esa-sceva/satcom-chunk-collection huggingface dataset:
# A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications
## 4 Numerical Examples
### Example 3
In Figure 12, the time histories of the vertical velocities are reported for the locations described in Figure 11c. The initial wave packet could be reproduced at most sensors, with \(s_{2,3}\) showing the most pronounced differences in phase and amplitude. These sensors represent velocities purely in x and y directions, respectively, and show the limitations \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameters** & \(\mathbf{C_{11}}\) & \(\mathbf{C_{22}}\) & \(\mathbf{C_{33}}\) & \(\mathbf{C_{12}}\) & \(\mathbf{C_{13}}\) & \(\mathbf{C_{23}}\) & \(\mathbf{C_{44}}\) & \(\mathbf{C_{55}}\) & \(\mathbf{C_{66}}\) & \(\mathbf{\alpha}\) (1/s) & \(\mathbf{\rho}\) (kg/m\({}^{3}\)) \\ \hline GFRP & 44.82 & 27.97 & 3.26 & 14.13 & 6.97 & 1.26 & 4.91 & 4.22 & 3.03 & 8000 & 1750 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model parameters for numerical simulation of composite panel. Unit is GPa unless specified otherwise. Figure 11: (**a**) Delaminations identified through wavenumber damage imaging. The color bar represents the wavenumbers scaled by the thickness in mm. (**b**) Cross-sections through 3D FFT of experimental wavefield data showing dispersion curves. (**c**) Mesh detail: loading surface (blue), bottom and left delaminations, sensor locations \(s_{1-6}\) (red) at the abscissae and ordinates \(\{0,-35,-50,-100\}\) mm for numerical solution. of the aforementioned assumptions. Reflections from the damaged area and/or panel boundaries could realistically be reproduced in most cases, with only \(s_{3,6}\) showing a significant difference in time of flight. Overall, given all the aforementioned limitations, these results can be considered satisfactory.
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## 2 Algorithm Description
### Interface Definition  
The RFI detection and mitigation system interfaces with multiple data streams and control signals, as illustrated in Figure 2. Each interface serves a specific purpose in the processing chain.  
The system's primary inputs are two 1-bit quantized data streams (\(x\) and \(y\)) representing the in-phase and quadrature components from each receiver's X and Y polarizations. These signals are sampled at 57.69375 MHz and feed directly into the Observable Generation block. In parallel, the system receives PMS measurements (_pmsx_, _pmsy_) at a lower rate of approximately 28 kS/s, which provide total power information for each polarization channel. The final stage applies the generated blanking masks to both the high-speed signal path and the PMS measurements. The outputs include gain-corrected PMS measurements for both polarizations (_gammax_, _gammay_), mitigated and averaged PMS values (_mpmsx_, _mpmsy_), and unmitigated and averaged PMS values (_apmsx_, _apmsy_). These outputs provide both debug and clean measurements for subsequent radiometric processing.  
Observable Generation subblock (Figure 3) generates intermediate signals that are then used in the RFI Detection and PMS Blanking subblocks for further processing, as well as the input truncated signals of the advanced correlator. These intermediate signals correspond to the truncated and equalized STFT, obtained from the X- and Y-polarization input signals. This subblock includes a calibration procedure to extract statistical information from the input signals to equalize the generated STFT. The truncated signals can also be configured externally with a truncation factor (\(\delta_{p}\)). The main purpose of this block is to convert the X- and Y-polarization real and imaginary samples to a suitable format for RFI  
Figure 2: Simplified overview of the RFI Mitigation algorithm implementation showing the three main processing stages and data flow paths. Configurable or external inputs are shown with red arrows.  
detection. This intermediate format includes a 3-bit truncated STFT output, and a 16-bit equalized STFT output.
## 2 Theoretical Investigation of Monitoring Models
### Analysis of the Spatial Magnetic Field Distribution of Permanent Magnets
For brevity of expression, after integrating Equation (6), we introduce a function notation as follows:  
\[\Phi(\varphi_{1},\varphi_{2},\varphi_{3})=\left\{\begin{array}{l}\arctan \left(\frac{\varphi_{1}}{\varphi_{2}}\frac{\varphi_{3}-z_{0}}{\sqrt{\varphi_{1 }^{2}+\varphi_{2}^{2}+(\varphi_{3}-z_{0})^{2}}}\right)\,(y\
eq 0)\\ 0\,\,\,\,\,\,\,\,\,\,\,\,\\[ \begin{array}{l}B_{x}=-2K[\Phi\left(\frac{b}{2},\frac{a}{2}-\Delta x,z\right)+ \Phi\left(\frac{a}{2}+\Delta x,\frac{b}{2},z\right)\\ \quad\quad\quad+\Phi\left(\frac{a}{2}-\Delta x,\frac{b}{2},z\right)+\Phi\left( \frac{a}{2},\frac{a}{2}+\Delta x,z\right)]|_{0}^{h}\\ \end{array}  \tag{9}\]  
It is known that the following relationship exists between stress and strain in strand material [29]:  
\[\varepsilon=\frac{\sigma}{E_{p}}=\frac{P_{p}}{A_{p}\times E_{p}}=\frac{P\left( 1-e^{-(kx+\mu\theta)}\right)}{(kx+\mu\theta)A_{p}\times E_{p}} \tag{10}\]  
where:  
\(P_{p}\)--average tensile force of the prestressed tendon (N);  
\(P\)--tensile force at the tensioning end of the prestressed tendon (N);  
x--distance from the tensioning end to the calculated section (m);  
\(\theta\)--sum of tangential angles of the curved duct portion from the tensioning end to the calculated section (rad);  
\(k\)--design position deviation coefficient;  
\(\mu\)--friction coefficient between the prestressed tendon and the duct wall;  
\(E_{p}\)--elastic modulus of the steel strand. The permanent magnet sensor is affixed to the linear prestressed tendon, with the midpoint of the permanent magnet situated \(l\) away from the midpoint of the Hall probe's epoxy resin base (as illustrated in Figure 1). When a prestress of \(P_{p}=P\) is applied, a relative displacement \(\Delta x\) occurs between the permanent magnet and the Hall probe fixed to the epoxy resin base, resulting in a strain given by \(\varepsilon=\Delta x/l\). By integrating Equations (9) and (10), we can derive the relationship between the anchor cable tensile force \(P\) and the magnetic induction density \(B_{x}\) as follows:  
\[ \begin{array}{l}B_{x}=\quad-2K[\Phi\left(\frac{b}{2},\frac{a}{2}-\frac{P \left(1-e^{-(kx+\mu\theta)}\right)}{(kx+\mu\theta)A_{p}\cdot E_{p}}l,z\right) \\ \quad\quad+\Phi\left(\frac{a}{2}+\frac{P\left(1-e^{-(kx+\mu\theta)}\right)}{(kx +\mu\theta)A_{p}\cdot E_{p}}l,\frac{b}{2},z\right)\\ \quad\quad+\Phi\left(\frac{a}{2}-\frac{P\left(1-e^{-(kx+\mu\theta)}\right)}{(kx +\mu\theta)A_{p}\cdot E_{p}}l,\frac{b}{2},z\right)\\ \quad\quad+\Phi\left(\frac{b}{2},\frac{a}{2}+\frac{P\left(1-e^{-(kx+\mu\theta) }\right)}{(kx+\mu\theta)A_{p}\cdot E_{p}}l,z\right)]|_{0}^{h}\\ \end{array}  \tag{11}\]  
Equation (11) represents the theoretical model for this monitoring technology. Once the parameters of the sensor structure are determined, the relationship between stress and the magnetic induction density \(B_{x}\) can be calibrated. Subsequently, the stress in the steel strands of the anchor cable can be derived from the monitored magnetic signal parameters, thereby achieving stress monitoring.
## 6 Numerical Results  
The behavior of the long-span suspension bridges is often well-described by the cable only. Here, however, the focus is addressed to cases in which the beam significantly influences the overall mechanics, as it happens when the dimensionless parameter \(\rho^{2}\) is of order \(10^{-2}\), (see, e.g., [13; 18; 48]). Therefore, a case study is considered: (a) the geometrical and mechanical characteristics are \(l=195\) m, \(E_{b}I_{b}=2.4\times 10^{9}\) Nm\({}^{2}\), \(E_{c}A_{c}=4.75\times 10^{9}\) N, \(m_{b}=917.86\) kg/m and \(m_{c}=177.6\) kg/m; (b) the prestress is evaluated as \(T_{0}=mgl^{2}/8d\), where \(d=19.5\) m is the cable sag; (c) the damping ratio is taken as \(\xi_{A}=1.5\%\) in the antisymmetric mode, and \(\xi_{S}=1.3\%\) in the symmetric mode (corresponding to \(\eta_{b}=0.0044\) s, \(c_{eb}=55.88\) Ns/m\({}^{2}\), \(\eta_{c}=0.00022\) s, and \(c_{cc}=6.29\) Ns/m\({}^{2}\), whose calibration is shown in Appendix A). A rectangular boxed cross-section of dimension \(2\times 1\) m (and so with an aspect ratio 2:1) is assumed for the beam. The aerodynamic dimensionless coefficients in Equation (5) are drawn from literature (obtained by wind tunnel tests in [30]), namely \(A_{1}=-3.47\), \(A_{3}=-490.25\), \(A_{5}=44744.21\). The air mass density is \(\varrho_{a}=1.25\) kg/m\({}^{3}\).  
This example is representative of a class of bridges, whose nondimensional parameters are: \(\rho^{2}=0.023997\), \(\Lambda^{2}=1161.79\), \(b_{1}=-0.386264\), \(b_{3}=-54.5443\), \(b_{5}=4978.17\), \(\eta_{b}=0.0011\), \(\eta_{c}=0.000056\), \(c_{e}=0.223\). All numerical values assumed by the parameters are consistent with the ordering performed in the perturbation analysis.
# Narrowband Internet of Things (NB-IoT):
## 4 Narrowband-IoT Possible Deployment Strategies
### Synchronous NB-IoT Deployment in Small Cells and Macro Cells  
In this strategy, NB-IoT is enabled in the small cells as well as in macro cell on the same PRBs. Macrocell UEs are configured to use higher transmit power as compared to small cell UEs while keeping the same PRBs for NB-IoT while others left for legacy LTE. Possible co-channel interference may occur in small cell edge UEs if the UEs are scheduled on the same resource units. The impact may further increase for UEs under mobility which might require the use of handover for smoothing the UEs transition from one serving cell to another. From our review, no work has addressed the interference cancellation mechanism for such a case. It is imperative to employ the existing geographical planning, frequency reuse, frequency hopping, and power control while considering the low complexity but high coverage range NB-IoT.
## 2 Observations and Method for Data Analysis
### Climatology, Seasonal and Interannual Variability of the Nonmigrating Diurnal Tides
We underline that only the cross-correlation function at latitudes where the respective seasonal components have large amplitudes makes physical sense. A strong positive correlation of 0.7 between the QBO and the AM of DE3 (near the equator) is immediately apparent in Figure 4a, as the AM of DE3 lags behind the QBO with a month. The other two seasonal components of the DE3 also reveal positive correlation with smaller coefficients, respectively 0.35 for the AC and 0.45 for the SAC, but  
Figure 3: (**a**) Latitude-time structures of the AM (**upper** plot), AC (**middle** plot), and SAC (**bottom** plot) separated from the monthly mean DE3 amplitudes at altitude of \(110\) km; (**b**) the same as (**a**), but for DE2; (**c**) the same as (**a**), but for DW2. with longer delay, \(\sim\)2-3 months for the AC and \(\sim\)6 months for the SAC, respectively. It is worth noting that the quasi-2-year variability of the DE3 in the MLT region has been investigated with simulations with the GAIA model [11]; the authors reported that it is generated by quasi-2-year variation in the zonal mean zonal wind between 40 and 70 km, which is modulated by the stratospheric QBO. The seasonal components AM and AC of DE2, however, demonstrate a negative correlation (Figure 4b), \(-\)0.6 for the AM and \(-\)0.4, respectively, for the AC, as both components lag behind the QBO with \(-\)6 and \(-\)8-9 months. It has been mentioned above that the SAC of DE2 is located between latitudes of \(\pm\)20\({}^{\circ}\). The bottom plot of Figure 4b shows positive correlation between the QBO and the SAC of DE2 at 20\({}^{\circ}\) N, 0.45, and a weaker negative one, \(-\)0.20, at 20\({}^{\circ}\) S; both maxima lag behind the QBO with 6-8 months. We consider that the QBO variability of the DE3 and DE2 tides studied by [PERSON] et al., 2010 [27], applying a different approach on only a 6-year interval (2002-2007), indicated that this dependence was well evident. The cross-correlation between the QBO and the seasonal components of DW4 (Figure 4c) is weaker in comparison with those of DE3 and DE2. While the AM around the equator shows a negative correlation of \(-\)0.4 with a time lag of \(\sim\)2 months, those at \(\pm\)(40-50\({}^{\circ}\)) are out of phase with the QBO. Similar but weaker correlations are seen for the AC and SAC near the equator, as well as with longer time lags, \(\sim\)7-9 months. In summary, the seasonal components of the considered DE3, DE2, and DW2 tides in the lower thermosphere demonstrate quite significant QBO variability, particularly the DE3 tide; its AM seasonal component near the equator demonstrated a positive correlation of 0.7, with a month lag behind the QBO index at 30 hPa. Figure 4: **(a, left column of plots) Cross correlation functions between the QBO and the AM (upper plot), AC (middle plot) and SAC (bottom plot) of the DE3; (b, middle column of plots) the same as (a), but for the DE2, and (c, right column of plots) the same as (a), but for the DW2. **
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
## 1 Introduction
The ultra-high spatial resolution of UAV images makes preprocessing complex and requires much processing time for classification [11]. Another important issue is that it is not always possible to construct a time-series UAV image set for crop classification. Although the acquisition of UAV images is less affected by atmospheric conditions than satellite images, it may be difficult to take UAV images in some season [12], particularly the rainy season which coincides with the growing season of crops in Korea. From an operational viewpoint, the acquisition of time-series UAV images for crop classification essentially has a prerequisite that operators make several visits to the area of interest. From a practical point of view, it is necessary to acquire optimal images at certain times, achieving classification accuracy comparable to the use of a complete time-series image set. Crop classification using UAV images is primarily conducted using a single UAV image [21; 28], but accuracy comparisons with the case using a time-series image set are yet to be considered fully. In addition to data acquisition issues, selection of proper classification methodology is important in order to generate reliable crop classification results. Since the 2000s, machine learning algorithms such as random forest (RF) and support vector machine (SVM) were widely applied to crop classification with remote sensing data [29; 30; 31; 32; 33; 34]. Along with the aforementioned issues related to crop classification with UAV images and selection of appropriate classification methodology, this paper focuses on the evaluation of the effectiveness of texture information for crop classification with UAV images. In particular, the classification performance using a single-date UAV image is compared with that of a time-series image set. In this study, two machine learning algorithms, RF and SVM, are applied as classification models, and the GLCM-based texture features [20] are used as additional features to reduce noise effects. From a practical viewpoint, we also investigate how much the utilization of texture information can improve classification accuracy when only a single-time UAV image is available. A case study of crop classification with UAV images in Anbandegi, a highland Kimchi cabbage cultivation area in Korea, was conducted to illustrate and discuss the two issues including the limited use of the UAV image and the impact of GLCM-based texture features on classification performance.
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 2 Materials and Methods
### Ex Vivo Analysis
#### 2.2.3 Immunohistochemical Analysis  
IHC analysis involving immunostaining was performed at the Calgary Laboratory Services (Core Pathology Laboratory). Aortic tissue samples were fixed with 10% buffered formaldehyde solution upon harvesting in the operating room, embedded in paraffin, on edge, and cut at 4 um sections. A Dako EnVision Flex Detection Kit (Agilent Technologies, Santa Clara, CA, USA) was used for the evaluation of inflammatory cells; specifically, the presence of the following markers was quantified: helper T-cells (CD4+), cytotoxic T-cells (CD8+), and macrophages (CD68+) [4]. This ready to use kit was used to the manufacturer's specifications (no dilution or secondary antibody retrieval necessary) with batch controls (tonsil). Analysis for CD4 (SP35) was run with H15 \(\times\) 15 (15 min antibody incubation, 15 min detection kit incubation) at 1/25, for CD8 (C8/144B) with H20 \(\times\) 20, and for CD68 (KP1) with H15 \(\times\) 20. IHC analysis was performed in order to characterize and quantify the level of inflammation in the media and adventitia layers as the mean number of cells per 1 mm\({}^{2}\) specimen area. The immunostaining was counterstained with hematoxylin allowing the discrimination of the adventitia layer from the media layer. The presence of each marker was evaluated by manually counting the positively stained cells in a 1 mm\({}^{2}\) area identified as a hot-spot with the highest density of stained cells.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 3 FPT Hardness of Approximation
### Hardness from Gap Hypotheses
#### 3.2.2 Strong Inapproximability of Multicolored Densest \(k\)-Subgraph and Label Cover
It turns out that relaxing \"perfect agreement\" to \"approximate agreement\" does not affect the proof too much, and the latter still implies that \(h\) satisfies all but \(\delta\) fraction of clauses as desired. As for the proof of the agreement testing theorem itself, we will not delve too much into detail here. However, we note that the proof is based on looking at different \"agreement levels\" and the graph associated with them. It turns out that such a graph has a certain transitivity property, which allows one to \"decode\" back the global function \(h\). This general approach of looking at different agreement levels and their transitivity properties is standard in the direct product/agreement testing literature [64; 65; 66]. The main challenge in [59] is to make the proof works for \(\zeta\) as small as \(1/k\), which requires a new notion of transitivity. To end this subsection, we remark that the Multicolored Densest \(k\)-Subgraph is known as the 2-ary Constraint Satisfaction Problem (2-CSP) in the classical hardness of approximation community. The problem, and in particular its special case called Label Cover, serves as the starting point of almost all known NP-hardness of approximation (see e.g., [67; 68; 69]). The technique in [59] can also be used to show inapproximability for Label Cover with strong running time lower bound of the form \(f(k)\cdot N^{\Omega(k)}\)[70]. Due to known reductions, this has numerous consequences. For example, it implies, assuming Gap-ETH, that approximating \(k\)-Even Set to within any factor less than two cannot be done in \(f(k)\cdot N^{o(k)}\) time, considerably improving the lower bound mentioned in the previous subsection.
# Iterative Positioning Algorithm for Indoor Node Based on Distance Correction in WSNs
## 3 Iterative Positioning Algorithm for the Target Node Based on Distance Correction
### The Iterative Positioning Algorithm  
In the actual physical environment, signals are easily disturbed by noise in the transmission process. Therefore, there is a large deviation between the distance obtained by the log-distance distribution model and the real distance value. To further reduce the positioning error, a node iterative positioning algorithm based on distance correction is introduced.  
The basic principle of the iterative positioning algorithm based on distance correction is as follows: the distance deviation coefficient is introduced to evaluate the degree of deviation of the distance measured by the log-distance distribution model and the triangle centroid positioning algorithm, respectively. The distance between the given anchor node and the unknown target node is recalculated based on the distance deviation coefficient. By constantly updating the distance between the given anchor node and the unknown target node, the target node coordinates are iteratively calculated.
# Wastewater Treatment with Bacterial Representatives of the _Thiothrix_ Morphotype
## 3 Methods for Identification of _Thiothrix_ in Wastewater Treatment Plants
### Sulfate Removal
Thiocaldithrix sulfafiontis’, _Thiothrix subterranean_ Ku-5\({}^{\rm T}\), _Thiothrix_ sp. STA_22, _Thiothrix unzii_ A1\({}^{\rm T}\), _Ca_. Thiothrix anitrata’ A52, _Ca_. Thiothrix moscovensis’ RT, _Thiothrix_ sp. 207, _Thiothrix nityanj_ JP2\({}^{\rm T}\) and _Ca_. Thiothrix singaporeensis’ SSD2 and on new 16 th genome of _’Ca_. Thiothrix namsareavi’. The genes in the other three panels are shown based on representative species: _Thiolinea disciformis_ DSM 14473\({}^{\rm T}\) and _Thiolinea eikelboomii’_ in the case of the genus _Thiolinea_; _Thiothim flexile_ in the case of the genus _Thiofilm_. Figure was prepared using EVenn diagram software ([[http://www.ehbio.com/test/venn/](http://www.ehbio.com/test/venn/)]([http://www.ehbio.com/test/venn/](http://www.ehbio.com/test/venn/)), accessed on 11 July 2024) [83] and considers data in Refs. [4; 84; 5]. (**b**) Reactions of sulfur dissimilatory metabolism. Moreover, in the latter case, among the nearly full-length 16S rRNA gene sequences of the entire bacterial community, sequences related to _Thiothrix lacustris_ accounted for 38% [86]. The ability of _Thiothrix_ to oxidize hydrogen sulfide could explain that enrichment by _Thiothrix_ improved the removal of sulfamethoxazole (SMX) (sulfur-containing organic matter) and mitigated SMX-induced stress to other microorganisms [40]. In addition to the biological removal of sulfide, the _Thiothrix_ morphotype can be involved in other metabolic processes that proceed during the biological treatment of wastewater. _Thiothrix_ was shown to be a significant part of the microbial community involved in the processes of nitrogen and phosphorus removal (1.51%), nitrification-denitrification (9.41%) and the aerobic removal of carbon (4.29%) [87].