esa-sceva/satcom-chunk-collection huggingface dataset:
# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 4 Results
### Ablation Experiment
#### 4.4.3 Performance Validation of Dark Channel and Bright Channel Prior information  
To validate the effectiveness of the dark channel and bright channel prior information in remote sensing image cloud detection, this study introduced various combinations of different channels into the input of the adapter and conducted corresponding experiments and analyses. While retaining the RGB channel image as the input to the backbone, seven different image data input methods were introduced in the adapter: RGB channels, dark channel, bright channel, concatenation of dark channel and RGB channels, concatenation of bright channel and RGB channels, concatenation of dark channel and bright channel, and concatenation of dark channel, bright channel, and RGB channels. The experimental results are presented in Table 3.  
From the experimental results, the following trends can be observed: When combining the dark channel and bright channel prior information and inputting them into the adapter, the model achieves the highest detection accuracy. This indicates that the fusion of these two types of prior information in remote sensing image cloud detection tasks provides the model with more comprehensive and accurate information, leading to superior performance. Furthermore, the experimental results for separately inputting the dark channel or bright channel prior information follow, with both demonstrating better performance compared to solely inputting RGB channels. This result emphasizes the role of the dark channel and bright channel prior information in enhancing model performance. However, a decline in accuracy is observed when attempting to concatenate the RGB channels with the prior information and inputting them into the adapter. This could be attributed to conflicting information or increased model complexity, leading to poorer interaction between channels, consequently affecting the effectiveness of the prior information.  
Figure 8: Comparison of models with single feature layer and multi-feature layer large window attention. (**a**) Input image. (**b**) Multi-feature Layer. (**c**) Single feature Layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Channel** & **MIoU (\%)** & **MAcc (\%)** & **PAcc (\%)** \\ \hline RGB & 92.43 & 95.62 & 96.95 \\ Dark & 92.71 & 95.77 & 97.09 \\ Bright & 92.68 & 95.83 & 97.05 \\ Dark + RGB & 92.33 & 95.48 & 96.91 \\ Bright + RGB & 92.14 & 95.51 & 96.84 \\
**Dark + Bright** & **92.89** & **96.04** & **97.12** \\ Dark + Bright + RGB & 92.03 & 95.45 & 96.78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of effects from different channels input image data.
###### Abstract  
In arid regions with limited water resources, improving cropland water-use efficiency (WUEc) is crucial for maintaining crop production. This study aims to investigate how changes in meteorological and vegetation factors affect WUEc in drylands and to identify its primary drivers, which are essential for understanding how cropland ecosystems respond to complex environmental changes. Using remote sensing data, we analyzed the spatiotemporal patterns of WUEc in Xinjiang from 2002 to 2022 by applying STL decomposition, Sen's slope combined with the Mann-Kendall test, and an XGBoost-SHAP model, quantifying its key controlling factors. The results indicate that from 2002 to 2022, WUEc in Xinjiang showed an overall declining trend. Prior to 2007, WUEc increased at 0.05 gC\(\cdot\)m\({}^{-1}\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), after which it fluctuated downward at \(-\)0.01 gC\(\cdot\)m\({}^{-1}\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). Intra-annual peaks consistently occurred in May and during September-October. Spatially, WUEc exhibited significant heterogeneity, increasing from south to north, with 53.26% of the region showing declines. Temperature (T) and leaf area index (LAI) emerged as the primary meteorological and vegetation drivers, respectively, influencing WUEc change in 45.7% and 17.6% of the area. Both variables were negatively correlated with WUEc, with negative correlations covering 60% of the region for T and 83% for LAI. These findings provide scientific guidance for optimizing crop structure and water-resource management strategies in arid regions.  
water-use efficiency (WUE); actual evapotranspiration (ET); relative contribution; machine learning; cropland; MODIS +
Footnote †: journal:  
Article
# Developing a Recommendation Model for the Smart Factory System
## 3 Methodology  
According to the past research related to SFS, there is a paucity of research on SFS recommendations. Therefore, this study constructs an SFS recommendation model to make up for these research deficiencies. It combines the methodology of the Engel-Kollat-Blackwell Model (EKB Model) to ensure the needs of users and the Modified Delphi Method to derive SFS recommendation indicators from experts. Through analyzing weights, the ELECTRE II is used to obtain importance of each dimension by calculating the Modified Compound Advantage Matrix. The stages of operation are as follows.  
Stage 1: Applying EKB Model for Prototype Recommendation Model Construction  
According to the EKB Model, this includes (1) Demand Confirmation, (2) Supplier Information Collection, (3) Alternative Evaluation, and (4) Selection and Purchase, to construct the prototype recommendation model.  
Stage 2: Prototype Indicators Construction by Updated Information System Success Model based on the EKB Model.  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Scholars** & **Associated research** \\ \hline [PERSON] \& [PERSON] (2005) & Using Software Quality Characteristics to Measure Business Process Quality \\ \hline [PERSON] et al. (2009) & Assessing the Quality of a Web-based Learning System for Nurses [34] \\ \hline [PERSON] et al. (2015) & Investigating the Success of an E-Government Initiative Validation of an Integrated Information System Success Model [12] \\ \hline [PERSON] et al. (2016) & Integration of Updated DeLone \& McLean Success Model, KANO model and QFD to Analyze the Quality of an Information System [35] \\ \hline [PERSON] et al. (2017) & Understanding the Quality Factors That Influence the Continuance Intention of Students Toward Participation in MOOCs [36] \\ \hline [PERSON] et al. (2018) & Understanding Health Professionals’ Intention to Use Telehealth in Yemen Using the DeLone \& McLean Information System Success Model [9] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Related research of the Information System Success Model.  
Figure 2: Updated Information System Success Model [32].  
This study proposed 6 Information Quality, 5 System Quality, 5 Service Quality, 23 SFS Function characteristics, 16 Information Security, 8 Perceived Value, 6 Perceived Risk, and 5 UI Design recommendation indicators. In total, there are 74 recommendation indicators. Stage 3: Amendment and Simplification of Prototype Indicator After analyzing the suggestions from experts, we revised the recommendation indicators to 6 Information Quality, 5 System Quality, 4 Service Quality, 18 SFS Function characteristics, 13 Information Security, 7 Perceived Value, 6 Perceived Risk, and 5 UI Design. This led to a total of 64 recommendation indicators. Stage 4: Recommendation System Development This step involves providing SFS suppliers ranking, SFS suppliers scoring, and SFS purchase ability analysis, and assessing the system user satisfaction using the Updated Information System Success Model.
## 4 Results and Discussions  
Firstly, we investigated the system performance affected by nonlinearity under the BTB case. As shown in Figure 5, a receiver sensitivity decrease of about 2 dB was observed using the LUT algorithm. That is to say, nonlinearities were present within the system, which affected the system performance. Thus, the prerequisite of nonlinearity was considered in the subsequent simulation.
# Adopting Signal Processing Technique for Osteoporosis Detection Based on CT Scan Image
## 3 Methodology  
Osteoporosis does not only tend to manifest in older people, but also in young people who experience bone problems due to specific medical factors and an unhealthy lifestyle. For people who have osteoporosis, their bone density becomes reduced and more brittle. Hence, the expected X-ray response of osteopororotic bone will differ from that of normal bone.  
X-ray-based scanning is a common practice for diagnosing osteoporosis at low to high dose rates. Low doses are good for health; however, they will produce a less clear image with a lower resolution or contrast. Meanwhile, the use of high doses of X-rays can improve image quality but also increase radiation exposure. The energy of exposure by X-ray is expressed in the Hounsfield value (HU). It can be seen that if the evaluation of bone quality is based on the HU value, it will greatly depend on the X-ray energy used. This will result in ambiguity when determining the cut-off or limit of normal and osteoporosis indications based on this HU value.  
This feasibility study proposes a new parameter that can be used to indicate bone quality without relying on HU, so that it can resolve the ambiguity when CT scans are conducted at different doses. Several imaging attributes adopted from signal processing are generated on each image data scanning. Different modalities of scanning consisting of CT and DEXA scanning were used to see the robustness of the technique. Experiments were carried out on the normal and osteoporosis bone. To quantify the degree or strength of the attribute, an attribute index number is defined as the density of attributes. The definition of the attribute index is as follows:  
\[\text{Attribute }Index=\frac{\text{Total Attribute at ROI}}{\text{Number of pixels at ROI}} \tag{1}\]  
Adopted attributes from the signal-processing technique were calculated on the selected region of interest (ROI), which was picked manually. The target area or ROI was based on the indication of a common osteoporosis location. The ROIs are used in the normal and osteopororotic bone at the same location. Figure 2 shows an example of ROI selection at the pelvic and hip locations, which will be used for both normal and osteoporosis data.  
Figure 2: Examples of ROI selection for attribute index calculation at **(a)** pelvic, and (b) hip locations.
# Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites
## 2 Detection Process of Surface Defects in SiCp/Al Composites  
### Establishment of the Image Dataset for Machined Surface Defects  
Ultrasonic-vibration-assisted machining, a type of non-conventional machining, has significant advantages for difficult-to-machine materials. Therefore, ultrasonic-vibration-assisted grinding experiments on SiCp/Al composites were performed in order to obtain a dataset of surface defects in SiCp/Al composite machining and to subsequently analyze the effect of process parameters on machined surface defects on the SiCp/Al composites. Four grinding process parameters and two grinding wheel parameters were chosen as the experimental variables, and a univariate experimental approach was used, as shown in Table 1. The machine used in the experiment is a DMG Ultrasonic 70-5 linear ultrasonic machining center (DMG MORI, Bielefeld, Germany), and the workpiece material is a SiCp/Al2024 composite with a 45% volume fraction, where the average size of the SiC particles is 5 \(\upmu\)m. Table 2 presents the mechanical properties of the Al2024 matrix and the silicon carbide particles. Considering the shock load in ultrasonic-vibration-assisted grinding, a nickel-bonded diamond grinding wheel for ultrasonic machining from SCHOOTT Diamond Tools Corp. was selected as the grinding tool. Furthermore, since the dimension of defects on machined surfaces is commonly on the micrometer scale, an ultra-deep-field microscope is used instead of a CCD camera for image acquisition. All images are 1600 \(\times\) 1200 pixels in size. Figure 1 shows the whole grinding experimental setup and the machining surface image  
Before training the surface defect detection model, the obtained surface images needed to be annotated to build the processed surface defect dataset. LABELIMG software (version 1.3.0) was used to annotate images, and a file containing the image size, defect type, and location information was generated after annotation. To enable the neural network to better learn the difference between the background and defects and to improve the detection accuracy, defect-free images were added to the dataset as negative samples. In addition, images containing oil contamination were also added to the dataset as negative samples during training to enable the model to better distinguish oil residues from machined surface defects. Some dataset images are shown in Figure 3.
## 3 Proposed PBFT Architecture and Model
### Consensus Process of Proposed PBFT
#### 3.3.4 Dynamic Group and Hierarchy Management
```
1:\(L_{\textit{inter}}\leftarrow\) Update the inter-cluster communication latency
2:\(L_{\textit{intra}}\leftarrow\) Update the intra-cluster communication latency
3:\(BW\leftarrow\) Update the average network bandwidth of nodes participating in the blockchain network
4:\(N_{\textit{node}}\leftarrow\) Update the total number of nodes participating in consensus within the cluster
5:Find\(GHI_{\textit{best}}\) based on Equation (17) with \(L_{\textit{inter}}\), \(L_{\textit{intra}}\), \(BW\)
6:return\(GHI_{\textit{best}}\)
```  
**Algorithm 2** Get_Best_GHI()  
In Algorithm 2, it starts by updating the \(L_{\textit{inter}}\) value, which represents the communication latency between different clusters in the network. Subsequently, it updates the \(L_{\textit{intra}}\) value, which means the communication latency within a single cluster. The next step is to update the bandwidth parameter \(BW\). This represents the average network bandwidth of the nodes that are participating in the blockchain network. The algorithm then updates the \(N_{\textit{node}}\) value, which means the total number of nodes participating in the consensus process within a particular cluster. The algorithm calculates \(GHI_{\textit{best}}\) with these updated values by utilizing Equation (17). This equation takes into account the inter-cluster latency (\(L_{\textit{inter}}\)), intra-cluster latency (\(L_{\textit{intra}}\)), and the average bandwidth (\(BW\)) to determine the best group and hierarchical configuration for the blockchain network. Finally, the algorithm returns the \(GHI_{\textit{best}}\) information, indicating the best grouping and hierarchical structure based on the current network conditions and the specified equation.
# SFCA: A Scalable Formal Concepts Driven Architecture for Multi-Field Knowledge Graph Completion
## 1 Introduction  
Knowledge Graphs(KGs), as a structured representation of interlinked descriptions of concepts, entities, relations and events, provide effective support for question answering [1], recommendation systems [2, 3], information retrieval [4, 5], and natural language processing [6]. By analyzing the public KGs (Freebase [7], YAGO [8], and DBPedia [9]), the incompleteness of KGs is the inevitable problem limited by existing KGs construction technology, requiring KGC to infer new facts. Among all available Knowledge Graph Completion (KGC) research, the knowledge graph embedding (KGE) model shows efficiency and significant performance, which embeds KGs components (entities and relations) into a latent space to learn topological structure information.  
Existing KGE-based KGC methods can be divided into two streams: (1) Structure information-driven methods [10, 11, 12, 13, 14, 15, 16]. This stream focuses on learning the embedding of entities and relations from KG structural information to score the plausibility of triples forlink prediction. (2) External information fusion method [17; 18; 19; 20; 21; 22]. This stream augments the training or inference process by fusing additional information with structural information to improve the link prediction performance.
# DBCW-YOLO: A Modified YOLOv5 for the Detection of Steel Surface Defects
## 1 Introduction
YOLO still needs to be optimized for improved detection accuracy. Thus, this research aims to design a steel defect detection model that can ensure high detection accuracy and a reasonable detection speed. Based on these characteristics, to enhance the accuracy of defect detection, this paper introduces a new one-stage inspection model, DBCW-YOLO, on the basis of YOLOv5. The algorithm uses YOLOv5 as the baseline model, and for the up-sampling part, an up-sampling module (CARAFE) [16] is proposed to enhance the receptive field and obtain much semantic information. For the YOLOv5 head, add the dynamic detection head (DyHead) [17] to enhance the detection ability of the original. For the YOLOv5 model's loss function, the WIoU [18] is used to improve the baseline model's training stability, improving the model's training efficiency. Therefore, the main contributions are listed as follows:  
1. Enhanced feature fusion capability using cross-scale connectivity and embedding lightweight up-sampling (CARAFE) into the YOLOv5 network to cope with the steel defect fusion capability with a large scale of variation and to ensure the lightness of the network by improving the receptive field. 2. We use the dynamic non-monotonic focusing mechanism to replace the CIoU boundary loss function in the original model with the WioU, which enhances the competitiveness of middle-quality anchor frames and simultaneously reduces the harmful gradient generated by low-quality examples. 3. Embed the self-attention mechanism detection head (DyHead) into the YOLOv5 detection stage to enhance the detection ability of the model. Our model is targeted to improve the characteristics of steel defects. First, BiFPN and the up-sampling module, CARAFE, are used to enhance the algorithm's focus on multi-scale information for steel surface defects with large-scale variations. Second, to address the inadequacy of the CIoU aspect ratio of the original model loss function, we introduce a WIoU to enhance the capability of the boundary loss function. For the weak detection ability of the model, we embed a dynamic detection head (DyHead) to improve the detection ability of the model. In addition, appropriate ablation experiments are designed to validate the effectiveness of the models and the individual modules. The experimental results indicate that DBCW-YOLO can maintain high detection accuracy while also having real-time detection capability. Experiment results denote that DBCW-YOLO has an mAP of 81.1% and 33.8 FPS (frames per second) an mAP improvement of approximately 6% over the YOLOv5 model. It can provide a solution to the problem of low defect detection on steel surfaces due to large changes in defect size and strong background interference in industrial scenarios.
## 4 Simulations and Discussion
### Characteristics of MSON before and after Compensation  
In order to solve the DOF attenuation problem caused by the oblique incidence in principle, a voltage-controlled liquid crystal located between the scan lens and the 2D galvanometer system can be used to compensate for the additional phase difference caused by oblique incidence. The characteristics of the MSON obtained before and after phase compensation using voltage-controlled liquid crystal are compared in Figure 5. As shown in Figure 5a, when the incidence angle \(\theta=0^{\circ}\), \(10^{\circ}\), or \(20^{\circ}\), the DOF before compensation was \(3.09\lambda\), \(2.93\lambda\), and \(1.50\lambda\), respectively. It can be found from the simulation results that the attenuation of DOF in the range of \(10^{\circ}\) was rather small. By adding a voltage-controlled liquid crystal to compensate for the additional phase difference caused by oblique incidence, the MSON with a constant DOF (\(3.09\lambda\)) in a specific angular range can be obtained. In addition, as shown in Figure 5b, the FWHM of the MSON remained unchanged at \(0.42\lambda\). Optical focusing focal spot size is divided into sub-resolution (\(0.61\lambda\)/NA) and super-oscillation criterion (\(0.38\lambda\)/NA) [21]. In the area between the two criteria, while achieving super-diffraction-limited focusing, the effect of side lobes can be effectively suppressed. Therefore, the MSON not only achieves super-diffraction-limited focusing, but also effectively suppresses side lobes.  
The aspect ratio of the MSON is measured by the ratio of DOF to resolution as shown in Equation (11) [22]. The aspect ratio of the MSON is as high as \(7.36\).  
\[AR=DOF/FWHM \tag{11}\]
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
## 3 The Estimation of Iridium Satellite Signal Doppler-Shift in Weak Signal Environment  
The Doppler-shift measurement is the basis of positioning using Iridium satellite SOPs. A new method of estimating Iridium satellite SOPs Doppler-shift in weak environment is proposed in this section. The detail principle of the new method is given, and every part of the new method is introduced in detail. We first give the principle and the process of the new method. The differences from the  
Figure 1: Iridium frequency allocation and frame structure.  
traditional Iridium Doppler-shift estimation method and the advantage of the new method are also carried out. Then, the mathematic model of the new method is presented.
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
## 5 Experiment and Analysis
### Network Performance Analysis
Figure 4: Comparison of cumulative throughputs. Figure 3: Comparison of death nodes. It can be seen from the number of retransmissions in Figure 7 that, as the number of simulation rounds increased, the number of retransmissions increased, which conforms to the transmission law of the WBAN. Compared with other algorithms, the number of retransmissions related to our algorithm was less in the early stage, because the link connectivity probability prediction with a sliding time window could more effectively evaluate the link reliability and realize a more stable data transmission. Figure 5: Comparison of the average delay. Figure 6: Comparison of the average hops.