esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Accuracy Analysis of Sampling Number  
In order to assess the expected value of each parameter with a limited number of observations and to ensure sufficient confidence, a reasonable number of samples must be determined [25]. The spatial distribution of soil properties has its inevitability and chance [26], and this experiment focuses on the chance study of the spatial distribution of soil properties. According to [PERSON]'s formula, the number of samples needed for the whole watershed at a certain confidence level and relative error can be found, and the calculation results are shown in Table 2.  
It can be seen from Table 2 that with the increase in soil depth, soil variability decreases first and then increases, and the number of samples required to reach the same confidence level and error also decreases first and then increases. With the reduction in confidence level and relative error requirements, the number of samples required for each layer of soil is also greatly reduced. [PERSON]'s research indicates that the relative error of reasonable sampling should be determined according to the actual level and critical level of soil salinity. In this study, average soil salinity of layers between 0 m and 0.25 m is 1.27%, when the corresponding errors are 5%, 10%, and 20%, the fluctuation range of soil salinity is 1.21-1.33%, 1.14-1.40%, and 1.02-1.53%. Average soil salinity of layers between 1 m and 2 m is 0.87%, and the fluctuation range of corresponding error is 0.83-0.91%, 0.78-0.96%, and 0.69-1.04%. Average soil salinity of layers between 2 m and 3 m is 0.79%, and the fluctuation range of corresponding relative error is 0.75-0.83%, 0.71-0.87%, and 0.63-0.95%. According to the salinization classification standard, the fluctuation range of soil salinity in the layers between 0 m and 0.25 m under the three error levels is within the range of severely salinized, and the fluctuation range of soil salinity in the layers between 1 m and 2 m, and between 2 m and 3 m under the three error levels is basically within the range of moderately salinized. Therefore, a reasonable choice of sampling number in terms of economy and accuracy as well as sampling efficiency was made, and the reasonable sampling numbers of soil salinity in the layers between 0 m and 0.25 m, between 1 m and 2 m, and between 2 m and 3 m were 89, 70, and 91, respectively, and the actual sampling number met the requirement of reasonable sampling number under this condition and was suitable for variability analysis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Depth/m**} & \multicolumn{3}{c}{**95\% Confidence Level**} & \multicolumn{3}{c}{**95\% Confidence Level**} \\ \cline{2-7}  & **5\%** & **10\%** & **20\%** & **5\%** & **10\%** & **20\%** \\ \hline
0–0.25 & 1422 & 355 & 89 & 866 & 217 & 54 \\
1–2 & 1126 & 281 & 70 & 686 & 171 & 43 \\
2–3 & 1459 & 365 & 91 & 889 & 222 & 56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of sampling number of soil salt content.  
Figure 5: Histogram of frequency distribution of salt content.
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 6 Experimental Results  
### Experimental Setup  
#### 6.1.1 Experimental Platform  
For the verification of our system, we selected three experimental platforms of different radius. Figure 7 shows the configurations of these robots. All of them are equipped with Kobuki, Hokuyo UST-10 LX 2D lidar sensor, Kinect V2 RGB-D camera, and Hasee z/m-kp7 gc laptop whose CPU is Intel i7-8750h, GPU is gtx1050 ti, RAM is 12 GB. All of their shapes are circular, and the max radiuses are 0.18 m, 0.23 m and 0.28 m respectively, as shown in Figure 7.  
Figure 6: The comparison of \(\mathbf{A^{\star}}\) algorithm and segmented global path planning. **(a)**\(\mathbf{A^{\star}}\) algorithm. **(b)** Topological map: the red dotted line is the topological path. **(c)** auxiliary navigation points (ANPs) sequence (\(\mathbf{d}\)**-\(\mathbf{h}\)**) segmented global path planning. The colorful cell means the cells traversed.  
Figure 7: Robot platform: the three lengths in the right column means different maximum robot radiuses.  
The navigation algorithm is based the robot operating system (ROS), which provides various libraries and tools to help create robot application. The robot pose was estimated by using AMCL [38], the ROS implementation of the Monte Carlo localization algorithm [39], which is feed the laser measurement data and odometry data and resample the particles representing the belief of robot pose. For the controllers and algorithms for controlling the robot, the ROS move_base stack [40] is implemented, with segmented global path planning and dwa local path planning. The former segments the global path into every topological area based on topological map, and run Astar path planning, respectively. The latter provides the controller that drives a robot in the plane, by assessing every local paths' costs of traversing through the grid cells and determine the linear and angular velocity to send to the robot. Meanwhile, ROS move_base stack provides the global cost map and local cost map [41]. We configured the 2D costmap with the static layer (the unchanging portion of the costmap generated by SLAM), the obstacle layer (the obstacles created by the sensor data), and the inflation layer (the inflation of the obstacles to represent the configuration space of the robot and avoid the collision).
# Levitating Control System of Maglev Ruler Based on Active Disturbance Rejection Controller
## 5 Discussion  
The levitating system of the MR is characterized as a nonlinear, strongly coupled, and inherently unstable system. This paper establishes a mathematical model of the levitating system through magnetic circuit analysis and force analysis, revealing the relationship between the current of the levitating control coil and the air gap magnetic field, as well as the levitating height (\(z_{a}\)) and angle (\(\psi\)) of the MR. It also reveals the principle of strong coupling of the levitating system and converts the levitating system from a four-input, two-output system to a two-input, two-output system, proposing a levitating control system based on ADRC.  
In the literature [1], the selection of magnetic circuit components and parameter settings for the MR were designed, and a large number of experiments were conducted to demonstrate that the four air gap magnetic fields can be independently controlled, as well as the fluctuation range of the levitating force of the MC at different positions. Based on the literature [1], this paper establishes a mathematical model of the suspension system through magnetic circuit analysis and force analysis. From Equations (50) to (54), it can be seen that there is a coupling between the suspension heights at both ends of the mover core, and the suspension system is a second-order system, not a typical linear system. These are not proposed in the literature [1].  
The standard ADRC controller is capable of controlling second-order systems and decoupling effectively [2; 12; 19; 20; 21]. However, the levitating system of the MR is a four-input two-output system, which is different from the system in the literature. This paper simplifies the levitating system into a two-input two-output system using Equations (50) and (51). And improvements have been made to the ESO of ADRC, as shown in Equation (57). The improved ADRC can be applied to the suspension system of magnetic levitation rulers. Through simulation and experiments, it has been proven that the suspension control system of the magnetic levitation ruler can quickly follow the set value, effectively suppress sudden interference and white noise, have small overshoot, and have good dynamic and static characteristics as well as robustness.  
In addition, classical PID controllers cannot achieve decoupling control and cannot be applied in four-input, two-output systems.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.3 Ablation Experiment  
To demonstrate the direct impact of the improved algorithm on detection performance, a series of ablation studies were conducted. Using the original YOLOX-Nano as the backbone network, various optimization algorithms were incrementally added to assess their effects on network detection performance. The results of these experiments are presented in Table 3. After incorporating the Mosaic and Mix up strategies, the \(mAP\) value increased to 83.52%. Substituting the \(IoU\) loss function with \(EIoU\) led to a further increase in algorithm performance, due to more accurate target identification. With the improved Focus and SPP modules, the \(mAP\) value further increased to 85.31%. Upon integrating various AM algorithms, the network performance varied, with CBAM showing the best effect, elevating the \(mAP\) to 86.04%. However, the use of ECA and SE resulted in a decrease in the \(mAP\); thus, the final model adopted the CBAM attention mechanism. In summary, the incremental addition of each optimization algorithm led to a steady improvement in \(mAP\) values, validating the effectiveness of each step in the improvement strategy. This resulted in the RS-YOLOX-Nano algorithm achieving better results in the detection of debris flow geological disasters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Para/MB** & **FPS** & **Precision/\%** & **Recall/\%** & \(\mathbf{F_{I_{0}}}\)s & \(mAP_{0.8}\)/\%** \\ \hline YOLOv3 & 235.0 & 36.98 & 86.00 & 73.76 & 0.79 & 79.07 \\ \hline YOLOv4-tiny & 22.5 & 162.05 & 80.43 & 64.84 & 0.72 & 72.34 \\ \hline YOLOv5-s & 26.98 & 77.16 & 89.06 & 56.58 & 0.69 & 81.79 \\ \hline YOLOv7-tiny & 23.12 & 92.46 & 88.93 & 59.80 & 0.72 & 81.24 \\ \hline YOLOv8-Nano & 11.65 & 92.16 & 87.95 & 71.36 & 0.79 & 80.58 \\ \hline YOLOX-Nano & 3.71 & 63.48 & 88.51 & 84.12 & 0.86 & 82.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of comparative performance experiments on mainstream lightweight networks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Para/MB** & **FPS** & **Precision/\%** & **Recall/\%** & \(\mathbf{F_{I_{0}}}\)s & \(mAP_{0.8}\)/\%** \\ \hline YOLOX-Nano & 3.71 & 63.48 & 88.51 & 84.12 & 0.86 & 82.51 \\ \hline RS-YOLOX-Nano & 3.83 & 62.45 & 89.61 & 85.61 & 0.88 & 86.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of comparative experiments between the original and modified models.
## 2 Related Work
### Positioning This Work  
Recent contributions by the authors have explored several foundational components relevant to this study. For instance, a federated learning and edge AI system for real-time anomaly detection in 5G-enabled IoT ecosystems was proposed in Edge-FLGuard, demonstrating high detection accuracy using lightweight models on public datasets such as TON_IoT and CIC-IDS2018 [17]. Another work focused on smart city contexts combined AI-driven anomaly detection with 5G and edge computing infrastructures, highlighting the role of multimodal data fusion for security [18]. Additionally, edge AI for real-time threat detection in smart homes was presented in a prior study, validating the feasibility of deploying lightweight federated models in latency-sensitive environments [19].  
These studies establish the viability of decentralized, AI-based anomaly detection systems. However, they primarily address individual challenges--such as model architecture, edge integration, or domain-specific applications--without a unified treatment of smart home environments, lightweight model constraints, and 5G-related architectural considerations within the same framework.  
This paper builds directly upon the preliminary Edge-FLGuard framework [17] and significantly extends it in four key ways:* Model Innovation: Edge-FLGuard+ introduces a new lightweight GRU-based autoencoder architecture that improves performance while reducing memory and computation requirements, in contrast to the earlier LSTM-based models.
* Smart home Focus: The current work targets 5G-enabled smart home environments specifically, rather than generalized IoT deployments.
* Privacy Enhancements: Edge-FLGuard+ incorporates advanced privacy mechanisms including differential privacy and secure aggregation, which were not implemented in Edge-FLGuard.
* Broader Evaluation: The present study includes ablation experiments, quantization analysis, and comparisons with centralized/local baselines--offering a more comprehensive evaluation than the prior work.
# Long-Range Acoustic Communication Using Differential Chirp Spread Spectrum
## 5 Experiment
### Lake Trial
In the 3 rd experiment, the CIR had only 1 multipath and a SNR similar to the 1 st experiment. The delay-Doppler spread spectrum showed that it moved to \(3.644\times 10^{-3}\) knots with a Doppler frequency of about 0.02 Hz. Through the channels analyzed for each experiment, we compared the performance of the CSS and DCSS techniques and also analyzed in which environment the chirp-based communication was stronger. First, the communication performance is represented in Table 4. The results shown in Table 4 indicate that the performance in the 3 rd experiment was the best and the 1 st experiment was the worst. The spectrograms of the received signals on each experiment are shown in Figure 8.  
According to the channel measured on the 1 st experiment, the symbolic length of the signal with a data rate of 100 bps was 10 ms, which caused inter-symbol interference (ISI) to be generated and reduced the output of the matched filter; the SNR was 6.361 dB. In this situation, increasing the output of the matched filter by increasing the time-bandwidth product should improve the performance. The existing CSS method had a BER of 0.1696, but the proposed DCSS method showed a significant improvement in performance with 0.03570. In the 2 nd experiment, Doppler existed because the  
Figure 6: The lake-measured channel in the 2 nd experiment: (**a**) CIR; (**b**) delay-Doppler spread spectrum; (**c**) power delay profile; (**d**) Doppler power spectrum. transmitter was not fixed, and the SNR was also lower than in the other experiments. The multipath was very small in size, so only the direct path was clearly identified; the SNR was 1.879 dB. The existing method had a BER of 0.1071, and the proposed method had a BER of 0.0238 on the following channels. This shows that the proposed method also leads to improved performance in terms of Doppler effects; however, it was possible to infer that performance was improved even by a low SNR. Finally, the 3 rd experiment did not have many multipaths, and the SNR was the highest among the three experiments of the study. Doppler was limited because both the transmitter and receiver were fixed; the SNR was 8.067 dB. Both the channel's time volatility and SNR were better than on the other experiments, so the existing method also had a fairly good performance at 0.00895. The proposed method further improved this and had a 0 BER. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**SNR**} & \multicolumn{2}{c}{**BER**} \\ \cline{3-4}  & & **CSS** & **DCSS** \\ \hline
1 & 6.361 dB & \(1.696\times 10^{-1}\) & \(3.570\times 10^{-2}\) \\
2 & 1.879 dB & \(1.071\times 10^{-1}\) & \(2.380\times 10^{-2}\) \\
3 & 8.067 dB & \(8.950\times 10^{-2}\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Lake experiment results. Figure 7: The lake-measured channel in the 3 rd experiment: (**a**) CIR; (**b**) delay-Doppler spread spectrum; (**c**) power delay profile; (**d**) Doppler power spectrum.
## 2 Materials and Methods
### Accuracy Evaluation Method  
The confusion matrix was used to calculate the user accuracy (UA), producer accuracy (PA), overall accuracy (OA), and kappa coefficient [52] of the classification results (as shown in Equations (9)-(12)), reflecting the location accuracy of the classification results in the study area.  
\[PA=\frac{k_{ii}}{\sum_{j=1}^{t}k_{ij}} \tag{9}\]  
\[UA=\frac{k_{jj}}{\sum_{i=1}^{t}k_{ij}} \tag{10}\]  
\[OA=\frac{1}{n}{\sum_{i=1}^{t}k_{ii}} \tag{11}\]  
\[Kappa=\frac{n\sum_{i=1}^{t}k_{ii}-\sum_{i=1}^{t}\left(k_{i+}\times k_{+i} \right)}{n^{2}-\sum_{i=1}^{t}\left(k_{i+}\times k_{+i}\right)} \tag{12}\]  
Among them, \(n\) represents the number of sample points, \(i\) represents the row, \(j\) represents the column, \(t\) represents the total number of rows or columns, \(k_{ii}\) denotes the element in row \(i\) and column \(i\), \(k_{ij}\) denotes the element in row \(j\) and column \(j\), \(k_{ij}\) denotes the element in row \(i\) and column \(j\), and \(k_{i+}\) and \(k_{+i}\) denote the sum of the elements in row \(i\) and column \(i\), respectively.
###### Abstract  
This study investigated the outage performance of a terrestrial FSO communication system that uses mixed series and parallel decode-and-forward (DF) relay-assisted (i.e., cooperative diversity) configurations, taking into account the influence of both atmospheric turbulence and pointing error effects. Turbulence-induced optical signal fading is modeled by gamma-gamma or the negative exponential distribution for weak to strong and saturated turbulence conditions, respectively. Additionally, weak to strong non-zero boresight misalignment-induced optical signal fading is modeled by the generalized Beckmann distribution. Under these conditions, an outage analysis of the examined FSO system is performed, in terms of both outage probability and mean outage duration metrics. Thus, fairly accurate closed-form mathematical expressions for both performance metrics are derived, while their corresponding analytical results demonstrate concrete performance and availability improvements for the total FSO system, especially when the number of the connected in parallel DF relays increases. Moreover, the obtained results are verified through the corresponding simulation results.  
free space optical communication systems; mixed series and parallel DF relay-assisted configurations; nonzero boresight misalignment-induced fading; turbulence-induced fading; gamma-gamma distribution; negative exponential distribution; Beckmann distribution; outage probability; mean outage duration +
Footnote †: journal: _technologies_
# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### Practical Application Cases  
The application cases in two real manufacturing scenarios are shown in Figure 15.  
Figure 14: Analysis of the impact caused by missing parts.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Each** \\ \hline \(\overline{\hat{u}}_{\ell}\) (\({}^{\circ}\)) & 0.26 & 0.24 & 0.23 & 0.22 & 0.23 & 0.22 & 0.21 & 0.22 & 0.23 & 0.23 & 0.23 \\ \hline \(\overline{\hat{t}}_{\ell}\) (mm) & 27.24 & 26.40 & 27.12 & 26.11 & 25.48 & 25.26 & 24.51 & 23.96 & 23.74 & 23.70 & 23.71 \\ \hline \(\overline{ite}\) (round) & 9.12 & 8.73 & 8.81 & 8.93 & 8.25 & 7.97 & 7.93 & 8.11 & 8.13 & 8.14 & 8.14 \\ \hline \end{tabular}
\end{table}
Table 5: Adaptability of the difference between virtual and real viewpoints.  
The first row shows a large-sized truss-type product painting task, and the second row introduces the assembly and gluing of porous structural components assisted by AR. The core task of both cases is pose estimation, and their respective problems correspond to a part of the experimental platform. The test results show that the proposed method can improve the initial error of the former from 5.0\({}^{\circ}\), 80 mm to 0.8\({}^{\circ}\), 17 mm, and increase the hole projection deviation \(\overline{IoU_{e}}\) of the latter from <0.1 to >0.5. Some valuable setting strategies and potential limitations analysis are as follows.  
(1) Setting strategies. (a) Divide object set based on assembly relationships and part sizes, and define a rule for sub-object to gradually participate in evolution; (b) given an empirical viewpoint, obtain the initial pose through the strategies provided in this article or studies [48; 49; 50]; (c) considering both uniform distribution and matching residuals, determine the constraint features for feasible region initialization; (d) choose to adjust VPS or replace it with other matching strategies based on the object structure; (e) select the number of termination evolution rounds based on observations; and (f) choose whether calibration optimization is needed based on the characteristics of the scene.  
(2) Potential limitations. (a) No significant structural features (such as flexible bodies). Lack of initial pose will result in the inability to initialize the feasible region. One possible solution is to adopt a method like MegaPose [34] that can adapt to unseen objects to provide an initial pose. (b) Excessive initial pose deviation is always a challenge. For possible failures, we recommend some possible methods from an engineering perspective to constrain the initial pose, including planning viewpoint, empirical adjustment, fixtures, etc. (c) Equipment failure. The coverage of the field of view and the rationality of the depth of field needs to be given priority consideration during the planning phase.
# Magneto-Inertial Fusion and Powerful Plasma Installations (A Review)
## 3 Laser-Driven Magneto-Inertial Fusion  
Laser-driven magneto-inertial fusion (LD MIF) is a wide range of installations, in which, laser systems are used to preheat the target with powerful laser beams and/or compression by lasers with high pulse energy of the target plasma located in a pre-generated (external) magnetic field [42; 43; 44; 45; 46; 47].  
Among laser installations and the generators based on them, NIF, OMEGA, Gekko, LFEX and others stand out. The new MagNIF pulse system allows the creation of magnetic fields of up to 30 T on the NIF installation [42; 43].  
Recently, experiments on magneto-inertial fusion have also been conducted at the NIF facility. The complex contains 192 high-power lasers that direct pulses after multistage amplification to a millimeter target with fusion fuel. Tables 2 and 3 with laser parameters are provided below.  
The paper [48] assumes the compression of plasma and magnetic flux, due to the quasi-symmetric laser implosion of a magnetized target. A one-dimensional, radiation magnetohydrodynamic code and a formulation of two-fluid equations for modeling a compressible, nonequilibrium, magnetized target plasma have been developed.  
The manuscript [49] demonstrates the possibility of generating super-strong magnetic fields that are entirely optical using supercomputer modeling. This article [50] uses the modeling of extended magnetohydrodynamics (MHD).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \begin{tabular}{c} **Particle** \\ **Retention** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Retention** \\ \end{tabular} & \begin{tabular}{c} **Plasma** \\ **Density** \\ \end{tabular} & \
\begin{tabular}{c} **Time** \\ **Confinement** \\ \end{tabular} &
\begin{tabular}{c} **Magnetic Field,** \\ **T** \\ \end{tabular} & **Optical Depth** \\ \hline ICF & Inertial & Inertial & 10\({}^{5}\) kg/m\({}^{3}\) & \(\tau_{\mathrm{E}}\)–10 ps & - & R\(\sim\)10 \\ \hline LD \({}^{*}\) & \multirow{2}{*}{Inertial} & \multirow{2}{*}{\(\sim\)0.01 g/km\({}^{3}\)} & \(\tau\)E–100 ns & Initial–1 & \(\sim\)0.1 g/cm\({}^{2}\) \\ \cline{3-6} PL ** & & Magnetic & \(\sim\)1019 cm\({}^{-3}\) & \(\tau\)E–100 \(\mu\)c & Final 1000 & \(\sim\)0.01 kg/m\({}^{2}\) \\ \hline MCF & Magnetic & Magnetic & 10\({}^{-6}\) kg/m\({}^{3}\) & \(\tau_{\mathrm{E}}\)–1 s & \(\sim\)10 T & - \\ \hline \end{tabular} \({}^{*}\) LD—MIF with laser driver, ** PL—MIF with plasma liner, formed by merging high-speed jets.  
\end{table}
Table 1: The magneto-inertial as fusion concept.