esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Based System for Black Rubber Roller Surface Inspection
## 2 Visual Inspection System
### Defect Detection Based on Proposed Traditional Method  
Rubber roller defect images only provide useful information about the defect area, typically tiny in comparison with the overall rubber region, especially if the rubber surface condition is fine. Therefore, a large portion of the image is considered background and needs to be removed for efficiency. However, in most cases interference from the imaging environment introduces a large amount of noise, making it difficult to distinguish important image regions from unwanted regions. As a result, it is necessary to develop image preprocessing operations to minimize environmental disturbances. OpenCV is utilized to make the process as fast and efficient as could be expected. OpenCV implements many computer vision algorithms and is written in the C programming language. Image preprocessing is done in several steps, which are described below.  
Since the visible area of the camera is wider than the working area, we first extract the working area from the video obtained during image acquisition. The extracted area will be further processed in the image preprocessing stage. The traditional defect detection process is depicted in Figure 5 below.  
Figure 4: Lighting scheme.  
Figure 5: Proposed traditional method.  
After extracting the ROI region from the input video, images extracted from the ROI region are put into preprocessing. We use the same input data set for both methods, and an image can include more than one defect, as shown in the table below. Then, the thresholding stage highlights the error on the video background using black and white values. Morphology and edge detection make defects clearer and easier to see. If edge detection returns a value other than zero, the defect area will be colored, and the rubber roller with defects can be detected, and vice versa, if the rubber roller is in good condition.
## 3 Results
### Improvement Comparison Between TL-LUE and VPM
For example, at the US-GLE site, R\({}^{2}\) increased by 0.07 while RMSE decreased by 0.12 gCm\({}^{-2}\) day\({}^{-1}\). At all six sites, R\({}^{2}\) improved by 0.02 to 0.09  
Figure 11: Improvement comparison of GPP estimation at different sites by TL-LUE model and VPM. Differences in R\({}^{2}\) and RMSE between TL-LUE model and VPM for corresponding modeling schemes were calculated. and RMSE decreased by 0.03 to 0.36 gCm\({}^{-2}\) day\({}^{-1}\). For the forest sites, the results by the schemes SM*VPD and VPD coincided very well, but the SM*VPD scheme still performed slightly better than the VPD scheme, and they both clearly outperformed the schemes LSWI and SM*LSWI. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **R(GPP\({}_{\text{obs}}\)-LSWI)** & **R(GPP\({}_{\text{obs}}\)-VPD)** & **R(GPP\({}_{\text{obs}}\)-SM)** \\ \hline US-Var & 0.88 & \(-\)0.39 & 0.40 \\ US-SRG & 0.61 & \(-\)0.02 & 0.21 \\ US-Wkg & 0.63 & 0.11 & 0.29 \\ US-ARc & 0.84 & 0.17 & 0.28 \\ US-AR1 & 0.39 & 0.19 & 0.29 \\ US-Goo & 0.75 & 0.26 & 0.05 \\ FR-TOU & 0.78 & 0.14 & 0.45 \\ DE-Gri & 0.69 & 0.18 & 0.39 \\ IT-MBo & 0.72 & 0.24 & 0.44 \\ IT-Tor & 0.79 & 0.26 & 0.51 \\ BE-Bra & 0.63 & 0.11 & 0.32 \\ US-PFa & 0.14 & 0.64 & 0.18 \\ US-MMS & 0.86 & 0.57 & 0.36 \\ BE-Vie & 0.36 & 0.67 & 0.23 \\ US-GLE & 0.23 & 0.77 & 0.08 \\ CZ-BK1 & 0.14 & 0.82 & 0.28 \\ FR-Bil & 0.24 & 0.78 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation of various water stress indicators with GPP at flux sites. Figure 12: Performance of simulation results with different water stress indicators (including VPD, LSWI, SM, and LSWI*SM) in TL-LUE model at four grass-type sites.
###### Abstract  
This study aimed to evaluate the applicational possibilities of enzymatically modified apple pomace (AP) in conjunction with probiotics as value-added ingredients for the production of jelly candies. AP was enzymatically modified with Pectinex(r) Ultra Tropical, Viscozyme(r) L, and Celluclast(r) 1.5 L (Novozyme A/S, Bagsvard, Denmark), and the soluble and insoluble dietary fibre content was determined using the Megazyme kit (Megazyme International Ireland Ltd., Wicklow, Ireland), reducing sugar content using the 3,5-dinitrosalicylic acid assay. The technological properties of the modified AP, such as its swelling capacity, water-retention capacity, oil-retention capacity, bulk density, and static and thermal emulsion stability, were evaluated. Enzymatically modified AP hydrolys with Celluclast(r) 1.5 L was used for the production of jelly candies supplemented with _Bifidobacterium animalis_ DSM 20105. The survival of probiotics in the jelly candies during in vitro digestion, the viability of probiotics during candy storage, and candy quality characteristics were analysed. Enzymatically modified AP had different carbohydrate compositions and technological properties, depending on the enzyme preparation used. Although the viability of probiotics in the jelly candies decreased during storage, a significantly higher viability of _B. animalis_ was determined in jelly candies supplemented with hydrolysed AP compared with control candies made without AP after digestion in the saline, gastric, and intestine phases. This study shows that Celluclast(r) 1.5 L can be used for increasing the soluble dietary fibre in AP (18.4%), which can be further applied, in conjunction with _B. animalis_, for added-value jelly candy production.  
dietary fibre; enzymatic hydrolysis; probiotic viability; added-value product Article
# Functional Characterization of _Saccharomyces_ Yeasts from Cider Produced in Hardanger
## 3 Results and Discussion
### Volatile Compound Production Profiles of the Saccharomyces Ferents  
The concentrations of the volatile compounds are listed in Table 2. We measured a total of twenty-six aroma compounds, eighteen esters (seven ethyl esters of fatty acids, two ethyl esters of branched acids, and nine acetate esters), three C6 alcohols, and five volatile phenols in experimental ciders. The main group of aroma compounds in our experiment were C6 alcohols, ethyl esters of fatty acids, and volatile phenols. When we sum all aroma compounds, we see a large variability between strains, ranging from 2503 \(\mu\)g/L (_S. cerevisiae_ 2095) to 8654 \(\mu\)g/L (_S. uvarum_ 2376). The major aroma compound in all ciders was hexanol, which varied from 1222 \(\mu\)g/L (_S. uvarum_ 2083) to 2539 \(\mu\)g/L (_S. cerevisiae_ 2349). According to [PERSON] et al. [33], the olfactory threshold value for 1-hexanol is 8000 \(\mu\)g/L, which means that its contribution to the aroma profile is most likely negligible. In the study of Scandinavian and British ciders, 1-hexanol was the major C6 alcohol [34], ranging from 32 to 6541 \(\mu\)g/L. In our recent study, Norwegian ciders, on average, contained 5137 \(\mu\)g/L of 1-hexanol, and French ones 6555 \(\mu\)g/L [1]. Higher alcohols are known to be the most abundant group of aroma compounds in cider and apple juice [35; 36], but they are mainly important as precursors of esters, which are known for their fruity and sweet aroma [37]. The concentration of higher alcohols generally decreases or disappears during cider fermentation [16; 34], but in some cases, it also increases or remains unchanged [36]. Seven different ethyl esters of fatty acids were quantified, namely ethyl propanoate, ethyl butyrate, ethyl hexanoate, ethyl octanoate, ethyl decanoate, ethyl valerate, and ethyl dodecanoate. The amounts varied greatly between strains, as shown in Table 2. Ethyl esters are known to be an important component of the Norwegian cider flavor (\(\mathcal{O}\)[PERSON] et al., 2023, in press [1]) and also for other ciders [34; 38]. Although some esters may be originally present in apple juice before fermentation, most esters in the ciders are formed by the esterification of alcohols with carboxylic acids during fermentation and aging [36; 39]. The yeast with the lowest total concentration of fatty acid ethyl esters (average 269.3 \(\mu\)g/L) (_S. cerevisiae_ 2273) produced almost no ethyl decanoate and ethyl dodecanoate (0 and 15.5 \(\mu\)g/L, respectively). Whereas in the cider with the highest total ethyl ester of fatty acids contents among all yeast strains (_S. uvarum_ 2376) (5123.67 \(\mu\)g/L), these two esters were among the two most abundant ones (1905.93 and 1899.66 \(\mu\)g/L, respectively). None of the _Saccharomyces_ yeasts produced ethyl valerate in higher concentrations (on average, less than 1% of all ethyl esters of fatty acids).
## 4 Modeling and Prediction Results
### Prediction Results of Cable Force  
Following the flowchart for cable force prediction, as depicted in Figure 2, the QPSO-RBF-SVM algorithm model was trained 100 times, resulting in a stable and significantly low mean square error between the output value and measured value. The test set data were thoroughly analyzed and processed, with detailed findings presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spreader Bar** & **Length** & **Linear Density** & **Elastic Modulus** & **Theoretical** \\
**Number** & **L (m)** & **(kg/m)** & **E (kN/mm\({}^{2}\))** & **Cable (kN)** \\ \hline D1 & 7.149 & 42.20 & 195 & 1196 \\ D2 & 10.100 & 42.20 & 195 & 1276 \\ D3 & 12.385 & 42.20 & 195 & 128  
The results depicted in Figure 6 demonstrate the predictions generated using the QPSO-RBF-SVM model. The close match between the predicted and true values is clearly demonstrated, highlighting the high accuracy achieved by the model. The average relative error on the test set was only 0.65%, indicating that the model maintains an extremely low error on the test set.  
It is also worth noting that the coefficient of determination R\({}^{2}\) value ranges from 0 to 1, quantifying the proportion of variability in the dependent variable that can be explained by the independent variables in the model, and the closer it is to 1, the better the fit is. The coefficient of determination R\({}^{2}\) value for the QPSO-RBF-SVM model was 0.97, indicating that the model fits the data very well and exhibits excellent performance. The high correlation between predicted and actual values indicates not only the accuracy of the model but also the reliability of the model in capturing potential patterns in the data set.  
In order to better illustrate the predictive performance of the model, Figure 7 displays the relative error diagrams comparing the predicted and measured values of the hanger cable force on the test set. It is evident that the maximum error of the hanger cable force is 2.42%, and the overall error falls within 5%. This indicates that the model's prediction accuracy meets the requirements for controlling hanger cable force, and its results strongly support the reliability of the practical application.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Number** &
\begin{tabular}{c} **Actual Measured** \\ **Value (kN)** \\ \end{tabular} & **Projected Value (kN)** & **Relative Error** \\ \hline
1 & 1261.89 & 1268.57 & 0.53\% \\
2 & 1275.78 & 1274.01 & \(-\)0.14\% \\
3 & 1348.07 & 1341.01 & \(-\)0.52\% \\
4 & 1303.29 & 1300.34 & \(-\)0.23\% \\
5 & 1347.37 & 1339.11 & \(-\)0.61\% \\
6 & 1342.61 & 1333.67 & \(-\)0.67\% \\
7 & 1170.76 & 1169.54 & \(-\)0.10\% \\
8 & 1412.81 & 1403.64 & \(-\)0.65\% \\
9 & 1228.15 & 1257.88 & 2.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test set data prediction results.  
Figure 6: Sling rope tension prediction results.
## 3 Results  
### Model Validation  
The comparison between the 8-day ET from MOD16 and that from PML-V2 is presented in Figure 3. Good agreement was found between the two datasets, with the R\({}^{2}\) of 0.86 (\(p<0.01\)). While most of the scattered points fell above the 1:1 line, indicating that PML-V2 ET underestimated ET compared to MOD16, but the distribution of the scatter points was relatively uniform, indicating that the PML-V2 model could better described the seasonal variation characteristics of ET in TGRA [36]. The verification result suggested that at the 95 global flux sites globally (R\({}^{2}\) = 0.72) [39], and it was also slightly better than the performance of PML-V2 ET in northern China [40]. Overall, the validation result confirmed the reliability of using the PML-V2 data product to reveal the spatial and temporal variation of ET in TGRA.  
Figure 2: The water level in the TGD (unit: m) and division of impoundment periods.
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Jufra
#### 3.2.3 True Negatives  
For most of the sites, there were no changes within the buffered areas during the analysis period, and this could be confirmed by visual examination of imagery and through field visits to a sample of these sites. For example, the algorithm found no change at EAMENA-0001515 (a well), which was confirmed in the field in June 2020 (Figure 24). Unfortunately, a subsequent visit in September 2020 after the analysis period found bulldozing.  
Figure 23: EAMENA-0001513 is between a hillslope and a road. Photograph looking from the west, towards the site, by [PERSON] in 2013.  
Figure 24: EAMENA-0001515, a well. Photograph by [PERSON] in June 2020.
## 6 Conclusions and Future Works  
In this study, we proposed an online service-time allocation method that can reduce the energy consumption of a MES while guaranteeing the queuing latency requirement for each class queue. We combined the Lyapunov optimization framework with a deep learning model and devised a lightweight algorithm that can quickly determine an optimal solution at the beginning of each time slot using the observed queue length and predicted workload. Using simulation studies, we verified that the proposed method is superior to conventional methods in reducing the energy consumption of a MES while maintaining the queuing delay below a given threshold value.  
Since our method deals with the service-time allocation problem when a MES is not overloaded, it can complement a MES congestion resolution method and a task migration method. If a MES detects that the average queue length cannot be guaranteed because the task input rate to a MES exceeds the capacity of the MES, it can invoke a MES congestion resolution method to move some tasks to the other MESs or to a cloud server. In addition, when a MES detects that a device moves out of its service area, it invokes a task migration method to move tasks to other MESs.  
As a future work, we plan to extend our resource management method into more complex scenarios where MESs migrate tasks and the backhaul network capacity is limited. To further improve the system performance and QoS requirements of tasks, we will jointly optimize a service-time allocation strategy, a MES congestion resolution policy, and a task migration algorithm.  
Conceptualization, J.P. and Y.L.; methodology, J.P.; software, Y.L.; formal. All authors have read and agreed to the published version of the manuscript.  
The present research has been conducted by the Research Grant of Kwangwoon University in 2021. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1F1A1047113).  
The authors declare no conflict of interest.
# Deflection Modeling and Curvature Manipulation of a Variable-Stiffness Flexible Needle
## 2 Materials and Methods
### Simulation of the Effect of Styled Extension Length on Deflection  
Fixing the styled diameter to 0.4 mm, the deflections of the styled at different extension lengths are described in Figure 6. To verify that \(w\propto L_{s}^{3}\), a polynomial fit with a degree of 3 (the equation is \(a*x^{3}+b\)) is selected and the fitted curves are \(y_{1}=0.001583x^{3}+8.05,R^{2}=0.9977\) (Structure 1), \(y_{2}=0.0004836x^{3}+7.828,R^{2}=0.9881\) (Structure 2), and \(y_{3}=0.0004906x^{3}+7.831,R^{2}=0.9957\) (Structure 3).  
Obviously, Structure 1, shown in Figure 6a, has a larger deflection and is most affected by the extension length. The tip deflections of the three structures in Figure 6d are proportional to the cube of the styled extension length, indicating that curvature can be controlled by adjusting the length of the styled extending out of the tube.  
The theoretical deflection of Structure 1 can be derived from Equation (15) and is plotted in Figure 6d. It should be noted that in Equation (15), the other parameters, such as Young's modulus, total length, etc., are consistent with the simulation settings in Table 2 except for the external force \(F\), which is the transverse component of the simulated load of 0.3 N, i.e., \(F=0.3\cos(\pi/6)\). The fitted curve for the theoretical deflection is \(y_{t}=0.001631x^{3}+7.925,R^{2}=0.985\). The theoretical and simulated deflections of Structure 1 exhibit good agreement when the axial force \(Q\) is neglected. At an extension length of 15 mm, the relative error is 4.9%. The error may be due to the effect of the mesh quality and the neglect of the lateral force \(Q\).  
Figures 5d and 6d show that both the stylet diameter and length influence the deflection. However, the small range of variation in stylet diameter results in limited control over the deflection. Once the needle is manufactured, its diameter is fixed and difficult to change. In contrast, the effect of the stylet extension length on the tip deflection is more significant, and the length of the stylet protruding from the tube is adjustable. Consequently, the solution of adjusting the stylet extension length is adopted and used to design the flexible needle with variable stiffness. Structure 1, whose deflection is most strongly affected by the stylet extension length, offers greater maneuverability and is thus selected.
## 5 Experimental Results
### Reliability after Coverage Hole Repair  
The number of transmitted packets for the proposed MiA-CODER mechanism after coverage hole repair is compared with other existing approaches with respect to the same simulation time. By comparing Figures 10 and 13 it is clear that the number of transmitted packets of the proposed MiA-CODER mechanism is increased after the coverage hole repair, thereby resulting in improved reliability. This is due to the implementation of coverage hole detection and repair in which the coverage hole is detected by the CH using the virtual sector-based hole detection (ViSHD) protocol in which the coverage holes both within the cluster and among the clusters are detected. Furthermore, the coverage hole repair is carried out by the BS using the MA-SARSA algorithm in which the coverage hole is repaired by selecting an optimal mobile node nearer to the hole and replacing it in the hole region. By doing so, the efficient transmission of data is achieved, thereby resulting in improved QoSensing in the network. The lack of efficient coverage hole detection and repair strategies affects the reliability of the existing approaches.  
The number of received packets of the proposed MiA-CODER mechanism after coverage hole repair is compared with other existing works with respect to the simulation time. From Figures 11 and 14 it is clear that the number of received packets increased after the coverage hole repair resulting in increased reliability to the appro  
Figure 12: Number of control packets (before coverage hole repair) vs. simulation time.  
Figure 13: Number of transmitted packets (after coverage hole repair) vs. simulation time.  
detection of coverage holes and repair of coverage holes in an efficient manner resulting in effective reception of data, thereby achieving improved QoSensing.  
The comparison of the proposed MiA-CODER mechanism with various existing methods after coverage hole repair with respect to the simulation time is provided in Figure 15. It can be said that the number of control packets increases after the coverage hole repair which means that the execution of coverage hole repair increases the reliability of the proposed mechanism. The increase in the number of control packets is due to the efficient coverage hole detection and repair carried out by the proposed mechanism. The detection of coverage holes both inside the cluster and between the clusters improves the accuracy of the hole detection process which helps in the recovery of the coverage holes through an effective hole repair mechanism. Hence, the reliability of the proposed mechanism is higher than the existing approaches.
# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
## 2 Previous Research
### Selecting a Bridge Exterior Image Acquisition Target and Collecting Damage Images  
To develop a technique to automatically detect damage in bridges, it is necessary to apply supervised learning to analyze the damage. Therefore, images were taken of the Yi Sunshin Bridge in Yeosu, Jeollanam-do, a facility that is expected to have relatively high damage due to high traffic volume. According to the Special Act on Safety Management of Facilities, facilities rated C or lower should be subjected to a regular inspection at least once every half year, a detailed inspection at least once every two years, and a detailed safety diagnosis once every five years. The visually inspected items in these facilities include cracks, concrete delamination/decay, exposed rebar, water leakage, and efflorescence. In general, cracks 0.3 mm or more and less than 0.5 mm should be less than 10% of the total cracks, and non-cracks are classified as Grade C if the surface damage area is less than 2-10%.  
A total of 33,924 images were collected through drone photography, and images containing various and complex damage were acquired. Table 1 lists the quantity of each damage item among the acquired image data. Noticeably, the frequency of leakage/whitening, scaling/spalling, and corrosion is relatively low compared to that of cracks. For deep learning analysis, the larger the number of training objects, the higher the classification detection accuracy. Table 1 presents the quantity of each damage category in the acquired image data. The occurrences of leakage/whitening, scaling/spalling, and corrosion are relatively low when compared to the frequency of cracks [2]. Each class has been meticulously designed for separate evaluation, ensuring that the comparison of each class among the BIRD U-Net, U-Net, and ResNet algorithms remains valid. Therefore,  
Figure 2: A&R-topped drone.  
we performed augmentation of up-down symmetry (FLIP_TOP_BOTTOM) and left-right symmetry (FLIP_LEFT_RIGHT) in this study to augment the 33,924 images collected for each damage item, and 135,696 images were ultimately used for analysis.  
The reason for the scarcity of no-damage data is because damage presents itself in various shapes, sizes, and depths. This diversity results in the ability to capture damage instances multiple times from different angles, under different lighting conditions, and in different situations. In contrast, images without damage tend to have a relatively consistent appearance, enabling representativeness with a relatively small number of images. In this study, we limited the representation of no-damage data because our focus was primarily on characterizing the different types of damage.