esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 4 Experimental Results and Analysis
### Experimental Results of Real Haze Images  
In order to verify the effect of the model in this paper on recovering real foggy images, four real outdoor foggy images were selected for the defogging experiments, and the experimental results are shown in Figure 7. There was overexposure in the sky region after the method in [4] for haze removal (e.g., panels 2 and 3 of Figure 7b) and more serious color bias in the processed images (e.g., panel 4 of Figure 7b). The defogged image of the method in [5] solved the defect of the method in [4], in which the halo appeared in the sky region, but the serious problem of color bias of the processed image remains unresolved (e.g., the fourth panel of Figure 7b). The method in [9] had more fog residue after defogging the test images. The fog removal by the method in [10] was better, with no visible fog, but the fogged images showed local color deviations (e.g., the building area in panel 1 in Figure 7b). A few images after the method of haze removal in [15] often showed noise points in the picture quality, (e.g., the building area in the second panel in Figure 7f). Moreover, the restoration effect of the fog picture was not as good as the algorithm in this paper (the middle area of the first and third pictures in Figure 7f). Through the above sufficient experiments, it is proven that the proposed method had high efficiency and could effectively remove fog without distortion in the sky area. Furthermore, the picture as a whole colorless partial phenomenon had good visual effects.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Evaluation** & **Ref.** & **Ref.** & **Ref.** & **Ref.** & **Ref.** & **Propose** \\
**Indicators** & **[5]** & **[6]** & **[11]** & **[12]** & **[15]** & **Method** \\ \hline \multirow{2}{*}{NTIRE’20} & PSNR & 7.80 & 12.13 & 12.30 & 10.86 & 10.28 & 15.74 \\  & SSIM & 0.24 & 0.28 & 0.26 & 0.33 & 0.31 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The quantitative evaluation of different defogging methods on the NTIRE2020-Dhaze dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Ref.** & **Ref.** & **Ref.** & **Ref.** & **Ref.** & **Propose** \\
**[5]** & **[6]** & **[11]** & **[12]** & **[15]** & **Method** \\ \hline SOTS & 0.91 & 0.98 & 0.81 & 0.83 & 1.16 & 0.74 \\ NTIRE’20 & 0.93 & 0.95 & 0.79 & 0.85 & 1.36 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The running time of the experimental algorithm.
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 7 Discussion  
We have proposed a deep learning-based primary screening diagnostic model focused on the accuracy of glaucoma classification. Reflecting medical knowledge of glaucoma, we composed four types of input data that can discern the structural features of the retina fundus images. We addressed the persistent issue of data imbalance in medical data through data augmentation and a modified loss function, which notably contributed to performance improvements by focusing specifically on glaucoma classification.  
In order to enhance classification accuracy, we structured an ensemble model and calculated weights based on similarity to the correct answer to ensure that misclassified data could be correctly classified. The performance was validated across various datasets through cross-validation, demonstrating the robustness and reliability of our approach.  
The results showed that the models developed for each dataset achieved 1% to 10% higher accuracy and 8% to 29% improved sensitivity compared to conventional single-image analysis. On the REFUGE dataset, we achieved a high accuracy of 0.9875 and a sensitivity of 0.9. In particular, for the ORIGA dataset, which is challenging in terms of achieving high accuracy, we confirmed a significant increase with an 11% improvement in accuracy and a 29% increase in sensitivity.
# Joint Power Allocation and Link Selection for Multi-Carrier Buffer Aided Relay Network
## 1 Introduction
### Related Work and Motivation  
Orthogonal frequency division multiplexing (OFDM) is considered as a promising solution to provide high data rates due to its robustness against multipath fading, high spectral efficiency, and flexibility in the resource allocation [22]. The resource allocation for traditional relaying transmission without buffer under OFDM modulation has been well studied in the last decade [23; 24]. Thus, the combination of OFDM and BAR is a promising candidate for higher throughput in cellular networks. Considering equal power allocation at all nodes, authors in [25] maximized throughput with adaptive link selection and a varying number of antennas. Next, the sub-carrier assignment and transmission mode (direct/relay) selection were presented in [26]. The optimal power loading over different OFDM sub-carriers plays the most significant role in achieving the real benefits of the multi-carrier transmission [27]. However, to the best of our knowledge, the problem of power allocation has not been taken into account for OFDM-based BAR networks.
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 4 Experiment
### Experimental Results
#### 4.3.3 Experiment Scenario 3
The experimental results are displayed in Figure 29.  
Regarding the acceleration, Table 12 was constructed with a comfort level using Figure 30 of acceleration. Table 12 confirms that the ODG MPC works more comfortably. The experimental results are more unstable than the two static obstacles. However, in Figures 31 and 32, and Table 13, the results show that ODG MPC works farther from obstacles than other algorithms. At the first obstacle, the obstacle avoidance operation operates 12.3% farther than the PF algorithm and 16.7% farther than the PF MPC algorithm. The second obstacle is operated by avoiding obstacles 42.3% farther than the PF algorithm and 31.6% farther than the PF MPC algorithm. Figure 30: Results of the acceleration from Scenario 3 for one dynamic obstacle and one static obstacle. Figure 29: Results of the path from Scenario 3 for one dynamic obstacle and one static obstacle. Figure 31: Results of the obstacle distance from Scenario 3 for a dynamic obstacle. Figure 32: Results of the obstacle distance from Scenario 3 for a static obstacle. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comfort Level (Score)** & **ODGMPC** & **PF** & **PFMPC** \\ \hline Comfortable (10) & 10.06\% & 6.44\% & 5.01\% \\ \hline A little uncomfortable (8) & 10.21\% & 6.59\% & 7.06\% \\ \hline Fairly uncomfortable (6) & 12.79\% & 9.54\% & 9.83\% \\ \hline Uncomfortable (4) & 20.99\% & 13.91\% & 11.34\% \\ \hline Very uncomfortable (2) & 23.43\% & 14.96\% & 16.77\% \\ \hline Extremely uncomfortable (0) & 22.52\% & 48.56\% & 49.99\% \\ \hline Average Comfort Level Score & **3.90** & 2.60 & 2.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Average result scores for 10 experiments with one dynamic and one static obstacle.
## 5 Discussion on Sensitivity Parameters  
The magnitude of the drawdown difference generally represents the extent of surrounding groundwater affected by groundwater drawdown inside the pit. A large drawdown difference means that the groundwater level outside the pit is lowered much slower than that inside the pit during dewatering, which imposes a very slight effect on the surrounding environment. On the other hand, when both the groundwater level inside and outside the pit decreases quickly, the magnitude of groundwater drawdown difference is small, indicating significant water flow from the surrounding soils into the pit. To investigate the effects of the groundwater drawdown inside the pit (\(S_{h}\)), the radius of the foundation pit (\(R_{w}\)), and the length of the waterproof curtain (\(L\)) on the groundwater drawdown difference, a series of sensitivity analyses were conducted using the proposed analytical method. Note that these sensitivity parameters were made dimensionless for simplicity, as shown in Table 5.
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 3 Cost-Schedule Integration and Big Data Technology
### Requirements for Cost-Schedule Integration  
The improvement of cost-schedule integration by leveraging big data technology needs to incorporate data characteristics while resolving the limitations within existing cost-schedule integration models. For developing an applicable method in construction projects, three requirements have been identified as followed.  
* **Data integrity and flexibility**: the integrity of lowest-level information units for cost and schedule data is fundamental for addressing mismatched information levels between them. It is essential to build a flexible data structure to accommodate various information units at different LODs from multiple dimensions. It should allow data decomposition and aggregation in response to purposes of construction management, including quantity, cost, schedule, productivity, labor, periodic payment, and material of construction projects.
* **Efficiency in establishing and transforming data**: The enormous time and effort required to develop the integrated database of cost and schedule data play a role as the root cause of the failure to adopt existing models in practice. A novel approach for efficiently and cost-effectively establishing and updating a database as projects proceed is necessary for its wide application in practice.
* **Practicability**: the new method needs to support a range of data extraction and analysis for practical cost and schedule control, and it should be easy to use for practitioners. In addition, the method should be fit for the work process and documentation in construction projects. The adoption of new technologies changes not only work activities but also work paradigm, including work processes, collaboration methods, information and administrative activities, working knowledge, and organization networks [28]. In this context, the new method should reflect sufficiently current construction management practices to minimize the changes to minimize practitioners' hesitation to use.
###### Abstract  
Passive seismic interferometry is a technique that reconstructs virtual seismic records using ambient noise, such as random noise or microseisms. The ambient noise in passive seismic data contains rich information, with surface waves being useful for the inversion of shallow subsurface structures, while body waves are employed for deep-layer inversion. However, due to the low signal-to-noise ratio in actual passive seismic data, different types of seismic waves mix together, making them difficult to distinguish. This issue not only affects the dispersion measurements of surface waves but also interferes with the imaging accuracy of reflected waves. Therefore, it is crucial to extract the target waves from passive source data. In practical passive seismic data, body wave noise and surface wave noise often overlap in frequency bands, making it challenging to separate them effectively using conventional methods. The synchrosqueezed continuous wavelet transform, as a high-resolution time-frequency analysis method, can effectively capture the variations in frequency of passive seismic data. This study performs time-frequency analysis of passive seismic data using synchrosqueezed continuous wavelet transform. It combines wavelet thresholding and Gaussian filtering to separate body wave noise from surface wave noise. Furthermore, wavelet cross-correlation is applied to separately obtain high-quality virtual seismic records for both surface waves and body waves.  
synchrosqueezed continuous wavelet transform; seismic interferometry; cross-correlation; wavelet thresholding Article
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
## 4 Conclusions  
A comprehensive financial assessment comparing three offshore wind monopile joint types--grounded joints, bolted flange joints, and PIP slip joints--reveals the PIP slip joint as the most cost-effective and financially superior option. While gross income remains consistent across all types, the PIP slip joint stands out due to its lowest initial investment, simplest installation, and minimal annual operational costs. Although the grouted joint shows slightly higher net income PV, the PIP slip joint offers improved short-term profitability, reduced financial risk, and better suitability for projects prioritizing streamlined implementation.  
Across key financial metrics such as NPV, LCOE, IRR, ROI, BCR, and payback period, the PIP slip joint consistently ranks highest. For instance, it has the highest IRR (43.42%), lowest LCOE (0.0136 EUR/kWh), and the shortest payback period (2.92 years), confirming its overall dominance. While bolted flange and grouted joints offer similar financial results, their marginal differences indicate that practical factors like ease of installation and structural performance may influence the final choice between them.  
The expenditure analysis further supports the PIP slip joint's long-term cost efficiency, with the lowest average annual expenditure (EUR 937,893) and highest financial performance sustainability. This joint type's 1.4% higher NPV, 5.72% shorter payback period, and 1.76% lower LCOE compared to the grouted joint highlights its superior economic return and energy cost reduction capabilities. Overall, the PIP slip joint emerges as the optimal choice for offshore wind projects aiming for financial viability, reduced risk, and sustainable energy production.  
Conceptualization, M.A.I, S.A. and D.L.; methodology, M.A.I. and D.L.; software, M.A.I. validation, M.A.I. and D.L.; investigation, M.A.I. and D.L.; data curation, M.A.I, S.A. and H.P.; writing--original draft preparation, M.A.I.; writing--review and editing, M.A.I, S.A., H.P. and D.L. visualization, M.A.I. and S.A.; supervision, D.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (RS-2022-KP002820).  
Not applicable.  
The data presented in this study is available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 2 Materials and Test Protocol  
### Materials  
The styrene-butadiene-styrene (SBS)-modified asphalt was used and purchased from Shell (Xingyue) Co., Ltd., located in Foshan City, Guangdong Province, China. Its fundamental performance parameters were tested. The results are shown in Table 1. The diabase, limestone, and limestone powder were selected for the coarse aggregate, fine aggregate, and filler, respectively, sourced from a test section of the highway in Foshan City, Guangdong Province, China. Their fundamental performance parameters were tested. The results are summarized in Table 2. Furthermore, the aggregate gradation of AC-13 and SMA-13 is presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance Parameters** & **Unit** & **Test Result** & **Method** \\ \hline Density & g/cm\({}^{3}\) & 1.051 & ASTM D70-17a \\ Penetration (25 \({}^{\circ}\)C, 100 g, 5 s) & 0.1 mm & 44.1 & ASTM D5-13 \\ Ductility (5 \({}^{\circ}\)C, 5 cm/min) & cm & 25.8 & ASTM D113-17b \\ Softening point & \({}^{\circ}\)C & 93.6 & ASTM D36-14 \\ Flash point & \({}^{\circ}\)C & \textgreater{}230 & ASTM D92-12b \\ Mass loss of residue after TFOT & \% & 0.4 & ASTM D6-11 \\ (Thin-film oven test) & \% & 72.5 & ASTM D5-13 \\ Penetration ratio of residue after TFOT (25 \({}^{\circ}\)C) & \% & 72.5 & ASTM D13-17b \\ Ductility of residue after TFOT & cm & 21 & ASTM D113-17b \\ (5 \({}^{\circ}\)C, 5 cm/min) & \% & 72.5 & ASTM D113-17b \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic performance parameters test results of SBS-modified asphalt.
# Micro-LEDs, a Manufacturing Perspective
## 2 Epitaxy and Chip Processing
Such a reversed electrode structure also makes the array compatible with an n-type metal-oxide-semiconductor transistor-based CMOS driver for faster modulation. Reduction in the size of micro-LEDs and spacing between devices can cause problems in chip processing and deteriorate LED performance. One important problem is the degradation of the quantum efficiency and homogeneity due to sidewall effect related to high density of surface defects caused during device processing, which leads to degraded electrical injection in the p-GaN [5; 30]. Acting as non-radiative centers, the surface defects on the sidewalls significantly degrade the internal quantum efficiency of the micro-LEDs. A decrease in maximum external quantum efficiency (EQE) from \(\sim\)10% to \(\sim\)5% with reduction in chip size from above 500 \(\upmu\)m \(\times\) 500 \(\upmu\)m to 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m has been reported for micro-LEDs, as shown in Figure 2[31]. The effect of size-reduction on the  
Figure 1: PL peak wavelength uniformity map for 2, 4, 6, and 8 inch wafers with zero edge exclusion. Their PL uniformity is very comparable, all around 2 nm in standard deviation except the eight inch wafer due to non-optimized wafer pocket design [23]. Reprinted by permission from Springer Nature, Copyright (2011). performances is more noticeable for smaller LEDs with larger perimeter-to-area ratios. For LEDs with the smallest dimensions (below 5 \(\upmu\)m), the peak quantum efficiency was reported to be below 10% or even 1% [19]. Therefore, processing technologies to minimize sidewall damage and avoid, if not totally eliminate, degradation of electrical injection are needed. [PERSON] et al. have reported that in pyramidal micro-LEDs, a SiO\({}_{2}\) leakage current confinement layer can decrease the reverse leakage current by two orders of magnitude, and the light output of the micro-LEDs can be improved by 115% averagely [32]. [PERSON] et al. have demonstrated that using atomic-layer deposition (ALD) of SiO\({}_{2}\) for sidewall passivation, size-dependent factors on peak EQE of micro-LEDs can be minimized [33]. Enhancement in light extraction and reduction in leakage current caused by surface recombination and sidewall damage led to an EQE of 33% for 20 \(\upmu\)m \(\times\) 20 \(\upmu\)m micro-LEDs with ALD passivation, compared to 24% without sidewall passivation. Be changing mesa etching process formation between p-contact and hybridization pads into a softer plasma and changing p-contact metal from Ni-based to Ag-based, [PERSON] et al. have improved the EQE of 10 \(\upmu\)m pixel pitch micro-LEDs from 4.8% to 6.8%, and achieved a brightness of 10\({}^{7}\) cd/m\({}^{2}\)[5]. [PERSON] et al. have reported that high EQEs may be maintained when decreasing the chip size from 100 \(\upmu\)m \(\times\) 100 \(\upmu\)m to 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m, with peak EQEs of 48.6 and 40.2%, respectively [30].
## 1 Introduction  
For the obtention of tomographic information, different measurement methods can be followed with respect to the sensing procedure: intrusive, non-intrusive, invasive, and non-invasive. The choice of an approach is the result of a wise choice made by the users. By 'invasive' is meant that the sensor surface is parallel to the inner zone of the wall of the channel under study, while the term 'intrusive' is used when the sensor itself interacts with the flow being visualised. In opposition non-invasive refers to cases in which the sensor is located on the outside of the wall, without crossing it, while non-intrusive indicates cases in which the sensor placement forces the pipe wall to be crossed, but without disturbing the flow under inspection [1]. In recent years, non-invasive procedures have shown a high degree of potential growth in many areas, such as in the chemical industry. When compared with non-intrusive procedures, non-invasive approaches bring benefits to the above-mentioned industry sectors, such as reduction of hazards due to operation of corrosive or radioactive materials, increase in operator safety, etc. Consequently, the non-invasive approach is the imaging strategy most commonly used in process plants.  
For hard-field tomography, the case must be considered in which there the measured parameter distribution and the sensitivity of the imaged media in the entire 3D domain are independent of one another [2]; optical, ultrasonic and X-ray are examples of this type of tomography. On the other hand, soft-field tomography refers to cases in which a dependency is found, over the entire volume, between the medium sensitivity and the distribution of the measures of the desired variable [3]. Electrical tomography is the most relevant member of this class, but it presents an important effort regarding the resolution of the inverse problem on the domain under observation.
# Effects of Polypropylene Fiber on the Liquefaction Resistance of Saturated Sand in Ring Shear Tests
## 3 Results
### Cyclic-Torque-Controlled (CTC) Tests
The cycle number of applied shear stress amplitude in cyclic shearing process until liquefaction or reaching the steady value was recorded. The cycle numbers of both group specimens are presented in Figure 8. Obviously, the number of cycles until liquefaction was different for the specimens with different fiber contents. The number of cycles increased with the increasing fiber content for the specimens with the same density. The more fiber inclusion was expected to increase the useful contact area between fiber and sand grains, and therefore effectively limited or even prevented the lateral failure of the sand induced by liquefaction, resulting in needing more cycles to induce liquefaction. With same fiber content, the cycle number of denser specimens was markedly larger than that of loose specimens. Compared with low-density samples, high-density samples have a closer internal structure and greater friction between sand particles, which requires more cycles to liquefy. As summarized in Table 3, the shear energy of cycles (\(W_{\text{cycle}}\)) and total shear energy until liquefaction (\(W_{\text{total}}\)) increased with the fiber content. For the specimens with a density of 1.29 g/cm\({}^{3}\), \(W_{\text{total}}\) in reinforced specimens with a fiber content of 0.2% and 0.4% approximately 16,189 J/m\({}^{2}\) and 46,866 J/m\({}^{2}\), which were 7.4 and 21.5 times that of unreinforced specimens, respectively. For the specimens with a density of 1.34 g/cm\({}^{3}\), \(W_{\text{total}}\) in reinforced specimens with fiber of 0.2% and 0.4% were approximately 1.2 and 1.5 times that of unreinforced specimens, respectively.