esa-sceva/satcom-chunk-collection huggingface dataset:
# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
## 2 Model and Methods
### Improved YOLOv8 Model  
At present, there is still potential to improve the detection accuracy of algorithms in the field of insulator fault diagnosis. At the same time, there are also problems such as the presence of multiple multi-scale targets in insulator images and the different scales of similar faults in the images, which brings problems to model training. Therefore, the purpose of this study is to improve the original YOLOv8 algorithm to realize multi-scale fault recognition of insulator images and improve the accuracy of insulator fault diagnosis. This paper proposes an insulator fault diagnosis algorithm based on the improved YOLOv8 model. The new algorithm is named YOLOv8-DCP, and the overall structure of the improved algorithm is shown in Figure 2.  
In order to solve the current problems in insulator fault diagnosis, this paper adopts three methods to improve the performance of the model. At the same time, in order to more directly show the correspondence between these improvement methods and the model improvement ability, Figure 3 is drawn. This figure shows the problems that need to be studied. For these problems, three improvement modules are proposed to improve YOLOv8 in a targeted manner and improve the accuracy of insulator fault diagnosis.
## 2 Literature Review and Hypotheses
### Firm Performance  
Firm performance (FP) is one of the most prominent concepts in organizational and strategic management literature [19]. Generally, FP is considered to reflect how well a company achieves its goals and objectives [34], but its complex and multifaceted nature has led to divergences and debates regarding its theoretical conceptualization and empirical measurement [19], causing inconsistencies that impact scientific rigor [35]. [PERSON] and [PERSON] assert that _\"improving performance is at the heart of strategic management\"_ [19] (p. 801), emphasizing the importance of academic efforts to understand FP for practical implications. Consequently, various research efforts have sought to enrich the understanding and operationalization of the concept. Below, the main conceptualizations and measurements of performance are detailed.  
[PERSON] et al. [35] identify three approaches to conceptualizing and working empirically with performance: the latent multidimensional approach, the separate constructs approach, and the aggregated construct approach. In their meta-analysis, they identified a prevalent inconsistency where most articles conceptualize FP from a latent multidimensional approach but measure it empirically with the separate constructs approach [35]. Recognizing this discrepancy, we seek to align theoretical conceptualization with empirical measurement, considering congruence with the objectives, suitability to the theoretical framework, feasibility, and contextual relevance. We chose the latent multidimensional approach as it provides a holistic understanding that reflects the interconnected and complex nature of modern business dynamics. This approach conceptualizes FP as an abstract and general construct, measured empirically by evaluating both financial and non-financial dimensions. It provides analytical flexibility, allowing the synergistic influence of EO and ITC on FP to be captured by considering it as an underlying construct influenced by a network of interconnected factors rather than isolated outcomes. It is worth mentioning that more recent works have supported the multidimensional nature of firm performance measurement [36]. Identifying capabilities that lead to superior FP is valuable for both academics and practitioners. Next, we will discuss EO, which is widely recognized in the literature as a vital capability [17; 18].
## 7 Experimental Results
### Original BEE  
When evaluating a clustering algorithm, power consumption is normally the only concerned metric, to the detriment of other issues such as network coverage. Coverage sensitivity longevity has been proposed to evaluate network lifetime rather than number of nodes. Network coverage is determined by the distribution and sensing range of the sensors. Some of the sensors in the network are more important than the others in terms of network coverage. The sensors that do not have a significant impact on the coverage of the network are allowed to die sooner than the others. The experimental results for original BEE are shown below.
###### Abstract  
As machine learning continues to evolve, traditional image steganography techniques find themselves increasingly unable to meet the dual challenge of deceiving both the human visual system and machine learning models. In response, this paper introduces the Visually Robust Image Steganography (VRIS) model, specifically tailored for this dual deception task. The VRIS model elevates visual security through the use of specialized feature extraction and processing methodologies. It meticulously conducts feature-level fusion between secret images and cover images, ensuring a high level of visual similarity between them. To effectively mislead machine learning models, the VRIS model incorporates a sophisticated strategy utilizing random noise factors and discriminators. This involves adding controlled amounts of random Gaussian noise to the encrypted image, thereby enhancing the difficulty for machine learning frameworks to recognize it. Furthermore, the discriminator is trained to discern between the noise-infused encrypted image and the original cover image. Through adversarial training, the discriminator and VRIS model refine each other, successfully deceiving the machine learning systems. Additionally, the VRIS model presents an innovative method for extracting and reconstructing secret images. This approach safeguards secret information from unauthorized access while enabling legitimate users to non-destructively extract and reconstruct images by leveraging multi-scale features from the encrypted image, combined with advanced feature fusion and reconstruction techniques. Experimental results validate the effectiveness of the VRIS model, achieving high PSNR and SSIM scores on the LFW dataset and demonstrating significant deception capabilities against the ResNet50 model on the Mini-ImageNet dataset, with an impressive misclassification rate of 99.24%.  
image steganography; dual deception; feature-level fusion; random noise factor; adversarial training +
Footnote †: journal: Applied sciences  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Calculation Model and Parameter Selection  
### Pressure Filtration Calculation Model  
The simplified mud slurry compression and filtration model is shown in Figure 5, and the mud slurry can be regarded as one-dimensionally compressed under pressure, with deformation occurring only in the horizontal direction. On both sides of the mud slurry are permeable plates, which allow the pore water in the mud slurry to seep out freely. In fact, the pressure filtration dewatering process of the mud slurry includes two stages. The first stage is the preliminary dewatering stage of the fluid-mixed mud slurry (the mixture of mud slurry, flocculant, and curing agent), and the interaction between the soil particles begins to form a soil skeleton, as shown in Figure 5a,b. At the second stage, the mud cake with a soil skeleton continues to be dewatered under pressure to form a mud cake with a low water content, as shown in Figure 5b,c.  
At the first stage, the mixed mud slurry is a suspended system due to the incompletely formed soil skeleton, and the consolidation theory is not applicable to the deformation calculation at this stage, but this stage can be completed quickly for the flocculated conditioned mud slurry with a better dewatering performance, so the second stage of pressure filtration can be regarded as the deformation of the mud cake by draining and consolidating it under the external force, which is an important stage in the determination of the time required for pressure filtration; therefore, the one-dimensional large-strain consolidation calculation introduced in this paper is limited to the second stage of pressure filtration.
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 3 Results
### Experimental Validation
Note that for large incoherent integration times (i.e., 200 ms) the ringings cannot be identified for both the L1 and L5 examples. Figure 12: Land-to-water transition during an MIR at L1 test flight over Devilbend Reservoir (38.29\({}^{\circ}\)S, 145.1\({}^{\circ}\)E). (**a**) Geo-located reflectivity, and approximated representation of the first four Fresnel zones; (**b**) time evolution of the reflectivity, and a zoom-in of the step transition on the (**c**) left and (**d**) right. Figure 14 presents another example on the same flight. In this case, there is an additional transition to a small portion of a peninsula being crossed by the GNSS-R track. It can be noticed that at short integration times, some ripples are produced when changing from land to water, at the beginning and the end of the track. In addition, in the central part, when passing over the peninsula the combination of the different responses produces very large amplitude ripples. In this case, large incoherent integration times (i.e., 200 ms) are not blurring the ripples in the center of the image. However, if even larger integration times were used (i.e., 1 s) no ringings could be identified. Figure 13: Land-to-water transition during a MIR at L5 test flight over Devilbend Reservoir (38.29\({}^{\circ}\)S, 145.1\({}^{\circ}\)E). (**a**) Geo-located reflectivity, (**b**) time evolution of the reflectivity, and a zoom-in of the step transition on the (**c**) left and (**d**) right. Finally, Figure 15 shows another double transition when passing over Sorrento, south of Melbourne. The left part corresponds to an open ocean area (Sorrento ocean beach), while the right part of the image is inside Port Phillip Bay (Sorrento front beach). Looking at the ocean-land transition in Figure 15c on the left-hand side, the reflectivity step is larger, but longer over time, and reflectivity values are noisy because of the increased surface roughness and speckle noise associated with a mostly incoherent scattering. The ripples described in the previous sections and illustrated in the previous examples can no longer be identified. However, as compared to the ocean side in Figure 15c, in Figure 15d the reflectivity step is smaller (calm water), but shorter in time (i.e., sharper), and similar ripples to the lake case can now be identified. Figure 14: Land-to-water transition during an MIR at L1 test flight over Devilbend Reservoir (38.29\({}^{\circ}\)S, 145.1\({}^{\circ}\)E). (**a**) Geo-located reflectivity, and (**b**) time evolution of the reflectivity.
## 3 Channel Characteristics Comparisons  
### Channel Impulse Responses  
For a wideband mmWave channel, the receiver can resolve multiple paths according to their delay and the CIRs are commonly represented by the superposition of many plane waves. Furthermore, in our measurements, only two students who operated the measurement platform stayed in the conference room, and they tried to keep stationary during the measurement. Thus, time variance can be ignored here. The CIRs are given as follows:  
\[h(\tau)=V\delta(\tau-\tau_{0})+\sum_{n=1}^{N}a_{n}e^{j\phi_{n}}\delta(\tau- \tau_{n}), \tag{1}\]  
where \(V\) presents the deterministic (typically LOS) component with the excess delay \(\tau_{0}\). The parameters \(a_{n}\), \(\tau_{n}\), and \(\phi_{n}\) are the amplitude, excess delay, and initial phase of the \(n\)th stochastic component, i.e., typically non-LOS (NLOS) component, respectively. \(N\) denotes the number of multiple paths.  
For VLC channels, the ray tracing process will end with an output file, including the detected optical power and path lengths from sources to the detector for each ray. Then, we can obtain the CIRs based on these data, as in [18]:  
\[h(\tau)=\sum_{i=1}^{N_{r}}P_{i}\delta(\tau-\tau_{i}), \tag{2}\]  
where the \(P_{i}\) and \(\tau_{i}\) are the received optical power and propagation delay of \(i\)th ray, and \(N_{r}\) denotes the number of received rays at the detector.  
The CIRs at the 10 th position are given in Figure 6a,b for mmWave and VLC, respectively. It is observed from Figure 6b that the delay of the first peak (LOS) is 18 ns, with which multiplied by the speed of light (\(3\times 10^{8}\) m/s) we can obtain the LOS distance (5.4 m). It can be verified that the LOS distance is equal to the distance from TX to the 10 th position (5.43 m) in Figure 3b, as the blue line shows. The second peak in Figure 6b is close to the LOS, so we can infer that it mainly comes from the reflections of the east door (red lines in Figure 3b). Moreover, the delay of the third and fourth peaks is 21.4 ns and 22.8 ns, i.e., the propagation distances are 6.42 m and 6.84 m, respectively. The green route in Figure 3b means \(T_{X}\rightarrow\) the south wall \(\to R_{X}\) and the path distance is 6.41 m (\(0.49\times 2+5.43\approx 6.42\) m). Therefore, the third peak mainly comes from the reflections from the south wall. Furthermore, the fourth peak is mainly from the north wall, as the black route presents with a path distance of 6.83 m (\(0.7\times 2+5.43\approx 6.84\) m) in Figure 3b.  
We find that mmWave channels have more peaks than those in VLC channels, which can also be found at other points. This result indicates that the mmWave signals can be received at RX through the reflections of more scatters but a few reflections by specific scatters can reach the detector for VLC. Moreover, mmWave channels present a larger delay fluctuation (130 ns) than that of VLC channels (20 ns), which can be explained by the diffuse reflections of VLC, i.e., the light rays split into many random rays and they are reflected in all directions when interacting with scatters. Finally, only small rays can reach the detector.
## 2 Sensor Design
### Spectral Phenomena  
During static testing of the patches, the reflected small spectrum was analyzed and characterized to determine major changes in the spectrum design. A deformation of the spectrum, together with growing side lopes at higher loads, can lead to misinterpretations of the Bragg wavelength. If the side lopes are as high as the main peak, or the main peak starts to broaden extremely, the mathematical algorithm used by the interrogator system cannot distinguish between several peaks and, consequently, is not able to accurately define the Bragg wavelength. In order to interpret the reflected signal correctly, a side lope suppression of at least 5 dB under the Bragg peak is necessary, as defined in the VDI guideline [3]. The reflected spectra at each load step for a patch with embedded bare fiber is shown in Figure 10.  
Figure 10: Reflected spectrum of the FBG patch with embedded bare fiber at different load steps.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature** & \(-\)**40**\({}^{\circ}\)C & \(-\)**20**\({}^{\circ}\)**C** & **23**\({}^{\circ}\)**C** & **70**\({}^{\circ}\)**C** & **80**\({}^{\circ}\)**C** \\ \hline Gauge factor & \(0.7955\pm 0.0081\) & \(0.7937\pm 0.0103\) & \(0.7799\pm 0.0175\) & \(0.7805\pm 0.0081\) & \(0.6896\pm 0.0451\) \\ \hline \end{tabular}
\end{table}
Table 1: Strain gauge factors determined from combined temperature and loading cycles.  
A maximum load of 5 kN is equivalent to a strain of approximately 1500 um/m. In order to interpret the spectral phenomena, a patch with embedded bare fiber was chosen. In this way, transverse force effects that directly act onto the bare fiber without a protecting coating could clearly be studied.  
Figure 11 reveals that, indeed, side lopes rise equally to the left and right of the Bragg peak during the different loading steps, but the Bragg peak itself does not broaden significantly. Presumably, there are no transverse forces acting on the grating. It is assumed that the side lopes were a result of the patch manufacturing process. During curing, the grating is likely partly pre-strained due to small local residual stresses. This may lead to a partially chirped embedded grating. When starting the patch loading, the grating is stretched non-uniformly due to initial chirp. This may lead to the increase of the side lopes. However, the increasing side lopes do not affect the functionality of the patches. The side lope suppression is significantly 13 dB below the main Bragg peak, and fulfills, therefore, the requirements of the VDI strain sensor guideline [3].  
The spectral effects of a combination of longitudinal and transverse load and local strain gradients on the embedded FBG, like it is the case in a multi-axial strain field, is of a very complex nature and has not been focused in this context.
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 5 Simulations, Results, and Discussions
### Weighted Factors' Effects on the HSN  
#### 5.4.1 Simulation  
In this section, we are concerned with the impact of the weighted factors in the comprehensive evaluation. Let 'a' represent the weight coefficient for the bandwidth, 'b' represent the weight coefficient for the delay, and 'c' represent the weight coefficient for the packet-loss rate. The magnitudes of these weight coefficients vary based on the specific transmission requirements. The simulation normalizes the bandwidth, delay, and packet-loss rate of different paths. To illustrate the influence of these weight coefficients on channel selection and network performance, this study selects three typical scenarios.  
**Scenario 1**: High-throughput transmissions, only concerned with the network bandwidth and not the delay or packet loss, i.e., a = 1, b = 0, and c = 0.  
**Scenario 2**: Strong real-time transmissions, only concerned with the network delay and not the bandwidth or packet loss, i.e., a = 0, b = 1, and c = 0.  
**Scenario 3**: High-reliability transmissions, only concerned with the packet loss and not the bandwidth or delay, i.e., a = 0, b = 0, and c = 1.  
In this simulation, most parameters are set to the same values as before, such as the number of nodes in the backbone network being four, and the number of nodes in each access network being four. The transmission direction of each node is random, with transmission sizes ranging from 2 Kb to 8 Mb. The number of wireless connections is four times the number of nodes in the access network. What differs from other subsections in the simulation is the parameter settings for generating random transmissions, such as the typical scenarios (1, 2, and 3) and the parameters (a, b, and c).
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 1 Introduction
The proposed improvement to the LDPC-SNNMS decoding scheme, achieved through extensive training and real-time data feedback, enables automatic adjustments to its internal parameters, allowing for better adaptation to continuously changing channel conditions. It maintains outstanding performance even in complex multi-path propagation and high-speed motion scenarios, such as those encountered by drones. Furthermore, this decoding scheme offers lower computational complexity, meeting the resource constraints of UAVs. Deep learning neural networks can be highly optimized for hardware to ensure efficient execution on embedded UAV computing platforms, minimizing resource utilization and thereby extending flight times and enhancing mission efficiency. The innovative contribution lies in harnessing the potent capabilities of machine learning and applying them to the UAV data link domain. This novel approach introduces deep learning neural networks that combine mathematical foundations and key concepts with machine learning to achieve adaptive channel decoding. This innovative method addresses some limitations of previous solutions. Due to the large amount of power data transmitted between drones, to design a high-accuracy and low-complexity decoder for LDPC long-code decoding this study proposes a shared-parameter neural-network-normalized minimum sum algorithm based on codebook quantization (shared-NNMS-CQ). The main contributions of the paper can be summarized as follows:  
* Firstly, we incorporate the concept of the model-driven approach and leverage the advantages of neural networks and Tanner graphs to expand the iterative decoding process of checking the update and propagation of messages between nodes and variable nodes in the minimum sum algorithm (NMS) into a deep feed-forward neural network. We use a novel method of sharing network parameters to improve the NNMS network, which is distinct from the shared-parameter method proposed in [31]. This approach improves the BER performance by reasonably reducing network parameters. * Furthermore, to further improve the novel LDPC decoding algorithm, we introduce a weight quantization method based on a codebook. This approach not only reduces the precision of each weight but also decreases the number of weight types required for training the network, leading to reduced computational complexity and resource consumption. The reasonable selection of the quantization schemes can even have a positive impact on the decoding performance. * Finally, to validate the BER performance of the proposed improved LDPC decoding algorithm, we demonstrate a performance and complexity analysis under a Rician channel in the context of drone communication scenarios.