esa-sceva/satcom-chunk-collection huggingface dataset:
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 2 Related Work
### CNN-Based Image Dehazing Methods  
Initial CNN-based methods approached dehazing by estimating the transmission map as an intermediary and then applying the atmospheric scattering model to reconstruct the haze-free image. For example, DehazeNet [13] extracts features from local image patches using multiple convolutional filters to estimate the transmission map, which is then used for image reconstruction. MSCNN [14] employs a multi-scale, two-stage architecture that first captures coarse global structures and then refines finer details, enabling effective handling of haze with varying density and spatial distribution.  
Recent CNN-based approaches favor end-to-end learning pipelines that directly map hazy inputs to clear outputs, eliminating the need to estimate intermediate variables. GCANet [15] incorporates smoothed dilated convolutions and gated feature fusion to enhance local structure and texture recovery. FFA-Net [16] introduces a Feature Fusion Attention mechanism that combines channel and pixel attention to model both spatial and contextual dependencies, leading to improved dehazing performance. [PERSON] et al. [17] proposed a novel dehazing method that jointly leverages spatial and frequency information through a dual-guided framework that learns detailed representations in both domains. This joint learning strategy enables more natural restoration of textures and structures, particularly in complex scenes, thereby minimizing detail loss.  
DEA-Net [18] introduces a Detail-Enhanced Attention Block (DEAB) composed of a Detail-Enhanced Convolution (DEConv) and a Content-Guided Attention (CGA) module. DEConv leverages difference convolution to enhance the representational capacity of standard convolution by capturing subtle variations. CGA assigns a Spatial Importance Map (SIM) to each channel, allowing the network to focus more effectively on informative regions during feature encoding. Despite these advancements, the inherently limited receptive fields of standard CNNs hinder their ability to capture long-range dependencies, particularly in visually complex environments.
## 2 Materials and Methods  
Previous studies addressed drug inhomogeneity using  
* Manual agitation (labor-intensive, error-prone).
* Microfluidic mixers (costly and incompatible with bedside settings SBS-LAB-125, Steinberg Systems, Berlin, Germany).
* Ultrasonic or magnetic stirrers (bulky and impractical for syringe pumps).
* Piezoelectric/electric eccentric motor/actuators (limited adoption due to cost and integration challenges), model-JXBOVE = 6 mm-3 v.  
None of these methods has been integrated into commercial syringe pumps nor validated in long-duration infusions typical of ICUs.  
This work is built on  
* Plasma/UV laser surface modification of polymers to reduce adsorption [8].
* Near-infrared stabilization of protein structures [9].
* Vibration-induced mixing for low Reynolds number fluids.
* Medcaptain HP-80 syringe pump (Medcaptain Medical Technology, Shenzhen, China), the syringe pump model in which the technology was implemented and tested.  
We contribute a compact, affordable, and clinically feasible system combining these principles, with validation across multiple drug types.  
Previous studies addressed drug inhomogeneity using various mechanical and optical means, but none have been successfully integrated into commercial syringe pumps. Our work combines compact piezo-driven vibration and photonic surface treatment into a practical, clinically compatible solution [9; 10; 11; 12; 13] (Figure 1).  
Several studies have investigated the effects of sedimentation in drug infusion systems, highlighting that flow interruptions and prolonged infusion times exacerbate the risk of concentration variability [14; 11; 15; 16; 17]. For example, research on lipid emulsions and protein-based drugs has demonstrated that separation occurs over time, leading to inconsistent dosing. Agitation-based solutions, such as magnetic stirrers and ultrasonic mixing, have  
Figure 1: Abstract graphic, technology comparison: current technology vs. technological proposal.  
been proposed, but these methods are often impractical for bedside applications due to their energy requirements and potential interference with infusion dynamics [1; 3; 7; 12; 18].  
In contrast to previous approaches, our system uses a dual-axis vibration homogenizer, minimizing sedimentation effects and ensuring a stable concentration profile throughout the infusion process. By incorporating this functionality into a standard syringe pump design, we improve usability while maintaining compatibility with existing clinical protocols. Our approach builds on previous research in fluid dynamics and mechatronics, integrating vibration-assisted mixing into a practical and energy-efficient solution suitable for intensive care environments. Another issue addressed is the adhesion of chemicals to the walls of plastic syringes such as polypropylene. This adhesion is inhibited by our system by applying photonic technology to the syringe walls during the pre-fill process. Another technology approach is the application of pulsed photonic technology to maintain homogeneity by preventing the formation of aggregates (700 nm IR) [7].
## 4 Monitoring System Testing and Analysis
### Measurement Performance Test and Analysis of State Parameters  
#### 4.2.1 Current Test  
The rated current value of the experimental line is 500 A, and the data monitored according to the existing current sensor data values and the current transformer calibrator are shown in Table 5. When the measured current exceeds 20% of the rated current, the ratio difference and phase difference change values are small, the overall measurement errors are approx. 3%, which is very small, and the ratio difference change is approx. 10\({}^{\circ}\). This demonstrates that the stability of current sensing monitoring is good.
# Safety Analysis of Buried Corrosion-Defect Gas Pipeline in Ceramic Soil Layer Under Blasting Action
## 4 Numerical Simulation of Corroded Defective Gas Pipelines
### Analysis of Stress Effect of Blasting on Corroded Gas Pipelines with Different Defect Lengths  
The stress variation rules of corroded gas pipelines with different defect lengths caused by blasting are shown in Figure 12 below.  
The stress variation law of corroded gas pipeline due to different defect lengths (1 cm, 2 cm, 3 cm, 4 cm, 5 cm) under the same blasting parameters is studied in this paper. Through accurate experimental measurement and data analysis, we reveal the internal relationship between the defect length and the pipeline stress. When the defect length is 1 cm, the maximum axial stress of the pipeline is 280 MPa, and the maximum circumferential stress is 250 MPa. At this time, due to the short defect length, the overall structural integrity of the pipeline has not been seriously damaged, so the stress level is relatively low.  
With the defect length increasing to 2 cm, the maximum axial stress of the pipeline rises to 310 MPa and the maximum circumferential stress to 280 MPa. This indicates that the increase in defect length has begun to have a significant impact on the structural integrity of the pipeline, resulting in some increase in stress levels. When the defect length is further increased to 3 cm, the maximum axial stress of the pipeline reaches 345 MPa, and the  
Figure 11: Effect of blasting on stress of corroded gas pipelines of different thicknesses.  
Figure 12: Effect of blasting on stress of corroded gas pipelines with different defect lengths.  
maximum circumferential stress 315 MPa. At this time, the increase of defect length has a more obvious impact on the stress of the pipeline, and the stress level continues to rise.  
When the defect length reaches 4 cm, the maximum axial stress of the pipeline jumps to 380 MPa, and the maximum circumferential stress is 350 MPa. This indicates that with increased defect length, the structural integrity of the pipeline is severely damaged, leading to a sharp increase in the stress level. When the defect length increases to 5 cm, the maximum axial stress of the pipeline finally reaches 420 MPa, and the maximum circumferential stress reaches 390 MPa. At this time, the stress level of the pipeline has reached or is close to its ultimate carrying capacity, and there are serious safety risks.  
It can be seen from the above data that with the increase of defect length, the maximum stress of corroded gas pipeline under the action of blasting vibration presents a linear or approximately linear upward trend. This change law reveals the significant influence of defect length on pipeline stress, that is, the longer the defect length, the higher the stress level of pipeline under blasting vibration, and the greater the safety risk.
## 4 Application Design and Implementation
### Message Structure and Dissemination Protocol
#### 4.2.2 Ad Hoc Domain
Periodically, each SU examines its local bit-vector, and missing chunks are requested back (in broadcast) to neighbors, using the _REQUEST_ message. Regardless of the nature of a node (PU or SU), upon the reception of a _REQUEST_ message, a node checks its local bit-vector and tries to satisfy, if possible, the request. As opposed to other X-NETAD messages, the _REQUEST_ message is intrinsically single hop and is never re-broadcast in order to maximize the reception probability of missing chunks and to avoid repeatedly saturating the network with useless packets. A node replies to a _REQUEST_ message with one or more _DATA_ messages if the requested chunks are locally available and according to a probabilistic function related to its distance from the sender. This replying probability is expressed accordingly to the following \"reverse\" IF probability assignment function:  
\[p=\exp\left\{-\frac{\rho_{s}d}{c}\right\} \tag{2}\]  
where \(\rho_{s}\) is the spatial density of vehicles in the VANET; \(d\) is the distance between transmitter and receiver; and \(c\) is a shaping coefficient. Clearly, this function assigns a higher replying probability to nodes that are located near to the original sender, namely those with a smaller packet loss probability. It is important to observe that the replied (e.g., transmitted upon reception of a _REQUEST_ message) _DATA_ messages are disseminated using the IF protocol. In Figure 5, an illustrative representation of the X-NETAD session of message exchange between the AS, one PU and a multitude of SUs is shown.
## Appendix B Healthcare and Educational Data--Systems or Databases
### Educational Data Systems/Databases  
* Systems or Databases--Sistema Educacenso, SISU and Plataforma Lattes (Brazil), National Center for Education Statistics Databases (US), Canadian Centre for Education Statistics Databases (Canada), Systeme d'Information sur le Suivi de l'Eleve (SISE--France), German Academic Exchange Service and Destatis Databases (Germany), National Pupil Database (NPD--UK), Center for Student Services and Development Databases (China), Unified District Information System for Education Plus (UDISE+--India).
* Common Attributes Present--Student ID, name, date of birth, university or school enrollment, grade level, attendance records, academic performance, graduation status, research groups with member's name (Lattes Platform).
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
###### Abstract  
The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make predictions; however, there is a tendency to ignore the neighboring relations outside the enclosing subgraph, thus leading to inaccurate predictions. In addition, they also neglect the rich commonsense information that can help filter out less convincing results. In order to address the above issues, this paper proposes a commonsense-guided inductive relation prediction method with a dual attention mechanism called CNIA. Specifically, in addition to the enclosing subgraph, we added the multi-hop neighboring relations of target nodes, thereby forming a neighborhood subgraph where the initial embeddings are generated. Next, we obtained the subgraph representations with a dual attention (i.e., edge-aware and relation-aware) mechanism, as well as the neighboring relational path embeddings. Then, we concatenated the two embeddings before feeding them into the supervised learning model. A commonsense re-ranking mechanism was introduced to filter the results that conformed to commonsense. Extensive experiments on WN18 RR, FB15k-237, and NELL995 showed that CNIA achieves better prediction results when compared to the state-of-the-art models. The results suggested that our proposed model can be considered as an effective and state-of-the-art solution for inductive relation prediction.  
Keywords:inductive relation prediction; commonsense; dual attention; contrastive learning +
Footnote †: journal: Applied sciences
## 1 Introduction
### Boundary Conditions  
The wind velocity profile at the inlet was determined on the basis of the logarithmic law, with a roughness length (\(z_{0}\)) of 0.025 mm, using Equation (1). In this equation, \(u_{ABL}^{*}\) denotes the friction velocity of the atmospheric boundary layer (ABL), \(\kappa\) represents the von Karman constant (0.42), and \(z\) is the height coordinate. The turbulent kinetic energy (\(k\)) was estimated from the average wind velocity and the measured turbulence intensity by means of Equation (2), where \(I_{u}\) represents the stream-wise turbulence intensity. The kinetic energy factor (\(a\)) was specified at 1, in accordance with the proposal of [PERSON]  
Figure 3: Cross-section and close-up views of the grid structure.  
Figure 2: Schematic representation of the computational domain.  
et al. [50]. The turbulence dissipation rate, \(\epsilon\) was determined by Equation (3) and the specific dissipation rate (\(\omega\)), by Equation (4), where \(C_{\mu}\) is an empirical constant set to 0.09.  
\[U(z)=\frac{u_{ABL}^{*}}{\kappa}\ln\left[\frac{z}{z_{0}}+1\right] \tag{1}\]  
\[k(z)=a[I_{u}(z)U(z)]^{2} \tag{2}\]  
\[\epsilon(z)=\left[\frac{u_{ABL}^{*}}{\kappa(z+z_{0})}\right] \tag{3}\]  
\[\omega(z)=\frac{\epsilon(z)}{C_{\mu}k(z)} \tag{4}\]  
In the flow domain, the lateral and upper walls were defined as having zero specific shear stress. Besides, the outlet was described as a pressure outlet, and the entry plane was designated as a velocity inlet. The inlet parameters, including velocity magnitude, turbulent dissipation rate, and turbulent kinetic energy, were derived from the Atmospheric Boundary Layer (ABL) data file. The ground surface was defined as a boundary wall with a no-slip condition applied. The roughness constant (\(C_{s}\)) was assigned a value of 0.5, resulting in a roughness height of ground sand grain (\(k_{s}\)) of 0.0006 m when plugged into Equation (5) [25; 27; 52; 53].  
\[k_{s}=\frac{9.79z_{0}}{C_{s}} \tag{5}\]
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 3 Proposed FRL-SR Algorithm
### Neighborhood Discovery  
Since the topology of the satellite network changes dynamically and the links between satellites are unstable, the neighbor nodes of the satellite change. Therefore, agents must periodically check the neighbor nodes so that they know where to forward packets when they arrive.  
Satellite nodes receive information about their neighbors by receiving 'hello' packets from neighboring nodes [26]. In addition, the \"hello\" packet contains a Q-table, link information, and available resources that nodes can use to calculate reward values and update their own Q-table. If a node does not receive \"hello\" packets from a neighboring node for a long time, it assumes that the neighboring node is down and will not send packets to it later. In this paper, nodes only send 'hello' packets to their neighbors. Compared to flooding 'hello' packets in terrestrial networks, this method saves energy cost for the satellite and does not burden the network.
## 4 Experiment
### Ablation Study  
In this section, we perform ablation studies to analyze the effects of different components in our proposed framework for pest recognition.  
To assess the performance of the DAM-Net when isolated from the ResNet-50 model, we conduct experiments on the D0 and IP102 datasets. The DAM-Net achieves an Acc of 99.6%, an MF1 score of 99.5%, and a GM of 99.5% on the D0 dataset. On the IP102 dataset, the results are an Acc of 71.4%, an MF1 of 63.7%, and a GM of 58.0%. These results underscore the discriminative power and robustness of the DAM-Net, confirming its suitability as the main task. The DAM-Net excels in learning global, attention-enhanced, and distinctive regional features, facilitating the recognition of subtle details and discriminative features for classification.  
We also train the ResNet-50 independently to evaluate its standalone performance. On the D0 dataset, the ResNet-50 achieves an Acc of 99.3%, an MF1 of 99.1%, and a GM of 99.1%. On the IP102 dataset, it achieves an Acc of 70.9%, an MF1 of 63.5%, and a GM of 59.5%. These results highlight the competent classification performance of the ResNet-50. By providing extensive global contextual information and an enriched feature representation, the ResNet-50 complements the main task, enhancing the overall performance within the multi-task learning framework.  
In our multi-task learning framework, we integrate the DAM-Net as the main task and the ResNet-50 as the subsidiary task, using an adaptive weighted loss mechanism. As shown in Table 4, this configuration improves performance compared to each model operating independently. On the D0 dataset, Acc increases to 99.7%, MF1 to 99.6%, and GM to 99.5%. On the IP102 dataset, Acc improves to 74.1%, MF1 to 65.9%, and GM to 59.5%. These enhancements confirm that combining the DAM-Net and the ResNet-50 leverages complementary features, boosting classification performance and enhancing generalization through task knowledge sharing. The adaptive weighted loss mechanism adjusts the importance of each task based on uncertainties. This approach benefits complex real-world scenarios by focusing on the most confident and informative features and reducing the impact of noisy data. It optimizes the integration of fine-grained details and global contextual information, thereby enhancing overall performance.  
The ablation study validates the critical role of each component within our multi-task learning framework and demonstrates the superiority of the multi-task learning approach over single-task models. The results emphasize the pivotal role of the adaptive weighted loss mechanism in achieving further enhancements in multi-task recognition efforts.  
Figure 7: Training vs. validation accuracy and loss curves of the proposed framework. (**a**) Curves on D0 dataset. (**b**) Curves on IP102 dataset.
## 3 Results
### Assessment of Climate Change Vulnerability
#### 3.2.2 SSP-245 Scenario  
The CCVI was determined using the climate change exposure, sensitivity, and adaptive capacity by sub-watershed calculations for the SSP-245 scenario. The results are outlined in Table 12.  
The highest climate change exposure in the SSP-245 scenario was 79.21 in the Nakdong Estuary sub-watershed, and the lowest was 21.08 in the Geumhogang sub-watershed. The highest climate change sensitivity was 55.68 in the Nakdong Miryang sub-watershed and the lowest was 19.56 in the Habcheon Dam sub-watershed. As in the SSP-126 scenario, the highest climate change adaptive capacity was 96.18 in the Geumhogang sub-watershed and the lowest was 2.61 in the Wicheon sub-watershed. Moreover, the highest CCVI was 66.18 in the Geumhogang sub-watershed and the lowest was 18.88 in the Wicheon sub-watershed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sub-Watershed**} & **Climate Change** & **Climate Change** & **Climate Change** & **Climate Change** \\  & **Exposure** & **Sensitivity** & **Adaptive Capacity** & **Vulnerability Index** \\ \hline Andong Dam & 36.56 & 24.86 & 22.72 & 26.71 \\ Imha Dam & 42.39 & 50.32 & 14.63 & 30.50 \\ Andong Dam Estuary & 34.42 & 33.33 & 38.51 & 36.19 \\ Naseeongcheon & 40.62 & 44.69 & 22.31 & 32.48 \\ Younggang & 42.57 & 28.73 & 23.36 & 29.51 \\ Byeongseong-cheon & 35.62 & 40.74 & 60.63 & 49.40 \\ Wicheon & 32.22 & 39.58 & 2.61 & 19.25 \\ Nakdong Gumi & 38.22 & 26.48 & 19.17 & 25.76 \\ Gamcheon & 33.61 & 34.25 & 37.20 & 35.57 \\ Nakdong Waegwan & 36.46 & 36.62 & 74.10 & 55.32 \\ Nakdong Sangju & 42.88 & 39.83 & 14.57 & 27.96 \\ Geumhogang & 25.06 & 51.28 & 96.18 & 67.18 \\ Hoeheon & 50.12 & 20.49 & 9.76 & 22.53 \\ Nakdong Goryeong & 35.83 & 36.03 & 56.53 & 46.23 \\ Habcheon Dam & 49.98 & 19.56 & 18.99 & 26.88 \\ Hwanggang & 53.75 & 26.70 & 19.62 & 29.92 \\ Nakdong Changryeong & 53.19 & 43.79 & 12.05 & 30.27 \\ Namgang Dam & 73.60 & 35.44 & 22.25 & 38.39 \\ Nakgang & 74.36 & 52.23 & 37.25 & 50.27 \\ Nakdong Miryang & 50.34 & 55.68 & 32.47 & 42.74 \\ Miryanggang & 45.23 & 38.58 & 28.47 & 35.19 \\ Nakdong Estuary & 75.50 & 39.31 & 58.72 & 58.06 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Climate change vulnerability in the SSP-126 scenario.
## 4 Experimental Results
### Comparison with Conventional Methods  
Since there exist powerful static image processing techniques like Hough Transform, Gabor Filter, Discrete Wavelet Transform, etc. [10; 11], that have been utilized considerably in classical research work in the context of reverse image search, we started out the study on some well-known classical research works and presented them for the sake of comparison with our proposed (RIS-DUGL) work's performance. We have implemented two of the conventional approaches, \"Similarity Evaluation in Image Retrieval using Simple Features\" [10] and \"Content-Based Image Retrieval using SVM, NN, and KNN Classification\" [14] in the context of CBIR. Table 2 summarizes and summarized comparison. These approaches use static digital image processing techniques. These techniques use shallow and hand-engineered fixed parameters and use simple features for content information from raw images and then use some similarity evaluation to do a reverse image search. Hence, the proposed RIS-DUGL method outperforms the retrieval accuracy compared to these two conventional techniques, as summarized in Table 2.