3.1.5. [PERSON]'s Liquefaction Probability Assessment Method  
[PERSON] et al. [22] compiled 36 sets of severe liquefaction data from the 2011 New Zealand Canterbury earthquake (\(M_{W}=6.3\)) and integrated them with 225 sets of Andrus' liquefaction data, creating a new database comprising 261 sets of sand and silt data. Using \(V_{s1,CS}\) and the natural logarithm of the \(CSR_{7,5}\) as the influencing parameters, they established liquefaction probability models for sand and silt based on the logistic model, probit model, log-log model, and complementary log-log model:  
Logistic:  
\[P_{L}=\frac{1}{1+\exp\{-[b_{0}+b_{1}V_{S1,CS}+b_{2}ln(CSR)]\}} \tag{14}\]  
Probit:  
\[P_{L}=\varnothing p[b_{0}+b_{1}V_{S1,CS}+b_{2}\text{ln}(CSR)] \tag{15}\]  
Log-log:  
\[P_{L}=\exp\{-\exp[-(b_{0}+b_{1}V_{S1,CS}+b_{2}\text{ln}(CSR))]\} \tag{16}\]  
Complementary log-log:  
\[P_{L}=1-\exp\{-\exp[b_{0}+b_{1}V_{S1,CS}+b_{2}\text{ln}(CSR)]\} \tag{17}\]  
Here, \(b_{0}\), \(b_{1}\), and \(b_{2}\) are the model parameters, and \(\Phi\) is the cumulative distribution function of the standard normal distribution. Comparing the assessment results of the four models, the log-log model and logistic model were considered optimal. However, the log-log model is relatively less conservative, whereas the logistic model is more balanced. This paper exclusively selects the logistic model for comparative analysis, excluding the log-log model from the comparison scope. The liquefaction probability assessment formula is as follows:  
\[P_{L}=\frac{1}{1+\exp\{-14.3931+0.0552V_{S1,CS}-2.8628 ln(CSR)\}} \tag{18}\]
# An Eco-Driving Controller Based on Intelligent Connected Vehicles for Sustainable Transportation
## 2 The Cooperative Car-Following System Based on Eco-Driving
### The Communication Network of the Cooperative Car-Following System  
The interaction of the information flow between any of the two vehicles is defined as communication topology. In this proposed cooperative car-following system, information exchange among vehicles through different communication topologies can be described as Figure 3.  
Figure 3: The communication network of the cooperative car-following system.  
Figure 2: The hardware structure of ICVs.  
As shown in Figure 3, ICVs can accurately obtain real-time information, such as vehicular position, acceleration, velocity, spacing, and traffic signal timing, through multiple sensors on the perception layer. Then, communication is established between ICVs through V2X communication to identify the driving movements of surrounding ICVs, and they share the collected information. The communication protocols between two ICVs are shown in Table 1. Through V2X communication, the preceding ICV will transfer its velocity, acceleration, position, and other state metrics to the following vehicle. After receiving the information of the preceding vehicle, the decision layer of the following vehicle will fuse its own information and environmental information to calculate the optimal acceleration. Finally, the desired control parameters will be transferred to actuators on the control layer via the CAN-Bus.
# Region-of-Interest Based Coding Scheme for Live Videos
###### Abstract
This multiplier links the distortion metric D (which is directly related to the Quantization Parameter, QP) with the number of bits R (also associated with QP). By doing so, it facilitates the evaluation of all feasible coding modes and selects the one that minimizes the overall cost function. Thus, rate control fundamentally ensures an effective trade-off between visual quality and bitrate efficiency. \[cost=D+\lambda\times R \tag{1}\]  
All the previously developed rate control (RC) algorithms in HEVC have not adequately considered the varying importance of different regions within a video frame. In response, we propose a novel rate control scheme tailored for high bit rate live streaming systems that separately addresses Regions of Interest (ROI) and Regions of Uninterest (ROU). Given that the reference software HM's encoding speed is insufficient to meet the real-time requirements of live streaming transcoding, our proposed algorithm builds upon the open-source encoding software x265. The algorithm introduces three distinctive features: Firstly, it employs the open-source mm-detection [19] tool to automatically detect ROI in each video frame and generate an ROI map accordingly. And mm-detection is an object detection toolbox that contains a rich set of object detection, instance segmentation, and panoptic segmentation methods as well as related components and modules. Secondly, we introduce a reservoir model to predict and supervise the overall bitrate increase, enabling real-time adjustment of rate control allocation bits for both ROI and ROU regions. Lastly, we allocate target bitrates to each ROI and ROU region with a focus on maintaining the stability of the live stream size; recognizing the multitude of factors affecting rate stability, we propose a Convolutional Neural Network (CNN) model to predict joint rate control parameters for ROI and ROU. The paper is structured as follows: Section 1 presents an overview of the general rate control problem and existing HEVC solutions. Subsequently, Section 2 elucidates the details of our proposed rate control approach. The experimental evaluation and results are discussed in Section 3. Finally, Section 4 offers a thorough discussion of the findings, while Section 5 concludes the paper with summarizing remarks and future directions.
## 3 The Constitutive Relationship of Cellular Medium
### Homogenization Algorithm Model
The model assumes that each phase of material obeys Weibull distribution. The damage evolution process is actually the process of strain energy release. In the process of strain energy release, the material will soften, and the macroscopic manifestation is the degradation of elastic modulus and the decrease of load-bearing capacity. The functional relationship between damage factor \(\mathrm{D}\) and failure factor \(\mathrm{f}\) can be defined as a variety of functional forms, so that the occurrence and evolution of damage can be described flexibly. The damage evolution function \(\varphi(\mathrm{f})\) includes: the step function damage evolution model Equation (24), power function damage evolution model Equation (25), linear softening damage evolution model Equation (26) and exponential function damage evolution model Equation (27) [97; 102; 103; 104]. \[\varphi(\mathrm{f})=\left\{ \begin{array}{cl}0&\mathrm{if\ f<f_{min};}\\ \mathrm{D_{max}}&\mathrm{otherwise}\\ \end{array} \right. \tag{24}\]  
\[\varphi(\mathrm{f})=\left\{ \begin{array}{cl}0&\mathrm{if\ f<f_{min};}\\ \mathrm{D_{max}}\times\frac{\mathrm{f}^{\mathrm{ex}}-f_{min}^{\mathrm{ex}}}{ \mathrm{f}_{max}^{\mathrm{ex}}-f_{min}^{\mathrm{ex}}}&\mathrm{if\ f_{min}\leq f<f _{max};}\\ \mathrm{D_{max}}&\mathrm{otherwise.}\\ \end{array} \right. \tag{25}\]\[\varphi(f) =\left\{ \begin{array}{cc}0&\text{if $f<f_{\text{min}}$;}\\ \text{D}_{\text{max}}\times\frac{f_{\text{max}}}{f}\times\frac{f-f_{\text{min}}} {f_{\text{max}}-f_{\text{min}}}&\text{if $f_{\text{min}}\leq f<f_{\text{max}}$;}\\ \text{D}_{\text{max}}&\text{otherwise.}\end{array} \right. \tag{26}\] \[\varphi(f) =\left\{ \begin{array}{cc}0&\text{if $f<f_{\text{min}}$;}\\ \text{D}_{\text{max}}\times\left(1-\exp\left(-\frac{f^{\text{ex}\phi}-f_{\text {min}}^{\text{ex}\phi}}{\text{e}\text{e}\text{e}}\right)\right)&\text{otherwise.} \end{array} \right. \tag{27}\]  
where f is the failure factor of the material, fmin is the minimum value, and fmax is the maximum value. Dmax is the maximum value of material damage coefficient. \(\alpha\) and \(\beta\) are the material response parameters. Although the above models can describe the stress-strain relationship of cellular media well, they cannot explain the mechanism during stretching or compression. The constitutive models of high strain rate, cell size and doped particles cannot be obtained. Therefore, it is necessary to deeply investigate the constitutive models of loading conditions and cell structure of cellular medium.
## 2 RNP-WMN Problem
### Problem Formulation
#### 2.2.1 Connected Router  
The mesh router \(r_{i}\) is a connected router if and only if there exists a path from it to at least one gateway router in the WMN.  
To better understand the concepts of connected routers, consider the example shown in Figure 2, where a WMN consists of 18 mesh routers, 30 mesh clients and 1 gateway router (\(r_{gu}\)), located in an area of 2000 \(\times\) 2000 meters. For the current state, mesh routers \(r_{2},r_{3},r_{4},r_{5},r_{6},r_{8},r_{9},r_{10},r_{11},r_{12},r_{13},r_{14}, r_{15},r_{16}\) and \(r_{18}\) are connected routers because there are paths from these mesh routers to the gateway router (\(r_{gu}\)). The mesh routers \(r_{1}\), \(r_{7}\) and \(r_{17}\) are not connected routers because there is no path from these routers to the gateway router.  
Figure 2: An example of the topology of the WMN.
## 2 Data and Methods
### Variable Time Series and Trend Analysis  
The corresponding data of the five models of the Qinghai-Tibet Plateau from 2015 to 2100 are extracted to calculate the CUE, GPP, NPP, mean annual precipitation (MAP) (Figure A2 in Appendix A), mean annual temperature (MAT) (Figure A1) and CO\({}_{2}\) concentration (Figure A3). Based on the monthly data of the corresponding model range of the Qinghai-Tibet Plateau in the historical period (1951-2014), the annual average values of CUE, GPP, NPP, MAP and MAT in this period are calculated. The deviation values of the future period relative to the historical period among different model are analyzed to forecast the future climate change in the Qinghai-Tibet Plateau terrestrial ecosystem and the change trends of GPP, NPP and CUE under future climate scenarios in a long time series. We used the commonly used climate tendency rate to examine time gradient [42], where this is defined by:  
\[\mathrm{Y_{i}}=\mathrm{a_{0}}+\mathrm{at_{i}}, \tag{1}\]  
where \(\mathrm{Y_{i}}\) is dependent variable for each year (ith year), a is the slope (that is tendency rate, indicating the annual change trend of change factors) and \(\mathrm{t_{i}}\) is the independent variable that means the time series (ith year) and the \(\mathrm{a_{0}}\) is the intercept. In this paper, the change trend of the long time series of CUE, GPP, NPP, MAP, MAT, and CO\({}_{2}\) concentrations in the Qinghai-Tibet Plateau is expressed by the tendency rate. If \(p\)-value is less than 0.05, it means the original hypothesis can be rejected.
# Research on Ultra-Low-Frequency Communication Based on the Rotating Shutter Antenna
## 4 Experimental Results  
The experimental results are presented in two sub-sections, i.e., (a) measurement of the rotating shutter antenna and (b) communication based on the rotating shutter antenna.