esa-sceva/satcom-chunk-collection huggingface dataset:
# Assignment Approach for Electric Vehicle Charging Using Traffic Data Collected by SUMO
## 3 Proposed Solution
### Assignment Algorithm
#### 3.2.2 Algorithm  
After receiving the needs of the vehicles by the control center of the SG, the Algorithm 1 calculates the SOC\({}_{f}\) max (see Equation (3)) of each vehicle with each station, then it selects the stations with an available outlet at the minimum. If the number of outlets available at the station is greater than or equal to the number of vehicles assigned to the same station, the assignment is made. Otherwise, the average SOC\({}_{f}\) max of the EVs assigned to the same station is calculated and the minimum average is selected.  
```
Input: EV\({}_{i}\)[Soc\({}_{0}\),C\({}_{i}\),A\({}_{1}\)]; CS\({}_{i}\)[d\({}_{ij}\), d\({}_{ij}\), Kjam\({}_{ij}\)] Output:[Soc\({}_{f}\)max]
1. RSU broadcasts the EV charging service
2. For each EV (1,...,nEV) and for each CS (1,...,mcs)
3. Calculate SOC\({}_{\rm{Hj}}\) (_using Equation_ (3))
4. Select CS according to occupancy (number of available outlets)
5. If the number of outlets is less than the number of vehicles assigned to the same station, then
6. Calculate the average of Max(Soc\({}_{\rm{Hj}}\)) of the vehicles assigned to the same station
7. Select the minimum of the average SOC\({}_{\rm{Hj}}\)
8. Update outlet matrix after EV affectation
9. endif
```  
**Algorithm 1**Assignment Algorithm
# Automated 3D Image Processing System for Inspection of Residential Wall Spalls
###### Abstract  
Continuous spalling exposure can weaken the performance of structures. Therefore, the development of methods for detecting wall spall damage remains essential in the field of Structural Health Monitoring. Currently, researchers mainly rely on 2D information for spall detection and predominantly use manual data collection methods in the complex environment of residential buildings, which are usually inefficient. To address this challenge, an automated 3D image processing system for wall spalls is proposed in this study. First, UGV path planning was performed in order to collect information about the surrounding environmental defects. Second, to address the shortcomings of RandLA-Net, a dynamic enhanced dual-branch structure is established based on which consistency constraints are introduced, a lightweight attention module is added, and the loss function is optimized in order to enhance the ability of the model in extracting feature information of the point cloud. Finally, spalls are quantitatively evaluated to determine the damage to buildings. The results show that the Randla-Spall achieves 94.71% Recall and 84.20% mIoU on the test set, improved by 4.25% and 5.37%. An integrated process using a lightweight device is achieved in this study, which is capable of efficiently extracting and quantifying spalling defects and provides valuable references for SHM.  
wall spalling detection; Randla-Net; semantic segmentation; automated non-destructive inspection 2025 15 2140
## 4 Result and Discussion
### Simulation of Energy Recovery
The expansion ratio nearly equals the lowest pressure ratio that the front-end compressor (e.g., screw compressor in Figure 2) must provide. The required input energy of the cryogenic refrigerator could be calculated through the integration of compression work using Equation (5). Assuming the compressor operates under \(\eta_{C}=0.60\) and the mass flow rate of 0.03 kg s\({}^{-1}\), the input compression works under varied rotating speed were calculated and presented in Figure 10. The curves of compression work are similar to those of the compression ratio (expansion ratio in Figure 9), but the change trends vary less dramatically. Because there is an exponential relationship with them and the index is less than 1.0, as shown in Equation (4). In the figure of compression work with time, the fill area under the curve represents the integration of compression work with time, which is the total energy input in the cooling process, as shown in Figure 10b-d. The total input energies are 71861.1, 75110.3, and 78265.3 kJ at the rotating speed of 170, 190, 210 krpm respectively. Also, the differences are not as large as those of the expansion ratios. Figure 9: Dynamic expansion characteristics with a motor: (**a**) Expansion ratio; (**b**) expander efficiency.
# Biomedical Application of Cyclodextrin Polymers Cross-Linked via Dianhydrides of carboxylic Acids
## 6 Dermal Transport  
PMDA-NS was first used as a multifunctional semisolid ingredient for drug delivery to the skin by [PERSON] and the others in 2014. The effect of NS on the solubility and photostability of the photosensitizer benzoporphyrin-derivative monoacid ring A (BPDMA) and all-trans retinoic acid (ATRA), as well as the effect of diclofenac on skin permeation, were investigated [45].  
BPDMA (Figure 11) is used as a photosensitizer in the photodynamic therapy (PDT) of age-related macular degeneration. However, its application is limited due to the very low solubility in water, resulting in its ineffective aggregation state. Moreover, it shows poor solubility, low-light absorption, and low singlet oxygen quantum yield. Based on the obtained data, it was found that complexation of PMDA-NS prevented aggregation in the aqueous solution, reduced photobleaching during irradiation, and did not have any effect on the generation of singlet oxygen [45].  
Figure 11: Structure of benzoporphyrin-derivative monoacid ring A (BPDMA).  
Figure 10: Structures of synthetic strigolactones: (**a**) MEB55; (**b**) ST362.  
In the second case, the effect of ATRA stabilization in gel formation was investigated by the polymer. During irradiation, the ATRA degraded to 13-cis and 9-cis retinoic acids (Figure 12). The use of CD-NS increased the half-life approximately by 200-fold compared to ATRA in ethanol. Therefore, NS can be applied as an excipient in water-based formulations to stabilize photosensitive molecules [45].  
Diclofenac (Figure 13) belongs to the group of non-steroidal drugs with anti-inflammatory properties. This complex NS drug was investigated in local treatment. Typically, diclofenac is formulated as an ointment, gel, or cream-gel because its composition affects the ability to penetrate the skin. [PERSON] et al. found, that CD-NS does not significantly affect diclofenac skin penetration especially in aqueous solutions. However, it is believed that the administration of the drug can be controlled in a semisolid preparation by adding PMDA-NS [45].  
In conclusion, gels based on NS cross-linked via pyromellitic dianhydride have the potential for local drug delivery because of their ability to increase the solubility of lipophilic drugs in semisolid formulations containing water, improve photostability, and modulate their transport. In addition, a significant decrease in the permeating dose and increase in the location of the drug in the stratum corneum and viable epidermis was detected, indicating NS are promising multifunctional ingredients in mono and biphasic preparation for local applications [45].
## 3 Testing Procedure and Instrumentation  
During seismic excitations, common columns are mainly subjected to flexural loading as presented in Figure 1. Loading is mainly imposed through the connections with the beams. The specimens constructed for the study have been designed and constructed in the way that they accurately simulate the loading conditions experienced by real columns (Figure 1).  
The layout of the test setup and the instrumentation along with a photograph are presented in Figure 5. During the test, the column is positioned horizontally, whereas the cyclic loading is imposed via an actuator acting horizontal at the free end of the beam (Figure 5). Axial compressive loading is constantly applied during the testing procedure to the horizontal column of the specimen.  
The specimens were tested under the same cyclic loading protocol imposed in a strong wall and rigid floor testing area of the laboratory. The loading is achieved using a double-acting servocontrolled hydraulic actuator with a maximum capacity of 500 kN horizontally fixed on to the rigid wall (Figure 5).  
The cyclic loading protocol (Figure 5c) is the same for both specimens, it consists of 7 steps and each step includes 3 full cyclic loadings. The imposed deformations of the steps are presented in mm and in terms of story drifts. Story drift (SD) in the work represents the ratio of the imposed deformation to the length of the beam measured from the loaded point to the centerline of the column. The imposed deformations of the seven cyclic steps are \(\pm\)8.5 mm, \(\pm\)12.75 mm, \(\pm\)17.0 mm, \(\pm\)25.5 mm, \(\pm\)34.5 mm, \(\pm\)51.0 mm, and \(\pm\)68.0 mm and correspond to story drifts equal to 0.5%, 0.75%, 1.0%, 1.5%, 2.0%, 3.0%, and 4.0%, respectively (Figure 5c).  
Measuring instrumentation includes (a) a load cell horizontally fixed to the rigid wall for the imposition of the loading that is simultaneously used for the measuring of displacements and loading of the free end of the beam, (b) string-LVDTs that are diagonally mounted on the joint body of the connection part of the column with the beam for the measurement of the shear deformations of this part of the specimen, and (c) strain gauges sticked on steel bar reinforcements of the column and the beam.
## 2 Methods
### Sentinel-2 Constellation Data Acquisition and Processing  
Ocean color data from the S2 mission were acquired from 1 January 2016 to 31 December 2019. A preliminary analysis was carried out on the Copernicus Open Access Hub (COAH) [33] before the products' downloading to ensure that only cloud-free images over the ROI, the Sao Jacinto sea outfall area, were obtained. The steps followed for this first investigation are presented in Figure 2. Data acquisition and processing were carried out for clear atmosphere days; alternatively, in case where there were clouds over the ROI, the image was not retrieved.  
Figure 1: Study area. The main urban areas, Aveiro and São Jacinto, are identified. Likewise, the Port of Aveiro facility, Barra inlet channel, and the sea outfall are shown.  
Data acquisition was focused on Level-1 imagery to implement the data processing to Level-2 (L2) and to access additional helpful information for this work. Nevertheless, it was necessary to use the ONDA [34], an open-access platform that also supports users at various levels of expertise in exploiting geospatial data, as it contains all open-access Copernicus data, such as Sentinel-2 data. The reason another platform needed to be used was that some data from COAH came with missing spectral bands when downloaded. For the L1 to L2 imagery processing, the software ACOLITE [35] was used. It is free and open-source software useful for processing data from turbid waters and small inland water bodies in particular. It also performs both atmospheric corrections and can output several parameters derived from water reflectances according to the algorithms employed; finally, RGB composites and PNG maps can also be generated. This step ensured the final ocean color data had an equally spaced grid on geographically referenced images. Additionally, the software offers users several algorithms for water physio-chemical indexes.  
To improve the sea outfall turbid plume detection when the sea surface conditions were not favorable to identifying differences in the water masses' colors, we employed four algorithms available on ACOLITE. They were spm_nechad2016 (for SPM detection), t_nechad2016, and t_dogliotti (both for turbidity studies), as well as k\({}_{d}\)490_qaasw (for diffuse attenuation of light) [36]. As for qualitative analysis of the sea outfall plume visibility proposed by this study, the k\({}_{d}\)490_qaasw was found to be the most effective algorithm to detect the marine outfall outflow (the comparison among the four algorithms can be found in Supplementary Materials). The selected algorithm provides information about inherent optical properties (IOP) related to water clarity, derived from the diffuse attenuation coefficient [36].
## 2 Method
### The Clock Model
The detailed variance setting will be described in the next section. Figure 1: Diagram of the adaptive prediction clock model. Figure 2: Diagram of the RT-PPP (real-time PPP) time and frequency transfer using the clock model. When the difference between the local time and the reference time is calculated in two or more stations simultaneously, the time difference between station A and station B can be expressed as follows:  
\[\text{UTC }(i)-\text{UTC }(j)=(\text{UTC }(i)-\textit{ref})-(\text{UTC}(j)-\textit{ref}) \tag{7}\]  
where _ref_ is the reference time, such as IGST (International GNSS Time); and UTC (\(i\)) and UTC (\(j\)) are the local times at station A and station B, respectively.
# Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks
## 4 Risk Features Analysis of Space Station
### Risk Feature Quantitative Analysis  
The configuration risks are related to both the components and structure of the space station. The components represent the static features of the configuration risks, and the structure represents the dynamic features of the configuration risks. The static features can be described by the occurrence probabilities, and the dynamic features can be described by the propagation probabilities. Therefore, the goal of the risk features quantitative analysis is to determine the occurrence and propagation probabilities.  
The occurrence probability can be determined by the probability assessment methods [41; 42; 43] or by Equation (1)  
\[p_{oi}=n_{i}/n_{total},1\leq i\leq 52 \tag{1}\]  
If the components are treated as the nodes, the complex aerospace system can be regarded as a complex aerospace engineering system network. Research shows that a complex aerospace engineering system network has a larger clustering coefficient and smaller feature path length [44; 45; 46; 47], which is consistent with the small-world complex network characteristic. So, the propagation probability can be determined by the small-world complex network analysis method. Fifty-two key configuration risks are modeled as the nodes to build the network model with UCINET software, and the node eigenvector centrality (EC) [48; 49] index can be computed. The EC index describes the node's adjacent nodes and the adjacent nodes' importance, and it can express the node's risk propagation feature. So, the risk propagation probability can be defined by Equation (2).  
\[p_{pi}=EC(i)=\lambda^{-1}\sum_{j=1}^{52}a_{ij}x_{j},1\leq i\leq 52 \tag{2}\]  
where \(\lambda\) is the maximum eigenvalue of the complex network model adjacency matrix \(A\), \(a_{ij}\) is the element in matrix \(A\), and \(x=(x_{1},x_{2},\cdots,x_{52})^{\text{T}}\) is the eigenvector corresponding to \(\lambda\). Adopting the Leader Rank algorithm to calculate the nodes' risk propagation probabilities, the top 15 highest propagation probability risks can be taken as high load risks and shown in Table 5. Then, the space station configuration reliability model will be built based on the 15 high load risks, and the occurrence probabilities and propagation probabilities of the 15 high load risks will be given in the data collection section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Risk Level** & \begin{tabular}{c} **Between-Mission** \\ **Risk Numbers** \\ \end{tabular} & \
\begin{tabular}{c} **Between-System** \\ **Risk Numbers** \\ \end{tabular} &
\begin{tabular}{c} **Inner-System** \\ **Risk Numbers** \\ \end{tabular} \\ \hline Level V & 19 & 55 & 77 \\ Level IV & 8 & 25 & 46 \\ Level III & 17 & 23 & 26 \\ Level II & 9 & 46 & 54 \\ Level I & 4 & 17 & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-layer configuration risks synthetically evaluation results.
## 4 Studies of the Molecular Profiling of EVs as Potential Biomarkers
### Proteome Profiling Analysis of EVs in Multiple Cancers
Furthermore, proteomic analyses of urinary exosomes of NSCLC patients highlighted exosomal leucine-rich-alpha2-glycoprotein 1 (LRG1) as a candidate biomarker for non-invasive diagnosis, playing a role in epithelial-mesenchymal transition (EMT) and angiogenesis [109] (Table 1). Analyses of the EV proteome have also provided potential new biomarkers for glioblastoma cancer (GBC). [PERSON] and colleagues (2020) provided a list of potential biomarkers by secretome profiling through LC-MS/MS, which included annexin A2, vimentin, and tenascin-C, among others [110] (Table 1). Vimentin is an intermediate filament protein that plays a central role in GBC progression [147], and tenascin-C is a non-filamentous protein that mediates cell-cell and cell-matrix interactions, which affects negatively proliferation and invasion in GBC [148]. Lastly, proteomic approaches have improved the knowledge of less known tumors thanks to the specificity of the proteins found in EVs. In this context, EVs isolated from serum of thyroid cancer lymph node metastases (LNM) patients had high amounts of talin-1 (TLN1), integrin beta-2 (ITGB2), SRC, and CAPNS1, compared with thyroid cancer without LNM [4]. TLN1 is a cytoskeletal protein involved in regulating the activity of cell adhesion proteins by coupling them to F-actin [149], it is also involved in adhesion, proliferation, survival, and tumor progression [150]. ITGB2 belongs to a family of cell surface receptors that play a key role in cell adhesion, migration, proliferation, and survival by forming physical interactions between the cell and the extracellular matrix [151]. SRC kinase activity and protein levels are elevated in several cancers, and are correlated with malignant progression [4]. Similarly, CAPNS1 belongs to a family of 15 calcium-dependent intracellular thiol proteases, whose aberrant expression or activity is involved in several diseases including cancer. Specifically, high calpain-1 levels were associated with papillary thyroid cancer, but its role in regulation of the proliferation and migration needs further investigation [4]. In esophageal squamous cell carcinoma (ESCC), GPC1 was identified as a novel biomarker [111] (Table 1). Additionally, EVs isolated either from NPC C666-1 cells or immortalized nasopharyngeal epithelial cells (NP69 and NP460) had large amounts of pro-angiogenic proteins, including intercellular adhesion molecule-1 (ICAM-1), and a variant isoform of CD44 (CD44v5), while the angio-suppressive protein thrombospondin-1 (TSP-1) was present at low levels in NPC C666-1 EVs [112] (Table 1). ICAM-1 is a member of the immunoglobulin superfamily, and CD44v5 a transmembrane glycoprotein involved in many biological activities, such as cell migration, tumor invasion, and metastasis [151].
# ARMONIA: A Unified Access and Metro Network Architecture
## 4 Simulation Results
### QoT Estimation  
We first evaluated the accuracy of the proposed QoT estimator. Assuming the aforementioned topology and transmission parameters, we used the GN model as described in the previous section to obtain the SNR metrics for training. We considered three traffic loads of 80, 120, 160 random lightpaths, with uniformly chosen source-destination pairs. We used 80% of the lightpaths for linear regression training, 10% for validation, and 10% for testing and executed 300 iterations for each case. To evaluate the estimation algorithm three accuracy related metrics were considered: (i) the mean squared error (MSE), (ii) the maximum overestimation (where the reported SNR is better than the actual), and (iii) minimum underestimation (where the reported SNR is worse than the actual). Figure 7 shows the evaluation metrics in dB for various number of lightpaths. We notice that the MSE is quite low even with a relatively low number of lightpaths. The maximum overestimation is as low as 0.43 dB and the minimum underestimation -0.2 dB when we employ information from 160 lightpaths. We notice that the accuracy of the estimator is generally quite good. The maximum overestimation (0.43 dB) is a design margin that should be used to avoid establishing a lightpath with inappropriate QoT. This design margin is quite lower than the standard design margin of 2 dBs [3] that is typically used. The lower margins have been shown to result in equipment cost savings in various scenarios [6; 39].  
Figure 6: Metro network topology organized in two layers with nine metro-edge and six metro core nodes.
## 6 Conclusions  
This paper proposed a method for vineyard yield estimation based on the application of artificial illumination in a vineyard facility during the night in order to obtain high-resolution images of clusters of red grapes with small illumination variance. Five grape segmentation methods have been empirically assessed under such illumination conditions. The quality of the segmentation was computed by comparing the automatic segmentation results with a manual labeling of the clusters of grapes in the images. The direct segmentation results have been improved by applying a sequence of morphological operators in order to fill gaps and eliminate noisy pixels from the segmented images.  
Empirical results showed that the use controlled illumination at night combined with the high-resolution and high-quality images of the vineyard simplifies the detection of clusters of grapes, because the color variability is small and the number of pixels available in each grape is very large (250,000 pixels on average). In the case of clusters of grapes not affected by occlusion, nine optimized implementations have provided grape size errors under 15%. The best estimate of the area of the grapes was obtained when applying a threshold level to the transformed H color layer of the images, obtaining a cluster size estimate error of 13.55%, which was improved up to 10.01% when applying an optimized sequence of morphological operators. In this case, the H layer was shifted 180\({}^{\circ}\) in order to move the reddish components of the grapes into the center of the H circular vector. The additional advantage of threshold segmentation is the simplicity of its real-time implementation in an agricultural machine designed to locate clusters of grapes and to estimate their size.  
The area and estimated volume of the clusters of grapes obtained from the images analyzed have been converted to vineyard yield by using specific calibration curves, which require manual operation. The results obtained with the proposed methods have shown that the yield can be predicted with an error of 16% and \(-\)17% in the cases of using the size information of the segmented area of the grapes and the volume of the solid of revolution computed from the segmented area.  
The results of this paper will have application to vineyard management by optimizing the resources required to harvest, transport, store and, if needed, manufacture the vineyard product. Future work will be focused on developing a real-time system in order to estimate yield in large vineyard facilities and to evaluate the robustness of the estimators against diseases and the ripening stage of the grapes.