esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Section  
### Raw Materials  
Ordinary Portland cement with strength grade of 42.5, complying with the Chinese Standard GB175-2007 [17], was used. Silica fume (SF) was used as a fine mineral admixture with a specific surface area of \(1.5\times 10^{5}\) cm\({}^{2}\)/g and a SiO\({}_{2}\) content of more than 96%, according to the Chinese Standard GB/T 21236-2007 [18]. The chemical compositions of cement and silica fume are given in Table 1.  
Two types of quartz sand produced by Harbin Jinghua Water Treatment Material Company (Harbin, China) were used as aggregates, as shown in Table 2. A mixture of 80% coarse quartz sand and 20% fine quartz sand was used for preparing all the UHPC mixtures.  
A polycarboxylate-based superplasticizer (SP) from Harbin Qiangshi Company (Harbin, China) was used. It has a water-reducing efficiency higher than 30% and a solid content of about 40%. The steel fibers (as shown in Figure 1) used in this study are copper-coated, smooth fibers with a tensile strength of 2850 MPa. Each fiber is 13 mm long with a diameter of 0.2 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constituent** & **Cement (wt. \%)** & **Silica Fume (wt. \%)** \\ \hline CaO & 64.48 & 1.5 \\ SiO\({}_{2}\) & 21.41 & 96.3 \\ Al\({}_{2}\)O\({}_{3}\) & 5.43 & 0.54 \\ Fe\({}_{2}\)O\({}_{3}\) & 3.51 & 0.64 \\ MgO & 1.47 & 0.16 \\ Loss on ignition & 2.54 & 1.43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of cementitious materials.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quartz Sand** & **Particle Size** & \multicolumn{2}{c}{**Constituent (wt. \%)**} & \multirow{2}{*}{**Apparent Density (g/cm\({}^{3}\))**} & \multirow{2}{*}{**Bulk Density (g/cm\({}^{3}\))**} \\
**Type** & **(\(\mu\)m)** & & **SiO\({}_{2}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & \\ \hline Coarse & 360–600 & \(\geqslant\)99.6 & \(\leqslant\)0.02 & 2.65 & 1.405 \\ Fine & 180–360 & \(\geqslant\)99.6 & \(\leqslant\)0.02 & 2.65 & 1.329 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical performances of quartz sand.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 4 Applications of Label Correlation in MLC Methods  
Based on different types of label correlations, MLC methods can also be divided into those utilizing positive correlations, negative correlations, global correlations, and local correlations. In MLC methods, existing methods can be categorized into global relation methods, local relation methods, and global-local relation methods, depending on the granularity of label correlations considered. By distinguishing the granularity of shared global and local label correlations, a finer-grained understanding of the relationships between labels can be achieved, enhancing the adaptability of models in different scenarios.  
Based on whether label correlations are considered, MLC methods can be divided into two main categories: those that do not consider label correlations and those based on label correlations. Traditional label correlation-based methods focus on leveraging positive label correlations globally to enhance classification performance. However, this global assumption overlooks the significance of local label correlations and negative correlations. In fact, label correlations are not always positive; sometimes, there can be negative correlations, where the presence of certain labels decreases the likelihood of other labels occurring. Additionally, label correlations often exhibit locality, implying that the correlations between labels may vary across different subsets of data or in specific contexts.  
The following sections will introduce global methods considering positive label correlations, local methods considering positive label correlations, global-local methods considering positive label correlations, and global/local methods introducing negative label correlations from the perspective of label correlations types. The specific method classifications are shown in Table 2, where '/' indicates the absence of methods corresponding to that category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Global Methods** & **Local Methods** & **Global-Local Methods** \\ \hline \multirow{6}{*}{only positive correlations} & & & GLOCAL [66] (2018) \\  & LP [63] (2004) & & GLKEL [67] (2018) \\  & CLR [64] (2008) & & GLC [68] (2021) \\  & CC [55] (2011) & MLC [65] (2015) & RLIM-GL [69] (2022) \\  & ECC [55] (2011) & GCC [65] (2015) & LFCLC [70] (2023) \\  & ML-GCN [28] (2019) & MLSF [54] (2016) & Wang et al. [30] (2024) \\  & A-GCN [51] (2019) & GD-LDL-SCL [36] (2021) & LCFM [71] (2024) \\  & KSSNet [29] (2020) & & MLFS-GLOCAL [35] (2024) \\  & & & & \\  & & & \\  & & & \\ \end{tabular}
\end{table}
Table 2: MLC methods of using different types of label correlations.
## 2 Materials and Methods
### Data Sources and Preprocessing  
Sentinel-2 sensor images were acquired free of charge from ESA [26] as part of the Copernicus program [27]. Images from the Sentinel-2B satellite are used in this paper. The characteristics of the Sentinel-2B raw image time series are shown in Table 1.  
The Sentinel-2B datasets acquired for this study consisted of images from December 2015, 2017, 2020, and 2022. They covered the southeast of Togo, extending into the southwest of neighboring Benin. After extracting the layers and stacking the spectral bands, atmospheric correction was performed using the dark object subtraction (DOS1) method [29] to reduce the scattering effects in the satellite scenes. This method is an add-on to QGIS software [30]. The DOS function is one of the most commonly used atmospheric correction methods. It enables use of the stretch function by converting raw or TOA (top-of-atmosphere) digital numbers into reflectance values [31; 32]. The QGIS 2.18 masking feature was used to extract the survey area from the four enhanced scenes. The National Institute of Statistics  
Figure 1: Study area.  
(INSEED/Togo) database was used to collect secondary data on population, climate, and agriculture [33]. Downscaled data from socioeconomic field surveys were also used to interpret the processed images.
# Cavity Wavelength on Erbium-Doped Fiber Ring Laser Depending on Fabry-Perot Etalon Steering Angle
## 4 Conclusions  
This study investigates the LC-FP as an optimal solution for tuning laser wavelengths within the framework of erbium-doped fiber ring laser systems. The tuning of the laser cavity wavelength can be achieved by applying varying voltage levels to the LC-FP. Additionally, adjustments to the incident light for wavelength tuning can be accomplished by altering the steering angle of the LC-FP, a phenomenon attributed to the device's angular dispersion characteristics. The operational range for the LC-FP's steering angle extends from \(\pm 4\) to 18 degrees. This architectural design is proficient in enabling the generation of both single-wavelength and dual-wavelength lasers within the C band. The tunability range for a single wavelength is approximately 13 nm, while the dual-wavelength configuration exhibits a similar tunability, alongside a spacing of approximately 17.5 nm between the two wavelengths. These performance capabilities are fundamentally influenced by several factors, including the operational wavelength of the EDFA, the specified wavelength of the collimator that guides the fiber optic beam into the LC-FP, and the inherent fixed thickness of the LC-FP device. In conclusion, the proposed scheme demonstrates the capacity for straightforward and efficient adjustments to the laser wavelength. Furthermore, it achieves an optical signal-to-noise ratio exceeding 50 dB, making it suitable for both communication and sensing applications.  
Conceptualization, C.-K.Y.; methodology, C.-K.Y., T.-P.F., M.-C.C. and P.-C.P.; data curation, T.-P.F. and C.-K.Y.; model validation, T.-P.F. and C.-K.Y.; formal analysis, C.-K.Y., M.-C.C. and P.-C.P.; investigation, C.-K.Y., T.-P.F., M.-C.C. and P.-C.P.; visualization: C.-K.Y.; writing--original draft preparation: C.-K.Y.; writing--review and editing: C.-K.Y.; supervision, M.-C.C. and P.-C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-027-076-MY2.  
Not applicable.  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
Sensors_**2009**, \(9\), 4824-4844; doi:10.3390/s90604824  
_Series of_  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
_Article_  
**Medium Access Control for Opportunistic Concurrent**  
**Transmissions under Shadowing Channels**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{1,\star}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Center for u-Manufacturing, Pohang University of Science and Technology, Pohang, Korea;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]  
_Received: 12 June 2009; in revised form: 16 June 2009 / Accepted: 17 June 2009 / Published: 18 June 2009_  
**Abstract:** We study the problem of how to alleviate the exposed terminal effect in multi-hop wireless networks in the presence of log-normal shadowing channels. Assuming node location information, we propose an extension of the IEEE 802.11 MAC protocol that schedules concurrent transmissions in the presence of log-normal shadowing, thus mitigating the exposed terminal problem and improving network throughput and delay performance. We observe considerable improvements in throughput and delay achieved over the IEEE 802.11 MAC under various network topologies and channel conditions in ns-2 simulations, which justify the importance of considering channel randomness in MAC protocol design for multi-hop wireless networks.  
**Keywords:** exposed terminal; IEEE 802.11 MAC; Log-normal shadowing; media access control; multi-hop wireless networks
## 3 Results
### Reliability  
The results of the reliability analysis are presented in the following table.  
For all variables (Table 2), the ICC is greater than 0.9, except for LDL (0.811, with 95% confidence interval ranging from 0.447 to 0.935), which indicates excellent reliability of the measurements. For triglyceride, HDL, LDL, and cholesterol scores, the coefficient of variation is below 5%, which, along with the ICC coefficients, indicates reliable measurements. However, only three variables (body weight, body mass index, and waist circumference) have a coefficient of variation below 10%, indicating sample heterogeneity. In the case of body fat percentage and muscle percentage, the values are 14.912% and 13.687%, respectively, and for the skinfolds of the thigh, arm, and abdomen, the coefficient of variation is even greater than 30%, meaning that repeated measurements could yield different results for the average value. In some cases, this could affect the reliability of the measurements. [PERSON]'s test of sphericity indicates the presence of a statistically significant combined correlation among the study variables (H = 1607.721; \(p\) < 0.001).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & **ICC (95\%)** & **Coefficient of Variation (\%)** \\ \hline BW & 0.982 (0.948–0.994) & 8.262 \\ BMI & 0.988 (0.965–0.996) & 9.175 \\ TSF-TH & 0.989 (0.968–0.996) & 30.475 \\ TSF-UA & 0.985 (0.956–0.995) & 55.238 \\ WC & 0.962 (0.892–0.987) & 8.638 \\ BF\% & 0.984 (0.952–0.994) & 14.912 \\ MM\% & 0.957 (0.874–0.985) & 13.687 \\ TSF-AB & 0.983 (0.951–0.994) & 34.038 \\ TG & 0.924 (0.77–0.974) & 2.438 \\ HDL & 0.976 (0.928–0.992) & 3.800 \\ LDL & 0.811 (0.447–0.935) & 1.725 \\ TC & 0.907 (0.728–0.968) & 1.225 \\ \hline \hline \end{tabular}  
* BW—body weight; BMI—body mass index; TSF–TH—skinfold thickness ((upper arm); WC—wist circumference; BP\%—body fat percentage; MM\%—muscle mass percentage; TSF-AB—skinfold thickness (abdrom); TG—triglycerides; HDL—high-density lipoprotein; LDL—low-density lipoprotein; TC—total cholesterol.  
\end{table}
Table 2: Reliability and variability analysis of the sample for the research variables using ICC and the coefficient of variation.
## Appendix A
### VSM in Analyze Site Selection Criteria  
The diagram shows the integration of BIM, Value Stream Mapping (VSM), and GIS to optimize site selection [70]. VSM identifies workflow inefficiencies, while GIS provides spatial analysis and data visualization. BIM acts as a central platform, integrating GISdata to support collaborative, data-driven site selection aligned with project and stakeholder goals. The process starts with Mapping Current State, identifying bottlenecks like redundant approvals and manual data entry. Then, Identifying Waste applies the seven Lean Waste principles [87] to prioritize delays, unnecessary costs, and inefficiencies, using GIS analytics to reveal repeated site evaluation issues. An optimized workflow is then developed by automating data collection and spatial analysis through GIS, while BIM dynamically visualizes and compares site options. Centralized digital platforms streamline approvals and data sharing. Implementation and Monitoring include team training on GIS, BIM, and workflow improvements.  
KPI benefits include time and cost efficiency through reduced delays and optimized resource use, improved stakeholder collaboration via transparent, data-driven decisions, and waste reduction by eliminating manual inefficiencies. Process optimization is enhanced through real-time analytics, although quality and safety impacts are indirect, focusing on regulatory alignment.  
The step-by-step illustration in Figure A5 shows the implementation of Target Value Design (TVD) in the plan phase for Author Design and the Design Review Model of construction projects. It highlights how TVD integrates BIM, Lean tools, and emerging technologies such as CDE, AR, and VR to align design outputs with predefined project targets. Each step emphasizes collaboration, real-time feedback, and iterative refinement to ensure cost efficiency, stakeholder alignment, and value delivery throughout the design development process. Feedback loops demonstrate the iterative nature of TVD, enabling continuous improvement at every stage [84]. KPIs enhance the cost efficiency and savings are achieved by aligning design outputs with budget constraints, minimizing costly late-stage modifications. Time efficiency and delivery improve through real-time collaboration and rapid design iteration, reducing rework and delays. Stakeholder satisfaction and collaboration are enhanced as CDE centralizes design coordination, while AR/VR enables immersive stakeholder engagement and informed decision making. Process optimization and automation benefit from BIM-driven workflows, ensuring a structured,data-driven approach to design refinement. Additionally, waste reduction is realized by eliminating inefficiencies, optimizing resource allocation, and preventing design errors early in the process.
## 2 Acquisition, Pointing, and Tracking in Inter-Satellite Laser Links  
The establishment of a successful inter-satellite laser link consists of three main steps: acquisition, pointing, and tracking, collectively known as the APT system [19; 24]. The acquisition step, which involves the use of a very narrow laser beam, is a challenging task. The search area in which the receiver satellite is located can be divided into the acquisition area, pointing area, and tracking area. The uncertainty area, typically on the order of micro-radians, is the region where the transmitter is initially positioned. Given the extended transmission range, the approximate location of this area can be determined using the global positioning system (GPS). For LOS wireless laser links in ground-based networks, the location of the uncertainty area can be further refined using an electronic theodolite system. The pointing area is defined as the region where the receiver satellite acquires the beacon signal while searching for it within its field of view (FOV). This area must lie within the uncertainty area and encompass the tracking region. The tracking area, in turn, is the smallest in range and is defined as the region where the signal light enters and activates the signal detector. Once the signal is detected and locked, the tracking loop begins. The main tasks of the acquisition stage include selecting acquisition approaches and scanning techniques.
## 5 Experimental Setup and Results
### Simulation Results
* The performance of the grid case is superior to that of the random case, possibly because the number of nodes in the grid is nearly equal, leading to a lower rate of energy loss compared to the random scenario. * In both D-Chain and Q-Chain cases, the multi-chain single-hop exhibits better performance than the multi-chain multi-hop. This is due to the fact that, in the multi-hop case, the nodes carry an extra load, resulting in increased energy loss, that ultimately impacts performance. In the case of mobile CH, the reliability of D-Chain and Q-Chain are presented in Figures 22-24. Obviously, Q-Chain performs better than D-Chain in terms of energy reliability. MD-Chain has better reliability than SD-Chain, although D-Chain reliability fluctuates in all models, as illustrated in Figures 22a, 23a and 24a. This is due to the fact that the CH and LN are selected based on the distance (nearest node to the BS), which implies that the CH remains constant during rounds until it fails, at which point a new CH is selected. Furthermore, the reliability of a mobile CH is assessed continuously during each CH selection, unlike in a static CH scenario, where reliability is only evaluated until the CH dies. This assessment takes into account that the new CH has energy, while the previous one has depleted its energy. In addition, the CH experiences a significant energy loss because it is responsible for transferring all the received data to the BS. Moreover, the failed nodes are removed from the chain and the chain is rearranged, without taking into account the presence of the failed nodes. The Q-Chain models for mobile CH are illustrated in Figures 22b, 23b and 24b. MQ-Chain outperforms SQ-Chain reliability. Moreover, MQ-Chain-SH has better performance than MQ-Chain-MH. Figure 22: Single-chain mobile CH energy reliability-based proposed RBD: (**a**) D-Chain, (**b**) Q-Chain.
# CPAM: Cross Patch Attention Module for Complex Texture Tile Block Defect Detection
## 4 Experiments and Results
### Analysis of the Proposed Method
#### 4.4.2 Analysis of CPAM
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **mAP** \\ \hline YOLOv7-CPAM (k = 3) & 94.6 & 90.0 & 0.92 & 94.8 \\ YOLOv7-CPAM (k = 5) & 93.0 & 89.4 & 0.91 & 94.1 \\ YOLOv7-CPAM (k = 7) & 90.9 & 89.5 & 0.90 & 93.8 \\ YOLOv7-CPAM (k = 9) & 94.4 & 88.0 & 0.91 & 93.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of CPAM with different convolution kernel sizes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **mAP** \\ \hline YOLOv7-CPAM (patch = 9) & 94.6 & 90.0 & 0.92 & 94.8 \\ YOLOv7-CPAM (patch = 16) & 94.4 & 90.0 & 0.92 & 94.9 \\ YOLOv7-CPAM (patch = 25) & 94.5 & 88.3 & 0.91 & 94.5 \\ YOLOv7-CPAM (patch = 36) & 94.8 & 88.3 & 0.91 & 94.7 \\ YOLOv7-CPAM (patch = 49) & 94.0 & 88.2 & 0.91 & 94.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of CPAM with different patch sizes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **mAP** \\ \hline YOLOv7-CPAM (P3) & 94.0 & 87.4 & 0.91 & 94.8 \\ YOLOv7-CPAM (P4) & 94.2 & 84.7 & 0.89 & 94.5 \\ YOLOv7-CPAM (P5) & 94.6 & 90.0 & 0.92 & 94.8 \\ YOLOv7-CPAM (P3 + P4 + F5) & 92.7 & 88.6 & 0.91 & 94.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of CPAM with different embedding positions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **mAP** \\ \hline YOLOv7-CPAM-(a) & 93.8 & 88.5 & 0.91 & 94.4 \\ YOLOv7-CPAM-(b) & 93.2 & 83.9 & 0.88 & 93.4 \\ YOLOv7-CPAM-(c) & 94.6 & 90.0 & 0.92 & 94.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of CPAM with arranging patches in different spatial orientations. and ECA [17], and the results are shown in Table 8. The improvement effect of SE and ECA on the overall performance of the model is clearly visible, but CBAM is more effective in the tile block defect detection task. Since CBAM fits better with tile block defect characteristics, CBAM can extract regional characteristics of tile block defects, which allows CBAM to perform better. Compared with these attention mechanisms, CPAM has a more outstanding and stable overall performance. As CPAM can not only extract regional characteristics of tile block defects but also establish connections between different regions, it thus extracts tile block defect information more efficiently. In addition, this paper provides the heat map of different attention mechanisms by using grad-cam [30]. From Figure 5, it can be found that the model incorporating CPAM can better focus on the defective region, thus effectively distinguishing the defects and the non-defects. Figure 5: Heatmaps of different attention modules.
# Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data
## 1 Introduction
Actually, the distance between the spatial flows and their nearby flows is important in measuring the strength of the spatial association. For example, the closer the two types of taxi flows are, the more intense the competition is between them. Therefore, the measurement of spatial associations should take the distance decay effect into account [24; 25; 26; 27; 28]. Moreover, many types of individual spatial flows are constrained by road networks, e.g., individual home-work commuting flows and taxi flows. The planar space assumption of existing bivariate flow pattern mining methods is not suitable for individual spatial flows in road networks [15; 20]. Although the existing methods can detect bivariate flow patterns effectively, two problems still exist: (i) the existing studies pay less attention to the individual network-constrained spatial flows; (ii) the existing studies do not take the effect of distance decay into account when detecting spatial associations between two types of individual network-constrained spatial flows. To overcome these problems, this study developed the Geographically Weighted Cross K-Function for individual network-constrained spatial flows by extending the Cross K-Function [11]. The Cross K-Function [29] was originally proposed to measure pairwise spatial associations between two types of point processes. It has been widely used in many domains, such as ecology [30], crime [31], and biology [32]. To measure spatial associations between types of spatial flows, [PERSON] and [PERSON] (2019) extended the Cross K-Function for points to a spatial flow context and developed a global version and local version of the Flow Cross K-Function [11]. In this study, we also propose two versions of the Geographically Weighted Cross K-Function: the global version is proposed to measure the overall spatial association at multiple scales and the local version is proposed to identify the exact locations where a spatial association arises at multiple scales.