esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance of VIs and SIF Signals at the Peatland Plant Community Level
No such clear trends and strong relationships between VIs and SIF were found for heterogeneous vegetation groups of PR and FE. For both the PR and FE categories, relationships between \(\text{SIF}_{760}\) and greenness indices (i.e., SR and NDVI) were also positive, but the \(\text{R}^{2}\) ranged between 0.05 and 0.23 for SR and NDVI and was not statistically significant. Surprisingly, the same relationships of greenness indices with \(\text{SIF}_{687}\) were negative for PR (although non-significant). The relationship between EVI and \(\text{SIF}_{760}\) was weak and insignificant with \(\text{R}^{2}=0.01\)  
Figure 9: Bar diagram presenting the average values of the observed vegetation indices and SIF derived from HyPlant data; (**a**) SR, (**b**) NDVI, (**c**) EVI, (**d**) PRI, (**e**) \(\text{SIF}_{760}\), and (**f**) \(\text{SIF}_{687}\). The codes corresponding to the names of the individual plant communities are written at the bottom of the bars and correspond to the abbreviations provided with Figure 3b. Detailed description of these plant communities is provided in Table S1 (Supplementary Material). The first group of bars represents meadows (ME), the second group of bars represents peatland rush (PR) vegetation communities, and the third group of bars stands for the fen (FE) vegetation communities. Error bars represent standard deviation. \(p<0.834\) for the PR category and R\({}^{2}\) = 0.00, \(p<0.925\) for the FE category. Similarly, the relationship between EVI and SIF\({}_{687}\) was also very poor and insignificant for both the PR and FE categories. Figure 10: Correlation analysis of SIF at 760 nm and at 687 nm with selected remotely-sensed vegetation indices (SR, NDVI, EVI, and PRI) at vegetation community level restricted to peatland area. Plant communities were grouped in three categories comprised of meadows (ME), peatland rush vegetation (PR), and fen vegetation (FE), and were presented separately for each group. Codes names correspond to those provided with Figure 3. Error bars represent standard deviation. In contrast to ME, the relationships between PRI and \(\text{SIF}_{760}\) was negative for the PR and FE categories with R\({}^{2}\) from 0.31 to 0.53 for PR and FE, respectively; whereas, the relationships of PRI with \(\text{SIF}_{687}\) were positive and insignificant for the PR (with R\({}^{2}\) = 0.48, \(p\) < 0.64), as well as negative and insignificant for the FE (with R\({}^{2}\) = 0.24, \(p\) < 0.12), vegetation categories.
## 4 Simulation Results
### IEEE 30 Bus Test System  
This power test system is used to exhibit the efficiency of the HIC-GWA. The details for busses and line data are shown in [43]. The system active and reactive power are 283.4 MW and 126.2 MVAR  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristics** & **IEEE 30** & **IEEE 118** \\ \hline Buses & 30 & [47] & 118 & [48] \\ Branches & 41 & & 186 \\ Load voltage & 24 & [0.95, 1.05] & [0.94, 1.06] \\ Control variables \((N_{var})\) & 24 & 130 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power system’s parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ICA Parameters**} & \multicolumn{4}{c}{**GWA**} \\  & & & & & **Parameters** & \\ \hline \(\xi\) & \(N_{Country}\) & & & \(N_{imp}\) & & \(N_{w}\) & \\  & 30 bus & 118 bus & \(\beta\) & 30 bus & 118 bus & 30 bus & 118 bus \\
1.02 & 15 & 100 & 0.90 & 5 & 20 & 5 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Setting of the proposed HIC-GWA approach.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Acronym** & **Reference** & **Simple Objective** & **Multiobjective** & **Fuel Cost** & **Emission** & \(P_{\text{loss}}\) & **VD** & \(L\) **Index** \\ \hline MSA & [19] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ MPSO & [20] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ MDE & [21] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ MPO & [22] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ FFA & [23] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ ARCBBO & [24] & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ \\ RCBBO & [24] & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ \\ GWO & [25] & ✓ & ✓ & ✓ & & & & \\ DE & [25] & ✓ & ✓ & ✓ & ✓ & ✓ & & \\ MGBICA & [26] & ✓ & ✓ & ✓ & ✓ & & & \\ ABC & [27] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ HSFLA-SA & [28] & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ \\ LITBO & [29] & ✓ & & ✓ & ✓ & ✓ & ✓ & \\ TLBO & [30] & ✓ & & ✓ & ✓ & ✓ & ✓ & \\ HMPS-SFLA & [31] & ✓ & ✓ & ✓ & ✓ & ✓ & & \\ PSO & [31] & ✓ & ✓ & ✓ & ✓ & ✓ & & \\ GABC & [32] & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ \\ DSA & [33] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ EEA & [34] & ✓ & ✓ & ✓ & & ✓ & ✓ \\ EGA & [34] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ ALC-PSO & [35] & ✓ & & ✓ & ✓ & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recently published approaches to solve OPF problems.
# Enhancing Furniture Manufacturing with 3D Scanning
## 3 Experimental Setup and Methodology  
### Experimental Setup  
A portable hand-held 3D scanner was used (model Eva, Artec 3D, Luxembourg). Table 1 provides an overview of the scanner's technical specifications. In-depth information can be found on the Artec 3D website [27].  
Portable 3D scanners are suitable for 3D scanning medium-sized objects (from 30 cm up to 2 m). It is a perfect solution for the furniture industry. This structured light 3D scanner is suitable for making quick, textured and accurate 3D models of medium and large objects such as a human bust, an alloy wheel, a motorcycle exhaust system, or furniture products. It captures precise measurements quickly at high resolution. Light, fast, and versatile, Eva is one of the most popular scanners and a market leader in hand-held 3D scanners. Based on safe-to-use structured light scanning technology, it is an excellent all-around solution to capture objects of almost any kind, including objects with black and shiny surfaces [27]. All the 3D scans were processed using Artec Studio software (v. 15, Artec 3D, Luxembourg).  
Three-dimensional scanning requires high computing power, especially when working with many scans containing many points. This research used a workstation with an Intel Core i7 processor, 32 GB of RAM, and a Nvidia GeForce RTX 2060 graphics card (Legion 5 Pro Gen 8, Lenovo Group Limited, Hong Kong, China). This workstation is shown in Figure 1. In the first step, all scans were cleaned, aligned, and registered globally, and then a point-cloud 3D model was created (as an STL file). In the next step, this point-cloud 3D raw model must be imported into CAD software, such as Geomagic DesignX (3D Systems Corp., Rock Hill, SC, USA), SolidWorks (Dassault Systemes, Velizy-Villacoublay, France), or CATIA (Dassault Systemes) to create a 3D CAD model. It is important to note that scanning is more manageable than creating a CAD 3D model using an STL 3D model as a reference. This process requires high engineering and 3D modeling skills. The created CAD model is then ready for manufacturing. Before manufacturing, it is vital to check the dimensional accuracy of the created CAD models compared to the scanned data. The 3D scanning process is presented in Figure 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Specification** & **Value** \\ \hline
3D accuracy & Up to 0.1 mm \\
3D resolution & Up to 0.2 mm \\ Scanned object size & Larger than 10 cm \\ Full-color scanning & Yes \\ Target-free tracking & Hybrid geometry and color-based \\
3D reconstruction rate & Up to 16 frames per second \\ Output formats & All popular formats, including STL, OBJ, and PLY \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specification of 3D scanner Artec Eva.
## 6 Analysis of Shear Transfer Actions
### Aggregate Interlock  
Shear transfer actions caused by aggregate interlock according to the SCPT are activated rather late within the process of load application and primary occur at a rotation of \(\varphi\approx 1.0\) mrad (Figure 9b). This shear transfer action requires a sliding of crack lips and thus is activated with the onset of the shear crack turning into a curved path from a straight bending crack. These findings were also documented in references [19, 46]. Even though the center of rotation is not assumed at the crack tip, it follows the horizontal position of the propagating crack tip but at different vertical positions. Thus, the curved crack shape provokes increasing sliding deformations in the straight bottom crack branch, which are again a prerequisite for the activation of the aggregate interlock. It is interesting to note that, despite its retard activation in the shear failure process, the aggregate interlock is the most effective shear transfer action in the failure state according to the SCPT, providing more than 50% of the total shear strength of specimen B1-1. In the past, the widespread hypothesis that the aggregate interlock would decrease with the increasing rotation of the control section [11, 18, 23, 44, 45] or even vanish at the state of shear failure due to large crack opening values [7, 16, 47, 48, 49] has prevailed. This behavior of aggregate interlock could not be confirmed, according to the SCPT. Due to the increased crack rotation, the beneficial effect of large sliding deformations in the crack are much more dominant than the decrease of aggregate interlock stresses resulting from increased crack opening at the same time. In addition to that, crack propagation leads to the continuous development of new shear crack segments. These segments again have a small crack opening, resulting in a high effectiveness of the aggregate interlock mechanism.
## 3 The Proposed Evaluation Method
### Key Techniques  
The evaluation method proposed in this paper consists of two important technical parts: (1) weights determination and (2) evaluation. The process of the evaluation method is shown in Figure 4.  
Figure 4: Flowchart of evaluation method.  
In the weight determination phase, the quantitative TOPSIS method and qualitative AHP method are used to determine the weights of each evaluation indicator in the sub-criterion layer and criterion layer, respectively. The weighting ideas and specific steps are introduced in detail in Section 3.4.  
In the evaluation phase, the fuzzy comprehensive evaluation method is used to carry out the two-level evaluation. The evaluation ideas and concrete steps are introduced in detail in Section 3.5.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
###### Abstract  
This study developed a novel water-soluble Cellulose Acetoacetate (CAA)-chitosan (CS) composite hydrogel drug delivery system. In this system, CAA and CS molecules are cross-linked via dynamic enamine bonds, forming a three-dimensional network structure suitable for drug encapsulation and controlled release. The primary objective was to address the challenges associated with the short half-life and significant fluctuations in therapeutic concentration of cytokine drugs, such as interleukin-2 (IL-2). A hydrogel system with a three-dimensional spatial network structure was successfully constructed via dynamic enamine bonds cross-linking between the acetoacetate groups in CAA molecules and the amino groups in CS. This system exhibits the following characteristics: (1) Dynamic covalent bonds impart adjustable mechanical properties to the hydrogel, enabling precise control over gelation time and mechanical performance; (2) A hierarchical pore structure (average pore size of 100-200 \(\upmu\)m) provides a three-dimensional confined space for efficient drug encapsulation, achieving an IL-2 encapsulation efficiency of 83.3 \(\pm\) 3.1%; (3) In vitro release studies demonstrated that the cumulative release of IL-2 within 72 h ranged from 18.4% to 34.7%, indicating sustained-release behavior. Cell viability assays confirmed that the hydrogel maintained the survival rate of 1929 cells above 85% (as determined by the CCK-8 method), and live/dead staining revealed no apparent cytotoxicity. Overall, this three-dimensional network hydrogel based on dynamic covalent bonds represents a promising strategy for low-dose, long-lasting cytokine delivery.  
hydrogel; cellulose; acetoacetate; chitosan; drug-sustained-release +
Footnote †: journal: Applied sciences
## 2 Related Work
### Egocentric Betweenness Measure Used in Different Areas  
[PERSON] et al. [8] investigated the problem of the quality of service (QoS)-based topology control over wireless sensor networks. To this end, a weighted, bidirectional topology-control algorithm named edge-betweenness centrality (EBC) was proposed. EBC selects the suitable set of neighbours in which input QoS requirements may be satisfied. The idea here is to select from the target network appropriate logical neighbours of the former nodes, i.e., a subset of neighbours that can be employed to perform application-specific procedures (for instance, message delivery) without the need to include all nodes of the network. The authors have demonstrated that this approach allows achieving a high QoS in wireless sensor networks by means of evaluating the relationships between entities of the network (i.e., edges). This provides the capability of controlling the information flow, the message delivery, the latency and the energy dissipation among nodes.  
The authors of SimBetrouting [9] proposed an algorithm for forwarding data packets in disconnected delay-tolerant MANETs based on social network analysis techniques. For this purpose, they designed and implemented the routing protocol, which used two components: (i) betweenness utility, which exploits the exchange of pre-estimated egocentric betweenness centrality scores; and (ii) similarity utility, which selects the node that provides the maximum utility for carrying the message. Based on these components, SimBet chooses which node provides the maximum utility for carrying the message. Simulation results have shown that it achieves good performance comparable to epidemic routing, with low network overhead. Additionally, the authors have illustrated that the employment of the egocentric betweenness metric may prove useful in any distributed systems, where global topology knowledge is inaccessible and, especially, where the underlying networks present small-world characteristics.  
[PERSON] et al. [10] proposed a protocol for topology control in wireless mesh networks to improve the energy efficiency and the battery lifetime. The proposed mechanisms chooses which devices must act as routers, forwarding the data packets received from other hand-held devices to it. In order to select the devices, centrality metrics are applied, from social network analysis, to build a topology control mechanism based on a connected dominating set. The mechanism's implementation and evaluation have been carried out in two modes, i.e., centralized and distributed. In the centralized mode, the three most common centrality measures (degree, closeness and betweenness) were employed. In the distributed mode, the egocentric betweenness measure was applied. Through the experiment results, it was verified that the use of the centrality measures contributes to a better network performance.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 3 Method
### Overall Process of the Proposed Algorithm  
The overall process of our proposed algorithm that jointly optimizes the UAV's location and the transmit power of each buoy to achieve the optimal transmission throughput and the CRB for localization simultaneously is summarized in Algorithm 1. And the details for each stage are given in the following subsections.  
```
1:Initialize the UAV's location and buoy transmit power as illustrated in (Algorithm 2).
2:while termination condition is not satisfied do
3:for Each weight vector is selected do
4: Implement offspring generation by (Algorithm 3).
5:for every offspring is selected do
6: Constraint operation based on (Algorithm 4)
7: Update the parent population following (Algorithm 5).
8:endfor
9:endfor
10:endwhile
```  
**Output:**  
Initial UAV's location \(I_{\mathbf{U}}^{(0)}\), buoy transmit power \(\mathbf{p}^{(0)}\), weight vectors \(\mathbf{W}\) and neighborhood set of weight vectors \(\mathbf{e}\).  
**Process:**  
```
1:Randomly generate the initial parent population \(I_{\mathbf{U}}^{(0)}\) and \(\mathbf{p}^{(0)}\) following the uniform distribution.
2:Use [PERSON]'s method to generate \(N=\binom{2+U-1}{2}\) weight vectors \(\mathbf{W}=\{\mathbf{w}_{1},\mathbf{w}_{2},\ldots,\mathbf{w}_{N}\}\).
3:while\(i=1:N\)do
4: Generate M closest neighborhood weight vectors to \(\mathbf{w}_{i}\).
5:endwhile
6:Divide \(I_{\mathbf{U}}\) and \(\mathbf{p}\) to several non-domination levels by non-dominated sorting method.
7:Associate each value in \(I_{\mathbf{U}}\) and \(\mathbf{p}\) with unique sub-region.
```  
**Output:**  
Offspring solutions \(I_{\mathbf{U}}^{off}\) and \(\mathbf{p}^{off}\).
1:if\(RAND<\delta\)then
2: Choose k members of \(\mathbf{e}(i)\) randomly.
3:if no candidates exist in the elected sub-regions then
4: Stochasticly select k results from \(I_{\mathbf{U}}\) and \(\mathbf{p}\) to form \(\hat{I_{\mathbf{U}}}\) and \(\hat{\mathbf{p}}\), respectively.
5:else
6: Select k number of results from the elected sub-regions to form \(\hat{I_{\mathbf{U}}}\) and \(\hat{\mathbf{p}}\).
7:endif
8:else
9: Stochasticly select k results from \(I_{\mathbf{U}}\) and \(\mathbf{p}\) to construct \(\hat{I_{\mathbf{U}}}\) and \(\hat{\mathbf{p}}\), respectively.
10:endif
11:Apply polynomial mutation and simulated binary crossover (SBX) [52] to generate the offspring solutions \(I_{\mathbf{U}}^{off}\) and \(\mathbf{p}^{off}\). ```
**Algorithm 2** Initial Parameter Determination
## 4 Conclusions  
In this study, a series of linear cutting tests was performed using the cutting tool of a pre-cutting machine. Regression analyses were performed according to the tool forces, cutting volumes, and specific energies recorded from rock cutting. Furthermore, the ridge formation characteristics between the cutting grooves were analyzed from the cross-sectional profile of the rock after cutting obtained from 3D scanning. The conclusions of this study based on the test results are as follows.  
1. The tool forces increased as the penetration depth and spacing increased, similar to TBMs and roadheaders. However, information for predicting optimal cutting conditions can be obtained from the ratio of tool forces. The ratio of the peak cutting force to the mean cutting force was the maximum near 2 for \(s/p\), and the ratio of the cutting force to the normal force was also the maximum near 2 for \(s/p\). In conclusion, when \(s/p\) is near 2, larger rock chips are obtained, and the wear of the tool is minimized.
2. The cutting volume increased in the power relationship as the penetration depth increased, and was not affected by the rock strength. This volume reached a maximum at a specific \(s/p\), and the specific \(s/p\) was the same, regardless of the penetration depth. This result is similar to the case of the TBMs and roadheaders. Additionally, the cutting  
Figure 18: Relationships between \(s/p\) ratio and specific energy: (**a**) \(p=3\) mm, (**b**) \(p=6\) mm, and (**c**) \(p=9\) mm.  
volume was constant as \(s/p\) became greater than 8, and the ratio of the measured cutting volume to the calculated cutting volume reached a maximum when \(s/p\) was close to 1.5. This result shows that ridges start to form between the cutting grooves as \(s/p\) becomes larger than 1.5, and the cutting is performed in the unrelieved mode if it becomes larger than 8.
3. The ridge between the cutting grooves decreased as the penetration depth increased and increased with spacing. In particular, when \(s/p\) was 1.5, ridges started to form, and as \(s/p\) became larger than 8, the ridges reached the height of the rock surface before cutting. This supports the results obtained from the analysis of cutting volume.
4. The specific energy decreased in a power relationship as the penetration depth increased, similar to the case of the TBMs and roadheaders. In addition, the specific energy reached a minimum when \(s/p\) was between 2 and 4, regardless of the penetration depth and rock strength, similar to that of a roadheader using a drag tool.  
[PERSON]-[PERSON] proposed the concept of research, developed the study, and conducted the experiment. [PERSON]-g.H. and [PERSON] contributed to reviewing the final paper and made recommendations for revision. [PERSON]-k.Y. supervised the study and provided important suggestions. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2019R1A2C2003636).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Methods and Process Procedures
### Cloud and Cloud Shadow Identification
#### 2.3.4. Haze Pixel Identification  
In comparison to cloudy and cloud shadow pixel identification, haze pixel identification is more challenging because a haze pixel usually has mixed surface reflectance of objects on land surface. Considering that the blue band is one of the most sensitive bands to haze effects, we used the difference of blue bands between the observation and its synthetic image in Equation (1) to identify haze affected pixels.  
\[dDN_{h}(i,j)=DN_{o}(blue,i,j)-DN_{s}(blue,i,j) \tag{8}\]  
where \(DN_{o}\) and \(DN_{s}\) represent digital number of observation and its synthetic image in blue bands, respectively. \(dDN_{h}\) is the pixel value difference between the two images. Haze usually makes a pixel value larger than that of a haze-free pixel. Similar to the cloudy pixel identification, to detect a haze pixel we have:  
\[dDN_{h}(i,j)=\left\{\begin{array}{ll}\geq T_{h}&\text{ \emph{ haze pixel}}\\ <T_{h}&\text{ \emph{a haze free pixel}}\end{array}\right. \tag{9}\]  
where \(T_{h}\) is the threshold to be determined for distinguishing between haze and haze-free pixels.  
Using the same approach of determining the threshold \(T_{c}\) for cloudy pixel identification, the threshold \(T_{h}\) can be determined. However, the selection of the parameter value \(C\) was not as obvious as for cloudy and cloud shadow pixel identification. It depended on not only the degrees (e.g., thin or thick) of haze on the image but also the prediction accuracy of its corresponding synthetic image. As the difference ranges of \(dDN_{h}(i,j)\) in Equation (8), it was usually much smaller than \(dDN_{c}\) in Equation (4) or \(dDN_{cs}\) in Equation (6), and the difference of some thin haze pixels may be close to the largest prediction errors of the synthetic image, wherein a smaller \(C\) was recommended. For our test case, we selected \(C=3\) for haze pixel identification.  
To improve the accuracy of haze detection, we introduced an additional condition \(DN_{o}(blue,i,j)\geq DN_{o,min}\) to Equation (9). This states that for a haze pixel its observation value of blue band should be bigger than a minimum value \(DN_{o,min}\), otherwise the difference \(dDN_{h}(i,j)\) is considered as contribution from the synthetic image prediction error even though the value is bigger than the threshold \(T_{h}\). Therefore, some misidentified haze pixels can be excluded. The minimum value \(DN_{o,min}\) can be determined as:  
\[DN_{o,min}=DN_{o,menn}+n_{DN_{0}} \tag{10}\]  
where \(DN_{o,menn}\) is the mean value of all \(DN_{o}(blue,i,j)\) and \(\sigma_{DN_{0}}\) is their standard deviation. n is an acceptable factor from 0.5 to 2.