esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In a recent report, a 25-um thick NZTO (Na\({}_{2}\)Zn\({}_{2}\)TeO\({}_{6}\)) film formed by the AD method that possessed identical crystal structure with base powder was shown to exhibit electrical conductivity that is applicable for thin film sodium-ion battery [34]. In the field of biomedical, it was demonstrated that the AD method can be used for fabrication of dense silicon-substituted hydroxyapatite coatings on commercial titanium exhibiting excellent adhesion strength and cell culture compatibility compared to only titanium and pure hydroxyapatite coating [35]. These studies indicate the potential of the AD method for rapid fabrication of functional films at room temperature which is not only energy-saving but also applicable on substrates with low temperature stability such as polymeric substrates. In this work, a systematic study has been conducted to investigate the effect of deposition cycles on the structural, optical and dye-sensitized solar cells (DSSC) photoconversion efficiency of aerosol-deposited ZnO thin films. As there is yet any study that reported on the DSSC performance of ZnO films obtained via the AD method, the finding from this study would be beneficial in the development of rapid film formation for renewable energy application in room temperature. \begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Deposition Technique** & **Equipment Cost** & **Thickness Uniformity** & **Reference** \\ \hline
1 & Sol-Gel & Low & Uniform coverage & [15] \\
2 & Hydrothermal & Low & Thick film & [16] \\
3 & Dip-Ceating & Low & Porous and crack & [18] \\
4 & Sputtering & High & High and dense film & [28] \\
5 & CVD & High (complex steps) & - & [20] \\
6 & ALD & High & Smooth surface & [21] \\
7 & PLD & High & Columnar growth & [22] \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of deposition techniques for ZnO films.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 4 Experimental Results and Analysis
### Simulation Setup  
We consider class \(A\) EDs (\(N\)), uniformly distributed around a single GW within a 6-km radius. These EDs follow the frequency regulation of the European region, where the UL duty cycle of the EDs and GW is limited to 1% and 10%, for the default channels, respectively. The GW and ED antenna heights are set to 15 and 1.5 m, respectively. EDs mobility follows a random walk two-dimensional (2-D) mobility model. EDs choose a random speed of between 0.5 to 1.5 m/s (in [31], 2 m/s is used for outdoor positioning) and changes direction after every 1000 m [32]. Every ED transmits \(\lambda\) packets/day during four days of the simulation time, where the results are generated using an average of 10 simulations with different seeds. Table 2 presents the rest of the simulation parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation time [days] & 4 \\ GW & 1 \\ \(\lambda\) & 24 packets/day \\ Packet length [bytes] & 51 [24] \\ UL packet transmission limit & 8 \\ Path loss exponent & 3.76 [33] \\ Propagation model & log-distance \\ Shadowing & de-correlation distance = 110 m [34] and \\  & variance = 6 dB [35] \\ ED movement speed [m/s] & 0.5\(\sim\)1.5 \\ Transmit power [dBm] & 2\(\sim\)14 \\ Frequency region & EU-868 \\ Channel bandwidth [kHz] & 125 \\ Coding rate & 4/8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
Figure 5: Smoothed SNR using exponential moving average-based (EMA) filter.
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
## 6 Exosomes Innate Functions  
Exosomes function will vary depending on their origin, cargo, and recipient cells. These innate properties can serve for further development and upgrading of exosomed-based DDS. Exosomes' most fundamental and widely recognized function is to mediate cell-cell signalization, thus regulating cell physiological functions and responses. This mechanism relies on the exchange of molecules described in the previous chapter.  
Additionally, to date, many great articles arise with exosome function/effect description [65; 85]. Thus, in this article, we only mention properties influencing DDS. Exosomes derived from bone marrow, macrophages, and tumors were described as influencing the inflammatory response, either prolonging allograft rejection time or activating inflammatory cells to the target tumor [86; 87]. Exosomes also take part in the immune response and antigen presentation. Due to this fact, they can stimulate the immune response by MHC class I or II antigens presentation, which is not desirable in the case of DDS or allogenic therapeutics and can decrease their body circulation time. The current solution to this problem is exosomes isolated from mesenchymal or stromal stem cells [88].  
As mentioned, the natural exosomal cargo consists of various nucleic acids, proteins, and lipids, which can directly influence cell behavior and expression pattern. Similarly, artificial siRNA, mRNA, and miRNA delivered by exosomes will regulate cell expression as reported [89]. In a similar manner, proteins can also act as procancerogenic agents, e.g., exosomes containing high levels of TGF-\(\beta\) and PGE2 will promote tumor growth [90]. On the other hand, dendritic-cell-derived exosomes containing MHC class I molecules and tumor antigens are able to activate T-cell anticancer response [87].  
Exosomes are superior to artificial lipovesicles in terms of biocompatibility and internalization. They can also serve to reach hard-to-access and brain tissues due to the possibility of targeted delivery and blood-brain-barrier crossing, respectively [28].
## 2 Results and Discussion
### Method Validation
#### 2.1.2 Specificity  
Specificity is defined as the capability of the method to differentiate between the analyte and other components present in the sample matrix [49]. It allows for the unequivocal assessment of the analyte in a sample. Specificity evaluation was carried out by injecting blank matrix samples into the HPLC system [46; 49; 50]. No interfering matrix peaks were encountered in the blank samples, demonstrating that the excipients used did not interfere, indicating specificity of the method. The excipients used for the preparation of the physical mixture used as a blank sample in the case capsules were corn starch, monohydrated lactose, microcrystalline cellulose, polyvinyl-pyrrolidone and talcium (Merck, Darmstadt, Germany) and magnesium stearate, titanium dioxide (Sigma Aldrich, Taufikirchen, Germany).
## 2 Data and Segmentation Methods
### Dataset
#### 2.1.1 Medical Image Data Formats  
Processing 3D medical image data formats includes converting the provided input data from labelled medical data formats into simple stack of images and can be a confusing task for someone who does not know any details of different medical formats. Medical image represents an internal structure of an anatomic region in the form of an array of elements called pixels in 2D or voxels in 3D. It is a result of a sampling/reconstruction process mapping numerical values to positions in the space. Medical image data formats include common information such as pixel depth, metadata, pixel data and photometric interpretation. These formats also store more than one 3D data scans and therefore can be used for 4D image processing. All data used in this paper consist of only one 3D scan per patient [18].  
Common medical data formats include:  
* NRRD--Nearly raw raster data, general medical image data format, data suffixes differ from versions with attached header (.nrrd) and detached header (.raw/.mhd) where metadata are stored separately from the image data.
* Nifti--Neuroimaging Informatics Technology Initiative, this format is usually used for brain imaging data and uses suffix.nii.
* Dicom--Digital Imaging and Communications in Medicine, general image data format and most commonly used for different medical image data, uses suffix.dcm.
* Analyze--Analyze 7.5 format which uses suffixes.img/.hdr, also a detached header format.  
An important part of processing medical images is their representation in the 3D vector space. This can be quite challenging and during this process loss of part of the image information can occur. During our data preparation process we converted NRRD 3D scans into image slices and lost the information about image spacing in the third axis. This information had to be recovered and added in order to generate 3D models of the segmented tissue.
## 3 Methodology
### Linear Discriminant Analysis (LDA) Method  
LDA is a classification method that has been successfully applied in many fields. For a sample of n observations distributed in \(M\) groups, the objective of the LDA is to produce a new representation space where we can better distinguish the \(M\) groups [29, 30]. It uses the between-class scatter matrix \(S_{B}\) to evaluate the separability of different classes and within-class scatter matrix \(S_{W}\) evaluates the compactness within each class.  
Let \(X=(x_{1},\ x_{2},\ldots,\ x_{n})\) be the training set, each \(x_{i}\) xi belongs to a class \(C_{l}(l=1,\ 2,\ldots,M)\).  
Between-class scatter matrix \(S_{B}\), within-class scatter matrix \(S_{W}\), and total-class scatter matrix \(S\) are defined by (6), (7) and (8).  
\[S_{W}=\sum_{l=1}^{M}\sum_{i=1}^{n_{l}}(x_{i}-\mu_{l})(x_{i}-\mu_{l})^{\prime} \tag{6}\]  
\[S_{B}=\sum_{l=1}^{M}(\mu_{l}-\mu)(\mu_{l}-\mu)^{\prime} \tag{7}\]  
\[S=S_{w}+S_{B} \tag{8}\]  
with:  
* \(n_{l}\) is the number of data points in class \(L\),
* \(\mu_{l}\): The center of gravity of class \(L\),
* \(\mu\): The center of gravity of all observation.  
The principle of LDA is to find vector projection \(v_{1}\) that maximizes the ratio: between-class/ within-class scatter. For this purpose, the objective function of LDA is defined by (9).  
\[F(v_{1})=\frac{v_{1}S_{B}v_{1}^{\prime}}{v_{1}S_{W}v_{1}^{\prime}} \tag{9}\]  
Figure 3: Spectral density of (**a**) ball faulty bearing (**b**) inner ring faulty bearing.  
It can be shown that the optimal vector projection \(v_{1}\) is the eigenvector corresponding to the largest eigenvalues of \(S_{W}^{-1}S_{B}\). The projection with maximum class separability information corresponds to the eigenvectors whose eigenvalues are the highest.  
We will present in Section 4.1 the bearing fault diagnosis by using LDA approach.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 5 Simulations
As an indication of the computational efficiency of the proposed algorithm, our simulations indicate a ratio of required CPU time of the proposed algorithm relative to NLSE is Proposed algorithm:NLSE = 1:8.83.
## 3 Positioning Based on DVB-T
### Range Measurement
#### 3.2.2 Correlation in the Ideal Case  
In the ideal case of a single emitter in a noise-free channel, without multipath and frequency offset, the correlation, \(R_{M}(m)\), can be analytically evaluated for \(m\in\Delta_{m}\), where \(\Delta_{m}\) is a discrete-time interval containing all the possible delays between the incoming signal and the local replica (expressed in number of samples). By substituting \(d_{p}^{\prime}=d_{p}^{k}\) in Equation (9), it is possible to show [9] that the absolute value of \(R_{M}(m)\) is:  
\[|R_{M}(m)|=\frac{16}{9}\sigma_{p}^{2}\alpha\left|\frac{\sin(\pi Bm)}{\sin\left( \frac{\pi Bm}{N_{p}}\right)}\right| \tag{10}\]  
where \(\sigma_{p}^{2}\) is the power of the data and TPS subcarriers and \(B\) is defined as:  
\[B=\frac{P_{I}N_{p}}{N_{FFT}} \tag{11}\]  
where \(P_{I}\) is the interval between two adjacent SPSs. Notice that \(R_{M}(m)\) is a periodic function with a period equal to \(N_{FFT}/P_{I}\). This period restricts the range of delays that can be estimated. From this point of view, the quadruplets of Figure 5 are more convenient as \(P_{I}\) decreases from 12 to three.  
In order to mitigate the side lobe of the correlation function, we use a windowing technique. By adopting a hamming window, the correlation becomes:  
\[R(m)=\frac{1}{N_{p}}\sum_{p\in p_{s}(k)}d_{p}^{\prime}P_{p}^{k*}w(p) \tag{12}\]  
where \(w(p)=0.53836-0.46164\cos(2\pi p/(K-1))\) is the hamming window and \(K\) is the number of non-null subcarriers.
# Efficient CORDIC Iteration Design of LiDAR Sensors' Point-Cloud Map Reconstruction Technology +
## 3 Proposed Method  
The mobile LiDAR sensor circuit design is mainly used in autonomous vehicles and environmental sensing. Due to the trend of autonomous vehicles development in the future, the main goal of this paper is to design a high-performance mobile LiDAR sensing circuit to realize LiDAR packet decoding and improve the traditional CORDIC architecture to simplify the operation of 3D LiDAR point-cloud map reconstruction. In this section, we propose an algorithm that simplifies the number of CORDIC rotation iterations. We explain in turn how the packet is decoded and how the features of CORDIC are employed to reduce the iterative operation.
## 3 Results
### Validation of System Performance  
Next, to validate the system's efficiency in mapping the fine surface profile in wet conditions, we imaged a positive 1951 USAF resolution test target (R3L3S1P, Thorlabs Inc., Newton, NJ, USA). The resolution target was chosen as a well-defined sample with nanoscopic roughness. A water droplet was inserted between the resolution target and the reference glass, and, similar to the processes depicted in Figure 2, the system was exposed to the surrounding vibration during the PSI process. Then, to capture a surface profile for use in reconstruction, the resolution target was also scanned using an atomic force microscope (AFM, XE-100, Park Systems), which is a standard metrology microscope used for high-precision surface mapping. Because the scan range of the AFM is limited to 40 \(\times\) 40 \(\upmu\)m, we selected an area (30 \(\times\) 30 \(\upmu\)m) with a nanoscopic pattern (group 6) printed in chrome on the target substrate prior to AFM imaging; thus, the AFM result was compared with our result from the same region. Figure 3 shows the results of surface imaging. The PSI created four phase-shifted interference images (Figure 3a), from which an unwrapped phase map was derived (Figure 3b). Using Figure 3b, therefore, the surface topology of the target was reconstructed (Figure 3c), and showed height profiles of the patterns on the target. For the pre-selected region, which resembled that depicted in Figure 3d, an enlarged view of the box in Figure 3c was also mapped using AFM, as shown in Figure 3e. Figure 3f presents a graph that displays height profiles taken along the dotted lines in Figure 3e,d, and shows that the height profile derived using the proposed method coincides with that derived using AFM, with the exception of subtle differences on either side of the plateau, for which the averages were 144.79 nm (proposed) and 145.35 nm (AFM). The measurement error to the reference value (145.35 nm) from five repeated measurements averaged 0.37 nm, which represents the measurement accuracy of our system. Moreover, the lateral resolution of the system was measured via a line profile (Figure 3h) taken along a dotted line on the height map (Figure 3g); this resolution was found to be approximately 3 \(\upmu\)m from the smallest resolvable pattern (element 6 of group 7). This experiment confirms the capability of the proposed method to accurately capture fine surface profiles even in wet and noisy environments.  
Figure 3: Surface-mapping performance of the proposed method. (**a**) Phase-shifted interference imagesof a positive 1951 resolution test target immersed in water and exposed to external noise during PSI. (**b**) An unwrapped phase map of the test target, including the physical height of the nanoscopic pattern on the target. (**c**) The height map reconstructed from (**b**). (**d**) A magnification of the height map of the boxed area in (**c**), and the same region scanned with AFM (**e**). (**f**) Height profiles taken along the dotted line in (**e**,**d**), respectively. (**g**) A 3-D view of (**c**). (**h**) A 1-D height profile taken along the dotted line in (**g**).
# Fast Temperature Field Extrapolation Under Non-Periodic Boundary Conditions
## 4 Results and Discussions
### Computational Cost of the FVM and POD-FVM Reduced-Order Extrapolation Method  
There is a consensus that the remarkable advantage of the POD order-reduction method is its fast calculation speed because of the great reduction of the degree of freedom of the algebraic equations system. Figure 13 shows the degree of freedom of each POD mode set. Mode set 1 reduces the number of degrees of freedom of each time-step algebraic equations system from 830,995 to 6 compared with the FVM method, achieving cross-order reductions. Although there is a slight increase in the degrees of freedom as the number of modes increases, a significant reduction is achieved compared to the traditional FVM method, as shown in Figure 13.  
The degree of freedom of the system of algebraic equations determines the calculation speed. Figure 14 shows the total time for calculating 1500 time steps by FVM and the reduced-order method for each mode set. The POD reduced-order time is divided into three parts: the POD decomposition time of the temperature snapshot matrix (decomposition), the time of mode coefficient extrapolation (extrapolation), and the time of reconstruction of the future temperature field (reconstruction).  
Compared with the calculation time for the FVM with the POD, it can be seen that the total calculation time of 1500 time steps after POD reduction is less than 200 s for each  
Figure 14: Comparison of the calculation time of 1500 steps using different methods.  
Figure 13: Comparison of the degrees of freedom of different methods.  
mode set, which is about 1400 times faster than the 2.83 \(\times\) 10\({}^{5}\) s needed with the FVM method. If the time taken to obtain the snapshot matrix is also included in the calculation time taken with the POD-FVM method, the calculation time needs to be added to another 9.43 \(\times\) 10\({}^{4}\) s. As a result, this is still a 76.6% improvement over the FVM method. The calculation speed has been greatly improved, highlighting the superiority of the POD-FVM reduced-order extrapolation method.  
By comparing the calculation times of the four mode sets, the mode set with the greater degree of freedom takes longer to finish the calculations. The decomposition time is around the same for each mode set because of using the same temperature snapshot matrix. The extrapolation time and reconstruction time were affected by the degree of freedom. Thus, the calculation time shows a pattern of increasing with the increase in the degree of freedom.
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Bayesian Method  
The Bayesian model (BN) is a particular type of graphical model that takes into account both independent and conditional relationships between various variables [101]. A directed graph's nodes and edges are used by the BN. The Bayesian model is a particular type of graphical model that takes into account both independent and conditional relationships between various variables [96]. A directed graph's nodes and edges are used by the BN. This model is very powerful in searching anonymous probability computations [56]. Based on the reviewed literature, we explored different papers on the two main types of Bayesian methods, namely, the Bayesian belief network and Naive Bayes (NB). NB is an ML model that is based on the Bayes theorem and is used to predict membership probabilities per class [101]. It predicts a given data point label based on the probability that belongs to a particular category [56]. Some researchers utilized the NB model for financial fraud detection. For example, [PERSON] [101] utilized the NB model to produce a fraud detection approach in a financial transaction. The experiment was performed in a dataset that contains both normal and abnormal financial statements. The results showed the effectiveness of the proposed model in fraud detection. [PERSON] and [PERSON] [81] utilized the NB algorithm to address fraudulent transactions in the health sector based on medical procedure records. The research is aimed to classify the supplier's behavior with respect to whether it is anomalous or not. To better perform fraud detection, [PERSON] and [PERSON] [33] proposed a method for intelligently detecting fraudulent financial documents by gathering specific features from financial reports. In the experiment, the authors took into account several linguistic and financial management discussion variables related to financial information. The result showed that the proposed model outperformed other existing ML methods. Some studies also used Bayesian networks to predict fraudulent financial transactions [33; 63].
## 3 Test Results and Discussion
### Binder Properties
#### 3.1.2 Kaolinite Content  
The TGA result used to determine the kaolinite content of the raw clay samples, as illustrated in Section 2.1.3, is shown in Figure 4. The TGA results are the average of two repeated tests with a coefficient of variance of about 5-10%. The estimated percentage of kaolinite content of the three raw clay samples is shown in Table 3. As noted earlier, the higher the kaolinite content, the higher the performance expected from the LC\({}^{3}\) binders [6; 10]. A kaolinite content of at least 40% is expected to yield strengths similar to the reference OPC at 40-50% substitution. EXW seems to be the only clay sample to satisfy this requirement, though their pozzolanic oxide compositions (discussed later) appear similar, with relatively higher SiO\({}_{2}\) and lower Al\({}_{2}\)O\({}_{3}\). This implies that varied kaolinite content of raw clay can yield similar pozzolanic oxide content; the hydrates and strength properties are discussed later.
###### Abstract  
Fatty acid and retinol binding proteins (FAR) are unique proteins found in nematodes and are considered potential targets for controlling these parasites. However, their functions in nematode parasitism and pathogenicity and interaction with hosts are still unclear. In this study, we investigated the specific roles of rice white tip nematodes (RWTNs), _Aphelenchoides besseyi_, and a protein, Ab-FAR-1, to elucidate the parasitic and pathogenic processes of nematodes. The results showed that the expression level of _Ab-far-1_ was significantly up-regulated after _A. besseyi_ infection of the plant. The immunofluorescence and subcellular localisation showed that Ab-FAR-1 was secreted into plant tissues mainly through the body wall of nematodes and might act in the nucleus and cytoplasm of plant cells. The pathogenicity of RWTNs was enhanced in _Arabidopsis thaliana_ overexpressing Ab-FAR-1 and inhibited in _Ab-far-1_ RNAi A. thaliana. Yeast two-hybrid, Co-IP, BiFC, and nematode inoculation experiments showed that Ab-FAR-1 could interact with the _A. thaliana_ actin-depolymerizing factor protein AtADF3, and the _A. thaliana adf3_ mutant was more susceptible to nematodes. An in vitro actin filament depolymerisation assay demonstrated that Ab-FAR-1 could inhibit AtADF3-mediated depolymerisation of actin filaments, and the turnover process of cellular actin filaments was also affected in _A. thaliana_ overexpressing Ab-FAR-1. In addition, fig22-mediated host defence responses were suppressed in _A. thaliana_ overexpressing Ab-FAR-1 and adf3 mutants. Therefore, this study confirmed that RWTNs can affect the turnover of actin filament remodelling mediated by AtADF3 through Ab-FAR-1 secretion and thus inhibit plant PAMP-triggered immunity (PTI), promoting the parasitism and pathogenicity of nematodes.  
plant parasitic nematodes; fatty acid and retinol binding protein; parasitism and pathogenicity; actin cytoskeleton; plant immunity +
Footnote †: journal: Molecular Sciences