esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Clustering of Class Association Rules  
The goal of clustering rules is to group related rules; rules in one cluster should be related to and distinct from rules in other clusters.  
Hierarchical clustering techniques come in two types: top-down (divisive hierarchical clustering) and bottom-up (hierarchical agglomerative clustering). In the beginning, bottom-up algorithms treat each example as a separate cluster. However, after each iteration, they combined the two closest clusters into a single cluster that comprised all the examples. A tree (or dendrogram) is used to illustrate the cluster structure that results. In contrast to the hierarchical agglomerative clustering method, the top-down strategy. It considers every case in a single cluster before breaking it up into smaller pieces until every example forms a cluster or meets the stopping condition.  
We applied the complete linkage method of hierarchical agglomerative clustering. In the complete linkage (farthest neighbor) method, the similarity of two clusters is the similarity of their most dissimilar examples; therefore, the distance between the farthest groups is taken as an intra-cluster distance. Figure 2 illustrates the clustering phase of the developed algorithm in which each rule is first regarded as a separate cluster (let us assume \(N\) CARs are generated). Utilizing the distance measures outlined in Section 3.2.2, a similarity (distance) matrix is built. The hierarchical clustering process needs to be used twice: first, to determine the cluster heights, which will be used subsequently to determine the ideal number of clusters. The \"natural\" number of clusters is found when the dendrogram is \"cut\" at the point that represents the maximum distance between two consecutive clusters merging. Agglomerative hierarchical clustering is once more used to identify the clusters of class association rules after the ideal number of clusters has been determined.  
Figure 2: Clustering phase of our developed classifiers.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 2 Materials and Methods  
### UAV Data  
To achieve the objective of the study, a rural area in Slovenia was selected as the number of visible (cadastral) boundaries in such areas is higher compared to dense urban ones. In addition, the selected rural area includes roads, agricultural field outlines, fences, hedges, and tree groups, which are assumed to indicate cadastral boundaries [22]. The UAV images of the case study area were indirectly geo-referenced, using an even distribution of ground control points (GCP) within the field as criteria. The GCPs were surveyed with real-time kinematic (RTK) by using Global Navigation Satellite System (GNSS) receiver, Leica Viva, connected in the Slovenian GNSS network, SIGNAL. The signals were received from satellite constellations of GPS and GLONASS. The total number of GCPs was 12. The Position dilution of precision (PDOP) values ranged from 1.2 to 1.7. The flight altitude was 80 m and 354 images were taken to cover the study area. The images were captured on October 19 th, 2018 in the noon time (good weather conditions, clear sky) at solar zenith angle of approximately 35 degrees. The study site had a coverage area of 25 ha. The planimetric accuracy assessment of the UAV orthoimage was based on comparison between GCPs coordinates surveyed with the GNSS receiver and the coordinates of GCPs on the UAV orthoimage. The estimated root-mean-square-error (RMSE) was 2.5 cm. Table 1 shows the specifications of data capture and Figure 1 shows the UAV orthoimage of the study area.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**UAV Model**} & \multicolumn{2}{c}{**Camera/Focal Length**} & \multicolumn{2}{c}{**Overlap**} & \multirow{2}{*}{**Flight**} & \multirow{2}{*}{**GSD**} & \multirow{2}{*}{**Pixels**} \\  & & **Length** & **Forward/Sideward** & & & **altitude [m]** & **[cm]** \\ \hline \multirow{2}{*}{Ponova vas, Slovenia} & DJI Phantom & 1\({}^{\prime\prime}\) CMOS & & & & \\  & 4 Pro & 20 mp/24 & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Specification of unmanned aerial vehicle (UAV) dataset for the selected study area in Slovenia.
# PC12 and THP-1 Cell Lines as Neuronal and Microglia Model in Neurobiological Research
## 1 Introduction
Microglia-type cells like THP-1 or BV-2 often accompany neuronal cultures in studies focused on neurodegeneration since neuroinflammation plays a crucial role in disease progression and quite possibly in its development. Activated microglia participate in developing neuroinflammation, responding to toxic exogenous substances (like lipopolysaccharide; LPS) or endogenous substances (like amyloid \(\beta\)). Each cell line exhibits a unique set of properties in its morphology and expresses \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Advantages** & & **Disadvantages** \\ \hline \(\bullet\) & Relatively cheap and easy to maintain & \(\bullet\) & Obtained results cannot be easily related to the human organism \\ \(\bullet\) & Provide results faster than in vivo studies & & \(\bullet\) & Cells require proper handling and preparation to become a viable model \\ \(\bullet\) & Allow multiple replicates of the & & \(\bullet\) & Knowledge of cells characteristics and phenotype is essential for the interpretation of results \\ \(\bullet\) & experiment & & \\ \(\bullet\) & The abundance of cell types/lines & & \(\bullet\) & \\ \(\bullet\) & Cells can be easily genetically modified & & \\ \(\bullet\) & Do not require animal experiments (so no & & \\  & need to apply for the consent of the ethics & & \\  & committee for animal testing) & & \\ \(\bullet\) & Adjustable 3D structure and culture & & \\  & environment & & \\ \(\bullet\) & Co-cultures enable cellular cross-talk & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of using cell cultures in scientific research. specific antigens and bioactive substances (e.g., neurotransmitters and cytokines) involved in different pathways and response mechanisms. Therefore, studying mechanisms of neurodegeneration and CNS inflammation, as well as the search for possible therapies and targeted drugs, requires standardized and well-defined cellular in vitro models [1; 3; 14]. Although tissue-derived primary cultures are most preferred, it is difficult to obtain sufficient amounts of these cells. Indispensable substitutes are well-defined cell lines of neoplastic origin. However, these cells often acquire the desired morphology and functionality only after differentiation. Authors in their studies show different approaches using either induced to differentiation or undifferentiated cells as neurobiological models, often without considering this discrepancy in cell lines preparation protocol. Our study investigated the impact of PC12 or THP-1 cell differentiation on sensitivity to A\(\beta\) (considered as one of the major detrimental factors in the pathophysiology of AD) or lipopolysaccharide-saccharide (a pro-inflammatory factor of bacterial origin). PC12 and THP-1 cell lines were selected based on their prevalence in recent neurobiological studies according to PubMed archive (on 20 November 2020).
# Evaluation of the Impact of Fines on the Performance of Sub-Base Materials
## 3 Test Results and Discussion
### Effect of Fines on the Permeability of Soil Aggregate Mixtures  
Falling head permeability tests were carried out on each composite sample. The results of the permeability tests are presented in Figures 6 and 7 for gradation B and gradation C, respectively. The coefficients of permeability (k) for non-plastic and medium-plasticity and high-plasticity fines reduced from 4.4 \(\times\) 10\({}^{-4}\) cm/s to 4.4 \(\times\) 10\({}^{-6}\) cm/s, 1.1 \(\times\) 10\({}^{-6}\) cm/s, and 1.6 \(\times\) 10\({}^{-7}\) cm/s, respectively, for gradation 'B', while for gradation 'C', the k value reduced from 1.2 \(\times\) 10\({}^{-4}\) cm/s to 1.4 \(\times\) 10\({}^{-6}\) cm/s, 6.2 \(\times\) 10\({}^{-7}\) cm/s, and 1.1 \(\times\) 10\({}^{-7}\) cm/s with the addition of non-plastic and medium-plasticity and high-plasticity fines. The k values were used for specific road geometry that has a length of 24 ft and slope of 0.02 ft/ft to determine the drainage time for pavement design.  
For sub-base materials with 0-6% of non-plastic fines, the material depicted fair drainage characteristics. However, as the fine content increased from 6-11%, drainage is poor and, with the addition of non-plastic fines in excess of 11%, drainage characteristics are very poor and the material is characterized as nearly impermeable. Similarly, for medium-plasticity fines up to 2%, the sub-base material showed fair drainage characteristics, while drainage characteristics are poor for a fine content in the range of 2-5% and are the worst for medium-plasticity fines with a content of more than 5%. Sub-base materials with high-plasticity fines exhibit fair drainage characteristics with fines in the range of 0-1% and drainage characteristics are poor for a fine content in the range of 1-3%, while for contents of more than 3% of fines, very poor drainage characteristics are observed.  
Figure 6: Effect of fines on permeability for the composite samples (gradation B).
# The Cell Membrane of _Sulfolobus_ spp.--Homeoviscous Adaption and Biotechnological Applications
## 5 Applications
In most of the cases, the archaeal lipids were mainly used as an enhancer of the conventional lipids, due to their extraordinary characteristics. However, it was also demonstrated that the generation of \"pure\" archaeosomes is also feasible and these are more resistant to pH, making them act as great oral drug delivery systems. Growth environments differing in temperature, pH and growth rate cause modifications in the lipid structure in Sulfolobales, making their membrane more or less rigid. In the same way, based on this concept of homeoviscous adaption and the connection between lipid composition and membrane properties, it should be possible to steer the characteristics of the archaeosomes and to tailor them for specific applications. Nevertheless, until now, it has not been systematically investigated whether the different growth conditions resulting in different lipid modifications, such as cyclopentane rings, have an impact on the archaeosome properties. Figure 4: Summary of properties and applications of archaeosomes and comparison between archaeosomes and conventional liposomes.
## 3 Results Analysis
### Measurement
#### 3.2.2 Far-Field  
According to the near-far field conversion, the RCS corresponding to the scattered near field could be derived expediently, as presented in Figure 11. Due to the mirror reflection, the RCS for the plate target and the far field radiation pattern of the vortex antenna were similar to each other, which verified the rationality of the near-far field conversion for the measurement of scattering RCS. Unlike the mirror reflection of the flat plate, the zero depth of the forward RCS for the spherical target was weakened due to the diffraction effect. Compared with the mirror reflection of the metal plate, there was a high energy near the main beam of the dielectric plate RCS measured under the same conditions, which was due to the diffuse reflection of the dielectric plate at oblique incidence with more complicated scattering, as depicted in Figure 11a,b. In addition, for the normalized far-field RCS, the diffuse reflection of the dielectric plate at the frequency deviating from the operating frequency was more significant.  
Figure 12 compares the RCS of the metallic plate and FR4 dielectric plate at different incident angles. With incidence angle changes from 30\({}^{\circ}\) to 60\({}^{\circ}\), it was found that the RCS distribution of the metal plate was basically the same at the same frequency, which indicates that the RCS was mainly from the mirror reflection. However, for the FR4 dielectric plate, with the increase in the incidence angle, the diffuse reflection gradually intensified, and finally the RCS was completely distorted.  
Figure 10: The distributions of the reflected field of the plate (**a**) and scattering field of the sphere (**b**) at 10 GHz/30 GHz.  
Figure 9: Scattering physics model of the plate (**a**) and sphere (**b**) in FEKO.  
Figure 13 shows the far-field RCS under the different off-axis conditions. It can be seen that the symmetry of the RCS distribution gradually decreased with the increase in the off-axis degree, which corresponded to the distortion distributions of the near-field amplitude and phase as described in Figure 8. Both the near- and far-field results showed that the off-axis field distribution lost symmetry, and the scattering field distorted the vortex characteristics.  
Figure 11: Far-field RCS at different frequencies. (**a**) Metallic plate, (**b**) FR4 plate, and (**c**) sphere.  
Figure 12: Measured RCS distributions for different incident angles (first to third row are \(30^{\circ}/45^{\circ}/60^{\circ}\) in turn) at different frequencies. (**a**) Metallic plate and (**b**) FR4 dielectric plate.  
Figure 13: RCS distributions of the off-axis metallic sphere. (**a**) On-axis, (**b**) off-axis 50 mm, and (**c**) off-axis 100 mm.
## 3 Results
### Flight Proximity and Nap-of-the-Object Photogrammetry Accuracy
#### 3.1.2 Point Cloud Reproducibility Analysis  
Point cloud data processing is a crucial step in UAV photogrammetry; its outcomes directly impact the overall quality of the results [34]. Four sets of point cloud data, collected at various flight proximities, were imported into PCM V2.0 software and cropped (Figure 8). The cropped point cloud data were denoised and superimposed with the point cloud data generated for the control group (10 m). The mean and variance of the point cloud distances  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Processing Parameter** & **15 m** & **20 m** & **25 m** & **30 m** \\ \hline Number of images & 1678 & 1182 & 1008 & 886 \\ Number of calibration images & 1678 & 1182 & 1008 & 886 \\ Number of ground control points & 9 & 9 & 9 & 9 \\ Ground resolution (cm) & 0.4 & 0.5 & 0.7 & 1.2 \\ Mean reprojection error (pixels) & 0.010 & 0.012 & 0.012 & 0.014 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Processing results for four sets of acquired images.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Flight** & \multicolumn{2}{c}{**Average Error (mm)**} & \multicolumn{2}{c}{**Mean Absolute Error (mm)**} & \multicolumn{2}{c}{**Root Mean Square Error (RMSE) (mm)**} \\
**Proximity (m)** & **X** & **Y** & **Z** & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline
15 & 0.04 & 0 & \(-\)0.12 & 8.20 & 6.43 & 7.27 & 9.30 & 8.04 & 8.15 \\
20 & 0.05 & 0.06 & \(-\)0.07 & 8.21 & 7.07 & 6.76 & 9.98 & 8.70 & 8.11 \\
25 & 0 & 0 & 0.07 & 8.  
from the control group were calculated to observe the effects of different flight proximities on the results.  
The superimposed analysis of the four groups of point cloud data generated at different flight proximities compared with the point cloud data of the control group is illustrated in Figure 9. In terms of distance mean and distance variance, both exhibit an overall upward trend as flight proximity increases. The minimum distance mean and minimum distance variance were observed at a flight proximity of 15 m. Within the flight proximity range of 15-20 m, the overall point cloud superposition distance is closely consistent; when the flight proximity exceeds 20 m, the point cloud distance mean and distance variance both change significantly, reaching 0.175 m and 1.64 m, respectively, at a flight proximity of 30 m. Differences in flight proximity evidently affect the point cloud distance mean and distance variance.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 4 Biological Signals for Wearable-Based HR
### Time features  
Time features provide a proxy to the pulse wave velocity as well as the duration of the systolic and diastolic phases of each cardiac cycle [18].  
* Slope transit time is the time difference between the systolic and diastolic points \[STT=t_{sys}-t_{dia}\] (8)
* Inter-beat interval is the time difference between two consecutive diastolic points \[IBI(i)=t_{dia}(i+1)-t_{dia}(i)\] (9)  
where \(i\) denotes the \(i\)-th diastolic point.
# Tag-to-Tag Interference Suppression Technique Based on Time Division for RFID
## 4 Anti-Collision for RFID Based on TD
### RFID Signal Power Model Considering Interference Signals  
In RFID systems, once the tag is powered up by the transmitted power from the reader, the selected tag starts to respond to the reader. Since the modulated signal scattered from the RFID tag suffers from noise and interference signals that originate from other tags, the received signal at the RFID reader, which is given by Equations (1) and (2), consists of the modulated backscattered signal of interest, noise, and interference signals [36; 37]. At the reader, the amount of power available to the receiver antenna for it to operate is given by  
\[\begin{array}{ll}P_{R-receiver}[dB]&=P_{r}[dB]+G_{r}[dB]+G_{\mathrm{R- receiver}}[dB]+10 log_{10}\big{(}\frac{\sigma}{4\pi}\big{)}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad-20 log_{10}\big{(} \frac{4\pi}{\lambda}\big{)}-\ 40 log_{10}(d),\end{array} \tag{6}\]  
where \(P_{R-receiver}[dB]\) is the power received by the receiver antenna of the reader, \(G_{R-receiver}\) is the gain of the receiver antenna at the reader (which is equal to the transmitter antenna gain at the reader), and \(\sigma\) is the RCS of the RF tag, which is important for the system-level efficiency and reliability [35; 36]. The RFID tag's cross-section area can be written as [38; 39]  
\[\sigma_{antenna}=\frac{|\tau|^{2}G_{tag}^{2}\lambda^{2}}{4\pi} \tag{7}\]  
where \(\tau\) is the impedance-matching coefficient between the tag chip and its antenna. Substituting the value of \(\sigma_{antenna}\) into Equation (6), the modulated power from the RF tag to the reader becomes:  
\[\begin{array}{ll}P_{R-receiver}[dB]&=P_{r}[dB]+2G_{r}[dB]+2G_{tag}[dB]+20 log_{10}(\tau)\\ &\qquad\qquad\qquad\qquad\qquad-40 log_{10}\big{(}\frac{4\pi}{\lambda}\big{)}- 40 log_{10}(d).\end{array} \tag{8}\]  
Equation (8) can be expressed in a general form as  
\[P_{R-receiver}=\frac{P_{r}G_{r}^{2}G_{tag}^{2}\tau^{2}\lambda^{4}}{\big{(}4d \pi\big{)}^{4}} \tag{9}\]  
Equations (8) and (9) show that the total energy consumption of passive RFID systems depends on the power transmitted from the reader to the tag. Since those equations show that the effective distance \(r\) between the tag and the reader can be adjusted by maintaining the power supplied at the reader antenna, the total backscattering power of the passive RFID tag and interrogation range depends on the transmission power level of the reader. In our computer simulations, we used the above-mentioned RFID signal power models.
## 3 Results and Discussion
### Post-manufacture Inspection  
The datum method showed significant differences at the top of the part. The best fit method, however, produced lower levels of variation (Figure 13). The variance distribution % for both methods is given in Figure 14, where the datum method results in a significant % exceeding 0.5 mm deviation, whereas the best fit does not significantly exceed 0.3 mm.  
The maximum geometry variance of under 1 mm is deemed insignificant with regards to its application, and therefore build quality is acceptable. The majority of the deviation is most likely caused by recoater blade contact during the PBF powder layering process as opposed to the effect of residual stresses.  
Scans of the internal geometry confirmed that the wall thickness produced is identical to the desired value (Figure 15). Modelling of the pedal spindle support was affected by CT noise reduction algorithms and threshold selection and, due to its smaller wall thickness, geometry comparison of this feature was limited.  
Figure 13: Comparison of Actual and Nominal Models; (**a**) Datum-Reference Method; (**b**) Best Fit Alignment Method.
## 2 Materials and Methods
### Characterization of the Glass-Ceramic Samples
#### 2.4.2 Physical and Mechanical Characterizations  
The density of the sintered glass-ceramic cylinders was assessed by geometrical mass-volume measurements; the resulting values are presented in Table 2. The total porosity \(P\) (vol.%) was calculated as:  
\[P=\left(1-\frac{\rho_{s}}{\rho_{0}}\right)\times 100 \tag{1}\]  
where \(\rho_{s}\) is the apparent density of the glass-ceramic sample (mass-to-volume ratio) and \(\rho_{0}\) is the theoretical density of non-porous material.  
Three-point flexural tests were carried out for glass-ceramic samples with a rectangular section (sample dimensions: 52 mm \(\times\) 10 mm \(\times\) 4 mm); the bending stress \(\sigma_{b}\) (MPa) was calculated as [29]:  
\[\sigma_{b}=\frac{3F_{b}L}{2b^{2}h} \tag{2}\]  
where \(F_{b}\) is the applied load, \(L\) is the distance between the supports, and \(b\) and \(h\) are the sizes of the sample section.  
Prior to undergoing microhardness measurements, the surface of the samples was polished by SiC grit papers (#4000 max.). The Vickers hardness (\(HV\)) of the glass-ceramics was measured by using a microhardness tester (Leica Microsystem, Milan, Italy); indentations were performed at 5 N for 20 s and the value of \(HV\) (GPa) was calculated as:  
\[HV=\frac{2F_{H}\sin(\beta/2)}{d^{2}} \tag{3}\]  
where \(F_{H}\) is the load applied during the test, \(\beta\) = 136\({}^{\circ}\) is the angle of the indenting tip, and \(d\) (um) is the length of the diagonal of the imprint.  
The fracture toughness \(K_{\mathrm{IC}}\) (MPa\(\cdot\)m\({}^{1/2}\)) was estimated according to [PERSON] et al. [30] as:  
\[K_{IC}=0.016F_{H}\frac{(E/HV)^{1/2}}{c^{3/2}} \tag{4}\]  
where \(E\) is the [PERSON]'s modulus and \(c\) is the average length of the cracks created by the imprint.  
The elastic modulus \(E\) in Equation(4) was experimentally assessed in a non-destructive way by using the impulse excitation technique, which is based on the analysis of the specimen's transient natural vibration resulting from mechanical impact (Lemmens Grindosonic instrument, Leuven, Belgium) [31]. This approach was also followed by other authors for investigating dental materials [32].  
Density, porosity, and all the results of mechanical tests were expressed as mean \(\pm\) standard deviation calculated for five samples.