esa-sceva/satcom-chunk-collection huggingface dataset:
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 3 Task Planning Model of Space-Robot Cluster
### Constraints  
The problem of collaborative task allocation of space-robot clusters aims at designing charging piles for space robots at specific locations in accordance with the principle of sustainable work during the execution of tasks. Therefore, the path of space robot should be a closed path starting from the charging pile position. The space robot needs to satisfy some constraints to complete the maintenance task: reserve energy, number of space robots, closed path and so on.  
Defining variables  
\[x_{ij}^{k}=\left\{\begin{array}{ll}1&If\ robot\ k\ goes\ through\ node\ i\ to\ node\ j\\ 0&else\end{array}\right.,y_{i}^{k}=\left\{\begin{array}{ll}1&If\ robot\ k\ repairs\ node\ i\\ 0&else.\end{array}\right.\]  
The energy consumed by the \(k^{\prime}th\) robot performing the task can be expressed as  
\[Consumption_{k}=\sum_{i=0}^{m}\left(\alpha_{k}\cdot dis_{i,i+1}^{k}+\beta_{k} \cdot num_{i+1}^{k}\right)+\alpha_{k}\cdot dis_{m,0}^{k} \tag{10}\]  
where in:  
\[dis_{i,i+1}^{k}=c_{ij}\cdot x_{ij}^{k} \tag{11}\]  
\[\sum_{k=1}^{N}y_{i}^{k}\leq 1,\ i=1,2,\cdots,M \tag{12}\]  
\[\sum_{i=0}^{M}x_{ij}^{k}=y_{j}^{k}\ \forall\ k\in\left\{1,2,\cdots,N\right\},\forall\ j\in\left\{1,2,\cdots,M\right\} \tag{13}\]\[\sum_{j=0}^{M}x_{ij}^{k}=y_{i}^{k}\ \ \forall\ k\in\{1,2,\cdots,N\},\forall\ i\in\{1,2, \cdots,M\} \tag{14}\]  
where \(m\) represents the number of cluster nodes on the space robot path, \(\alpha_{k}\) represents the energy consumption per unit distance of the \(k^{\prime}th\) robot, \(dis_{i,j+1}^{k}\) represents the path length of the \(k^{\prime}th\) space robot from the \(i^{\prime}th\) node to the \((i+1)^{\prime}th\) node on the path, \(c_{ij}\) represents the distance between node \(i\) and node \(j\), \(i=0\) represents the starting position of the robot, \(\beta_{k}\) is the energy consumption of the \(k^{\prime}th\) robot repairing a single maintenance node, \(num_{i+1}^{k}\) represents the number of maintenance nodes in the \((i+1)^{\prime}th\) cluster node on the path of the \(k^{\prime}th\) space robot. Constraint (12) ensures that each maintenance point has at most one space robot for maintenance; constraint (13) ensures that each node in the maintenance path has only one starting point; constraint (14) ensures that the node in each maintenance path has only one end point. In general, these constraints can ensure that each maintenance point is assigned to at most one space robot, and the space robot passes all the assigned nodes and walks a closed loop.  
In order to ensure that the robot can safely return to the starting point, the energy of the space robot is greater than that of the charging pile at the last maintenance point. Each robot should return to the charging pile position before the safe energy of the battery is exhausted, namely:  
\[Consumption_{k}<\xi\cdot Energy_{k} \tag{15}\]  
In which \(Energy_{k}\) is the total energy of the \(k^{\prime}th\) robot, \(\xi\) is the safety factor.
# Cross-Cycled Uplink Resource Allocation over NB-IoT
## 3 Cross-NP Uplink Resource Allocation
### Property of the Proposed Algorithm  
**Theorem 1**.: _The time complexity of Algorithm 1 is \(O(DUM+UM\xi^{2}|K|+L)\) for an NP._  
Proof.: In Algorithm 1, the Transmission-Performance() function takes \(O(DIM)\) time to calculate the number of bits that each UE can transmit using different unit types and modulations. For a UE, Algorithm 1 selects a combination of unit type and modulation from \(UM\) selections. Then, Algorithm 1 finds sufficient subcarriers and subframes for a combination by looking for each DCI and scheduling delay. Because at most \(\xi\) UEs can be allocated in an NP, the process of Lines 5-16 takes \(O(UM\xi^{2}|K|)\) time. Then, because there are at most \(L\) subframes, the Subframe-Allocation() function and setting the corresponding parameters take \(O(L)\) time. Thus, the algorithm's time complexity is \(O(DUM+UM\xi^{2}|K|+L)\).
## 4 Discussion
### Recommendations for Agricultural Producers and Environmental Policymakers
#### 4.4.2 Recommendations for Environmental Policymakers  
Establishing a Salinization Dynamic Monitoring Network: It is recommended that governments at all levels and local agricultural departments establish a comprehensive salinization monitoring network. Through the regular collection of remote sensing data and ground-based field data, they should comprehensively grasp the distribution and change trends of saline-alkali land resources. By monitoring the changes in saline-alkali land in real-time, corresponding mitigation measures can be taken in a timely manner to ensure the sustainable management of saline-alkali land.
# User Pairing for Delay-Limited NOMA-Based Satellite Networks with Deep Reinforcement Learning
## 2 System Model
At the same time, the user with better channel quality, i.e., User \(t\), adopts the SIC strategy to decode and remove the interference from User \(c\); the decoding SINR can be derived as  
\[\gamma_{t\to c}^{\rm N}=\frac{\alpha_{c}^{p}\gamma\Phi_{t}G_{s}(d_{t})\big{|}g _{t}\big{|}^{2}}{\alpha_{t}^{p}\gamma\Phi_{t}G_{s}(d_{t})\big{|}g_{t}\big{|}^{2 }+1}=\frac{\alpha_{c}^{p}\gamma Q_{t}}{\alpha_{t}^{p}\gamma Q_{t}+1}. \tag{6}\]We can derive that \(\gamma_{c}^{\rm N}<\gamma_{t\to c^{\prime}}^{\rm N}\), since \(Q_{c}<Q_{t}\). Then, User \(t\) decodes its own information, and the achieved SINR is  
\[\gamma_{t}^{\rm N}=\alpha_{t}^{p}\gamma\Phi_{t}G_{\rm s}(d_{t})|g_{t}|^{2}\ = \alpha_{t}^{p}\gamma Q_{t}. \tag{7}\]
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 5 Experiments
### Generalization Experiment  
#### 5.3.1 FloW-Img Dataset  
First, generalization experiments were conducted on the FloW-Img dataset by comparing the proposed algorithm with SOTA algorithms to validate the generalization capability of USD-YOLO across different datasets. The results of the generalization experiments on the FloW-Img dataset are presented in Table 5.  
Compared with other SOTA algorithms, USD-YOLO achieved the highest values in precision metrics such as Recall, mAP50, and mAP50-95 on the FloW-Img dataset, demonstrating its superior detection precision. Additionally, USD-YOLO outperformed most algorithms in terms of FPS, indicating its fast detection speed. Compared with the baseline model YOLOv8s, USD-YOLO improved small object detection precision with only a slight sacrifice in speed, meeting the requirements of unmanned systems for both precision and speed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **mAP50** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **mAP50-** \\ **95 (\%)** \\ \end{tabular} & \begin{tabular}{c} **Params** \\ **(M)** \\ \end{tabular} & \
\begin{tabular}{c} **GFLOPs** \\ \end{tabular} &
\begin{tabular}{c} **FPS** \\ \end{tabular} \\ \hline YOLOv8s & 54.4 & 64.9 & 37.7 & 11.2 & 28.8 & 129.8 \\ TPH-YOLO [25] & 54.0 & 64.0 & 37.3 & 11.36 & 29.8 & 74.8 \\ SPD-YOLO [26] & 52.9 & 62.9 & 35.4 & 12.74 & 46.2 & 94.5 \\ YOLOv5 & 56.2 & 66.3 & 41.8 & 7.02 & 15.9 & 100.0 \\ YOLOv7 & 43.8 & 49.1 & 23.0 & 9.32 & 26.1 & 56.2 \\ YOLOv8n & 49.5 & 56.9 & 29.2 & 3.15 & 8.9 & 133.7 \\ YOLOv8m & 54.4 & 68.3 & 43.4 & 25.9 & 79.3 & 77.0 \\ YOLOv9 & 53.0 & 62.5 & 38.4 & 6.17 & 23.9 & 42.5 \\ YOLOv10 & 56.2 & 66.2 & 41.1 & 8.06 & 24.8 & 89.3 \\ SSD [22] & 41.8 & 44.8 & 18.8 & 24.48 & 62.2 & 47.5 \\ Faster R-CNN [42] & 46.2 & 64.0 & 24.6 & 136.98 & 199.6 & 6.2 \\ USD-YOLO & 56.7 & 70.8 & 46.2 & 12.35 & 30.5 & 126.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of comparison experiments on the Citypersons dataset.  
Therefore, the results of the generalization experiments demonstrate that USD-YOLO exhibits excellent generalization performance, maintaining robust detection capabilities and reliability across other datasets.
## 6 Navigation and Interface
### Predefined Trajectories  
The fundamental function of each AMR is to get from the current position within the environment to a user-determined position. Phollower provides two different approaches. Each of them is suitable for a different type of environment. However, the choice of the approach to be used is made solely by the user.  
The predefined trajectory is usually suitable for an industrial environment, where fixed rules for the movement of the motorized industrial systems are applied. This type of use is mainly represented as operations with material supplies needed for production lines. In such cases, the specific paths along which these systems can move and perform are precisely defined and marked. A mechanical barrier often separates these paths from the rest of the environment. This version with predefined trajectories also represents a virtual path (virtual magnetic tape for AGV systems) within a virtual map. All preset stops are interconnected by a strictly defined trajectory. Altogether it creates a route network around which the robot can move. For route calculation purposes, [PERSON]'s algorithm is used [PERSON] (1996).  
Figure 9 shows the route network together with the actual trajectory of the robot. A specialized web interface for the visualization of the map, stops, and trajectory was implemented for this type of operation. It gives the user the option to select individual targets and control the course of an autonomous movement. This type of navigation comes with the disadvantage that the robot is not able to dynamically bypass obstacles on the path. In this mode, Phollower only sends a notification about the barrier on the route to a nearby responsible human operator.
###### Abstract  
As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space-Air-Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios.  
U  
21 November 2024  
Revised: 24 December 2024  
Accepted: 30 December 2024  
Published: 6 January 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-Objective Optimization for Resource Allocation in Space-Air-Ground Network with Diverse IoT Devices. _Sensors_**2025**, 25, 274. [[https://doi.org/10.3390/452010274](https://doi.org/10.3390/452010274)]([https://doi.org/10.3390/452010274](https://doi.org/10.3390/452010274))
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## Appendix A Mathematical Formulation  
_Appendix A.1. Doppler Sub-Apertures Model_  
The SAR data belonging to the electromagnetic image are formed through the focusing process that involves the application of a two-dimensional matched filter acting in the range direction and in the azimuth direction. The SLC signal resulting from compression is given by [35]:  
\[\begin{split} s_{SLC}(k,x)&=2N\tau\exp\left[-J\frac{4 \pi r}{\lambda}r\right]\text{sinc}\left[\pi B_{c_{r}}\left(k-\frac{2R}{c}\right) \right]\text{sinc}[\pi B_{c_{D}}x]\\ &\text{for }x=kt,\ k=\{0,1,\ldots,N-1\},\ x=\{0,1,\ldots,M-1\},\ \text{with }N,M\ \in\mathbb{N}.\end{split} \tag{10}\]  
Equation (10) represents the focused SAR signal generated by the back-scattered electromagnetic energy of a point target supposed to be stationary. The parameter \(\lambda\) is the SAR radio-frequency wavelength. The terms \(B_{c_{r}}\), and \(B_{c_{D}}=\frac{4 Nd}{\lambda r}\) are the total chirp and Doppler bandwidths respectively. The total synthetic aperture is equal to \(L_{sa}=2 Nd\) and the azimuth resolution \(\delta_{D}\approx\frac{1}{B_{c_{D}}}=\frac{\lambda R}{2L_{sa}}\). In (10) the \(\frac{2R}{c}\) parameter identifies the position in range where the maximum of the sinc function is positioned, while in azimuth it is centered around \"zero\". In the case where the peak of the sinc function has a nonzero coordinate along the azimuth dimension, Equation (10) can be recast as:  
\[\begin{split} s_{SLC}(k,x)&=2N\tau\exp\left[-J\frac {4\pi r}{\lambda}\right]\text{sinc}\left[\pi B_{c_{r}}\left(k-L_{c_{g}}\right) \right]\text{sinc}\left[\pi B_{c_{D}}\left(x-L_{D_{h}}\right)\right]\\ \text{for }L_{c_{g}},\ L_{D_{h}}\ \in\mathbb{R},\end{split} \tag{11}\]  
in (11) \(L_{c_{g}}\), and \(L_{D_{h}}\) are the slant-range, azimuth position of the single-target beam center, into the image coordinates. In this context the DFT is equal to:  
\[\begin{split} S_{SLC_{f}}(n,q)&=DFT2\left\{2N\tau \exp\left[-J\frac{4\pi r}{\lambda}\right]\text{sinc}[\pi B_{c_{r}}k]\text{sinc }[\pi B_{c_{D}}x]\right\}\\ &=2N\tau\exp\left[-J\frac{4\pi r}{\lambda}\right]\sum_{k=0}^{N-1} \sum_{x=0}^{M-1}\text{sinc}[\pi B_{c_{r}}n]\text{sinc}[\pi B_{c_{D}}q]\\ &\exp\left(-J\frac{2\pi kn}{N}\right)\exp\left(-J\frac{2\pi xq}{M }\right)\\ &=2N\tau\exp\left[-J\frac{4\pi r}{\lambda}\right]\frac{1}{\pi B_{ c_{r}}}\text{rect}\left[\frac{n}{\pi B_{c_{r}}}\right]\frac{1}{\pi B_{c_{D}}} \text{rect}\left[\frac{q}{\pi B_{c_{D}}}\right]\\ &\exp\left(-J2\pi nL_{c_{g}}\right)\exp\left(-J2\pi qL_{D_{h}} \right),\end{split} \tag{12}\]  
which has a rectangular shape. In (12) the parameter DFT2 is the two-dimensional digital Fourier transform.
## 2 Materials and Methods  
### Human Vital Signs  
Vital signs need to be measured to determine a person's level of health. Changes in the value of vital signs measurements can be a piece of early warning information about potential health problems. Under certain conditions, such as medical observation of a potentially serious disease or epidemic, it is necessary to measure vital signs continuously.  
Normal vital signs vary with physiology, gender, age, exercise ability, weight, and overall health. The normal range of healthy vital signs for an inactive adult is:  
* Breathing rate: 12 to 30 breaths per minute [9];
* Heartbeat rate: 60 to 100 beats per minute [10].  
Even though a normal breathing rate is around 12 to 30 breaths per minute, some studies have concluded that a breathing rate as low as 8 breaths per minute is still in the normal category [11].  
To detect vital signs, the radar must be able to detect movement in the chest caused by heartbeat and breathing. The heart is located in the rib cage inside the chest, as shown in Figure 2a. The heartbeat that pumps blood throughout the body triggers the heart wall to hit the chest wall so that the chest wall moves at the same rhythm as the heartbeat.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & This Work & [5] & [6] \\ \hline Radar & FMCW & FMCW & FMCW \\ \hline Frequency & 77-81 GHz & 77-81 GHz & 76-81 GHz \\ \hline Multiple target & & & \\ separation & 2D-FFT & Angular position (AoA) & X \\ \hline Maximum Range & 2.55 m & 4.3 m & 2 m \\ \hline Concurrent Detection & 4 people & 2 people & 1 person \\ \hline Experiment & & & \\ Environment & Indoor environment & - &
\begin{tabular}{c} Bedroom \\ environment \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with other works.  
The same phenomenon occurs in breathing. Breathing is the exchange of gases in the lungs. Air with carbon dioxide needs to be removed from the lungs, and air with oxygen needs to replace it.  
The lungs are located in the chest cavity, as shown in Figure 2b. The movement of the lungs, expansion, and contraction when humans breathe causes the ribs to move. This movement of the ribs pushes the chest wall to also move up and down in the same rhythm as the movement of the lungs. Our research named this movement as chest displacement.  
This chest displacement is detected by radar to produce heartbeat and breathing rate after signal processing.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Algorithms for Joint Carrier Recovery
#### 4.1.3 Managing Channel Phase Differences  
Channel phase differences mainly arise from the comb generation process, but can also be caused by vibrations and temperature-induced changes when the different channels travel through different fibers. Since the joint carrier recovery schemes rely on the phases being the same, performance may be affected.  
In the case of master-slave carrier recovery, any phase difference will remain on the slave channels after the master phase has been applied to them. Performance-wise, it is no major challenge to remove these since they are typically much smaller than the common-mode phase noise. However, since the goal of the master-slave type of scheme is to reduce the DSP computational complexity, it is important that the phase difference compensation is computationally lightweight compared to the master phase estimator. For small phase differences, an adaptive equalizer will be able to perform the compensation provided that the error signal used for the tap update is sensitive to phase rotations. In this case, the main adaptive equalizer that performs polarization demultiplexing will also track phase differences. Another alternative is to use a separate feedback phase tracker, since they are computationally lightweight if the tracking-speed requirements are low.  
The joint-estimation schemes have different requirements. To not affect the main phase estimation, the phases of the channels need to be aligned before the joint estimation block. For example, in the modified BPS algorithm described above, a large phase difference between the channels will cause the cost function in Equation (7) to have multiple local minima, corresponding to the different channels. This will cause rapidly-varying estimation errors that are hard to compensate at a later stage in the DSP chain.  
The challenge of aligning the phases before the joint phase estimation block is that the channel-dependent phase noise is much smaller than the common phase noise among the lines and cannot be estimated easily before the common phase noise has been compensated for. This can be solved by using a feedback method where the remaining phase error after joint phase compensation is used to align the channels before the joint estimation. This is illustrated in Figure 7b. In a practical hardware implementation, the updating speed of such a phase-tracker will be limited by parallelization [108] and ultimately limit the allowable speed of the channel-dependent phase noise.
# Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
## 5 Simulation Experiment Results and Analysis
### CFKLQ-CFAR Detection Performance
#### 5.1.3 Performance Comparison Between CFKLQ-CFAR and FUMCA-CFAR  
In Ref. [15], a sub-reference window minimum selection unit constant false alarm rate detector (MCA-CFAR) is proposed, and based on this, four constant false alarm rate detectors with fuzzy fusion rules (FUMCA-CFAR) are designed. That is, the MCA-CFAR based on the \"MAX\" fuzzy fusion rules (MAX-MCA-CFAR), the MCA-CFAR based on the \"MIN\" fuzzy fusion rules (MIN-MCA-CFAR), the MCA-CFAR based on the algebraic product fuzzy fusion rules (PRODUCTS-MCA-CFAR), and the MCA-CFAR based on the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pair** & **Average Value** & **Number of Cases** & **Standard Deviation** & **Standard Error Mean** \\ \hline ASKLQ & 55.025440 & 10 & 0.0039390 & 0.0012456 \\ CFKLQ & 55.042930 & 10 & 0.0036878 & 0.0011662 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Paired sample statistics in a multiple-target environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sum of** & **Degree of** & **Mean Square** & **F** & **P** \\  & **Squares** & **Freedom** & **Mean Square** & **F** & **P** \\ \hline Between groups & 0.002 & 1 & 0.003 & 105.062 & 6.0995 \(\times\)\(10^{-9}\) \\ Within group & 0.000262 & 18 & 0.000015 & & \\ Total & 0.002 & 19 & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: ANOVA in a multiple-target environment.  
algebraic sum fuzzy fusion rules (SUM-MCA-CFAR). The performance comparison results of the proposed CFKLQ-CFAR and the five detectors are presented in Figure 3.  
As shown in Figure 3, in a multiple-target environment, the detection performance of CFKLQ-CFAR is superior to that of five CFAR detectors. After calculation, in Figure 3a, the average detection probability of CFKLQ is 56.78%, which is 3.34% higher than that of MCA, 2.28% higher than that of MAX-MCA, 2.35% higher than that of MIN-MCA, 2.11% higher than that of PRODUCTS-MCA, and 1.35% higher than that of SUM-MCA. In Figure 3b, the average detection probability of the CFKLQ is 55.04%, which is 5.34% higher than that of MCA, 1.40% higher than that of MAX-MCA, 1.49% higher than that of MIN-MCA, 1.23% higher than that of PRODUCTS-MCA, and 1.00% higher than that of SUM-MCA. In Figure 3c, the average detection probability of the CFKLQ is 56.02%, which is 5.73% higher than that of MCA, 2.17% higher than that of MAX-MCA, 1.28% higher than that of MIN-MCA, 1.94% higher than that of PRODUCTS-MCA, and 1.28% higher than that of SUM-MCA. Because CFKLQ-CFAR not only uses a fuzzy fusion strategy but also makes composite decisions based on the four fusion results. CFKLQ-CFAR improves the utilization of target detection information and improves the detection performance. The average detection probability of each detector in different environments with SNR ranging from 0 dB to 35 dB is provided in Table 9.