esa-sceva/satcom-chunk-collection huggingface dataset:
# Biodegradable Polymeric Architectures via Reversible Deactivation Radical Polymerizations
## 2 Biodegradable Polymeric Architectures
### Micelles, Vesicles and Capsules
#### 2.4.1 Micelles
In addition to RAFT polymerization, ATRP incorporating with ROP were also employed to synthesize a new class of supramolecular and biomimetic glycopolymer/poly(\(\varepsilon\)-caprolactone)-based polypseudorotaxane/glycopolymer triblock-copolymers. The polypseudorotaxane block was prepared by an inclusion reaction between biodegradable poly(\(\varepsilon\)-caprolactone) and \(\alpha\)-cyclodextrinrin. These triblock biohybrids were then utilized to fabricate micelles or vesicles that possess hydrophilic glycopolymer shell and oligosaccharides the threaded polypseudorotaxane core [99]. Likewise, quite similar biodegradable amphiphilic block copolymers with poly(\(\gamma\)-methyl-\(\varepsilon\)-caprolactone) (PmL), o-nitrobenzyl (ONB) and polyacrylic acid (PAA) blocks, and the same synthetic methodologies have also been prepared by [PERSON] et al. for fabrication of micelles as well. Furthermore, the as-fabricated micelles and vesicles are also photoresponsive due to the presence of a photodegradable OCN linker as a junction point between hydrophilic and hydrophobic chains [100]. Acetal is a pH sensitive group that is stable at pH 7 and prone to go hydrolysis at mild acidic pH of 4.0-5.0, with a half-life of 6.5 h, respectively. [PERSON] and coworkers [101] incorporated acetal groups into block copolymers comprising of a novel acid-labile polycarbonate and poly(ethylene glycol) (PEG) to generate pH-responsive biodegradable micelles as potential smart nano-vehicles for targeted delivery of anticancer drugs. Biodegradable cross-linked micelles were also prepared with a stimulus-responsive triblock copolymer synthesized via a bifunctional ATRP initiator containing intra-disulfide linkage [102]. When the micelles were prepared by stimulus-responsive copolymer and  
Figure 9: (**a**) Synthesis of triblock copolymer P(MEO2 MA-co-OEGMA)-\(b\)-PCL-\(b\)-P(MEO2 MA-co-OEGMA); (**b**) Schematic representation of the self-assembled thermo-sensitive core–shell micelles for temperature-stimulated drug release and gelation of tPBs. Copyright 2018, Elsevier; (**c**) Functionalized micelles and further attachment of fluorophores and targeting tripeptides. self-assembled on mica surface, pH manipulated switchable surface was achieved [103]. NMRP in combination with ROP were also utilized to prepare poly(\(\varepsilon\)-caprolactone-\(b\)-4-vinylpyridine) for preparation of micelles. As the so-prepared micelle impregnated a cationic core it can mediate the transportation of AuCl\({}_{4}\)\({}^{-}\) anions from aqueous phase to the micelle core to afford micelle protected Au nanoparticles after reduction with NaBH\({}_{4}\)[104].
## 4 Framework for Reliability Analysis Incorporating Noisy Response Prediction
### Implementation Details of the Proposed Framework  
The algorithm and implementation details of the proposed framework are outlined as follows and illustrated in Figure 4:Step 1. Construct the initial DoE: Generate \(n_{0}\) samples \(\mathbf{X}=\left\{\mathbf{x}^{(1)},...,\mathbf{x}^{(n_{0})}\right\}^{T}\) using Latin hypercube sampling (LHS) in the standard normal space.  
Step 2. Evaluate the limit-state function responses: Evaluate the limit-state function on the sample points \(\boldsymbol{\mathcal{Y}}=\left\{g(\mathbf{x}^{(0)}),...,g(\mathbf{x}^{(n_{0}) })\right\}^{T}\).  
Step 3. Construct a Kriging model: Establish a Kriging model using the initial DoE \(\left\{\mathbf{X},\boldsymbol{\mathcal{Y}}\right\}\)  
Step 4. Perform reliability analysis: Utilize the subset simulation method to estimate the failure probability \(P_{f}\) with the current Kriging model.  
Step 5. Conduct hybrid active learning process: Generate a candidate pool and select sample points within the \(\gamma\)-quantile of \(\left|\mu_{\tilde{G}}(d)\right|\). Subsequently, identify the farthest points from the selected sample points to the current DoE points. The Kriging model can be updated either before moving to the next step or after completing multiple enrichment processes. Proceed to the next step to identify multiple enrichment points. Utilize the \(K\)-means clustering algorithm to determine centroids of clusters. Finally, identify candidate points that are closest to each centroid of the cluster.  
Step 6. Complete enrichment process: Based on identified points \(\hat{\mathbf{x}}\) and the corresponding limit-state function responses \(\boldsymbol{\mathcal{Y}}(\hat{\mathbf{x}})=\left\{g(\hat{\mathbf{x}}^{(0)}),...,g(\hat{\mathbf{x}}^{(K)})\right\}^{T}\), update the experimental design of the Kriging model.  
Step 7. Check if the convergence condition, such as stability in the reliability index or reaching the maximum number of DoE points added, is satisfied. If the condition is met, conclude the process with the estimated failure probability; otherwise, return to Step 5.  
Figure 4: Flowchart of the HAK algorithm.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 8 Performance Analysis  
In this section, we present a detailed analysis of the performance of the proposed secure homomorphic K-means clustering protocol using the Paillier cryptosystem. The computational and communication efficiency of the protocol is evaluated. We use four datasets for our experiments, namely wine data, breast cancer data, ionospheric data, and yeast data. The final experimental results are shown in Figures 2-5.
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 3 Problem Statement
### Assumed Temperature Sensor Fault Conditions  
The following is an explanation of the temperature sensor fault situation assumed in this study. As it is difficult to intentionally cause a sensor fault, the fault is emulated using the measured values of a normal sensor. Various types of sensor faults have been  
Figure 1: Experimental system: (**a**) external view. (**b**) Schematic diagram of the microreactor system.  
reported [18]. In this study, we consider the case where the sensor deviates from its predetermined position due to a situation such as the sensor being unglued, and shows a measured value that is different from the original temperature of the controlled object. This situation is shown in Figure 2. The fault is represented as a step-like fault element \(d_{w}\) added to the original temperature \(y\) for the measured value \(y_{f}\), as shown in Equations (1) and (2). Note that \(A\) is the amplitude of the fault signal, \(B\) is the time constant of the fault signal, and \(T_{F}\) is the fault time. The reason for this is explained below. When the position of the sensor suddenly changes as shown in Figure 2b, the ambient temperature of the sensor changes. The Fourier law and thermal radiation create a thermal gradient in the surrounding area. Therefore, a step-like offset change generally occurs while the sensor is moving. Step-like changes also occur due to the thermal inertia of the sensor itself. In temperature, where heat transfer and heat conduction are dominant compared to thermal radiation, the offset change during the fault is expected to be a first-order delay due to the thermal inertia of the sensor. Only fault patterns with such offsets will be considered. The original measured value \(y\) is not used in the experiment and \(y_{f}\) is used in all control systems.  
\[y_{f}=y+d_{w} \tag{1}\]  
\[\left\{\begin{array}{ll}d_{w}=0&(T<T_{F})\\ d_{w}=A(1-\exp(-B(T-T_{F})))&(T\geq T_{F})\end{array}\right. \tag{2}\]  
An example of the normal and faulty measured deviation at that time is shown in Figure 2.
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 4 Conclusions
**Nomenclature**  
ANN Artificial neural network  
CPT Conditional probability table  
DAG Directed acyclic graph  
DEF Efficiency degradation of fan  
DEHC Efficiency degradation of high-pressure compressor  
DEHT Efficiency degradation of high-pressure turbine  
DEKF Extended Kalman filter with the data buffer bank  
DELC Efficiency degradation of low-pressure compressor  
DELT Efficiency degradation of low-pressure turbine  
DGF Flow rate degradation of fan  
DGHC Flow rate degradation of high-pressure compressor  
DGHT Flow rate degradation of high-pressure turbine  
DGLC Flow rate degradation of low-pressure compressor  
DGLT Flow rate degradation of low-pressure turbine  
D-S Dempster-Shafer  
GPA Gas path analysis  
HBN Hybrid Bayesian networks  
HPC High-pressure compressor  
HPT High-pressure turbine  
KF Kalman filter  
LPC Low-pressure compressor  
LPT Low-pressure turbine  
LST-SATM-Net Lightweight Spatial-Temporal Model Fusion Self-Attention Mechanism  
\(N_{1}\) Low-pressure rotor speed  
\(N_{2}\) high-pressure rotor speed  
\(pt_{1}\) Air inlet total pressure  
\(pt_{13}\) Fan outlet total pressure  
\(pt_{25}\) High-pressure compressor inlet pressure  
\(pt_{3}\) High-pressure compressor outlet pressure  
\(pt_{45}\) Low-pressure turbine inlet pressure  
SDEKF Self-tuning extended Kalman filter with the data buffer bank  
SVM Support vector machine  
\(T_{1}\) Air inlet temperature  
\(T_{125}\) Fan outlet total temperature  
\(T_{25}\) High-pressure compressor inlet temperature
## 1 Introduction
However, a few studies deal with the application of natural additives or byproducts rich in antioxidants as free radical scavengers to protect bituminous materials from oxidation phenomena [18; 19; 20]. The antioxidant properties of many raw materials borrowed from renewable natural resources have yet to be tested such as, e.g., the polyunsaturated fatty components of natural phospholipids. The application of cheap and environmentally friendly anti-aging additives to virgin bitumen would enjoy the double advantage of using sustainable and renewable compounds and at same time reducing the carbon footprint of the products of asphalt industries according to the circular economy's recommendations. The objective of the present work is to investigate whether the addition to bitumen of antioxidant agents obtained by natural resources improves its resistance towards short artificial aging by Rolling Thin Film Oven Test (RTFOT). The research design is accomplished by investigating the effects of the aging process on both the mechanical response of binders by running Dynamic Shear Rheology (DSR) tests, and the free radical content detected by Electron Paramagnetic Resonance (EPR) spectroscopy on asphaltene and maltene bituminous fractions. The experimental approach is sketched in the flowchartof Figure 1. Bitumen manifests EPR spectra due to organic radicals [21; 22; 23] the signal shape of which is susceptible to changes in the oxidative state, and vanadyl ions VO\({}^{2+}\)[24] associated with porphyrin species, which instead appear to be unaffected by oxidative treatments. Here we will focus our attention on the two most representative components present in the bitumen, namely, asphaltenes and maltenes [25]. The former are macromolecular compounds, comprising polyaromatic nuclei linked by aliphatic chains or rings of various lengths and sometimes by functional groups [26]. A peculiar characteristic of petroleum asphaltenes is the presence of stable free radicals, well detectable by EPR, associated with a non-localized \(\pi\) system of electrons stabilized by resonance. Maltene represents the bitumen fraction soluble in n-pentane, which in turn can be split into saturates, aromatics (also apolar aromatics), and resins (also polar aromatics) [27]. The results of the present work show that it is possible to draw useful anti-aging additives from renewable sources capable to reduce the age-hardening effect and concomitantly protect bitumen against oxidative aging.
## 4 Result  
Each AI method used 540 datapoints for training and 270 for testing. Then, the best parameters were used for classification. We used 270 datapoints for the case study and to compare the accuracies of the different AI methods. The results are shown in Table 2. Table 2 shows a comparison of the accuracy of fault classification by different AI methods at the sending end and receiving end.  
Figure 7: Training process of the support vector machine (SVM).  
Table 2 shows that PNN and SVM give the most accurate results, with 100% accuracy at both the sending and receiving ends. BPNN is the least accurate with an accuracy of 98.88% at the sending end. The sending end of BPNN has an error in the case of double line to ground fault (DLG) at 30-40% of the length of the transmission line and single line to ground fault at 40% of the length of the transmission line. BPNN shows errors because it detects DLG as a line-to-line (LL) and the single line to ground fault (SLG) is identified as a DLG. We also analyzed an additional case by varying the ground resistivity (1-100 ohm) in six cases of SLG and varying the load (70-120%) in ten cases of SLG and LL, making a total of sixteen cases. These additional cases were analyzed using the three AI models to test accuracy. Table 3 shows the percentage of accurate results in the additional cases.  
PNN was the least accurate, with an accuracy of 68.75% at the sending and receiving ends. PNN showed errors in the case of high load of LL because it identified the LL as SLG at both ends. BPNN showed an accuracy of 68.75% at the sending side and 100% at the receiving end. BPNN showed errors in the case of the high load of LL, which was identified as DLG. The SVM showed the most accurate results for the additional cases, with an accuracy of 100% at both sides. Therefore, the SVM is suitable for fault identification.
## 3 Review of Methods
### Ozone Treatment of Herbs
#### 3.3.1 Application of Ozone  
Currently, the use of ozone is considered the most effective method of the chemical decontamination of herbs. This technology is relatively young [34]. Ozone has a strong bactericidal effect, to which both the Gram-positive (_Listeria monocytogenes_ and _Staphylococcus aureus_) and the Gram-negative (_Escherichia coli_ and _Salmonella_), as well as viruses and yeasts are susceptible [38; 39; 40; 41]. Fungus is slightly less sensitive to ozone than bacteria, and bacterial spores are more resistant than their vegetative cells [42]. Ozone has a high antioxidant potential and is therefore increasingly used in the food industry, among others, during the production and processing of vegetables and fruit [43; 44; 45; 46; 47]. It is also used to treat water (drinking and swimming pool water), as well as to sanitize equipment and surfaces, to disinfect rooms, to combat algae, etc. The growing use of ozone is also associated with the production of ozonators for home use. The recommended procedures for the herb decontamination use ozone dissolved in water; a condition for effective purification of the plant raw material is its good contact with this compound.  
In Poland, ozone is treated as an allowed means of decontamination of herbal raw materials. Compared to other methods of decontamination of herbal raw materials, the use of ozonators for this purpose seems to be the most advantageous and prospective solution. The leading herb producers in Poland turn to it, among others, the companies such as Dary Natury (Koryciny, Poland) and Herbapol (Wroclaw, Poland).  
There is also hybrid technologies of herb hygiization, which combine selected features of the methods mentioned above. Less than 2 years ago, the first in Poland production line for hygiization of seasonings and herbs in a continuous process was launched. This means the introduction of new standards of hygiization and herbal production in general.
## 3 Experiments
### Dataset Preparation  
Images of the droppers along the catenary of a high-speed railway were acquired through the catenary monitoring device. A total of 1532 pictures of droppers were collected. After manual screening and the elimination of images without droppers, a total of 1056 valid sample pictures containing droppers remained. Among the valid samples, the majority were normal samples, with relatively few abnormal samples. Too few abnormal samples will lead to overfitting. Therefore, it was necessary to expand the data to increase the diversity of samples and improve the accuracy and generalization performance of the dropper defect detection network. The number of images containing defects was expanded by CycleGAN, and the total sample size was expanded by data enhancement. A total of 4224 images were obtained, including 1612 abnormal samples and 2612 normal samples. The targets in the dropper images were classified into broken droppers, bent droppers, normal droppers, and droppers with small defects. The Labellmg software was used to label the images and generate detection labels of \"dxdx\", \"wqdx\", \"zcdx\", and \"xxqx\" for the respective categories. Some examples and corresponding category labels are shown in Figure 8. After labeling the dropper images, the dataset was randomly divided into a training set, verification set, and test set at a ratio of 7:2:1. Details of the experimental data are given in Table 1.
## 5 Results and Discussion
### Case Comparison
Table 3 shows that the exergy flow entering the fuel cell of case 3 is 3776.9 kW, where this value is 293 kW higher than case 1, and 308.9 kW higher than case 2. Lower operating temperature of the PCFC in case 1 and case 2 than case 3 causes larger exergy destruction. Increasing the operating temperature by increasing the inlet gas temperature with direct utilization of thermal energy of GT has reduced PCFC exergy destruction and improved its power output. The results also show that GT power in case 3 increases due to an increase in exergy flow entering the turbine. For the PCFC/GT hybrid system in case 3, Figure 6 shows that exergy efficiency, energy efficiency, and net power output of case 3 are 77%, 71%, and 1836 kW, respectively. Considering all of the results, case 3 is a suitable configuration for IR-PCFC/GT hybrid systems with high system efficiency. Compared to the hybrid systems mentioned in the literature, case 3 has 13-14% higher energy efficiency than IR-SOFC/GT hybrid systems, 9% higher than ER-SOFC/GT hybrid systems, and 5% higher than ER-PCFC/GT systems. Next, the hybrid system efficiency with variation of several parameters, such as fuel flow rate, fuel utilization factor, steam to carbon, and fuel variation, of case 3 is investigated.
## 4 Experimental Results and Analysis
### Modeling of Initial Health Assessment Model Based on MSET
#### 4.1.2 Data with Different Fault Degrees at the Same Speed  
The fault rolling bearing data are divided into 4 groups: 1730 r/min, 1750 r/min, 1772 r/min, and 1797 r/min, according to the operating conditions. Each group contains data with 3 fault degrees. The deviation of data with three fault degrees from the health baseline is quantified for each group. The results are shown in [27].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Fault Degree** & **Fault 1** & **Fault 2** & **Fault 3** \\ \hline Fault diameter (mm) & 0.1778 & 0.3556 & 0.5334 \\ \hline \end{tabular}
\end{table}
Table 1: Fault degree of the driving end rolling bearing.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Motor Load** & **0** & **1** & **2** & **3** \\ \hline Approximate motor speed & & & & \\ (r/min) & 1797 & 1772 & 1750 & 1730 \\ \hline \end{tabular}
\end{table}
Table 2: The speed conditions of the rolling bearing operation.  
Figure 4: (a) Deviation distance of 3 kinds of faults at 1750 r/min; (b) Health score of 3 kinds of faults at 1750 r/min.  
Figure 3: (a) Deviation distance of 3 kinds of faults at 1772 r/min; (b) Health score of 3 kinds of faults at 1772 r/min.  
Figure 2: (a) Deviation distance of 3 kinds of faults at 1797 r/min; (b) Health score of 3 kinds of faults at 1797 r/min.  
show that on the same speed condition, the calculated deviation distance is positively correlated with the fault degree: the greater the fault degree, the greater the corresponding deviation distance, which verifies that the deviation distance of the output of the health assessment model can monotonically represent the fault degree.
## 3 Results and Discussion
### \(H_{2}\)O\({}_{2}\) Decomposition Kinetics  
Figure 6 illustrates the kinetics of H\({}_{2}\)O\({}_{2}\) decomposition in continuous-flow mode, determined with varying initial H\({}_{2}\)O\({}_{2}\) concentrations. The extent of H\({}_{2}\)O\({}_{2}\) decomposition is approximately 10-15% of the initial concentration (Figure 6a,b). The low degree of H\({}_{2}\)O\({}_{2}\) decomposition is due to the relatively short contact time of the solution with the catalyst in the fixed bed, which is about 30 min.  
Figure 6c shows the kinetics lines plotted on a logarithmic scale. In the case where the initial H\({}_{2}\)O\({}_{2}\) concentrations are in the range from 3 to 15 mM, the plots are linear. This fact indicates that the H\({}_{2}\)O\({}_{2}\) decomposition kinetics follow the kinetics of a first-order reaction. In the case where the initial H\({}_{2}\)O\({}_{2}\) concentration exceeds the threshold value of 23 mM, the graphs become nonlinear (Figure 6c). This fact indicates that the decomposition of H\({}_{2}\)O\({}_{2}\) does not fit the kinetics of a first-order reaction. The graphs are concave downward, suggesting that the reaction order is higher than one.  
We observed a similar effect in our previous work [22] and explained it by side reactions. The hydroxyl radicals formed react with hydrogen peroxide, leading to the formation of hydroperoxide radicals (Equation (2)). The hydroperoxide radicals then react with each other to form O\({}_{2}\) and H\({}_{2}\)O\({}_{2}\) (Equations (3) and (4)). The rate of the side reaction (2) increases at higher initial H\({}_{2}\)O\({}_{2}\) concentrations. Therefore, the kinetic plots on a logarithmic scale deviate from linearity at higher initial H\({}_{2}\)O\({}_{2}\) concentrations.  
\[\mathrm{HO^{\bullet}}+\mathrm{H_{2}O_{2}}\rightarrow\mathrm{HOO^{\bullet}}+ \mathrm{H_{2}O} \tag{2}\]  
\[\mathrm{HOO^{\bullet}}\leftrightarrow\mathrm{H^{+}}+\mathrm{O_{2}}^{\bullet-} \tag{3}\]  
\[\mathrm{HOO^{\bullet}}+\mathrm{H^{+}}+\mathrm{O_{2}}^{\bullet-}\rightarrow \mathrm{O_{2}}+\mathrm{H_{2}O_{2}} \tag{4}\]