esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Validation Studies  
This section carries out a couple of feasibility and validation studies of the proposed conceptual architecture: functional integration with the process mining and the process analyzing. One is for the structural analysis; that is, as the implemented conceptual architecture, the systematic framework is implemented and applied to a couple of real datasets obtained from the 4 TU.Centre for Research Data [17], each of which is formatted in the IEEE-XES standardized event log format and contains very large scale event logs spawned from enactment histories of very large scale and massively parallel process models. Figure 9 shows the captured screens of the datasets, and the following are the brief descriptions of the datasets used for the experimental validation studies:  
Figure 8: The implemented systematic framework of the integrated functional architecture.  
Figure 9: The detailed descriptions of the datasets from the 4 TU.Centre for Research Data.  
* Experimental Dataset-1: Mining and analyzing a structured information control net process model from the enactment event log dataset of the large bank transaction process model:
* Title: Large Bank Transaction Process;
* Creator: [PERSON] ([PERSON]);
* Date accepted and published: 2014-08-21;
* Description: Synthetic Bank Transaction Process;
* Models: Petri net, large, stand-alone, and SESE-aided decomposed ;
* Logs: large, with and without noise, and two particular scenarios;
* Additional: model diagram, decomposition diagram, and activity re-naming.
* Experimental Dataset-2: Mining and analyzing a structured information control net process model from the enactment event log dataset of the loan application example (Configuration 1) process model:
* Title: Loan Application Example, Configuration 1;
* Creator: [PERSON];
* Date accepted and published: 2013-04-16;
* Description: A collection of artificial event logs describing four variants of a simple loan application process. Variant 1 is the most complex process with parallelism and choices. The other three variants have simpler, more sequential control flows, and some activities of Variant 1 are missing or split into two. These event logs are used to test different approaches of discovering a configurable process model from a collection of event logs.
# Recent Advances in Flexible Resistive Random Access Memory
## 3 Material System of Flexible Resistive Memory
### Flexible Substrate Materials  
In the practical use of flexible RRAM, devices will inevitably undergo frequent deformations such as bending, twisting and folding, which makes them vulnerable to mechanical damage. Therefore, the choice of flexible substrate material is very important.  
Flexible substrates shall have the following characteristics: (1) Mechanical properties: Strong mechanical flexibility is the most essential requirement of flexible substrates to comply with deformable transformation under severe stress and strain, in addition to the efficient stress-release property while maintaining the original functionality. Moreover, the substrate is the carrier of flexible RRAM memory cells and plays the role of mechanical support. Therefore, the substrate is required to have good dimensional stability, that is, the ability of the material to maintain its original size under different processing conditions and uses. (2) Chemical stability: In the process of device preparation and use, the substrate will  
Figure 11: Scanning electron microscopy images of the (**a**) AgNW film and the (**b**) PEDOT: PSS/AgNW hybrid film. Atomic force microscopy images of the (**c**) AgNW film and the (**d**) PEDOT:PSS/AgNW hybrid film [109].  
be exposed to a variety of chemical atmospheres, so the substrate must have high chemical inertness to ensure the normal production and normal operation of the device. (3) Barrier characteristics: Although the electrode materials and dielectric materials composed of the device have good electrical properties, they are often susceptible to the corrosion of oxygen, moisture and other atmospheres, resulting in the degradation of the functional layer and device performance. Therefore, the substrate material is needed as a protective layer to prevent or delay the corrosion of the functional layer. (4) Heat resistance: Including thermal transition temperature and coefficient of thermal expansion. The maximum temperature should not exceed the temperature of glass transition (T\({}_{g}\)) during work and processing. The difference in the coefficient of thermal expansion between adjacent layers in a multilayer device will exert additional stress on the lattice, resulting in the strain and cracking of the device during heating or cooling. (5) Surface uniformity: A good substrate should have a smooth surface. The electrode is directly deposited or grown on the substrate surface, so the excessive roughness of the substrate surface will lead to uneven electrode thickness, which may lead to short circuits and other problems. In addition, issues such as machinability, cost and weight should also be considered.  
Flexible substrate materials can be roughly divided into polymer materials, metal materials and other materials based on their physical properties. In the development of flexible electronic devices, polymer substrates have always been the primary material of choice because they have excellent mechanical properties, stable chemical properties and good processability.
## 3 Results  
### Mortar Mix Ratio and Compressive and Flexural Strength  
A total of 1%, 3%, and 5% of the cement were replaced with equal masses of DE. With a fixed cement-sand ratio of 1:1.3, the amount of water reducer was adjusted to make theflowability of the cement paste be 180 \(\pm\) 5 mm. See Table 2 for the mix proportions of mortar. The compressive strength and flexural strength are shown in Figure 3.  
As can be seen from Table 2, when wet DE is used to replace cement to achieve the same flowability, it increases the internal curing water by 2% to 10% but also requires an increase in the dosage of water reducer by 0.01% to 0.04%. For concrete, strength is its most basic performance indicator and the primary consideration in structural design and quality inspection of construction work [34]. Therefore, even though the introduction of internal curing materials can effectively suppress shrinkage and cracking in cement mortar, it cannot be done at the cost of compromising the strength of the paste. As shown in Figure 3b, the early compressive strength of the mortar significantly decreases with the incorporation of DE. As the age increases, the compressive strength of the mortar continues to develop and gradually stabilizes, gradually approaching or even exceeding the compressive strength of the reference mortar during the same period. When the age is 28 days, the compressive strengths of groups A2, A3, and A4 increase to 102%, 98.6%, and 89.7% of the 28-day compressive strength of the reference mortar, respectively. This also indicates that the incorporation of DE slightly reduces the 3-day and 7-day compressive strengths of the mortar compared to the reference group but has a smaller impact on the 28-day compressive strength.  
In summary, replacing 1% of cement with an equal mass of DE has a certain impact on the workability of cement mortar, but this can be addressed by appropriately adjusting the dosage of water reducer. Incorporating DE reduces the 3-day and 7-day strengths of the mortar but has a smaller effect on the 28-day compressive strength.
# A Numerical Study of the Vibration Characteristics of a Haptic Actuator for a Dial Gear Shifter
## 2 Materials and Methods
### Finite Element Analysis for Transducer Design  
Modal analysis is an important technique for analyzing and predicting the vibration characteristics of complex structures or systems. For a multi-degree of freedom (MDOF) system, the equation of motion for \(\mathbf{x}\)(t) is written as  
\[\mathbf{M}\ddot{\mathbf{x}}+\mathbf{C}\dot{\mathbf{x}}+\mathbf{K}\mathbf{x}= \mathbf{F}(t), \tag{1}\]  
where \(\mathbf{M}\), \(\mathbf{K}\), and \(\mathbf{C}\) denote the mass, stiffness, and damping matrices of the vibration system, respectively. \(\mathbf{F}(t)\) is the excitation force in the MDOF system. For the harmonic forcing condition, the vibration output is determined as follows:  
\[\mathbf{x}(t)=\mathbf{X}(\omega)\mathrm{sin}(\omega t+\varphi), \tag{2}\]  
where \(\omega\) is the radial frequency, \(\mathbf{X}(\omega)\) is the frequency response function, and \(\varphi\) is the phase difference. Using modal analysis, the displacement for the k-th mode can be written as  
\[\mathbf{x}_{\mathrm{k}}(t)=\mathbf{X}_{\mathrm{k}}(\omega)\mathrm{sin}( \omega t+\varphi_{\mathrm{k}}), \tag{3}\]  
The transfer function for the k-th mode, Hk(\(\omega\)), characterizes how the vibrational response at a certain spatial point responds to harmonic excitation across a range of frequencies. It is a complex function of frequency, providing both magnitude and phase information, which is crucial for understanding the resonance behavior and dynamic characteristics of the system.  
\[\mathbf{H}_{\mathbf{k}}(\omega)=\frac{\mathbf{X}_{\mathrm{k}}(\omega)}{ \mathbf{F}_{\mathrm{k}}(\omega)}. \tag{4}\]  
Figure 1: Structure of the haptic actuator in (**a**) the exploded view and (**b**) the assembly view.
## 2 Results and Discussion
### Candidate Genes for Farnesyl Diphosphate Synthase  
Contig 3967 from our dataset was clearly identified as farnesyl diphosphate synthase (FPPS). In order to establish the suggested route to thapsanes, a synthase would be needed for the formation of sesquilavandulyl diphosphate (SPP) [9]. However, while the predicted protein encoded by contig 3967 exhibited 78% identity with its _Arabidopsis_ ortholog (NP:199588), the second closest sequence in our dataset had only 25% identity. To investigate the possibility that a second FPPS-like gene (with potential SPP synthase activity) was present in the genome of _T. laciniata_ but was not represented in the transcriptome dataset, we attempted to amplify other FPPS homologues from genomic DNA using degenerate primers. However, no additional FPPS sequences were retrieved. Thus, despite the structural similarity of FPP and SPP, it is likely that the enzyme involved in the formation of SPP has little sequence identity to FPPS. Previously, the only irregular prenyl synthase enzyme that has been biochemically characterized is chrysanthemyl diphosphate (CPP) synthase from Sagebrush [10; 32], although the requirement of an enzyme with irregular farnesyl pyrophosphate synthase activity has been described [9]. Thus, the investigation of irregular prenyl diphosphate synthases is an interesting field for discovery of enzymes with novel and very chemically interesting functions.  
\begin{table}
\begin{tabular}{c c c c} \hline
**TCA cycle enzyme** & **Arabidopsis query** & **Contig (identity)** & **Reads** \\ \hline Citrate synthase & NP\_850415 & Comp7488 (84\%) & 12696 \\ Aconitase & NP\_567763 & Comp1618 (85\%) & 46037 \\ Isocitrate dehydrogenase & NP\_175836 & Comp214 (85\%) & 194838 \\
2-oxoglutate dehydrogenase & NP\_191101 & Comp359 (85\%) & 162552 \\ Dihydrolipoyllysine succinyltransferase & NP\_200318 & Comp2028 (71\%) & 35422 \\ Dihydrolipoyl dehydrogenase & NP\_567487 & Comp12031 (84\%) & 7805 \\ Succinyl-CoA synthetase & NP\_001119263 & Comp3388 (90\%) & 28993 \\ Succinate dehydrogenase flavoprotein subunit & NP\_201477 & Comp2812 (94\%) & 29604 \\ Succinate dehydrogenase iron-sulfur subunit & NP\_001118718 & Comp3447 (76\%) & 21231 \\ Fumarase & NP\_001078075 & Comp8438 (87\%) & 13149 \\ Malate dehydrogenase & NP\_190336 & Comp4342 (77\%) & 19283 \\ \hline \end{tabular}
\end{table}
Table 1: _T. laciniata_ tricarboxylic acid cycle (TCA) encoding transcripts. Individual components of the TCA cycle were identified by using corresponding Arabidopsis enzymes as queries in a tBLASTn search of the assembled _T. laciniata_ contigs. The identified contigs are listed with their amino acid identity to their _Arabidopsis orthologues_ shown in parenthesis. The number of reads from which the contigs were assembled, giving an absolute expression value, is shown in the fourth column.
# Hydroxyryrosol-Rich Olive Extract for Plasma Cholesterol Control
## 1 Introduction  
The Mediterranean diet is typically high in fat and there is evidence about its role in the prevention of cardiovascular disease [1, 2], in which the proportions of unsaturated and saturated fatty acids have been shown to play an important role [3, 4]. One of the hallmarks of the Mediterranean diet is the high consumption of olives and olive oil as the primary source of dietary fatty acids and major sources of antioxidants [5]. The high nutritional value of these products is due to their richness in monounsaturated fatty acids (MUFA), fiber, vitamin E, and a number of phytochemicals [6]. In effect, olive oil provides an exceptional lipid matrix that is rich in molecules with different bioactive chemical entities [7]. The main phytochemicals that have been identified and quantified in olives and olive oil are phenolic and non-phenolic compounds [8]. Olive phenolic compounds belong to six different classes, including phenolic alcohols (hydroxytrosol and tyrosol),flavones (Iuteolin, luteolin-7-O-glucose, apigenin, and apigenin-7-O-glucose), flavonoids (rutin), anthocyanins (cyanidin-3-O-glucose), phenolic acids (5-O-caffeoylquinic acid) and a hydroxycinnamic acid derivative (verbascoside), while triterpene acids (notably mashinic and oleanolic acids) are the main subclass of non-phenolic compounds that have been identified in olives [6].  
In 2011, the European Food Safety Authority (EFSA) published a health claim that was related to polyphenols in olive oil and their possible protection of blood lipids against oxidative stress, stating that 5 mg of hydroxycytrosol and its derivatives (e.g., oleuropein complex and tyrosol) should be consumed daily in the context of a balanced diet for the sufficient avoidance of oxidative damage [9; 10]. In effect, according to the most recent observations, olive hydroxytyrosol acts as a free-scavenger and metal-chelator [11], and it reduces the levels of low-density lipoprotein cholesterol (LDL-C) oxidation, platelet aggregation, and chronic inflammation, thus counteracting atherosclerosis-related cardiovascular (CV) disease (ASCVD) through the prevention of endothelial dysfunction and macrophages activation [12; 13; 14].  
In light of this evidence, the aim of this study was to evaluate the effect of daily dietary supplementation with highly standardized polyphenols--mainly hydroxytyrosol--which were derived from the olive oil production by-products of an Italian olive variety (Coratina Olive) on the plasma cholesterol of a sample of hypercholesterolemia individuals.
## 4 Results and Discussion
### Pineapple Fruit Processing
#### 4.2.1 Pineapple Packing Process  
Figure 8 shows the shared values of carbon, water, and energy footprints per process, grouped starting at the agricultural and industrial stages, and up to the packaged pineapple scenario. Regarding the carbon footprint, a share of 82.9% was shown to correspond to fruit cultivation. Within the fruit cultivation percentage, application of fertilizers had the highest participation at 43.1%, since its manufacture was included. Irrigation and land preparation followed at 17.8% and 10.4%, respectively. The industrial stage had a participation of 17.1%. Box assembly and fruit packaging were the processes with the greatest impacts within this stage at 13.2% of the total carbon footprint. For the water footprint, 95.9% the impacts generated came from the irrigation process, mainly during the growth and maturation of the plant. A share of 3.2% corresponded to the water supply for the application of agrochemicals throughout plant cultivation. Less than 1% of the remaining impact was generated during the packaging process; it came from cleaning the fruit.  
Regarding the energy footprint impact, a similarity to the carbon footprint was shown in the distribution of impacts due to its relationship with the consumption of fossil fuels. The greatest impact came from the agricultural stage with a share of 84.2%, while the industrial stage contributed a share of 15.9%, mainly related to the manufacturing and transportation of cardboard boxes.  
Figure 7: Global environmental impacts by stage and type of industry.  
Figure 9 shows the energy flows that made up the energy footprint from the cultivation process and the packaging of pineapples by the type of primary energy supplied. The largest proportions of energy flows were consumed by fertilizer manufacturing, irrigation, and soil preparation operations, which accounted for more than 70% of total consumption. In the industrial stage, the highest consumption was shown in the pineapple box assembly and packaging operations with a share of 11.3%. The same was observed with the other two pineapple processes, as shown below. The main source of energy supply came from the fossil primary energy, with a share greater than 90%.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Enabling Technologies
#### 4.3.3 Signal Processing  
Some signal processing methods such as compressive sensing and differential evolution have played roles in research on chaotic image encryption. These methods and their roles are investigated below.  
Compressive (Compression) Sensing  
Image encryption algorithms based on compressive sensing theory have been proposed. One such proposal is by [PERSON] et al. [196], where a unique source of chaos from _hybrid analog-digital electro-optic_ sources was used. Another proposal was by [PERSON] et al. [197], where Gauss random matrices were used for compression. Both algorithms exhibited strong cryptographic strength through simulations and experimental analysis.  
An image encryption algorithm utilizing differential evolution was proposed by [PERSON] et al. [198]. The differential equation utilizes an algorithm based on natural evolution to find optimal decision variables to seed the [198] chaotic map optimization for image encryption using the triple objective differential evolution algorithm.
## 3 Field-Circuit Coupling Analysis Method
### Electric Field and Stray Parameters Calculation
The electric field coupling impedance is defined as follows:  
\[R_{\rm g}=f(E_{i}) \tag{7}\]  
In Equation (7), \(E_{i}\) is the instantaneous electric field strength between the MMC terminal conductor and the ground, and \(R_{\rm g}\) is the electric field coupling impedance of the MMC terminal. The value of electric field coupling impedance \(R_{\rm g}\) is determined by the electric field strength. When the transient electric field \(E_{i}\) is less than the air breakdown field strength at the position, the value of resistance \(R_{\rm g}\) is considered to be infinite. Conversely, when the transient electric field \(E_{i}\) reaches the air breakdown field strength, the resistance value \(R_{\rm g}\) becomes a relatively small value, approximately several 1 to 5 ohm or even lower. From Equation (5), it can be deduced that the MMC stray capacitances can be obtained through electric field analysis. A 3-dimensional electric field calculation method was used to perform detailed calculations of the MMC valve stray parameters and electric field distribution characteristics. In the electric field calculation model, the computed capacitance and electric field values are coupled into the system field-circuit coupling analysis model. During the system calculation process, the electric field characteristics of the MMC valve influence the fault characteristics.
# The Development of an Optimized Impact Pad for a Six-Strand Tundish Using CFD Simulations
## 4 Evaluation and Discussion
### A Comparison of Contours Based on Flow Velocity  
A plane section was created 10 mm below the slag level to observe the contours near the slag line. A velocity color spectrum ranging from 0.05 to 0.2 m/s was used. Based on the observations, it is possible to conclude that the velocity field has changed. With the SP-K2 and SP-K4 configurations, lower flow velocities were observed near the ladle shroud. This could have a positive impact on reducing the wear of the refractory lining in the central part of the tundish. This trend has also been confirmed by the results of the tracer distribution and the specified vectors shown in Figure 20.  
Based on the turbulent kinetic energy [36; 37] analysis in the KTHE/C impact pad area, a high value (~1.4 \(\times\) 10\({}^{-1}\) m\({}^{2}\)/s\({}^{2}\)) was recorded at the impact point, indicating significant turbulent activity. There is almost no flow near the walls of the tundish, which may lead to the formation of dead zones. At the melt surface, the activity is moderate, but with a risk of lining erosion and slag shearing, which may adversely affect the metallurgical quality of the steel.  
Compared to the SP-K4 impact pad configuration, the turbulent kinetic energy in the impact zone was significantly lower (~1.7 \(\times\) 10\({}^{-3}\) m\({}^{2}\)/s\({}^{2}\)), indicating effective damping of the influent flow. The flow near the walls is uniformly distributed with finely flowing regions, while the melt level remains stable and virtually free of turbulent activity, contributing to protection from slag contamination.  
In terms of the rate of dissipation [37] of the turbulent energy, the impact zone in the case of the KTHE/C impact pad is characterized by a broad, intensely dissipating flow that also reaches the surface, thereby disturbing it. This poses a risk of slag entrainment and uncontrolled mixing, which can impair melt purity.  
In contrast, with the SP-K4 impact pad, there is targeted dissipation in the impact area and a steady flow at the walls, leaving the surface almost untouched. This configuration allows for higher metallurgical quality, and although the mixing efficiency is lower, it is more controllable and uniform.
## 4 Design of TLQoS
### Routing Potentials
#### 4.3.2 Delay Potential Field  
We define the delay potential of a node \(n_{i}\) as  
\[P_{n_{i}}^{d}=D(n_{i}) \tag{4}\]  
Here, the function \(D(n_{i})\) refers to the normalized node delay at \(n_{i}\) and defined by  
\[D(n_{i})=\frac{D_{n_{i}}^{avg}}{D_{p}^{req}} \tag{5}\]  
Here, \(D_{n_{i}}^{avg}\) denotes the average node delay at \(n_{i}\), and \(D_{p}^{req}\) is the end-to-end delay requirement of a packet, in other words, the packet deadline. Thus, delay is normalized to the packet delay requirement.  
Then, we define the delay force, \(F_{d}(n_{i},n_{j})\) from node \(n_{i}\) to \(n_{j}\in NB(n_{i})\) as  
\[F_{d}(n_{i},n_{j})=\frac{P_{n_{i}}^{d}-P_{n_{j}}^{d}}{c_{n_{i},n_{j}}} \tag{6}\]  
\(F_{d}(n_{i},n_{j})\) allows a packet to traverse through the nodes with minimum latency on its path to the \(BC\). The delay potential field, however, is time variant which changes dynamically, and thus is not loop free. However, it can be avoided by integrating the field with time-invariant hop-count potential field which will be described later in this section.