esa-sceva/satcom-chunk-collection huggingface dataset:
# Increasing the Bioactive Compound Content of Olive Oil by Acidification of Olive Paste
## 4 Discussion  
### Extraction Efficiency and Water pH  
The statistical analysis presented in Table 2 demonstrated that neither the type of acid nor its dosage significantly influenced extraction efficiency (\(p\) = 0.4, \(\alpha\) = 0.05). Although the mean extraction efficiency for pastes treated with 1% ascorbic acid (68.83%) was slightly elevated compared to the control (67.81%), this observed difference did not achieve statistical significance. Consequently, the results indicate that the application of acids, within the tested parameters, did not produce a statistically significant change in olive paste extraction efficiency. These results confirm that the acids did not have direct technological effects. The primary objective of this study was to enhance \(\beta\)-_glucosidase_ activity, thereby increasing bioactive phenolic compound concentrations, by utilizing the enzyme's known preference for acidic pH and relatively low temperatures.  
As for pH in Table 2, the pH of the vegetation waters from extracted pastes exhibited a decrease with increasing acid concentration. Notably, the highest acid concentrations resulted in the most significant pH reductions. It is important to note that acid concentrations were expressed as a percentage of paste weight rather than as molar ratios. Because the molecular masses of the acids differ--acetic acid (60.05 g/mol), ascorbic acid (176.12 g/mol), and citric acid (192.12 g/mol)--proportionally more acetic acid was added than ascorbic or citric acid at the same percentage. This, combined with the differences in pKa values (acetic acid: 4.74, ascorbic acid: 4.10, citric acid: 3.15), resulted in water samples from acetic acid treatments being nearly three times more concentrated than those obtained with ascorbic or citric acid at the same weight percentage. Considering these factors, the pH values in Table 2 are justified. Among all treatments, pastes treated with 1% ascorbic acid exhibited the smallest reduction in water pH, decreasing from 4.95 in the control (no acid added) to 4.33 with 1% ascorbic acid.  
Consequently, the question arises whether the pH reduction in the pastes, induced by acid addition, may shift the equilibrium of certain phenolic compounds, responsible for the inherent acidity of the control pastes, towards their molecular form, thus enhancing their solubility in the oil phase. These authors posit that the phenolic compound group is diverse, with phenolic acids being prominent constituents that contribute to the inherent acidity of the control pastes. In contrast, secoiridoids are not characterized by high acidity. Their principal functional groups, alcohols, exhibit significantly lower acidity (pKa secoiridoids \(\gg\) pKa phenolic acids). Within olive paste, the dissociation of secoiridoids is further suppressed by the presence of phenolic acids and the prevailing pH, leading to their predominantly molecular form. The subsequent sections will elaborate on the influence of pH.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 2 Related Work
### Update Object Appearance Template  
Most trackers use simple linear interpolation to update the template in every frame. One problem with this is that the appearance information of the object decreases exponentially over time, causing the appearance information of the object to focus only on the current few frames. As the object can change in appearance due to deformation, rapid movement, or occlusion, and very little information is retained about the historical appearance of the object, this can lead to the misidentification of the object, which can result in assigning a wrong ID to the object. [PERSON] [24] proposed a conservative update strategy that does not consider the reliability of tracking objects to reduce the influence of drift under background interference. In the tracking process, the whole sequence is decomposed into multiple long periods, and each long period is subdivided into multiple small periods. Multiple trackers are established instead of one tracker, and the number of each tracker is equal to the number of small-time periods in each large period. Each tracker has a specific update strategy, but common updates must be executed frame by frame within the previous small cycle of each large tracker, so each tracker stops updating after a certain small cycle and then restarts. [PERSON] [25] employed a Long Short-Term Memory (LSTM) to estimate the current template by storing previous templates in memory during online tracking, which is a computationally expensive and rather complex system. [PERSON] [26] also used a template memory, but he uses reinforcement learning to select one of the stored templates. This method fails to accumulate information from multiple frames.
# Popularity-Aware Service Provisioning Framework in Cloud Environment
## 5 Evaluation Results
### Effect of \(\lambda\)  
Figure 5a,b show the effect of the unit incoming rate \(\lambda\) on the average OPEX of the service provider \(\zeta_{O}\) and the average response time of the service \(\psi_{R}\), respectively. A higher unit incoming rate indicates that more resources should be requested to maintain a sufficiently low response time. However, since the comparison schemes did not consider  
Figure 4: Effect of the response time requirement of the service. (**a**) Average OPEX of the service provider. (**b**) Average response time of the service.  
the incoming rate, their OPEXs remained constant regardless of \(\lambda\) (see Figure 5a). On the other hand, the PASPF requested more resources when a higher unit incoming rate was given. Thus, its OPEX slightly increased with the increase in the unit incoming rate. However, the average response time of the PASPF can be maintained below the response time requirement \(\theta_{R}\) (see Figure 5b). Meanwhile, since MINIMUM requests insufficient resources, it had the lowest OPEX (see Figure 5a), but its average response time significantly increased as the unit incoming rate increased (see Figure 5b). Even though MAXIMUM can achieve the lowest average response time of the service (see Figure 5b), its OPEX is highest (see Figure 5a). In addition, the average response time of the NON-POP could be maintained below the response time requirement (see Figure 5b), but its OPEX was higher than those of the PASPF and MINIMUM (see Figure 5a). This means that the NON-POP always requests an excessive amount of resources regardless of popularity.  
Figure 5: Effect of the unit incoming rate. (**a**) Average OPEX of the service provider. (**b**) Average response time of the service.
## 5 Results and Discussion
### Equivalent Stress and Material Damage Corresponding to the Five States
#### 5.1.2 Material Damage Analysis  
To analyze the influence of the material damage of the flanging lip deformation, the simulations were carried out on the material damage of the flanging lip in the latter four states. Figure 7 shows the damage degree of the flanging lip in the latter four states.  
Figure 7a shows the damage contours in the limit state of \"State 2\". At this time, the flanging lip has no surface damage or only minor surface damage, and the material damage can be ignored. Figure 7b shows the damage contours in the limit state of \"State 3\".  
Figure 7: Damage degree of the flanging lip in the latter four states: (**a**) Material damage in “State 2”; (**b**) Material damage in “State 3”; (**c**) Material damage in “State 4”; (**d**) Material damage in “State 5”.  
At this time, obvious surface damage appears on the flanging lip with damage values ranging from 0.14-0.28. The damage occurs at a depth of about 1/8-1/6 of the flanging lip thickness in the depth direction; this state has no significant impact on the practical application. The stress and deformation degree should be controlled so that the damage depth does not exceed 1/4 of the flanging lip thickness, and then requirements can be met. Figure 7c shows the damage contours in the limit state of \"State 4\". At this time, although the fit between the flanging lip and the housing chamfer is stable, the damage value of the flanging lip is larger. The material on the outer edge of the flanging lip has been penetrated in the area where damage values is about 0.5, and the damage value of the flanging lip reaches 0.86. Figure 7d shows the damage contours in the limit state of \"State 5\". At this time, the material on the outer edge of the flanging lip is excessively squeezed, and most of its damage value is above 0.85. The quality defects of this state cannot meet the practical application.  
The analysis of the different deformation degrees shows that the loading control parameters and the loading control method of the roller staking process should be comprehensively considered. A better staking quality can be obtained by making the stress-strain and deformation degree of the flanging lip in \"State 3\" as good as possible, i.e., quality inspection requirements of push-out load, the fit between the flanging lip and the housing chamfer, and the starting torque are all met.
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCA Models for Different Power Systems  
#### 3.2.1 The LCA of a Diesel-Powered Ship  
Before analysing the considered alternative power systems, the LCA of the power system currently used in the fishing sector (diesel-powered ship) needs to be performed. It serves as a baseline scenario in the final comparison of the environmental impact of possible fishing fuels. The energy needs of the diesel-powered ship are presented in Section 3.1.  
The processes included in the LCA of a diesel-powered ship are shown in Figure 6.  
The environmental impact of diesel engines is included within the M phase, which refers to the manufacturing process of the materials that the engine consists of. As presented by [PERSON] et al. [62], the weight of each material that serves as an input to GREET 2022 is calculated by multiplying the materials' weight ratios by the weight of the engine \(m\) (t), calculated by the following equation [62]:  
\[m=\frac{2\cdot P_{ave}}{450}. \tag{10}\]  
Figure 6: Processes included in the LCA of a diesel-powered ship.  
The WTT phase of diesel includes crude oil extraction in the Middle East and its transportation to Croatia via tanker for 4000 km. Once the oil is imported at the Croatian terminal, it is brought to the refinery in Rijeka through the oil pipeline. At the refinery, the oil is refined. The produced diesel is then distributed to the pump in the port by tank truck for 100 km.  
The TTW phase refers to the combustion of diesel in the marine engine. Depending on their emission factor (Table 3), these emissions are calculated with Equation (1).
## 3 Results and Analysis
### Overlapping-Droplet-Classification-Test Validation and Analysis
#### 3.1.2 Comparative Study of Ablation Before and After Model Optimization  
The confusion matrix is shown in Figure 11. In this study, more than 3000 overlapping fog droplets were classified using an optimised multi-classification model with an accuracy of 98%, which effectively meets the need for subsequent segmentation. This approach not only improves the classification performance of the model, but also proves its feasibility and effectiveness in practical application scenarios.  
Accuracy, precision, recall, and F1 score are used as the evaluation indexes of the classification model to analyse the performance comparison of the model before and after Bayesian optimisation of the tuning parameters, as shown in Figure 12. The accuracy, precision, recall, and F1 score of the model are significantly enhanced by Bayesian optimisation of the hyperparameters, which comprehensively enhances the classification performance.  
Figure 10: Feature Analysis and Optimization Algorithm Performance Evaluation. (**a**) Feature Importance Ranking (XGBoost-based); (**b**) Bayesian Iterative Optimization Process.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Hyperparameters** & **Initial Value** & **Search Space** & **Tuning Results** \\ \hline colsample\_bytree & 0.9 & [0.4, 1.0] & 0.5353 \\ n\_estimators & 100 & [1, 70, 150] & 123 \\ learning\_rate & 0.1 & [0.005, 0.3] & 0.0896 \\ gamma & 3.8 & [0, 8] & 0.0776 \\ max\_depth & 6 & [2, 15, 1] & 11 \\ alpha & 0 & [0, 5] & 0.3580 \\ lambda & 1 & [0, 5] & 3.8116 \\ subsample & 0.9 & [0.4, 1.0] & 0.6297 \\ \hline \end{tabular}
\end{table}
Table 5: Adjustment of model hyperparameters.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 5 Results and Discussion  
In order to exhibit the efficacy and fulfillment of the designed antenna, a prototype was developed, and experimental measurements were carried out. Figure 10 shows the fabricated triple band-notched SWB antenna in reference to the above-mentioned parameters in Table 1. An Anritsu MS46122B vector network analyzer (VNA) was used to measure the antenna parameters. Even though the designed antenna functioned well for the frequency spectrum of 1.67-47.5 GHz, the empirical results were recorded only up to 20 GHz because of the higher frequency constraints of the available VNA.  
Both simulated and measured VSWR results of the presented antenna are provided in Figure 11. Simulated and measured result showed that the designed antenna operated at 1.67-47.5 GHz with VSWR below 2, except the triple notched bands at 1.8-2.2 GHz, 4-7.2 GHz, and 9.8-10.4 GHz for rejecting the AWS and C and X band signals, respectively.
# Impulse Signal Detection for Bearing Fault Diagnosis via Residual-Variational Mode Decomposition
## 3 Simulation and Case Study
### Case I
The two results by RVMD with the traditional definition of kurtosis indicate the redefined kurtosis can evaluate periodic impulses more accurately. And all these results for the inner and outer race fault signals verify the effectiveness of our method and highlight its advantage. Figure 8: Results by the RVMD for the signals from MFPT: (**a,b**) correspond to the first and second sub-modes of the inner race fault bearing; (**c,d**) correspond to the first and second sub-modes of the outer race fault bearing. Figure 9: SES of the results from RVMD: (**a**) corresponds to the result shown in Figure 8b; (**b**) corresponds to the result shown in Figure 8c. Figure 11: Results by ICF-VMD for the signals from MFPT: (**a**,**b**) correspond to the TDW and SES of the inner race fault bearing; (**c**,**d**) correspond to the TDW and SES of the outer race fault bearing. Figure 12: Results by RVMD with the traditional definition of kurtosis for the signals from MFPT: SES for (**a**) the inner and (**b**) outer race fault signals. Figure 10: Results by FK for the signals from MFPT: (**a**,**b**) correspond to the kurtogram and SES of the inner race fault bearing; (**c**,**d**) correspond to the kurtogram and SES of the outer race fault bearing.
## 4 Application to an Additive Manufacturing Benchmark Test
### Mesh Convergence Study  
A mesh convergence study was first conducted to evaluate the effect of the mesh size of the 3D finite element model on the simulation results. We selected three different mesh sizes, compared their results with the experimental results, and concluded that a mesh of size 0.3 mm was best suited for further studies. The comparison study was conducted using the residual strain results at z = 9.536 mm. In Figure 8, the numerical results are similar to the experimental results. Table 2 presents the decrease in error norm between the experimental and numerical results with mesh size. For the error analysis, we used the \(L^{2}\) relative error norm which is defined as  
\[L^{2}=\sqrt{\frac{\sum_{i=1}^{i=n}(u_{E}(i)-u_{s}(i))^{2}}{\sum_{i=1}^{i=n}u_{ E}(i)^{2}}} \tag{24}\]  
in which \(u_{E}\) and \(u_{s}\) are the experimental and simulation data, respectively, whereas the total number of data is represented by \(n\). Each simulation was performed on 32 CPUs that each had an Intel E5-2683 v4 processor. From Table 2, it is clear that the error decreases with the decrease in mesh size, but at the same time, the computation cost increases. Therefore,  
Figure 7: Material properties of IN625 (a Nickel alloy) [43; 49].  
we chose a 0.3 mm mesh for our simulations, as it provides better results than the 0.4 mm mesh size, while it is much less computationally expensive than the 0.2 mm mesh size.
# Challenges of Engineering Skillsets Essential for Driving Circularity of Smart Cities
## 3 Materials and Methods
### Methods  
The qualitative data underwent a thematic analysis, which is the process of identifying and analyzing a pattern or effect in qualitative data [50]. Our critical literature review also reveals that the semi-structured interview is the most suitable for our research goal. Finding underlying themes or patterns in the data is crucial to identifying the knowledge gap: what skillset is needed for the implementation of CE in a smart city setting, and what skillset will produce the best results in practice? These can be deduced by the other questions in the questionnaire. Two of the biggest barriers to CE include difficulty setting up an effective circular supply chain and challenges with customer behavior and product redesign [51]. Three questions that are particularly important in resolving these challenges are 'How are privacy and security concerns due to smart city technologies in your work addressed?', 'Is proficiency in another language preferable for your work?', and 'How do you involve various stakeholders in your smart city project(s)?'. These three questions are indicators of skillsets that are often overlooked: first, the privacy concerns would indicate hard engineering skills such as network system knowledge, online security, and continuous learning. These skills are vital in the design of a circular economy and its technologies, meaning challenges with redesign will involve problem solving and critical thinking; therefore, understanding what steps industry experts take to combat security concerns will indicate the steps needed to tackle such a big challenge in the CE discipline. Language proficiency is a hard skill that is often overlooked because it is a common language skill in global science [52]; so, proficiency in other languages will likely indicate better soft skills, such as group communication and scientific discussion. Routine transparency and communication with stakeholders (involving customers) will also help to combat one of the biggest issues faced, namely, the challenges related to customer behavior. A company's proactive efforts to communicate with customers lead to a positive relationship between them [44]. Observing the communication skills displayed by industry experts with their project stakeholders will improve relationships when implementing CE in smart city projects, meaning fewer conflicts and issues will arise. It will also help to inform the stakeholders of the intricacies of the projects being implemented, which will help to maximize the project's efficiency given that a lack of knowledge is one of the main barriers to CE implementation [40].
# Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar
## 3 Results and Analysis
### Thermal Analysis  
The above-mentioned test results indicate that the adding of 0.6% PVA had a considerable effect on increasing the mechanical property and reducing the water absorption of cement mortar, while the excessive incorporation of 2.0% PVA hardly affected the properties of cement mortar. To understand the mechanism behind these opposite levels of effectiveness of PVA on cement mortar, the microstructure of cement mortar incorporating of 0.6% and 2.0% PVA was studied, for comparison, the property of cement mortar without PVA was also investigated.  
Firstly, the thermal decomposition performance of cement mortars containing 0%, 0.6%, and 2.0% PVA was investigated by using differential scanning thermal analysis (DSC), and the results are shown in Figure 9a. There are two major endothermic peaks in the three curves, as shown in Figure 9a. The first endoscopic endothermic peak is located at 95 \({}^{\circ}\)C, which is caused by the evaporation of adsorbed water. The second endoscopic endothermic peak is located at 430-450 \({}^{\circ}\)C, which is caused by the decomposition of Ca(OH)\({}_{2}\). The two absorption peaks of the three curves are integrated in Figure 9b to assess the effect of PVA on the thermal decomposition of cement mortar. Comparing the first endothermic peaks of all DSC curves, it can be found that PCM0 without PVA is 18.245 J/g, and the incorporating 0.6% PVA increased the endothermic enthalpy values by 13.8%. However, the adding of 2.0% PVA reduced the first absorption peak by 9.3% as compared to PCM0 without PVA. This result indicates that the incorporation of 0.6% PVA might accelerate the hydration rate of the cement matrix, while the addition of 2.0% of PVA delay the hydration rate. Comparing the second absorption peak of the three samples, it can be found that PCM0 had the highest value of 22.145 J/g, followed by PCM2 having a peak value of 19.51J/g, and PCM4 had the lowest value of 17.243 J/g. Figure 9b shows that the first endothermic peak corresponds to the evaporation of adsorbed and interlayer water of PCM2 was much higher than that of PCM0, while the second peak corresponds to the decomposition of Ca(OH)\({}_{2}\). This conflicting result may be due to the -OH bonds in PVA chain react with the -OH bonds of Ca(OH)\({}_{2}\), leading to the reduction of the content of Ca(OH)\({}_{2}\), which is further studied by using FTIR and reported in the next section.  
Figure 8: Capillary water absorption of cement mortar with different PVA contents (0–24 h).