esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Scheme
### New Node-Association
Once the child node has the list of CHs with their ranked value, the child node selects the CH which have highest rank. \(R_{i}\) of each CH node is computed using Equation (12) and is defined as follows. \[R_{i}=\frac{X_{i}^{-}}{X_{i}^{-}+X_{i}^{+}}\;\textit{ where}\;i=\;1...m\] (12) where, \(R_{i}\) is the ranking index of \(i\)th CH candidate node. The detailed mechanism of node(s) association phase is depicted in Algorithm 2. ```
1:procedureNodeAssociation
2:\(CHlist\leftarrow list\) of CHs in range of node
3:\(i\gets i\)th CHs
4:\(j\gets j\)th resource
5:\(w\gets weight\) of resource
6:\(IPS\leftarrow Ideal Positive Solution\)
7:\(INS\leftarrow Ideal Negative Solution\)
8:\(R_{i}\leftarrow Rank\) of CH\(i\)
9:\(a_{ij}\gets i\)th CH, \(j\)th critarea
10:if\((CHlist.length==1)\)then
11: Send join-CH message. 12:endif
13:if\((CHlist.length>1)\)then
14:for\(i\gets 1\)to sizeOfCHlistdo
15: Get CH candidate from CHlist
16:for\(j\gets 1\)to \(CH-resources\)do
17: Insert CH-resources in decision matrix
18: Normalize decision matrix e.g., \[{X^{*}}_{(i,j)}=\frac{X_{(i,j)}}{\sqrt{\sum_{j=1}^{n}X_{(i,j)}^{2}}}\text{ where }i=1...m,j=1...n\]
19: Assign weights to each criterion
20: Construct a weighted decision matrix. 21: Compute IPS\({}^{+}\) and INS\({}^{-}\)
22:\(IPS^{+}=\max\{(X^{\prime}_{1,j}),\ (X^{\prime}_{2,j})..(X^{\prime}_{m,j})\},\forall j\in n\)
23:\(INS^{-}=\min\{(X^{\prime}_{1,j}),\ (X^{\prime}_{2,j})..(X^{\prime}_{m,j})\},\forall j\in n\)
24: Compute distance from IPS\({}^{+}\) i.e., \(X^{+}_{i}=\sqrt{\frac{\sum\limits_{j=1}^{n}(X^{\prime}_{(i,j)}-\ IPS^{+})^{2}}}\) where \(i=1...m\)
25: Compute distance from INS\({}^{-}\) i.e., \(X^{-}_{i}=\sqrt{\frac{\sum\limits_{j=1}^{n}(X^{\prime}_{(i,j)}-\ INS^{-})^{2}}}\) where \(i=1...m\)
26: Calculate R\({}_{i}\) of each CH e.g., \[R_{i}=\frac{X^{-}_{i}}{X^{-}_{i}+\ X^{+}_{i}}\ \ \text{where }i=1..m\]
27: Select CH having highest Rank value. 28: Send join-CH message. 29:endfor
30:endfor
31:endif
32:endprocedure
```  
**Algorithm 2** New Node(s) Association Algorithm
## 4 Discussions, Conclusions, and Future Directions
### Conclusions  
The conclusions drawn from this study underscore the advancements achieved in JCAS systems, particularly through the application of DNNs. Superior performance was observed in communication systems, notably in tasks involving channel estimation and signal detection, when compared to the traditional methods. Realistic channel measurements were instrumental in evaluating both the communication and radar systems, highlighting the potential of DNNs to outperform the conventional approaches.  
Moving forward, further exploration and experimentation are warranted across various scenarios. This includes extending machine learning techniques to MIMO cases, incorporating specific waveforms, and implementing advanced techniques such as SSF or TDMA-based functioning for enhanced joint system design.  
The proposed system based on SSF presents notable advancements over the existing related works, demonstrating consistently lower BERs across various SNR conditions and improved radar sensing performance. This underscores the efficacy of leveraging DNNs to autonomously learn and adapt to complex data patterns, surpassing the limitations of the traditional methods reliant on predetermined algorithms or handcrafted features.
## 3 Results and Discussion
### Hydrophobicity of Foamed MOC Materials  
When water was dropped on the surface of the N-MOC (we added a small amount of methylene blue to the water for easy observation), the water immediately penetrated the sample through the holes, and after adding TEOS copolymer with FAS and FS samples, the same volume of water droplets could stay on the material on the surface, and the contact angle of water droplets on the surface of the material was greatly increased.  
In Figure 9, the FAS content of A1-A5 was 1%, n (FAS): n (TEOS) = 1:8; when the content of H2O2 increased from 4% to 12%, the contact angle changed. The results show the bulk density of F-MOC was reduced from about 1200 Kg-m\({}^{-3}\) to about 200 Kg-m\({}^{-3}\). The water contact angle increased first and then decreased. When the H2O2 content was 10%, the water contact angle reached the highest 154\({}^{\circ}\).  
B1-B5 were the changes in contact angle when the additional amount of H2O2 was fixed at 10%, and the TEOS content increased from n (FAS): n (TEOS) = 1:6 to n (FAS):n (TEOS) = 1:14. The result showed that the water contact angle increased first and then decreased, reaching a maximum of 142\({}^{\circ}\) at n (FAS): n (TEOS) = 1:8.  
C1-C5 were the fixed additional amounts of H2O2, which was 10%, n (FAS):n (TEOS) = 1:8; the FAS content increased from 0.5% to 2.5%. When the FAS content increased from 0.5% to 1%, the water contact angle ranged from 140\({}^{\circ}\) to 156\({}^{\circ}\) with a larger increase. When the FAS content continued to increase, the water contact angle remained above 150\({}^{\circ}\) and did not continue to increase.  
Figure 8: Triethoxy-1H, 1H, 2H, 2H-tridecylfluoro-n-octylsilane (FAS), and tetraethyl orthosilicate (TEOS) co-hydrolysis mechanism.  
Summarily, the additional amount of H\({}_{2}\)O\({}_{2}\) was 10%, n (FAS):n (TEOS) = 1:8. When the FAS content was 1%, the F-MOC obtained had the highest water contact angle and had the best hydrophobic effect. The pores of the material provided suitable roughness on the surface of the material. Thus, low surface energy fluorinated alkyl chain and certain roughness make the hydrophobic effect of F-MOC the best.  
Through a series of tests, the samples with the largest water contact angle were selected (A is F-MOC, B is N-MOC). Water contact angle could reach 156\({}^{\circ}\), as is shown in Figure 10a. As shown in Figure 10b, the sample was mechanically cut to retain the internal incision of the foam material. The water contact angle of all points on the surface of the foam material exceeds 150\({}^{\circ}\),and the solution will always remain on the surface of the F-MOC without penetration as shown in Figure 10c.which achieved the inherent hydrophobic and has more advantages in applications than the hydrophobic modification of surface coating and dipping.
## 4 Conclusions  
In order to design an automatic navigation system (ANS) for a self-driving tractor, a dynamic path search algorithm and fuzzy logic control-enhanced Stanley model (FSM) were integrated to easily implement the ANS. Based on the Dongfanghong tractor platform, the performance of the developed ANS was tested in straight-line path tracking and whole-field path tracking. The experimental results indicate that when the tractor traveled at an average speed of 1 m/s, the maximum tracking errors for straight-path and whole-field path tracking were 10 cm and 27 cm, respectively. Compared to the SM, the FSM improved the lateral tracking error by 20.6% for the whole-field path tracking, with an average lateral tracking error of 8.1 cm. As the tractor was only operated in straight paths inside the field, the large error appearing in the headland area did not affect its unmanned working ability.  
With the progress in unmanned farm technology, more diversified scenarios need ANSs, resulting in new requirements for the reliability and cost of ANSs. The positioning sensor employed in this study was only a dual-antenna RTK, which can be easily degraded by external disturbances, such as the occlusion of trees and electromagnetic interference from high-voltage wires, so a more reliable and low-cost integrated navigation system should be designed in future work. Furthermore, the field test was only carried out for tillage paths, which is not sufficient for other applications. Developing more approaches to whole-field path tracking is also the main focus of our next work.  
Conceptualization, B.C. and X.C.; methodology, B.C. and Y.Z. ([PERSON]); software, B.C. and X.C.; validation, X.C. and Z.M.; formal analysis, Z.M.; investigation, B.C.; resources, Y.L.; data curation, Y.Z. ([PERSON]); writing--original draft preparation, X.C. and B.C.; writing--review and editing, B.C. and X.W.; visualization, Y.Z. ([PERSON]); supervision, B.C.; project administration, X.W.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.  
Figure 16: Results of lateral deviation in whole-field path tracking.  
**Funding:** This research was jointly funded by the National Natural Science Foundation of China, grant number 32271999; the Primary Research & Development Plan of Jiangsu Province, grant number BE2021313; the Priority Academic Program Development of Jiangsu Higher Education Institutions, grant number PAPD-2023-87; the Sichuan Province Engineering Technology Research Center of Modern Agriculture Equipment, grant number XDNY2023-003; the Zhejiang Province Agricultural Machinery Research, Manufacturing and Application Integration Project, grant number YF20220801; and Open Funding from the Key Laboratory of Modern Agricultural Equipment and Technology (Jiangsu University), Ministry of Education, grant number MAET202301. The APC was funded by MAET202301.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Description of Experiments
### Laboratory Scale Fatigue Testing (UIC Fatigue Test)  
In order to identify the AE signature due to the fatigue crack growth, a laboratory scale testing with similar characteristics as the NAVAIR testing is designed. However, some simplifications are made in defining the loading condition and sample geometry. The loading condition is fatigue causing tensile stress at the notch tip. The circular geometry of the spline section is built as a planar geometry in order to apply loading in conventional test equipment, while the other geometric details of the spline section (e.g., thickness, spline details) are preserved, as shown in Figure 2. A more comprehensive discussion of the laboratory testing is presented in [16]. Similar to the NAVAIR test rig, a notch is introduced to control the initiation of crack. To ensure that the crack direction is perpendicular to the spline tooth, the cyclic load (4 Hz, \(R_{s}=0.1\), max load = 12 kN) is applied using the load control scheme of Instron loading machine (Instron, Norwood, MA, USA). The AE monitoring system includes three WD sensors placed in a triangulation form for source localization; one Nano 30 sensor (nano-30 FP 97 sensor manufactured by Mistras Group Inc.); and, piezoelectric wafer sensor array, see in Figure 2c. The AE sensors are bonded using super glue and connected to 40 dB gain pre-amplifiers. The AE data is collected using PCI-8 data acquisition board manufactured by Mistras Group Inc. Each AE waveform is recorded with 3 MHz sampling rate with the duration of 1 ms. The threshold is varied between 50 dB and 70 dB to control the hit rate [16]. In addition, strain data is collected throughout the fatigue test to capture the strain level change due to the crack growth. The crack length reaches to  
Figure 1: The Naval Air Systems Command (NAVAIR) gearbox test (**a**) the test rig, and (**b**) the types and positions of the AE sensors on the gearbox housing (CH (Channel) 1 and 2 are WD (wideband) sensors, and CH 3 and 4 are micro 30 sensors) (**c**) the sensitivity curves of Micro30s and WD sensor provided by Mistras Group Inc.  
7 mm after 590,211 cycles, as shown in Figure 3. The acoustic microscope image is taken using the Sonoscan Gen 6 C-SAM acoustic microscope (Sonoscan, Inc., Elk Grove Village, IL, USA) and 100 MHz transducer with 60 \(\upmu\)m resolution.
## 3 Performance Evaluation  
Three benchmark cases, the triaxial test of a sand specimen, cone penetration test and sand flow due to dam break, were used to evaluate the performance of the GPU parallel computing strategy. The parallel computing was conducted on a workstation with a GPU NVIDIA GeForce Titan V and a 6-core (12-thread) CPU Intel i7-6850K with a frequency of 3.6 GHz. On the GPU, a maximum of 3840 cores were available, and the global memory was 12 GB. For comparison purposes, the benchmark cases were simulated with a single-CPU core, multiple-CPU cores (OpenMP) and a single-GPU (CUDA), respectively, with a well-tuned code on each platform. All the simulations were using double-precision numbers as single-precision seems to be insufficiently accurate to describe the tiny particles with a radius of lessthan 0.1 mm [60]. A \"speedup\" factor for the whole incremental step or each operation within the incremental step is used to quantify the effect of parallel computing, expressed as:  
\[\text{Speedup}=T_{\text{Sequential}}/T_{\text{Parallel}} \tag{19}\]  
where \(T_{\text{Sequential}}\) and \(T_{\text{Parallel}}\) represent the average runtimes of CPU sequential and CPU or GPU parallel calculations per increment.
## 6 Quality Analysis of the Reconstructed Image
### Review and Analysis of Objective Measurement Results
#### 6.2.1 Objective Quality of the Test Image Horse  
The reconstructed images for each compression method and ratio are shown in Figure 14. At the lowest compression ratio (top row), the images look almost the same. At 0.4 bpp, the differences between JPEG 2000 and EEDC-BSSP are imperceptible, while the degradation begins for the image compressed with JPEG. At 0.2 bpp, artifacts begin to appear on the JPEG image, while degradation is observed on the JPEG 2000 image. On the EEDC-BSSP image, loss of detail is evident but structure is preserved. At 0.1 bpp, the JPEG image is severely degraded, the structure and details of the JPEG-2000 image are distorted, and the EEDC-BSSP image still has the original structure but lacks details. At 0.05 bpp, the JPEG cannot reproduce the image, JPEG 2000 is very blurred, while the EEDC-BSSP image is degraded in its structure and details, but still a picture of a horse is recognizable. The data shown in Tables 9-11 are the numerical evidence of the image degradation shown in Figure 14.  
From Table 9, it can be seen that, the lower the parameter _AAE_, the smaller the difference between the original and the reconstructed image, i.e., the smaller the distortion. Furthermore, the parameter _MSE_ should be lower, while the parameter _SNR_ should be larger, which means that the reconstructed image has better visual quality.  
In Table 10, the parameter PSNR is similar to SNR, i.e., its value increases with the quality of the reconstructed image. The values of SSIM, MS-SSIM in Table 10, and VIF in Table 11 range from 0 (no similarity) to 1 (identical images). According to the SSIM values, EEDC-BSSP is better than the other two compression methods at compression ratios of 0.05, 0.1, and 0.2 bpp, while it is the same as JPEG 2000 at 0.4 bpp and the same as JPEG at 0.8 bpp, but JPEG 2000 has the best result at low compression. MS-SSIM has similar results. The VIF results tend to be better for EEDC-BSSP at all compression ratios except 0.8 bpp.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**bpp**} & \multicolumn{3}{c}{**PSNR**} & \multicolumn{3}{c}{**SSIM**} & \multicolumn{3}{c}{**MS-SSIM**} \\  & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** \\  & **JPEG** & **2000** & **BSS  
Figure 14: Comparative overview of reconstructed images for image _horse_ ranging from 0.8 bpp (**top row**) to 0.05 bpp (**bottom row**). Compression methods: (**a**) JPEG, (**b**) JPEG 2000, and (**c**) EEDC-BSSP.  
\begin{table}
\begin{tabular}{c c c c} \hline
**bpp** & **JPEG** & **JPEG 2000** & **EEDC BSSP** \\ \hline
0.8 & 0.75 & 0.80 & 0.75 \\
0.4 & 0.59 & 0.66 & 0.67 \\
0.2 & 0.38 & 0.50 & 0.54 \\
0.1 & 0.23 & 0.33 & 0.38 \\
0.05 & / & 0.17 & 0.23 \\ \hline \end{tabular}
\end{table}
Table 11: Results of the VIF metric for image _horse_.
## 6 Implementation and Case Study
### Dynamic DU Sleep Scheduling  
We formulate the DU sleep scheduling problem as a constrained optimization task:  
\[\min_{s_{t}}\sum_{t=1}^{T}E_{t}(s_{t})\quad\text{s.t.}\quad\ell_{t}(s_{t}) \leq\ell_{\max}\quad\forall t\in\{1,\dots,T\} \tag{14}\]  
where \(E_{t}\) and \(\ell_{t}\) represent energy consumption and latency at time slot \(t\), with \(s_{t}\) denoting the sleep state decision. Our FL model uses a two-layer LSTM architecture for load prediction, and to further optimize real-time deployment performance, the LSTM model was pruned to 128 hidden units and quantized to FP16 precision. This approach reduced the computational overhead by 35% without compromising model accuracy. Furthermore, hardware acceleration using NVIDIA A100 GPUs and CUDA-optimized kernels enabled sub-millisecond inference times, which are critical for the management of URLLC slices.
# Moon Sensor Station to Improve the Performance of Lunar Satellite Navigation Systems
## 2 Satellites-Only and Performance Evaluation
Pseudorange Computation: the pseudorange is computed by also adding the RX clock bias and the other sources of errors (RX, multipath, and satellite clock all independent, uncorrelated, and Gaussian-distributed): \[\hat{\mathbf{\rho}}^{T}(\mathbf{\theta})=\left[\hat{\rho}_{1}(\mathbf{\theta})\quad\hat{\rho}_ {2}(\mathbf{\theta})\quad\ldots\quad\hat{\rho}_{N}(\mathbf{\theta})\right]+\mathbf{n}^{T }(\mathbf{\theta}) \tag{1}\]0 (9) \[\hat{\mathbf{\rho}}^{T}(\mathbf{\theta})=\left[\hat{\rho}_{1}(\mathbf{\theta})\quad\hat{\rho}_ {2}(\mathbf{\theta})\quad\ldots\quad\hat{\rho}_{N}(\mathbf{\theta})\right]+\mathbf{n}^{T }(\mathbf{\theta}) \tag{1}\]1 Each error term, \(\epsilon\), is modeled as a zero-mean Gaussian distribution, with a specified standard deviation, as described in the paper. Moreover, the standard deviation of the multipath error depends on the satellite's elevation angle, as described in the paper. 4. Position Estimation: The iterative WLS algorithm is used to estimate the receiver position for each trajectory point. 5. Statistical Evaluation: For each point, \(N\) Monte Carlo trials are performed, and the root mean square (RMS) error across trials is computed. In this way, the geometrical user-satellite effects are taken into account when introducing the satellite position error, together with a realistic time bias of the receiver. The other error contributions are merely added to the expected pseudorange. Finally, the same approaches for CRLB computing or Monte Carlo simulation can also be used in case of additional measurements, as shown in the following sections.
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 1 Introduction
### Related Works
However, their model was intelligent reflecting-surface-assisted cognitive radio networks with non-orthogonal multiple access. In addition, most papers consider the channel between UAVs and IoT devices as a line of sight channel. However, due to obstacles such as buildings, it is highly likely that there is a non-line-of-sight connection between UAVs and IoT devices ([PERSON] et al., 2018), so non line of sight channels will be considered. In this paper, we consider probabilistic LoS channels, with energy efficiency and spectral efficiency as objective functions. We compare our work with several related works, as shown in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Model** & **Objective** & **Variables** & **Method** \\ \hline [PERSON]. [PERSON] et al. (2015) & Multiple users, UAVs and satellites; line of sight channels & Penalty function constructed from throughput and energy consumption & Bandwidth allocation; UAV trajectory; UAV power; indicator variable about UAV and satellite & BCD algorithm; SCA algorithm \\ \hline [PERSON]. [PERSON] et al. (2016) & Multiple users, UAVs, one or more satellites; probabilistic LoS channel & Spectral efficiency & gateway selection; bandwidth allocation; UAV deployment & Dinkelbach method; simulated annealing algorithm; SCA algorithm \\ \hline [PERSON]. [PERSON] et al. (2018) & 3D integrated SAG networks with multiple vertical layers & Data rate; latency; reliability; energy efficiency and so on & Only a framework was provided without specific explanation & NSGA-II; no specific steps provided \\ \hline [PERSON] et al. (2019) & IRS-assisted CRNs with NOMA; perfect and imperfect CSI & Energy efficiency; spectral efficiency & the phase shift matrix of IRS; beamforming vector & BCD algorithm; \(\epsilon\)-constraint method; \(\mathcal{S}\)-procedure method; introduce auxiliary variables \\ \hline \multirow{3}{*}{This paper} & Multiple users, UAVs, one satellite probabilistic LoS channel & Energy efficiency; spectral efficiency & IoT device power; UAV power; sub-channel selection; UAV deployment & BCD algorithm; SCA algorithm; Dinkelbach method; \(\epsilon\)-constraint method; introduce slack variables \\ \cline{1-1} \cline{2-5}  & Multiple users, UAVs, one or more satellites; probabilistic LoS channel & Energy efficiency; spectral efficiency & IoT device power; UAV power; sub-channel selection; UAV deployment & BCD algorithm; SCA algorithm; Dinkelbach method; \(\epsilon\)-constraint method; introduce slack variables \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between this paper and related works.