esa-sceva/satcom-chunk-collection huggingface dataset:
# _Sci_
## 4 Discussion
### Modifying Satellite ICDS and KPPs for Laser Communications
#### 4.3.4 Key Performance Parameter (KPP) #1: The Transmitter and Telescope  
The fourth and final KPP addresses the transmitter and telescope. The impact of atmospheric interruptions cannot be ignored; because the U.S. Department of Defense is interested in mission assurance (and not typically financial constrained), why not require a dual-band transmit and receive system--combined radio frequency/laser bands that use the same telescopic antenna [59]? A combined radio frequency (in the ultra-high frequency band or higher, likely microwave) and laser antenna could simultaneously accommodate both bands \"thereby enabling, as a function of the operational environment, a shift from the higher band to the lower band and vice versa\" [59]. This would merit evaluation for ship and aircraft operations in foul weather, and to possibly serve as an aide in resolving the acquisition, tracking, and pointing challenge--the radio frequency signal could serve as a beacon to help zero the optical transmission [134].
## 2 Methodology
### Experimental Program
#### 2.2.2 Setups and Data Acquisition  
Figure 2 shows specific setups of Brazilian tests for evaluating the tensile behaviours of concrete. A hydraulic servomaterial testing machine (MTS-180-50) with a constant compressive velocity of 0.01 mm/s was utilized to load a concrete disc (diameter: \(\Phi\)75 mm) under the quasi-static condition. As shown in Figure 3, the Brazilian test was then performed under BTC and BTD boundary conditions, with the disc loaded by two steel plates in BTC, and the disc loaded by two steel-arc jaws with a diameter of 1.5 times larger than that of the disc in BTD tests, respectively (see Figure 3a).  
To capture the initiation of the tensile crack, a Photron FASTCAM SA.5 camera was adopted to record the surface deformation in the specimens. We then identified the initiation of crack patterns using a DIC technique by measuring the real-time strain concentration. Figure 2c presents the random distribution of grey levels by spraying speckles on the surface of specimens. The relative motion of the specimens could be tracked by comparing the grey-level distributions of images before and after deformations. In general, the development of cracks is highly correlated with strain concentration. The location of cracks can thus be determined by the strain concentration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Density** & **Compressive Strength** & **Passion’s Ratio** & **Compressive Young’s Modulus** \\
**GPa** & **kg/m3** & **MPa** & \\ \hline
32 & 2430 & 60 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quasi-static compressive mechanical properties of concrete.  
Figure 2: Experimental setups for the MTS machine (**a**) and DIC (**b**), and disc specimen with sparkles (**c**).  
In this paper, a camera equipped with a 50 mm lens was used to acquire the planar images at the resolution of 1024 \(\times\) 1024 pixel\({}^{2}\) during deformation. The images were captured. The parameters settings of the DIC analysis windows are given in Table 3.
### System Configuration Optimization
#### 2.3.1 System Configuration Optimization Model  
To optimize the PEDF system configuration, this work employs a dual-layer model based on planned operation. The objective function targets economic viability, low carbon emissions, and reliability. The inner layer utilizes a Cplex solver to solve this linear problem, while the Sinh Cosh Optimizer in the outer layer determines the optimal configuration scheme [36].  
Cplex solvers are commercial solvers that efficiently solve linear programming problems. The Sinh Cosh optimizer is a recently proposed metaheuristic algorithm based on the mathematical properties of hyperbolic functions. It features two stages for balancing global exploration and local utilization, a bounded search strategy to ensure convergence, and a mechanism to switch between stages. This results in strong evolutionary capabilities, fast search speeds, and robust optimization ability compared to other algorithms [36].  
The solution process and information exchange for the planning and operating dual-layer model, based on Sinh Cosh optimizer and Cplex, are illustrated in Figure 9. The planning layer initializes configuration for photovoltaics and energy storage and sends them to the operating layer. The operating layer then optimizes the comprehensive goals of economic efficiency, low carbon emissions, and reliability using Cplex, providing feedback on operation and maintenance costs. The planning layer utilizes the Sinh Cosh optimizer to determine the optimal solution, thereby forming a closed loop that results in the best lifecycle cost for the system.
## 5 Simulation and Analysis  
From the analysis presented in Section 3, it is evident that spread-spectrum technology effectively enhances the SNR post-despreading, thereby influencing the precision and sensitivity of QD position detection. To validate the efficacy of this method, experiments were conducted using the experimental system constructed in Section 4 to assess the precision and sensitivity of QD position detection under beacon spread-spectrum modulation. The PRN signal was set to 10 MHz, with modulation PRN depths of 1023, 127, and 31; these corresponded to spread-spectrum gains of 30.09 dB, 21.03 dB, and 14.91 dB, respectively. These configurations were compared with direct detection performed under non-spread-spectrum technology to verify the effective detection of light-spot positions under beacon spread-spectrum modulation; this was also in order to evaluate the improvements observed in the precision and sensitivity of QD position detection across different spread-spectrum depths.
## 3 Methods
### Feature Extractor  
The feature extractor plays a critical role in learning node representations by preserving node attributes and graph topology, facilitating the generation of synthesized nodes. Graph convolutional networks (GCNs) can typically be used to accomplish this task. However, a GCN has certain limitations and may not handle various types of local topologies well, leading to limited effectiveness when dealing with new graph structures. To overcome these issues, GraphSAGE is introduced as an improvement over GCNs because it can effectively learn from multiple types of local topologies and possesses strong generalization capabilities. Considering this, the ESA-GCN adopts a GraphSAGE block as the core module of the feature extractor to achieve learning of node representations. Within this block, the message passing and fusion processes are formulated as Equation (1).  
\[\mathrm{H}_{\mathrm{v}}^{\mathrm{I}}=\sigma\Big{(}\mathrm{W}^{\mathrm{I}}\cdot \mathrm{CONCAT}(\mathrm{F}[\mathrm{v},:],\mathrm{F}\cdot\mathrm{A}[:,\mathrm{ v}])\Big{)} \tag{1}\]  
Here, F represents the input node attribute matrix, and F\([\mathrm{v},:]\) represents the attributes of node v. \(\mathrm{A}[:,\mathrm{v}]\) is the v-th column of the adjacency matrix, \(\mathrm{h}_{\mathrm{v}}^{\mathrm{I}}\) is the embedding of node v, \(\mathrm{W}^{\mathrm{I}}\) is the weight parameter, and \(\sigma(\cdot)\) refers to activation functions such as \(\mathrm{ReLU}(\cdot)\).  
Figure 1: Conceptual framework of the ESA-GCN model.  
The ESA-GCN model uses GraphSAGE in the GNN as the feature extractor, which learns node representations through message passing and fusion processes to measure similarity and interpolation operations between nodes.
## 3 Discussion and Results
Figure 7: Design of Droop Nose Leading Edge (DNLE) and Morphing Trailing Edge (MTE) airfoil configurations versus the baseline airfoil. (**a**) the basic concept of the morphing wing with combined morphing leading and trailing edge deflections; (**b**)the two cases of MTE designs. Figure 9a that the laminar-to-turbulent transition region is located at 18% of the chord (0.18c) at an angle of attack of 6\({}^{\circ}\) and at 43% of the chord (0.43c) at an angle of attack of 2\({}^{\circ}\).The variations of the skin friction coefficient along the chord were assessed at different angles of attack using the Transition \((\gamma-Re_{\theta})\) SST model in Figure 9.  
The velocity magnitude contours plots at three angles of attack (4\({}^{\circ}\), 10\({}^{\circ}\), 16\({}^{\circ}\)) at \(\mathrm{Re}=2.4\times 10^{6}\) are shown in Figure 10. The turbulent intensities play an important role in understanding the flow behavior over an airfoil, and they are shown in Figure 10 for the angles of attack of 4\({}^{\circ}\) and 10\({}^{\circ}\). The contour lines remain attached to the airfoil at low angles of attack such as 4\({}^{\circ}\), as shown in Figure 10a; the flow starts to separate at the trailing edge of the airfoil at an angle of attack of 10\({}^{\circ}\) as shown in Figure 10b and changes into a fully separated flow at the angle of attack of 16\({}^{\circ}\) as shown in Figure 10c. It is important to understand that boundary layer separation takes place at an angle of attack of approximately 10\({}^{\circ}\). The separation region shown in Figure 10c causes increased drag as it induces a large wake that completely changes the flow downstream of the point of separation. It is clear that the turbulent intensity is minimal at the airfoil leading edge at a low angle of attack of 4\({}^{\circ}\), as  
Figure 10: Velocity magnitude contour plots at three angles of attack at \(\mathrm{Re}=2.4\times 10^{6}\). (**a**) the contour lines at low angles of attack such as 4\({}^{\circ}\); (**b**) the contour lines at low angles of attack such as 10\({}^{\circ}\); (**c**) a fully separated flow at the angle of attack of 16\({}^{\circ}\). Figure 9: Skin friction coefficients variations with the chord obtained at different angles of attack. (**a**) the friction coefficient variations with the chord for the angles of attack of 0\({}^{\circ}\), 2\({}^{\circ}\) and 6\({}^{\circ}\); (**b**) the initial flow separation, the separated region and its reattachment for upper and lower surface. seen in Figure 11a, while at the angle of attack of 10\({}^{\circ}\), the turbulent intensity increases over the airfoil surface, including at its leading edge.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 6 Conclusions  
In this paper, we solve the problem of low efficiency in complex task decomposition and dynamic resource allocation in multi-satellite collaborative computing. The innovation of this study lies in proposing a dynamic resource mapping method based on task priority using graph theory decomposition, game theory, and deep reinforcement learning, as well as a real-time task scheduling method based on temporal logic.  
The experimental results show that in the selected dataset, the objective function value of our method is reduced by at least 32.4% compared to the comparative algorithms (such as IDCE-GA), and the task execution time is significantly reduced, especially in large-scale scenarios where the resource utilization advantage is obvious. However, this method still has shortcomings in terms of parameter tuning and computational complexity control, and further optimization of the algorithm's convergence and real-time performance is needed in practical applications. In future research, we will explore low-complexity joint optimization strategies and combine digital twin technology to achieve virtual real linkage, in order to further enhance the dynamic adaptability of the algorithm in heterogeneous satellite network collaborative scenarios.  
**Author Contributions:** Conceptualization, S.W.; methodology, S.W. and J.L.; software, C.Z.; validation, S.W. and C.Z.; formal analysis, S.W.; investigation, Z.S.; resources, S.W. and J.L.; data curation, Z.S.; writing--original draft preparation, S.W.; writing--review and editing, S.W. and C.Z.; visualization, Z.S. and C.Z.; supervision, L.L. and J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant No. U2003208 and No. 61972398), the Science and Technology Plan of Hunan Province (GrantNo. 2016 TP1003), the Key Technology R&D Program of Hunan Province (Grant No. 2018 GK2052), the Foundation for Equipment Procurement (Grant No.6141A020227), and the Theory and Method of Multi-Satellite Collaborative Application Foundation (Grant No. 2019-JCJQ-ZD-342-00).  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 5 Experimental Evaluation
### Experimental Framework  
Our validation methodology encompasses three complementary approaches designed to establish both theoretical soundness and practical viability.  
Statistical simulation involves 16 distinct scenarios with 32,000 total test iterations (500 per scenario \(\times\) 16 scenarios \(\times\) 4 algorithms), providing rigorous statistical validation through one-way ANOVA \(F(3,1996)=30.30\), \(p<0.001\) with 95% confidence intervals using Monte Carlo methods and Latin Hypercube Sampling. Our analysis includes comprehensive effect size calculations, revealing that 64.6% of algorithm comparisons yield large effects ([PERSON]'s \(d\geq 0.8\)), with maximum effect sizes reaching \(d\) = 4.57 for HATC versus KFTC in EMI scenarios.  
Hardware implementation includes FPGA synthesis and verification on PolarFire SoC with comprehensive timing closure analysis.  
Security validation provides comprehensive attack detection and mitigation evaluation against six threat scenarios including stealth timing, desynchronization, burst interference, replay attacks, multi-channel coordination, and firmware compromise.  
The simulation framework models realistic operational conditions across multiple dimensions, including Gaussian, uniform and burst error distributions for noise modeling, environmental factors that span temperature ranges from \(-40\)\({}^{\circ}\)C to \(+85\)\({}^{\circ}\)C with vibration and EMI effects, and comprehensive coverage of attack scenarios that represent real-world threat conditions. Scenario-specific correlation analysis validates algorithm performance, with HATC maintaining 40.4% success rate during burst noise conditions where other algorithms show significant degradation.
# Investigating the Reliability and Dynamic Response of Fully 3D-Printed Thermistors
## 3 Results and Discussion
On the other hand, the adhesion performance of PEDOT:PSS film on Ag film was very robust compared to the PC substrate, having an adhesion strength of approximately 4B with almost no material removal, as demonstrated in Figure 6b. Similar outcomes have been reported by different researchers [48; 49]. The reason for the better adhesion is related to the possible electrostatic attraction between these layers, where the negatively charged sulfonate groups in PSS formed electrostatic interactions with the positively charged Ag silver film. Another possible reason could be the print orientation of the PEDOT:PSS film relative to the underlying substrate. Although PEDOT:PSS was dispensed at 0\({}^{\circ}\), the actual orientation varied depending on the substrate material. For example, on the PC substrate, the relative orientation was 45\({}^{\circ}\), whereas on the Ag film, it was 0\({}^{\circ}\) with respect to the surface of each material. Therefore, the effect of print orientation on mechanical reliability should be further investigated. To further investigate the variation in the adhesion depending on the underlying feature, SEM images were analyzed. As shown in the SEM image in Figure 7, a distinct gap line between the PEDOT:PSS and PC substrate was observed. This gap likely weakened the adhesion mechanism between the two layers, resulting in the PEDOT:PSS films detaching more easily. This is attributed to the coarse surface of the PC substrate, which contributed to weak adhesion and needs surface treatments. Figure 7: SEM image of PEDOT:PSS and PC substrate structure manufactured for adhesion test, a clear line-like gap is visible between these. The proposed method involves a multi-step manufacturing process that requires manual handling of the substrate between printing stages, which can introduce variability and alignment challenges. Additionally, unlike traditional substrates with smooth surfaces that offer better surface quality and interfacial adhesion, this study utilizes 3D-printed substrates without further surface treatment. The inherent roughness of these substrates leads to irregularities such as porosity, resulting in poor uniformity and inadequate interfacial adhesion between the PC and PEDOT:PSS layers. This is evidenced by the relatively weak adhesion observed between these layers. Therefore, further optimization to improve interfacial stability between layers is essential. Addressing these challenges through enhanced substrate preparation, process automation, and adhesion promotion strategies will be crucial for improving the reliability and scalability of fully 3D-printed thermistors.
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 5 Conclusions  
This study proposed an intra-domain transfer learning strategy to tackle the challenge of insufficient training samples in fault diagnosis applications. To alleviate the impact of negative transfer, the intra-domain transfer learning strategy first uses the vanilla transfer of an off-the-shelf inter-domain model to a data-abundant source domain that is akin to the target domain. The learned feature extractors are then reutilized in the target domain via shallow-layer freezing, followed by a finetuning step with small samples.  
We verified the proposed transfer learning strategy in a gearbox fault diagnosis case study and compared it to two other learning schemes. In the case study, we adopted CWT as a preprocessing tool to convert 1D vibrational waveform to 3D time-frequency images, and selected ResNet-50 as the base model. Under various small sample settings (splitting ratio of training and testing samples in the target task), we carried out extensive experiments and observed superior performance of the proposed strategy in both convergence speed and accuracy. Finally, we introduced heat map visualization to demystify the learned deep neural network. We leave the quantitative assessment of a fault diagnosis model using heat maps to future work.  
Conceptualization, L.Z. and J.L.; data curation, J.Z. and Y.P.; investigation, L.Z., Y.P. and J.L.; methodology, L.Z.; validation, J.Z., Y.P., and L.Z.; writing--original draft, L.Z. and J.Z.; writing--review and editing, Y.P., and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Science Foundation of China (NSFC) under Grant 71801045, and the research start-up funds of DGUT (GC300502-46).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methods and Materials
### Methods  
#### 2.1.1 Conventional DAS for Plane Wave Imaging  
In this section, the process for compounded plane wave imaging is described. The same linear ultrasound probe composed of \(N\) equally spaced elements is used for both transmission and reception. To reconstruct one image, \(M\) plane waves are transmitted. For a given plane wave \(m\), with a transmission angle \(\theta_{m}\) and a given element \(n\) of the array, the recorded signal is \(p_{n,m}(t)\). The data acquired are beamformed to reconstruct the pixels in the grid \((x,z)\), where \(x\) is the lateral axis parallel to the array, and \(z\) is the depth axis.  
Each pixel \(r(x,z)\) of the RF image is obtained through combination of the \(N\times M\) correctly delayed samples \(q_{n,m}(x,z)\). Each pixel is extracted from the raw echo signal \(p_{n,m}(t)\). To correctly select the \(q_{n,m}(x,z)\) samples, two assumptions are required. The speed of sound in tissues is believed to be constant \(c_{0}=1540\) m\(\cdot\)s\({}^{-1}\), and a point scatterer is believed to back-scatter the spherical wave front. In this way, the echo back-scattered by a point located at \((x,z)\) corresponds to the samples \(q_{n,m}(x,z)\) with:  
\[q_{n,m}(x,z)=p_{n,m}(\tau_{n,m}(x,z)), \tag{1}\]  
where \(\tau_{n,m}(x,z)\) is the time of flight of the wave, which is the sum of \(\tau_{TX,m}(x,z)\), the time in transmission for the steered plane wave \(m\) to get to the scatterer, and \(\tau_{RX,n}(x,z)\), the time in reception from the scatterer to element \(n\) of the probe:  
\[\tau_{n,m}(x,z)=\tau_{TX,m}(x,z)+\tau_{RX,n}(x,z), \tag{2}\]\[\tau_{TX,m}(x,z)=\frac{1}{c_{0}}\big{[}x\cdot\sin\theta_{m}+z\cdot\cos\theta_{m} \big{]}, \tag{3}\]  
\[\tau_{RX,n}(x,z)=\frac{1}{c_{0}}\sqrt{(x-x_{n})^{2}+z^{2}}, \tag{4}\]  
where \(x_{n}\) is the lateral position of the \(n^{th}\) element. Then, the \(N\times M\) correctly delayed samples \(q_{n,m}(x,z)\) are summed on the plane wave dimension, in order to obtain \(N\) compounded delayed samples \(s_{n}(x,z)\):  
\[s_{n}(x,z)=\sum_{m=1}^{M}q_{n,m}(x,z). \tag{5}\]  
Finally, in the case of conventional DAS beamforming, the samples \(s_{n}(x,z)\) are simply summed along the element dimension, in order to obtain the pixel value \(r_{DAS}(x,z)\):  
\[r_{DAS}(x,z)=\sum_{n=1}^{N}a_{n}\cdot s_{n}(x,z), \tag{6}\]  
where \(a_{n}\) are the weighting coefficients of the apodization window (e.g., Tukey, Hann, and others). Finally, to obtain the B-mode image, the RF image is subjected to envelop detection along the depth dimension. Of note, if the central frequency of the ultrasound array is \(f_{0}\), then the RF image lines oscillate around the spatial frequency \(f_{z}=\frac{2}{c_{0}}f_{0}\). Thus, for the sake of simplicity, the parts that follow make direct use of the temporal frequency \(f_{0}\) rather than the spatial frequency \(f_{z}\).  
In this study, all of the beamformers presented follow the same process for delaying and plane wave compounding. The only difference lies in the way \(s_{n}(x,z)\) are combined to obtain the pixel value \(r_{DAS}(x,z)\).