esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The efficiency of using the collector pressing scheme in the SPS process has been confirmed to improve the optical, physical, and mechanical properties of YSZ ceramics with an increased shape factor. A method for developing a seal surface and determining the optimal way of increasing pressure and temperature during SPS on this surface can be used to optimize the consolidation modes of the materials. The obtained results quantitatively confirmed that the most promising method for sintering transparent YSZ ceramics is to combine the SPS method with one of the rational methods of powder deformation, implemented according to the collector pressing scheme.  
It has been shown that transparent/translucent YSZ ceramics with an increased shape factor (14 mm in diameter and up to 5 mm in height, h/d = 0.36) can be successfully fabricated by the SPS technique combined with the collector pressing scheme. The optical properties and microhardness of ceramics obtained using the collector scheme is comparable or better to the optical properties of ceramics obtained using the conventional uniaxial pressing scheme. However, samples made in SPS according to the collector deformation scheme demonstrate a smaller CSR size and lattice microstresses, which can positively affect other operational properties of ceramics.  
The reasons for the anomalous effect of the sample thickness on light absorption noted in our experiments, as well as a generalized assessment of the effect of the collector pressing scheme on the optical properties of transparent ceramics during SPS, is the subject of further studies.  
Conceptualization, O.K., E.D. and V.P.; methodology, E.D. and O.K.; validation, O.K., E.D. and M.F.; formal analysis, E.D. and V.P.; investigation, T.A., V.P. and A.A.; resources, O.K. and V.M.; data curation, S.S. and D.V.; writing--original draft preparation, V.P.; writing--review and editing, O.K., E.D., S.S., D.V. and A.C.; visualization, S.S. and D.V.; project administration, O.K. and M.F.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by Tomsk Polytechnic University Competitiveness Enhancement Program grant Number VIU-ISNPT-204/2020.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The study was performed by \"Innovation Centre for Nanomaterials and Nanotechnologies\" of the National Research Tomsk Polytechnic University.  
The authors declare no conflict of interest.
# Model Updating of Bridges Using Measured Influence Lines
## 2 Materials and Methods
### Comparison of the Initial FE Model and Measurements  
#### 2.4.1 Strain IL  
Strain ILs were calculated for longitudinal (E11) strains at the E_MG1 and E_MG2 positions. Since only the driving lane vehicle crossings were considered, the strain ILs were obtained with two pairs of 0.5 kN point loads (representing the vehicle wheels) varied across the last three spans, starting at the beginning of S5 and ending at the end of S7, at the expansion joint. The strain ILs were calculated for point loads spaced approximately 1.0 m apart, representing 92 static analyses required to extract each strain IL. Lastly, the modelled strain IL was discretised to 0.01 m with a cubic interpolator for the comparison with the measured strain IL. Figure 14 displays the E_MG1 (**a**) and E_MG2 (**b**) strain ILs of the initial FE model, the related measured strain ILs, and the differences between the two.  
As expected, the modelled strain ILs overestimated the measured ones, with the peaks exceeding the measured values by approximately 45% for both sensors.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 3 Experimental Results  
Experiments are carried out on the proposed model PyraGAN_F32_2 and PyraGAN_F16_3. Then, we compare the performance of them with HiDDeN [21] and SteganoGAN [22], which achieved groundbreaking end-to-end steganography. HiDDeN [23] was proposed by the team of Stanford, and it realizes secure and robust data hiding despite the presence of noise such as JPEG compression, Gaussian blurring, cropping, and dropout. However, its weakness is that capacity of hiding data is extremely small. SteganoGAN [22] from the team of MIT gained a relatively large capacity for hiding data. The above two networks both take the image as a carrier. Few references take the video as the carrier [27, 28], especially taking into consideration the features of video compression such as the CU partition mode. Inspired by the idea of the end-to-end steganography network, we aimed to construct a lightweight, secure, and large capacity steganography network for the latest VVC videos. We reproduce HiDDeN [21] and SteganoGAN [22] using open-source code for comparison experiments. For the sake of fairness, their performances are evaluated on the same dataset.
## 3 Results and Discussion  
### Drying Curves of Arbutus unedo L.  
The fruit samples' initial and final moisture contents of the _Arbutus unedo_ L. varied from 1.8857 to 2.0834% d.b and from 0.8857 to 0.6041% d.b, respectively. The horizontal solar radiation was lower than the inclined one during the daytime (Figure 2).  
The evolution of the moisture content and drying rate for each air-drying temperature as a function of time is presented in Figure 3a. The air temperature impacted significantly the drying time of _Arbutus unedo_ fruit samples. The drying time decreased as the air-drying temperature increased. The drying curves for the studied samples are similar to those previously described in other crop fruits [31; 50; 51; 52; 53; 54; 55]. This confirms the quick evaporation of moisture from the samples at the beginning of the drying, then a later decline as the drying time increased. The observed continuous decline in moisture content proves that the internal mass transfer is solely controlled by the diffusion process. While the air-drying temperature increased, an extra energy rate was applied to the _Arbutus unedo_ L. fruits which resulted in an increase in the drying rate (Figure 3b). The constant rate was not observed for all curves, and presumably, all the drying cases took place within the declining rate period. This observation validate that the diffusion was mainly physical mechanism controlling moisture movement during the drying process. These observations align with those reported on other fruits [56; 57; 58].  
Regardless of the air-drying temperature, the recorded drying rate neared zero. This observed decline is attributed to the sample's lower evaporation rate [59]. At each studied  
Figure 3: The trend of the moisture ratio (**a**) and drying rate (**b**) with drying time at 60 \({}^{\circ}\)C, 70 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C.  
air-drying temperature, the drying time decreased in response to the increase in the drying rate. These properties are widely observed in numerous biological products [60].  
In contrast, the moisture content per sample decreased with the increase in temperature. The unique presence of phase II (phase 0 and I were absent) for drying curves of _Arbuttus untedo_ L. was noticeable. This is most likely attributed to the difference between the saturated (humidified) air temperature and the initial temperature of the fruit samples, considered negligible. [PERSON] and co-workers (2011) reported that the moisture content decreases in food products during the drying process [61]. This means that during the drying operations, the water moves from higher moisture to lower moisture points, and the movement of water from the inside to the surface of the food matrix is gouverned by molecular diffusion. These findings were also reported by other authors, such as [PERSON] and co-workers (2021) for rosemary in a solar- convection dryer [62], [PERSON] (2012) for sweet potato [63], [PERSON] et al., (2020) for carob seeds in a convective solar dryer [59], and [PERSON] and co-workers (2021) for apple in a hybrid solar-electrical forced convection dryer [64].
# Spatiotemporal Evolution of Gas in Transmission Fluid under Acoustic Cavitation Conditions
## 4 Discussions
### Complexity and Limitations on Gas Spatiotemporal Evolution Modelling  
Under acoustic cavitation conditions, solving the equations governing the spatiotemporal evolution of gas in the transmission fluid is somewhat complicated. For instance, cavitation involves complex interactions between liquid and gaseous phases, including bubble nucleation, growth, collapse, and coalescence governed by different physical principles. Cavitation also results in significant heat transfer and phase changes due to local temperature and pressure conditions. Furthermore, the interaction effect between the acoustic waves and fluid forms a dilemma as the acoustic waves can induce cavitation, and cavitation bubbles can, in turn, influence the propagation of acoustic waves. Considering the complexity of gas spatiotemporal evolution, this research for gas spatiotemporal evolution has certain limitations.  
However, from the perspective of pressure amplitude and fluctuation in this work, the amplitude and changing trend of transmission fluid pressure are the same as those of the results by [PERSON] et al. [25]. The results of vapor void fraction, cavitation zone movement distance, and duration are also proved by their work [25], including the vapor void fraction distribution presenting an inverted V-shape, the maximum vapor void fraction being about 0.5%, and the rarefaction wave and compression wave effects on cavitation.  
This gas void fraction model of gas-liquid two-phase flow in a slender tube, also the related research findings, can be applied in many fields where acoustic cavitation exists, including automotive and mechanical engineering, maintenance and diagnostics, hydraulic systems, pump and turbine design, acoustic engineering, environmental and safety considerations, environmental mitigation, etc.
## 2 Materials and Methods
### Data Analysis  
The removal efficiency by the chlorine concentrations were expressed as log\({}_{10}\) reduction of bacterial population as described by [42]:  
\[\text{LR}=\log_{10}(N_{t}/N_{0}) \tag{1}\]  
LR = log reduction of bacteria count at time \(t\); \(N_{0}\) = initial bacterial concentration at time 0, \(N_{t}\) = final bacterial concentrations after a treatment time \(t\).  
The disinfection kinetic parameters of _E. coli_ at higher chlorine concentrations were determined by fitting inactivation data to the Chick-Watson empirical model [43]. The empirical logarithm equation was expressed as:  
\[\log(N/N_{0})=-kC^{n}T \tag{2}\]  
where, \(N\) = bacterial concentration at time \(t\), \(N_{0}\) = initial bacterial concentration at time 0, \(C\) = free chlorine concentration (mg/L), \(T\)= contact time (min), \(k\) = inactivation rate constant, \(n\) = coefficient of dilution.  
For log reduction/time plots for survivors, inactivation data was fitted to the empirical model of [43; 44]. Data were log\({}_{10}\) transformed and statistical analysis was performed by linear regression using Origin Pro 2017.  
The Hom model is expressed as:  
\[\log(N/N_{0})=-kC^{n}T^{m} \tag{3}\]  
where \(N\) = bacterial concentration at time \(t\), \(N_{0}\) = initial bacterial concentration at time 0, \(C\) = residual free chlorine concentration (mg/L) at time \(t\), \(T\) = contact time (min), \(k\) = inactivation rate constant, \(n\) = chlorine exponent (Hom model), \(m\)= time exponent  
Using multiple linear regression analysis, the constants were evaluated as:  
\[\log(N/N_{0})=\log k+n\log C+m\log \tag{4}\]  
Statistically significant differences were determined between untreated and chlorine treated _E. coli_ strains. Statistically significant differences in inactivation were also determined among the chlorine treated _E. coli_ strains by one-way analysis of variance (ANOVA) and Turkey Post Hoc comparison test using SPSS (IBMSPSS Statistics 23). Differences between means were considered significant at \(p\) < 0.05.
## 2 Synthesis and Characterization of MIP
### Polymerization Parameter in MIP Synthesis
#### 2.1.1 Functional Monomers  
The selection of monomers is a significant step in the synthesis of MIPs. In 2016, [PERSON] et al. [14] proposed the selection of MIPs via UV-Vis spectra. In both non-covalent and covalent interactions, a monomer should be capable of bonding with a template. However, non-covalent interactions are the most common procedure for the synthesis of MIPs due to the lower intensity of the interactions involved [15]. Non-covalent is based on interaction between template and monomer through weak forces such as Van der Waals forces. This resulted facile template removal. Moreover, non-covalent interaction is simple and effective. However, it's easy disrupt during template-monomer complex. Covalent is a slower binding which more tedious during template removal. Acrylic acid (AA), 2- or 4-vinylpyridine (2- or 4-VP), and methacrylic acid (MAA) are the most commonly used functional monomers. MAA is often used as a monomer due to its ability to interact with the template via both non-covalent and covalent interactions [16].  
Furthermore, the presence of a carboxylic acid functional group acts as a hydrogen bond, and proton donors ensure that MAAs are widely used in the preparation of MIPs. Furthermore, when the chemical/compound used is present in acidic, basic, or neutral conditions, MAA creates hydrogen-bonding interaction with them. As such, an effective recognition component can be obtained by forming a potent complex between the functional monomer and template, of which is designed to complement the template. When the template contains primary functional groups, acidic monomers are preferred, for example, MAA. As the template contains acidic functional groups, a basic monomer such as 4-VP is preferred. 2-Hydroxyethyl methacrylate (HEMA) [17] is an example of an uncharged  
Figure 1: Number of published papers of MIPs and MIPs in natural product found in PubMed.  
monomer. To achieve good selectivity, acrylamide (AM) could be used. When AM is added to a prepolymerization mixture, for example, it produces materials with high selectivity towards carboxylic [18], amide [19], and alcohol groups [20; 21; 22; 23].  
MAA and 4-VP are used in a study by [PERSON] et al. [24] to formulate the molecularly imprinted solid-phase extraction (MISPE) procedure in 2010. This is because non-steroidal anti-inflammatory drugs (NSAIDs) can bond to the hydrogen atoms of the basic amine (-NH) functional group when the carboxylic group is neutral, resulting in high extraction efficiency. When it comes to NSAIDs, it mainly uses 2-VP and 4-VP as monomers [25]. In contrast, MAA and other polar monomers cannot create hydrogen bonds because it is inefficient at interacting with weakly polar or polar templates. Although hydrophobic monomers like styrene can be utilized in these situations, designing and synthesizing monomers with complementary structures to the template structure that interact via hydrophobic and Van der Waals forces is preferable [26; 27].
## 6 Conclusions  
Most existing privacy-preserving data publishing algorithms consider an offline data publishing scenario in which the data publisher first anonymizes the data in an offline manner, and then releases the anonymized data for public use. However, with the increasing demand for the sharing of microdata among various parties, an offline privacy-preserving data publishing scenario is insufficient to support the voluminous request for a release of data. Instead, it is more desirable to integrate the data anonymization functionality into existing systems that are capable of supporting online query processing. In this paper, with the aim of supporting efficient online privacy-preserving data publishing, we presented a novel scheme that is able to efficiently anonymize the query results on the fly. In particular, given a user's query, the proposed approach effectively estimates the generalization level of each attribute for achieving the \(k\)-anonymity property in the query result datasets based on the statistical information. The proposed algorithm achieves a high level of efficiency in applying \(k\)-anonymity by effectively sacrificing the information loss of the released microdata. The experimental results when applying a real dataset show that significant processing time gains can be achieved with the proposed method, while avoiding a significant reduction of information on the released microdata. Future work will include an investigation into the various types of queries containing complex operations, such as a join or aggregation.  
This research was funded by a 2021 research Grant from Sangmyung University.
## 5 EEG Signal Processing and Classification Techniques in Rehabilitation Research
### Feature and Channel Selection  
High-density EEG recordings generate voluminous data, creating a challenge for BCI applications. Channel selection techniques address this by identifying a smaller subset of optimal electrodes, maximizing classification accuracy and reducing computational cost. Similar to feature selection, candidate channel sets are generated and evaluated with dedicated criteria, ultimately prioritizing channels harboring the most task-relevant information. Filter methods rely on the statistical analysis of EEG properties, while wrapper methods directly test channel performance within the BCI system. In some cases, hybrid approaches combining both strategies are employed for optimal channel selection. This meticulous pruning of EEG data lays the foundation for accurate BCI operation and enhances its real-world potential.  
1. Filter Approach: Initiating with the full set of features, filter methods meticulously identify the optimal subset through dedicated selection criteria. These criteria often revolve around key characteristics like information gain, dependency, consistency, correlation, and distance measures [57]. A significant advantage of filter methods lies in their minimal computational requirements. Additionally, the feature selection process operates independently of the chosen classifier, providing greater flexibility. Widely utilized filter methods include correlation criteria and mutual information techniques, both meticulously honing in on the most informative features within the data landscape.
2. Wrapper Approach: Distinct from filter methods, wrapper approaches forge a direct partnership with the classifier to select features meticulously. They iteratively present candidate feature subsets to the classifier, diligently evaluating its performance. This feedback loop guides the selection process, prompting either acceptance of a subset based on established criteria or the proposal of new combinations for further evaluation. Algorithms within this realm encompass searching algorithms and evolutionary algorithms. The former embarks on their quest with an empty set, strategically adding or removing features until the classifier's performance peaks. Their journey typically concludes when a designated maximum feature subset size is attained. Meanwhile, evolutionary algorithms, such as particle swarm optimization (PSO) [58], and artificial bee colony (ABC) [59; 60], harness nature-inspired optimization techniques to uncover optimal subsets. While wrapper methods excel at identifying feature combinations that yield superior classifier performance compared to filter methods, their computational demands are considerable, rendering them less suitable for handling vast datasets.
# Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes
## 5 Conclusions  
Through a numerical simulation study of ultrasonic guided wave detection for non-defective and defective pipelines, the conclusions are as follows:  
1. The dispersion curve obtained by the semi-analytical finite element method is in good agreement with the simulation data. The successful excitation of the T(0,1) mode by the torsional excitation mode can be effectively used to detect axial cracks. The L(0,2) mode can be successfully excited by observing the displacement nephogram and can be effectively used to detect the defects in the circumferential weld.
2. Through the algorithm of combining wavelet transform and Hilbert transform, the relative errors are all less than 1%, so the positioning accuracy is high.
3. The quantitative analysis of defects in circular welds based on multi-node fusion and modal projection algorithm clearly extracted ultrasonic guided wave signals of different modes. L(0,2) guided waves are sensitive to pipeline defects with circumferential and radial dimensions.
4. Multi-node fusion improves the sensitivity and noise immunity of defect signals in field detection, which is especially suitable for pipeline detection in complex environments. Modal projection enhances the recognition of defects in complex structures such as welds by separating different guided wave modes. The combination of the two makes the field detection more accurate, adapts to complex working conditions, and supports real-time monitoring.
## 3 Challenges in FSO Communication
### Atmospheric Channel Effects Phenomena
#### 3.1.2 Atmospheric Turbulence  
AT arises from irregularities in air temperature, humidity, and pressure, which cause random variations in the atmosphere's refractive index. This results in turbulent eddies of different sizes and strengths, presenting significant challenges for the propagation of FSO signals. Such turbulence leads to detrimental effects like beam wander, beam spreading, scintillation, and phase distortion, all of which can degrade FSO communication system performance [2; 15; 20]. To develop reliable and efficient FSO systems, it is essential to understand and mitigate these effects. Promising solutions include advances in AO, diversity techniques, ECC, and beam steering technologies. Section 4 provides further details on these mitigation strategies.  
Several mathematical models are developed and analyzed to accurately characterize the probability density function (PDF) of random irradiance fluctuations under various turbulent conditions, such as weak, moderate, strong, and saturation. Examples of these models include the log-normal (LN), Gamma-Gamma (IT), \(K\)-distribution, and negative exponential (NE) models [15; 46; 47]. Section 3.2 discusses several AT models for the FSO system.
# Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
## 3 Proposed ECDH Architecture
### Memory Block  
The memory block in the proposed ECDH accelerator architecture is an \(8\times m\) size register file. Here, the numerical value \(8\) denotes the total registers while the value for \(m\) determines the size of each register. The memory block is responsible for preserving the intermediate and the final results during and after the execution of Algorithm 1 and Figure 2. The internal architecture of a memory block, as shown in Figure 3, consists of two \(8\times 1\) multiplexers that are responsible for reading the data. Moreover, it contains one \(1\times 8\) demultiplexer to modify the contents of each particular register address. The _Controller-2_ is responsible for generating control signals to perform read and write operations.