esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Plasmids and Cell Transfection  
4.4.1. miR-Overexpressing Vectors  
The effects of the selected miRNAs on the mRNA expression level of _PCSK7_ were examined 48 h after the transfection of the overexpressing miR8 in the Huh7 and HEK93T cell lines. Plasmids encoding pEGFP-C-miR-125a-5p, miR-143-3p, miR-409-3p, and miR-320a-3p and their corresponding control (miR-NC) were constructed. The nominated miRNA genes were amplified and cloned downstream of the _GFP_ gene into the pEGFP-C1 vector (Clontech, Japan). All the primer sequences used are available in Supplementary Table S1.  
Pre-miR miRNA precursor-overexpressing vectors (300 ng) were transfected in the Huh7, HEK293T, and HepG2 cell lines using FuGENE HD (Promega Corporation, Madison, WI, USA) in 12-well plates. The transfections were conducted in triplicate, and mock-related counterpart vectors were utilized as controls.
# Development of Dialogue Management System for Banking Services
## 4 Methods
### Language Identification  
Another problem that occurred during the development was handling messages in different languages used by the bank customers that are not fully supported by the NLU model of the chat-bot. The main reason for having different languages in our dataset is that certain groups of people in Azerbaijan speak Russian or English. For this reason, we get about 3% of the inquiries in non-Azerbaijani, most being in Russian, English, Turkish, etc. To tackle listed problems, a Language Identification component has been added to the bot model. Two main approaches can be implemented to identify the language of the message: applying existing pre-trained language models and developing custom language identifiers.  
First, the available approaches that support the Azerbaijan language, such as Facebook's \"fastText\" language identification model and \"langid\" Python package for language identification, were investigated. The \"langid\" package is trained over Naive Bayes Classifier using a mixture of byte n-grams. The package itself comes together with an embedded model, which supports 97 languages and is trained using the data extracted from variety of domains, in order to handle diverse types of textual data [53]. However, \"fastText\" text-based language identification model can identify 176 languages, including Azerbaijan [6]. Using both BoW and character n-grams approaches in the model architecture, as well as implementing hierarchical softmax, \"fastText\" achieves high performance and reduces computation time. For our work, \"fastText\" language identification has been utilized. The \"fastText\" model has two language models: the light model with the file size of 917 kB and the heavy model with the file size of 126 MB, which is considered to be more accurate. We implemented language identification using both models. Both models were pre-trained on a clean dataset from Wikipedia, Tatoeba, and SETimes, and the results were not accurate with respect to our noisy online chat dataset.  
Second, by implementing machine learning algorithms on the domain dataset, custom methods were established. For this purpose, we used Naive Bayes, Logistic Regression, Linear SVM, and KNN classifiers to train a two-class language identification model. These models are used to distinguish the Azerbaijan language from other languages, primarily English and Russian.
## 2 Improved Wavelet Threshold Denoising Method  
### Classical Wavelet Transform Methods  
A wavelet is a small range of waves with a limited length and a zero average value. Compared with the traditional Fourier transform using the infinite trigonometric function basis, the wavelet transform takes advantage of the attenuated and limited wavelet basis. Specifically, the wavelet basis can obtain the sequence characteristics in the frequency domain and time domain simultaneously, which enables a localized signal analysis. Continues wavelet transforms and discrete wavelet transforms are two frequently used methods.  
In continuous wavelet transforms, the given signal is projected on a continuous family of frequency bands. The function is the sum of the products of the target signal with the scaled and shifted wavelet basis over the entire time period, as shown in Equation (1):  
\[W_{g,s}(a,b)=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}s(x)g^{s}(\frac{x-b}{a} )dx \tag{1}\]  
where \(a\) defines the scale parameter and \(b\) defines the shift parameter. The former reflects the frequency features of the signal, and the latter reflects the temporal features.  
For a system containing big data, such as structural health monitoring system, discrete wavelet transform (DWT) is more suitable for practical analysis. DWT is obtained by selecting a discrete subset of the scale and shifting parameters according to the power of 2. In the previous continuous wavelet transform formula, let \(a=2^{j},\ b=k2^{j}\), where \(j\) is the number of discrete subset layers. The function of the discrete wavelet transform is shown in Equations (2) and (3).  
\[W_{g,s}(j,k)=\int_{-\infty}^{\infty}s(x)g_{(j,k)}^{*}(x)dx \tag{2}\]  
\[g_{(j,k)}^{*}(x)=\frac{1}{\sqrt{2}^{j}}g\bigg{(}\frac{x-k2^{j}}{2^{j}}\bigg{)} \tag{3}\]  
There are various methods to implement DWT. The Mallet algorithm is a classic method [20]. Through the repetitive decomposition process in the Mallet algorithm, the signal can be decomposed into several series of detail coefficients and one series of approximation coefficients. A schematic diagram of the Mallet algorithm is shown in Figure 1.
## 3 Security Analysis of ULRAS Protocol
### De-Synchronization Attack
* Adversary selects \(i^{\prime}_{sub}\in\{1,2,3,4\}/\{i_{sub}\}\) and calculates \(M^{\prime}_{3}=M_{3}\oplus((i_{sub}\oplus i^{\prime}_{sub})\lll x)\). 6. The adversary sends \(M_{2}\) and \(M^{\prime}_{3}\) to the tag. 7. Upon receipt of the messages, the tag calculates \(M^{\prime}_{2}=RR(RR(ID\oplus R_{t}\oplus T_{R},ID\oplus R_{t}),K+R_{t})\) with its local values and then verifies whether \(M^{\prime}_{2}\stackrel{{? }}{{=}}M_{2}\), which it is because the adversary has not changed \(M_{2}\). Hence, the tag: * successfully authenticates the back-end server; * gets \(i^{\prime}_{sub}\), where \(i^{\prime}_{sub}\
eq i_{sub}\). * generates a new sub-key as \(subkey=Rot(K(i^{\prime}_{sub}),K\oplus R_{t}\oplus T_{R})\); * and finally updates its \(IDS,K\) and \(T_{t}\) as below: \(IDS_{new}=Rot(IDS\oplus R_{t},K\oplus R_{t}\oplus T_{R})\); \(K_{new}\) generated by replacing \(K_{i^{\prime}_{sub}}\);  
In the above attack, the tag updates \(K_{i^{\prime}_{sub}}\) and \(i^{\prime}_{sub}=i_{sub}\oplus\Delta\
eq i_{sub}\) while the reader updated \(K_{i_{sub}}\). In this attack, if \(R_{t}\oplus T_{R}\) is not rotation invariant, the adversary's success probability to de-synchronize the tag and the reader would be '1' and its complexity is only one run of protocol and doing some offline computation and sending some messages. It should be noted in the given attack that the tag authenticates the reader and updates its parameters. Hence, keeping a record of old parameters by the back-end server does not prevent this attack and so the ULRAS protocol is not a secure protocol for use.
## 2 Materials and Methods
### FHIR Terminology Services  
The HL7 standard Fast Healthcare Interoperability Resources (FHIR) [30] is currently gaining traction in interoperable data modeling on the national and international level [31]. Apart from the majority of FHIR resources typically used to capture patient-centered information, FHIR also defines a range of Terminology Services in its Terminology Module [32]. These consist mainly of the resources CodeSystem (for representing clinical terminologies and classifications), ValueSet (to state a subset of codes taken from one or more CodeSystems) and ConceptMap (for stating the relation between codes of different origin), as well as a number of operations to query the former resources in a standardized fashion. The FHIR resources and operations relevant for this project are listed in Table 1, including a brief explanation of their usage.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Resource Type** & **Operation** & **Explanation** \\ \hline \multirow{4}{*}{CodeSystem} & \multirow{2}{*}{\$lookup} & Lookup information for a specific SNOMED CT concept. \\  & & Query the hierarchical relationship between two codes to determine a concept\textbackslash{}s domain. \\ \cline{1-1}  & & Check a given PCE for syntactic and semantic correctness. \\ \hline ValueSet & \$expand & Query the subset of possible concepts via ECL. \\ \hline CodeSystem supplement & & Save and load created PCEs. \\ \hline \hline \end{tabular}
\end{table}
Table 1: FHIR resources and operations needed within this project.  
Figure 3: Excerpt of a SNOMED CT Expression Template with static and variable components, as well as cardinalities.  
Consequently, a terminology server based on the FHIR standard is required, which further supports the use of SNOMED CT's Expression Constraint Language as well as postcoordination. CSIRO's Ontoserver [33] was chosen as the only candidate fulfilling the latter requirement in particular [34].  
Here, postcoordination support includes syntactic and semantic validation, as well as display string generation for existing PCEs and subsumption testing for pre- and postcoordinated expressions. Saving a PCE for later usage or inclusion in value sets requires its addition to SNOMED CT via a CodeSystem supplement [35].  
A local instance of Ontoserv running version 6.12.1 with enabled support for PCEs and providing several recent versions of the International Edition of SNOMED CT was available during development and for testing purposes.
## 4 Results and Analysis  
In this paper, we used the VGG16 network, a structure commonly used in VGG Net, with a 13 + 3 network layer structure, i.e., a convolutional layer plus a fully connected layer. Our experiments were based on channel-level sparsity training, and the inputs and outputs of channels with adjustment parameters close to 0 and their corresponding weights were pruned in the trained model. We used global thresholds as a metric. The use of global thresholds avoids too many iterations and too much retraining.  
According to the evaluation criteria, the number of parameters and FLOPs were selected as the criteria after pruning the model; the loss function was used as the important criteria when we trained the model and the accuracy rate was utilized as the criteria of the lip-recognition result. Additionally, the accuracy rate meant that the number of correctly identified samples was divided by the total number of samples.  
The network was pruned with global pruning rates of 20%, 40%, and 60%, and the number of training rounds was the same for each pruning rate during training. It was verified that pruning was effective in removing the redundancy of network parameters, FLOPs were compressed, and memory and running memory were reduced. The results are shown in Table 3 (where FLOP means the times of floating-point operations performed by the computer per second, memory means computer disk memory, and Mem R + W means running memory occupied by reading and writing), which lists the original parameters of the VGG16 network and the changes in the parameters under each pruning rate. We can see that the value of each index decreased significantly after pruning.  
After compressing the network, experiments compared algorithm convergence, algorithm recognition speed, and recognition accuracy between the Pruned-VGG16 and the original network under the three pruning rates on the self-made dataset.
## 3 Finite Element Analysis of a Railway Track Slab with a Gap
### Analysis of Computed Results by Finite Element Analysis
#### 3.2.3 Comparison of Maximum Acceleration Level  
After applying an impact load on the concrete slab surface, the maximum acceleration \(a_{\max}\) generated on the surface was investigated.  
As described in Figure 6, maximum acceleration \(a_{\max}\) was obtained on the concrete surface with change of impact load size at a given assumed soil stiffness of 40 MPa. As can be seen in Figure 6, in the case of without a gap, it was easily verified that the maximum acceleration \(a_{\max}\) increased with distance proportionally somehow from the impact position. In addition, near the center, maximum acceleration \(a_{\max}\) was lower than the values computed at the outside area. The maximum acceleration \(a_{\max}\) increased also with impact load size.  
However, in the case of with a gap, the maximum acceleration \(a_{\max}\) was larger near the gap center where the impact load was applied and compared with other places. Notably, the maximum acceleration \(a_{\max}\) produced a peak at the center, then decreased with distance from the center. The maximum acceleration \(a_{\max}\) contour looks like a conical shape. In the case of with a gap, Figure 6 illustrates that the nearer the loading point in the case of a gap, the higher the \(a_{\max}\) generated on the top surface of the concrete slab. All values of \(a_{\max}\) were obtained by FFT of generated data.  
After applying an impact load on the concrete slab surface, the maximum acceleration amplitude generated in the air was investigated. Figure 7 illustrates that much higher maximum amplitudes were generated intensively near the impact loading point in the case of with a gap as opposed to without a gap. Only a slightly small maximum amplitude was generated at the loading point in the case of without a gap. Additionally, increments of the maximum amplitude with increase of impact load level were clearly identified in the gap case compared to without a gap case.  
Figure 5: Comparison of acceleration amplitude in frequency domain in cases of with gap and without gap underneath the slab. (**a**) On concrete slab surface. (**b**) In the air.  
Figure 6: Comparison of maximum acceleration amplitudes on concrete surface in cases of with and without gap at different positions from impact loading point. (**a**) Without gap. (**b**) With gap.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 2 Positioning Architecture
### Slave EKF-Based Doppler Positioning Model
#### 2.2.2 LEO Downlink Measurement Model
\[y(k)=Hx(k)+v(k), \tag{14}\]  
\[z\equiv\big{[}\dot{\rho_{1_{1}}},\,\ldots,\,\dot{\rho_{m_{n}}}\big{]}^{T}, \tag{15}\]  
Moreover, the measurement matrix \(H\) was obtained using the Jacobian matrix of the nonlinear measurement function \(h(x(k))\). Finally, the matrix \(H\) is given by Equation (16). \[H\,=\,\,\left[\begin{array}{cccc}0_{1\times 4}&h_{p_{1,1}}^{T}&-h_{p_{1,1}}^{T}& 0&c\\ \vdots&\vdots&\vdots&\  
\[h_{v_{m_{n},n}}=\frac{\Delta p_{m_{n}}}{|\Delta p_{m_{n}}|_{2}}, \tag{18}\]  
\[\Delta p_{m_{n}}=[p_{r}-p_{m_{n}}]\ \Delta v_{m_{n}}=[v_{m_{n}}-v_{r}], \tag{19}\]
## 5 Simulations
### Reference-Tracking Performance in System with Varying Payloads and Uncertainties  
To test if the composite controller is more adaptive in the condition of varying payloads, three working conditions are considered. The first working condition is that the payload mass is gradual changed from 600 kg to 0 kg, starting from 0 s to 6 s. The other working conditions are that the payload mass is suddenly changed from 600 kg to 200 kg at 6 s and 2 s.  
Figure 12 shows the tracking performances and vibration suppression of the manipulator with uncertainties in different conditions with varying payloads. Table 4 is the composite controller performance comparisons with uncertainties, where three evaluation criteria for the trajectory tracking errors are adopted: the root mean squared error (RMSE), integral squared error (ISE), and integral absolute error (IAE) [36].  
Figure 12a-c indicates that the tracking error converges faster than the sudden change of mass, when the payload mass changes gradually. The settling time is about 1 s. In addition, the convergence rate of tracking error is almost the same whether the sudden time is 2 s or 6 s. Their error curves nearly coincide (see Figure 12a-c) and their RMSE, ISE and IAE are almost the same (see Table 4). Therefore, the sudden change in mass does not affect the tracking performance of manipulator system, whether in the transition process or in the steady state.
# Acoustic TDOA Measurement and Accurate Indoor Positioning for Smartphone
## 3 Robust TDOA Measurement Method
### Static Robust Positioning Algorithm Base on TODA
From Figure 11, it can be seen that the positioning result is affected by Gaussian noise. The ML localization method exhibits high stability with the standard deviation of noise being set as 1. In order to illustrate the problem, we use the ML method to conduct a positioning simulation for 5 test points in scenario 1. The cumulative distribution function (CDF) diagram is as follows:  
From Figure 12, static positioning results are relatively stable by using the ML method. So, we chose ML in the static positioning experiment. The positioning error formula used in this article is as follows:  
\[\text{PE}=\sqrt{\left(x-x^{\prime}\right)^{2}+\left(y-y^{\prime}\right)^{2}} \tag{9}\]  
where PE is the positioning error, (\(x,y\)) is the true position, and (\(x^{\prime},y^{\prime}\)) is the estimation.
## 1 Introduction
The CI affects the distribution and interception of canopy inner radiation, further controlling carbon uptake and the transformation of energy and evapotranspiration. In addition, the CI is crucial for the estimation and explication of vegetation evapotranspiration and photosynthesis [39] and plays a vital role in ecological and methodological models [40]. Ignoring the CI would lead to at least 12% overestimation of global GPP [39]. The contribution of shaded leaves to the total GPP is 56, 40, and 39% in EBF, ENF, and shrubs, respectively [41]. Additionally, the CI has a large influence on the estimation of the canopy level, which may be due to its effect on the ratio of sunlit leaves to shaded leaves (e.g., the TL-LUE model) or changes in the PAR interception area [39]. Currently, only a constant CI estimation (\(\Omega\)) in a one-year cycle (OYC) is applied in the TL-LUE model, but CI differences can be observed in different seasons, although the uncertainty of seasonal disturbances in the CI is obvious in most cases, with a lower CI value in the leaf-on season and a higher CI value in the leaf-off season, particularly in deciduous broadleaf forests and mixed forests [42]. Furthermore, CI has been able to detect seasonal changes in some vegetation structures, especially in deciduous broadleaf forests and shrubs [43]. Therefore, the TL-LUE model using the \(\Omega\) constant undoubtedly leads to uncertainty in GPP estimation in theory since the seasonal difference in vegetation CI leads to a change in the APAR values between sunlit and shaded leaves, which would affect the accuracy of GPP estimation in the TL-LUE model. In addition to having ample carbon cycle observation data, such as crop yield inventory, SIF, atmospheric OCS, and particularly flux data measured by eddy covariance towers, North America also spans a wide range of latitudes and has a wide variety of vegetation types. As a result, it is a reasonable and suitable place to research the carbon cycle in terrestrial ecosystems. The study intends to improve the GPP estimation in the TL-LUE model by considering the seasonal changes in CI (TL-CLUE). The OYC for leaf life is divided into two or three seasons based on the MODIS land surface phenology products (MCD12Q2), and the corresponding values of \(\Omega\) to the seasons are estimated. We simulate the eight-day GPP of North America during 2002-2020 by the TL-CLUE model driven by meteorological and vegetation data. This study provides an alternative way to improve GPP estimation and provides insights into the carbon cycle for different vegetation types across North America.
# Optimization of Microalgae-Bacteria Consortium in the Treatment of Paper Pulp Wastewater
###### Abstract  
The microalgae-bacteria consortium is a promising and sustainable alternative for industrial wastewater treatment, since it may allow good removal of organic matter and nutrients, as well as the possibility of producing products with added value from the algae biomass. This research investigated the best bacterial and microalgae inoculation ratio for system start-up and evaluation of removing organic matter (as chemical oxygen demand (COD)), ammonical nitrogen (NH4\({}^{+}\)-N), nitrite nitrogen (NO2\({}^{-}\)N), nitrate nitrogen (NO3\({}^{-}\)-N), phosphate phosphorus (PO4\({}^{3-}\)-P) and biomass formation parameters in six photobioreactors with a total volume of 1000 mL. Reactors were operated for 14 days with the following ratios of pulp mill biomass aerobic (BA) and _Scenedensmus_ sp. microalgae (MA): 0.1 (PBR1), 1.0 (PBR2), 1.1 (PBR3), 3.1 (PBR4), 5.1 (PBR5), and 1.3 (PBR6). Results show that COD removal was observed in just two days of operation in PBR4, PBR5, and PBR6, whereas for the other reactors (with a lower rate of initial inoculation) it took five days. The PBR5 and PBR6 performed better in terms of NH4\({}^{+}\)-N removal, with 86.81% and 77.11%, respectively, which can be attributed to assimilation by microalgae and nitrification by bacteria. PBR6, with the highest concentration of microalgae, had the higher PO4\({}^{3-}\)-P removal (86%), showing the advantage of algae in consortium with bacteria for phosphorus uptake. PBR4 and PBR5, with the highest BA, led to a better biomass production and sedimentability on the second day of operation, with flocculation efficiencies values over 90%. Regarding the formation of extracellular polymeric substances (EPS), protein production was substantially higher in PBR4 and PBR5, with more BA, with average concentrations of 49.90 mg/L and 49.05 mg/L, respectively. The presence of cyanobacteria and Chlorophyceae was identified in all reactors except PBR1 (only MA), which may indicate a good formation and structuring of the microalgae-bacteria consortium. Scanning electron microscopy (SEM) analysis revealed that filamentous microalgae were employed as a foundation for the fixation of bacteria and other algae colonies.  
microalgae-bacteria consortium; paper pulp wastewater; photogranules; organics removal; nutrient removal; biomass sedimentability +
Footnote †: journal: Applied Sciences