esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Biodiesel Synthesis Processes
### Criteria for Catalyst Selection  
The rate of a chemical reaction is enhanced by a catalyst that functions by lowering the activation energy without itself being consumed. The catalyst does not appear in products as it is supposed to be unchanged at variance with stoichiometric reactions.  
A chemical equilibrium is quickly attained whereas the reaction thermodynamics remain unaffected during the action of catalyst; this process is known as catalysis that involves a little proportion of it. Similarly to all chemical reactions, the rate of transesterification is also improved by _acidic_ or _basic_ catalyst.  
_Acid catalysts_ cause slower the speed of reaction and causes the equipment corrosion, therefore they are not really applicable at a large scale. On the other hand, _base catalysts_ are not only cost effective, but also readily available and much more efficient. A major issue associated with base catalyzed reaction is that free fatty acids (FFA) should be 0.5% or less to hinder saponification.  
Catalysts used for transesterification are either homogeneous (acid/base), heterogeneous (acid/base) or enzymatic (lipases) in nature.  
1. Homogeneous catalysts are required in less concentration to provide rapid results but are difficult to recover and reuse, for example, a high yield of biodiesel is produced in the presence of sodium alkoxide at low concentration in short reaction time but is difficult to be recovered;
2. Conversely, heterogeneous catalysts are recoverable and reusable but less reactive due to leaching. A selection of highly efficient and active catalysts is the main focus of researchers for production of high yield of biodiesel. In most cases, base catalysts are in the form of sodium and potassium carbonates that form hydroxides and alkoxides and the rate of reaction is faster as compared to acid catalyzed reactions;
3. A catalyst with large surface area of a catalyst can resolve different issues concerned with catalytic types and synthesis of nano-sized catalyst fulfils the above-mentioned criteria, hence, it can be the best option for producing high quality biodiesel with maximum yield in reasonable budget in less time;
# Effect of Different Gradings of Lightweight Aggregates on the Properties of Concrete
## 4 Evaluation of the Properties of Concretes with Different Aggregate Gradings
### Thermal Property Evaluation  
The thermal responses of the LWC samples are experimentally and numerically investigated. The Hot Disk device that satisfies the European standard is used to measure the thermal conductivities of the LWC samples, and three to five specimens in each case are repeatedly measured on different sides of the specimens to increase accuracy. The obtained thermal properties from the experiments are utilized for the numerical simulation and compared with those data. Here, the ABAQUS package is also used for the simulation, and the sample investigation is presented in Figure 6.  
Figure 7 presents thermal conductivity values of the samples obtained from both experimental and numerical methods with respect to the specimen density. In this figure, a general trend of the thermal conductivity related to the density and grading can be identified. Likewise, for the mechanical cases in Figure 5, LW 30 shows the largest thermal conductivity as well as the specimen density, while LW 25 has the lowest values; this denotes that the thermal conductivity of LWC is also affected by the density of the specimen. In addition, the thermal conductivity values of LW 23 and LW 45 show differences for both experiments and simulations, even though their experimental densities are almost the same. However, LW 23 and LW 25 have different experimental densities, although their thermal conductivity values are almost the same in both experimental and numerical approaches. This indicates that the thermal conductivity of LWC can be affected by the density as well as the type of grading of LWA; when the specimen density is almost the same, the specimen that contains less volume of finer aggregates shows lower thermal conductivity (LW 23 and LW 45), while the specimens with different densities can have almost the same thermal property by adopting an appropriate grading (LW 23 and LW 25).
## 4 Experiments and Results
### Experimental Data Introduction and Processing  
The experimental dataset SECOM [44] (Semiconductor Manufacturing) is derived from the UC Irvine machine learning repository. SECOM consists of production line monitoring data and semiconductor quality data, containing 1567 observations, each of which is a vector of 590 sensor measurements, plus a pass/fail label of the product.  
Notably, there are some missing values in the dataset, and only 104 of the 1567 observations recorded that the product failed the quality test, while the vast majority of products passed the quality test with a ratio of approximately 1:14. To this end, the experimental data were pre-processed, and the main work included (1) establishing the index of the dataset; (2) deleting columns with more than 50% of their data missing; and (3) interpolating missing values in the dataset. In general, the missing values of each sample were imputed by using the mean value from n-neighbours found in the dataset. (4) We normalized the dataset by using the Max-Min normalization method. (5) We eliminated features that had a variation below a specified threshold, and (6) down-sampling technology was used for data balancing.  
After processing, a new experimental dataset was formed, including 449 variables, and the sample size was 416 (the ratio of pass and fail was 3:1).
# Design and Fabrication of a MEMS Bandpass Filter with Different Center Frequency of 8.5-12 GHz
## 1 Introduction  
In recent years, filters have become a new research hotspot, and some filters based on new materials and new theories have emerged, such as filters based on graphene materials [1], filters based on silicon nitride [2; 3], filters based on photon quantum theory [4; 5], the mathematical model of filters [6], and filters integrated with a micro-electro-mechanical system (MEMS) switch [7; 8; 9; 10]. The radio frequency of a micro-electro-mechanical system (RF MEMS) tunable filter, which is a type of electronic component that controls signals by RF MEMS switches, and offers higher integration, lower power dissipation, smaller size, better linearity, and better performance in frequency adjustment than conventional filters [11]. Therefore, over the last two decades, RF MEMS filters have had tremendous applications in modern communications systems, such as satellite communication, radar, medical electronics, and software [12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. As an important electronic device in signal transceivers, the tunable filter, without noise or various unwanted signals in each frequency band, reduces inter-channel interference, ensures the normal operation of communication equipment, and achieves high-quality communication [22]. Electronic devices that require RF filters to have low insertion loss and numerous RF MEMS filters, such as radar and microwave test equipment [23], have thus been proposed and fabricated in recent years. The University of Michigan has designed a tunable filter with four adjustable frequencies, but its out-of-band rejection is only 4-5 dB [24]. The University of California, San Diego, has designed a filter with a smaller volume and an out-of-band rejection greater than \(-\)30 dB, but its insertion loss is large (greater than \(-\)2 dB) [25].  
This paper proposes a tunable bandpass filter based on the micro-electro-mechanical system (MEMS) switch. It consists of a single parallel capacitive RF MEMS switch, two resonators, drive circuits, and active control modules. Resonators with different geometricalcharacteristics can filter electromagnetic waves of different frequencies. The filter of the corresponding resonator is controlled by changing the state of conduction and disconnection of the same RF MEMS switch, thereby achieving adjustable band pass filtering. Because aluminum is a relatively cheap metal material and the full structure is made of aluminum, some structures can be completed in one step, reducing the amount of photolithography. To reduce the difficulty of fabrication and reduce the production cost, we used a new all-aluminum structure to make the filter.
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 2 Materials and Methods
### Method of Comparing the Focus of a Scene Point between Images  
A three-dimensional matrix containing the focus estimations is obtained by applying the operators from the Table 3 to the acquired images. The estimation is calculated for each pixel of the captured images. In order to obtain a height map, it is necessary to compare the focus estimations in a \(z\)-axis (Figure 5).  
The image defocusing resulting from image plane displacement is a normal process. Therefore, the curve corresponding to the focus estimator in consecutive images should have the character of a normal distribution. The other authors use this relationship to estimate depth by using Gaussian function interpolation [29]. The impact of interference in real systems makes it necessary to use more robust methods.  
Two solutions were proposed:  
* Use of a maximum function--the assumption is made that the in-focus image has the highest value of the focus estimator. As the image plane moves away from the imaged surface, the image becomes increasingly blurred, reducing the value of the calculated focus estimator. The method allows the surface plane to be determined with a resolution of up to one plane;
* Use of the centre of gravity (CoG) method--the application of this method makes it possible to increase the resolution of the determination of the surface plane compared to the maximum function [30].  
The CoG algorithm involves calculating a weighted average where the weights for consecutive coordinates are the values of the calculated focus estimator (Equation (2)). An additional modification to the algorithm is to increase robustness to noise by disregarding points for which \(W_{i}\) is less than half of the maximum value of the focus estimator in a given profile (Equation (3)). The CoG function returns non-integer values of the position  
Figure 5: Diagram of the height map generation in the SFF method.  
of the focus plane. A non-integer value corresponds to the allocation of the focus plane between the image planes captured on the images.  
\[CoG=\frac{\sum W_{i}\times i}{\sum W_{i}} \tag{2}\]  
\[W_{i}=\begin{cases}W_{i}&W_{i}\geq\frac{max(W)}{2}\\ 0&W_{i}<\frac{max(W)}{2}\end{cases} \tag{3}\]  
where:  
_CoG_--the determined centre of gravity value corresponding to the position of the focus plane in the \(z\)-axis;  
_i_--coordinate corresponding to the image number (\(z\)-axis) in registered image stack;  
_W_--set of values of the focus estimator for a surface point in the image stack.
## 3 Results
### Single Cell  
#### 3.1.1 Baseline Condition  
Figure 3a shows the time evolution of the battery cell temperature under baseline conditions with \(q_{heating}\) = 4.0 \(\times\) 10\({}^{5}\) (W/m\({}^{3}\)), as evaluated from both the theoretical solution and numerical simulation. In both cases, the temperature gradually increases after the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Reference** \\ \hline Density & 500 (kg/m\({}^{3}\)) & Assumed *1 \\ Specific heat & 750 (J/kg) & [28] \\ Specific surface area & 10 (1/m) & Assumed *2 \\ Environment temperature, T\({}_{\text{env}}\) & 298 (K) & Assumed \\ Heat transfer coefficient, ht & 50 (W/m\({}^{2}\)-K) & Assumed *3 \\ External heating, \(q_{\text{heating}}\) & Single cell 4.00 \(\times\) 10\({}^{5}\) (W/m\({}^{3}\)) & Assumed *4 \\ (Baseline) & Multiple cells 3.0 \(\times\) 10\({}^{5}\) (W/m\({}^{3}\)) & Assumed *4 \\ \hline \hline \end{tabular}  
* For instance, this value is obtained when heating with a 10 W, 50 mm \(\times\) 50 mm polyimide heater.  
\end{table}
Table 1: Parameters for theoretical analysis and simulation for battery thermal runaway.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Latent heat, Q\({}_{\text{tot,i}}\) (baseline) & 3.22 \(\times\) 10\({}^{5}\) (/m\({}^{3}\)) & [19] \\ Frequency factor, \(k\alpha\) & 5.00 \(\times\) 10\({}^{13}\) (1/s) & [19] \\ Activation energy, E\({}_{\text{i}}\) & R \(\times\) 1.85 \(\times\) 10\({}^{4}\) (J/mol) & [19] \\ Constant, m & 1 & [19] \\ Constant, n & 1 & [19] \\ Constant, C & 1.01 & [19] \\ \hline \hline \end{tabular}  
*5: It varies depending on cell placement, conditions, etc. Herein, a provisional value is adopted.  
\end{table}
Table 2: Parameters of thermal decomposition reaction.  
heating starts, then rapidly rises around 200 s due to the thermal decomposition reaction, and subsequently decreases gradually. While the timing of the rapid temperature rise is slightly delayed in the theoretical solution, the profiles match well overall.  
The time evolution of the temperature rate of change (_dT/dt_) is shown in Figure 3b, and the time evolution of the unreacted fraction \(\alpha\) from the numerical simulation is shown in Figure 3c. In the numerical simulation, the heat generation rate begins to increase around 160 s, with a spike in heat generation observed around 170 s. This timing coincides with a rapid decrease in the unreacted fraction \(\alpha\). In contrast, the theoretical solution shows a linear increase in the heat generation rate starting around 190 s, reflecting the linear approximation of the heat generation rate.
## 3 Experiments
### Implementing the Metrics Calculation Tool  
Python version 3.9 was chosen as the programming language. Programming environments used are PyCharm Community and Visual Studio Code. List of used libraries presented in Table 4.  
Figure 3: Scheme of the algorithm for calculating the Monotonicity metric.  
Setting of the designed program launch parameters is carried out using the json files of the experiment configuration and the script. The program has a CLI interface and has an indication of the process of performing experiments. Calculation results are saved in CSV and JSON files. In addition, log files are saved. There is a separate module for plotting dependencies of metric values on parameters and saving them in PNG format, which is launched separately. It is possible to customize the models used, XAI methods, parameters for generating synthetic data, calculated metrics.  
There some modules implemented in the program:  
1. Synthetic data generation module. The synthetic data generation module includes classes that allow you to generate sets of features by sampling from various distributions (multivariate normal distribution, including conditional distribution according to the method described in paragraph 2.1), as well as markers for data sets using various methods (linear, piecewise -linear, non-linear function) for linear regression or classification models.
2. Module for calculating metrics of XAI methods. Each metric is implemented as a separate class: Faithfulness, Infidelity, Monotonicity. When any of these classes are initialized, an instance of the CustomData class and an instance of the machine learning model are passed. Each of the metric classes implements the evaluate function, which takes as input the initial data set, the weights obtained from the XAI method, and a parameter that controls the number of additionally generated samples for calculating the metric.
3. XAI methods application module. The module is responsible for initializing and applying the XAI methods. For the SHAP method, two classes are implemented that provide an interface for calling the shap.Explainer and shap.KernelExplainer classes included in the shap library. The library is the official implementation of the method, and its documentation contains many examples of applying the method to models of various types.
## 2 Materials and Methods
### Live/Dead Assay  
A Live/Dead(r) Viability/Cytotoxicity kit (Cat# L3224, Invitrogen) was utilized to study the cell viability and cytotoxicity in hFOB 1.19 cells in response to various concentrations (1 mM, 5 mM, 10 mM, and 50 mM) of MgO/Mg(OH)\({}_{2}\) NPs. With this two-color fluorescence-based assay, live and dead cells were determined by calcein acetomethoxy (calcein AM) and ethidium homodimer (EthD-1) dyes as a marker for cell viability and intracellular esterase activity and plasma membrane integrity. The intracellular esterase activity through the conversion of nonfluorescent cell-permeable calcein AM to intensified fluorescent calcein determined the live cells. Calcein was retained within the live cells to produce green fluorescence in the live cells (excitation/emission wavelengths of 495/515 nm), whereas EthD-1 entered through the damaged plasma membrane of the cells and showed a 40-fold enrichment of fluorescence upon binding to nucleic acids to produce a bright red fluorescence in dead cells (excitation/emission wavelengths of 495/635 nm).  
Samples were allowed to rotate in 1X phosphate-buffered saline (PBS) and sit for 10 min at RT, as mentioned in Section 2.4. Meanwhile, for the preparation of the dye solution, 18 \(\upmu\)L EthD-1 was added in 18 mL of PBS, followed by the addition of 18 \(\upmu\)L of calcein AM, to achieve 0.5 mL of dye solution per sample in a 24-well plate for MgO/Mg(OH)\({}_{2}\) NPs sample sets. Since each sample was in triplicate, for each sample set, 0.5 mL of freshly prepared dye solution was added slowly in drop-by-drop fashion. The 24-well plates were allowed to sit in the dark for 30-40 minutes, followed by observation under a fluorescence microscope (EVOS XL AMG).  
Live (green) and dead (red) cells were counted utilizing Image J software (Version 1.48, National Institutes of Health, Bethesda, Maryland, USA). The percent (%) live cells was calculated as  
\[\text{Percent }(\%)\text{ Live cells}=\frac{\text{live cells}}{\text{Total number of cells}}\times 100. \tag{1}\]  
Here, we calculated the total number of cells by adding the number of live cells to the number of dead cells. Further, the results for the percent (%) live cells were analyzed statistically by one-way analysis of variance (ANOVA) techniques using Prism version 5.0 (GraphPad Software, Inc., San Diego, CA, USA). The difference between means was considered statistically significant when the \(p\)-value was \(\leq\)0.05 when compared to controls. The results obtained were expressed as mean \(\pm\) standard error (SE) or standard deviation (SD).
## 5 Discussion  
### Reliability Analysis of 3D Deformation Fields Obtained from Different Data  
To validate the reliability of the three-dimensional deformation field results, GNSS horizontal and leveling vertical deformation velocities were used to assess the accuracy of the three-dimensional deformation field. Through comparison, it was found that the standard deviations of residuals in the east-west, north-south, and vertical deformation fields were 1.2 mm/yr, 1.4 mm/yr, and 0.88 mm/yr, respectively. Figure 9 depicts both the probability distribution of residuals and a comparison between observed values and results from the integrated GNSS-InSAR three-dimensional deformation field analysis. The fusion  
Figure 8: The slip rate and locking depth of the creep section of the XSHFZ. (**a**) slip rate; (**b**) locking depth; (**c**) the velocity profile of P1; (**d**) the velocity profile of P2; (**e**) the velocity profile of P3; (**f**) the velocity profile of P4; (**g**) the velocity profile of P5; (**h**) the velocity profile of P6.  
approach demonstrates robustness in obtaining reliable three-dimensional deformation field measurements.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 3 Detailed Implementation Steps
### Implementation Steps for Extracting Watermark  
The extracting procedure mainly includes the following steps:  
1. Filter \(A^{\prime}\) to reduce the out-of-band noise by low-pass filter.
2. Divide \(A^{\prime}\) into \(M\) audio segments \(A_{l}^{\prime}\), and \(M\geq L+16\).
3. Perform \(r\)-level DWT on \(A_{l}^{\prime}\) to get \(De^{\prime}(r,n)\).
4. Separate \(De^{\prime}(r,n)\) into \(De_{1}^{\prime}(r,j)\) and \(De_{2}^{\prime}(r,j)\), and then implement DCT on them to obtain \(C_{1}^{\prime}(r,j)\) and \(C_{2}^{\prime}(r,j)\).  
Figure 3: Framework diagram of this algorithm.  
* [Step 5]: Calculate \(M^{\prime}_{c1}\) and \(M^{\prime}_{c2}\).
* [Step 6]: If \(M^{\prime}_{c1}>M^{\prime}_{c2}\), the extracted binary is '1', otherwise, it is '0'.
* [Step 7]: Repeat Step 3 to Step 6 until the end of the extracting process.
* [Step 8]: When a group of \"11111111\" start sign appears in the extracted binary information, the watermark begins to be extracted. When a group of \"0000 0000\" end sign is present, the extraction is finished.
* [Step 9]: Generate the binary chaotic sequence \(c(q)\) according to Formulas (3) and (4), and then obtain the extracted picture according to Formula (24).
## 4 Experimental Results  
### Model Verification  
#### 4.1.1 Comparison with the TC Best-Track Wind Speed Estimates  
The training dataset and test dataset in Table 1 were used to verify the performance of our estimation method. The results of the validation are shown in Figure 7. The horizontal axis is the maximum wind speed estimated by the proposed model, and the vertical axis is the CMA TC best-track intensity data. Unexpectedly, the accuracy of the training dataset is higher, but there was little difference between the training dataset and test dataset when the intensity of TC is TY, STY, and super TY. The R\({}^{2}\) of our method verification result for the training dataset was 0.89, the RMSE was 4.19 m/s, the MAE was 3.20 m/s, and the BIAS was \(-0.008\) m/s. The R\({}^{2}\) of our method verification result for test dataset was 0.83, the RMSE was 5.94 m/s, the MAE was 4.62 m/s, and the BIAS was \(-0.43\) m/s. Figure 7b-f shows the differences between model-estimated TC intensity and the CMA best track data in four subgroups. Figure 7b,e relate to lower-intensity TCs, i.e., tropical depressions, tropical storms, and severe tropical storms. Figure 7c,f relate to typhoons, severe typhoons, and super typhoons. Compared with Figures 4 and 5, we can see from Figure 7 that using combined HY-2A and SSMIS is better than the use of either HY-2A or SSMIS separately.  
Figure 6: Parameters of different numbers used to determine the best estimation model. The X-axis is the number of parameters, the left Y-axis is RMSE, and the right Y-axis is R\({}^{2}\).
# Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks
## 3 PUTPOSH Model
### Framework
#### 3.2.1 Spectrum Sensing  
In the spectrum sensing module (Figure 1), the PU movement is continually observed by the CRU during transmission on the currently occupied channel. The spectrum sensing part can be achieved by integrating one of the existing sensing methods, i.e., matched filter (MF) [15], cooperative sensing (CS) [16; 42] or energy detection (ED) [17; 43; 44]. In the presented PUTPOSH model, the energy detection technique is used in the spectrum sensing part for the reason of its working difficulty and for the low computational features [45; 46; 52]. It is considered as a generalized approach when it is compared with the MF and CS. In the ED approach, the recipients do not need any information on the PU's signal [18; 53] and the signal is observed by comparing the given threshold value with the energy detector's output. The threshold value is fixed but depends on the earlier arrival of the PU. The apparent energy stage is increased when PU arrives on its licensed band. When an energy level's threshold is specified, the apparent energy stage is tested multiple times either in the presence and in the absence of the PU. As the power of the arriving signal increases than the given threshold, the handoff process is initiated to execute handoff actions.  
Figure 1: The proposed primary user traffic pattern based opportunistic spectrum handoff framework.