esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis  
R2016b MATLAB software was used to mimic the features, settings, and data. The following characteristics were used in this study: Intel Core i7 3.2 GHz processor, 16 GB RAM and Windows 10. MATLAB software provides various mathematical advantages over other applications, including the ability to share source codes across users, and thus was chosen to simulate the proposed method--the simulation parameters are represented in Table 1.  
Table 2 presents the comparative analysis of the proposed SIMHC_ACLDT with existing techniques, namely LEACH and PEGASIS, based on the network lifetime, average delay, coverage rate, throughput, and energy efficiency.  
Figures 2-6 present the comparative analysis results of the proposed SIMHC_ACLDT and existing LEACH and PEGASIS techniques. SIMHC_ACLDT obtained optimal results for clustering-based data transmission and low power consumption in the network. Specifically, the proposed method achieved a network lifetime of up to 96%, average delay of 53 ms, coverage rate of 83%, data transmission of 97%, and energy efficiency of 95%.  
\begin{table}
\begin{tabular}{l|l} \hline Parameter & Value \\ \hline Field size & 100 \(\times\) 100 m\({}^{2}\) \\ \hline Number of sensor nodes & 100 \\ \hline Energy of sensor nodes & 80\% have 2]; 20\% have 5] \\ \hline Base Station location & (0,0) \\ \hline Number of clusters & 9 \\ \hline Size of message & 4000 bits \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameters** & **LEACH** & **PEGASIS** & **SIMHC_ACLDT** \\ \hline
**Network Lifetime (\%)** & 94 & 95 & 96 \\ \hline
**Average Delay (ms)** & 60 & 55 & 53 \\ \hline
**Coverage Rate (\%)** & 75 & 78 & 83 \\ \hline
**Throughput (\%)** & 93 & 94 & 97 \\ \hline
**Energy Efficiency (\%)** & 94 & 94.6 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of proposed sensor system and network based on low-power communication and existing techniques.  
Figure 4: Comparative analysis of coverage rate.  
Figure 3: Comparative analysis of average delay.  
Figure 2: Comparative analysis of network lifetime.
# Kleene Algebra to Compute Invariant Sets of Dynamical Systems
## 5 Application to Dynamical Systems
### Paths  
**Definition 3**.: _Given a point \(\mathbf{a}\) and the system \(\mathcal{S}:\dot{\mathbf{x}}(t)=\gamma(\mathbf{x}(t))\). The path associated to \(\mathbf{a}\) is defined as_  
\[\boldsymbol{\
abla}_{\gamma}(\mathbf{a})=\Big{\{}\mathbf{z}|\forall\varepsilon >0,\exists t_{1}>0,\|\boldsymbol{\varphi}_{\gamma}(t_{1},\mathbf{a})-\mathbf{ z}\|\leq\varepsilon\Big{\}}. \tag{24}\]  
_Equivalently, we can write_  
\[\boldsymbol{\
abla}_{\gamma}(\mathbf{a})=\boldsymbol{\varphi}_{\gamma}([0, \infty],\mathbf{a}). \tag{25}\]  
The path \(\boldsymbol{\
abla}_{\gamma}(\mathbf{a})\) is a closed set and contains the equilibrium points or cycles to which the system will converge from \(\mathbf{a}\).  
**Definition 4**.: (Path inside a region). _Consider the system (1), a region \(\mathbb{Y}\), and a point \(\mathbf{a}\in\mathbb{Y}\). We define the path inside \(\mathbb{Y}\) as \(\boldsymbol{\
abla}_{\gamma|\mathbb{Y}}(\mathbf{a})\) where the function \(\gamma|\mathbb{Y}\) is defined as_  
\[(\gamma|\mathbb{Y})(\mathbf{x})=\left\{\begin{array}{cl}\gamma(\mathbf{x}) &\quad\text{if }\mathbf{x}\in\mathbb{Y}\\ \mathbf{0}&\quad\text{otherwise}\end{array}\right. \tag{26}\]  
This notion is illustrated by Figure 4 where two trajectories starting from \(\mathbf{a}\) and \(\mathbf{b}\) are represented. The path \(\boldsymbol{\
abla}_{\gamma|\mathbb{Y}}(\mathbf{a})\) contains a limit cycle, whereas \(\boldsymbol{\
abla}_{\gamma|\mathbb{Y}}(\mathbf{b})\) stops at the boundary of \(\mathbb{Y}\).  
Figure 4: Illustration of the definition of a path inside a region.
# Synchrotron X-ray Radiation (SXR) in Medical Imaging: Current Status and Future Prospects
## 3 Synchrotron X-ray Radiation in Medical Imaging
### Organ to Cellular Scale Imaging  
A hierarchical phase-contrast tomography (HiP-CT) has recently been proposed. The technique is based on the X-ray phase propagation and carried out using the European Synchrotron Radiation Facility (ESRF)'s Extremely Brilliant Source (EBS) [42]. HiP-CT made it possible to image the excised intact human organ (brain, lung, heart, kidney, and spleen) from organ to the cellular scale with a minimum of 1.3 \(\upmu\)m voxel size (Figure 8).  
Figure 8: Hip-CT images of (**a**) brain, (**b**) lung and (**c**) heart at voxel size 25 \(\upmu\)m (column ii), 6 \(\upmu\)m (column iii) and 2.5 \(\upmu\)m (column iv), respectively. (**d**) Kidney and (**e**) spleen images at voxel size 25 \(\upmu\)m (column ii), 6 \(\upmu\)m (column iii) and 1.3 \(\upmu\)m (column iv), respectively. Reprinted with permission from [42].
# Energy-Efficient Hardware Implementation of an LR-Aided K-Best MIMO Decoder for 5G Networks
## 3 LR-Aided K-Best Decoder
To implement Equation (7) the breadth-first K-Best algorithm is used, where in the shift and scale of \(\mathbf{y}\) transforms the points searched for into continuous numbers (\(-1\), \(0\), \(1\), \(2\dots\) ) in each dimension. The detection of candidates in each layer exploits the on-demand child expansion the Schnorr-Euchner (SE) enumeration, hence there will be no fixed format of the candidates. The value of the candidates in the current layer is only based on the initial child (\(child_{i}\)) and steps obtained. The order of the detection in each layer can be presented in Figure 1.  
The crosses in Figure 1 represent the points in the lattice, while the black point is rounded to \(-1\), which is the initial child in this example. The other candidates in the current layer will be based on the order as the Figure 1 shows. The constant k will determine the times of the jumping across the candidates. For the complex model used in this work, the order in two dimensions can be presented as Figure 2.  
Figure 1: Schnorr-Euchner enumeration order in one dimension. Figure 2: The SE enumeration order in two dimensions. The removal of the fixed format of the candidates, combined with the on-demand child expansion method, significantly reduces the correlation among the candidates, which can improve the performance of the MIMO detection process and reduces its complexity.
###### Abstract  
We report a continuous-wave single-frequency singly-resonant mid-infrared optical parametric oscillator (OPO). The OPO is based on 5 mol % MgO-doped periodically poled lithium niobate (MgO:PPLN) pumped by a continuous-wave single-frequency Nd:YVO\({}_{4}\) laser at 1064 nm. A four-mirror bow-tie ring cavity configuration is adopted. A low-finesse intracavity etalon is utilized to compress the linewidth of the resonant signal. A single-frequency idler output power higher than 1 W at 3.68 \(\upmu\)m is obtained.  
mid-infrared; single-frequency; optical parametric oscillator (OPO); MgO:PPLN crystal; continuous-wave (CW)
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 3 Methods
### Spatial Autocorrelation Analysis (Moran's I)  
To assess the potential for spatial dependence in LST data, a spatial autocorrelation analysis using [PERSON]'s \(I\) statistic was conducted. A spatial weights matrix was constructed using a \(k\)-nearest neighbors (KNN) approach, with \(k=8\), ensuring that each point was connected to its eight closest neighbors. The weights were row-standardized to account for variations in sampling density. [PERSON]'s \(I\) is a widely used global measure of spatial autocorrelation that quantifies the degree to which values at one location are similar to values at nearby locations [48; 49]. A positive [PERSON]'s \(I\) value indicates spatial clustering, where similar values tend to be located close together, while a negative value suggests spatial dispersion, where dissimilar values are clustered. A value close to zero indicates a random spatial pattern.  
[PERSON]'s \(I\) was calculated for LST measurements for each of the four diurnal periods. This stratified approach allowed for the assessment of spatial autocorrelation across different temporal contexts to assess clustering between different LULC classes. For each calculation, the associated \(p\)-values were examined to determine the statistical significance of any observed spatial autocorrelation patterns. The rationale for employing [PERSON]'s \(I\) was to rigorously examine the fundamental assumption of independence often required by classical statistical methods [50]. Given the stratified random sampling design within LULC classes, and the inherent spatial continuity of thermal phenomena, it was anticipated that LST values might exhibit spatial autocorrelation, particularly between LULC areas. The outcomes of this [PERSON]'s \(I\) analysis were intended to inform the selection of subsequent spatial analysis techniques, specifically regarding the appropriateness of methods that account for spatial dependence versus those that assume spatial independence.
## 2 Materials and Methods
### Experimental Design  
The experiment was conducted in completely randomized blocks with five treatments: (C) the control; (VT) 0.5 L/m\({}^{2}\) of vermicompost tea diluted in a proportion of 1:15; (Ap) 0.5 L/m\({}^{2}\) of _Arthospira platensis_ cultivated in VT; (MC) 0.5 L/m\({}^{2}\) of microbial consortium obtained from compost (1.2 \(\times\) 10\({}^{9}\) cells/mL) [13]; (Bk) 1.0 kg/m\({}^{2}\) of Bokashi. Each block consisted of two rows of garden bed that were 16 m\({}^{2}\); each plot had 2 m of length spaced by 1 m length from each other. A space of 1.0 m of length in the ends of the garden beds was considered (Figure 1a). The vegetables were planted at the same time in a consortium, including pepper, a single plant row spaced 1 m apart; lettuce, spaced by 0.3 m \(\times\) 0.3 m; arugia, six plants spaced by 0.15 m between the pepper and lettuce row; carrot seeds, which were applied in a row with 0.15 m from two rows of lettuce; and beetroot, six plants spaced by 0.15 m between the lettuce row and pepper (Figure 1b). All plants were planted by seedlings, except the carrots, which were planted by seeds. Around 5 days after the emergence, carrots were thinned to 6 plants per row. For the forestry component, two garden beds (one in block 2 and other in block 4) received some plants and trees: _Sphagneticola trilobata_, _Giricidia_ sp., _Mus paradisiaca_, _Ricinus communis_, _Diospyros kaki_, _Caesalpina pluviosa_, _Tabebuia roseo-alba_, _Mangirefa indica_, _Melia azdarach_, _Cajanus cajan_, and _Citrus_ sp. The experiment was conducted for the duration of two planting and harvesting cycles. The first started on October 2021 and the second on May 2022.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline
**pH** (**CaCl2**) & **OM** & **P-res** & **K** & **Ca** & **Mg** & **H + Al** & **Al** & **SB** & **CCE** & **V** & **m** & **S** & **B** & **Cu** & **Fe** & **Mn** & **Zn** \\ \hline  & **g** & **mg** & & & & & **mmol/dm\({}^{3}\)** & & & & & & & **mg/dm\({}^{3}\)** & & \\  & **dm\({}^{-3}\)** & **dm\({}^{-3}\)** & & & & & & & **\%** & & & & & **mg/dm\({}^{3}\)** & & \\ \hline
6.5 & 17 & 26 & 3.1 & 52 & 9 & 14 & \textless{}0.7 & 64.1 & 78 & 82 & 1 & 10.0 & 0.22 & 2.50 & 27.0 & 1.90 & 6.30 \\ \hline \end{tabular}
\end{table}
Table 1: Soil chemical properties before starting the experiments.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Summary  
Rubber-toughened epoxy resins can significantly enhance the toughness and impact resistance of epoxy resins while offering good processability and reduced production costs. Through extensive research and development, rubber-toughened epoxy resins have demonstrated significant application value in various fields. However, there are notable drawbacks: rubber toughening can lead to decreased thermal resistance, reduced aging performance, and diminished thermal stability of the epoxy resin. Addressing these issues remains a pressing challenge.
# Bounding Volume Hierarchy-Assisted Fast SAR Image Simulation Based on Spatial Segmentation
###### Abstract  
In order to improve the simulation efficiency under the premise of ensuring the fidelity of synthetic aperture radar (SAR) simulation images, we propose a BVH-assisted fast SAR image simulation method based on spatial segmentation. The beam scanning model is established based on RD imaging geometric relation, and the bounding volume hierarchy (BVH) algorithm is used to assist in obtaining the time-varying latticed radiation and shadow areas within the radar beam, combining them with the real-time position of the sensors to complete the simulation of the electromagnetic (EM) wave transmission. The ray tracing algorithm is used to calculate the multiple backscatter fields of EM waves, including various material properties of the target surface. The SAR spatial traversal is adopted to spatially segment the latticed radiation area, and the compute unified device architecture (CUDA) kernel function is designed using the echo matrix cell method to make each cell of the target echo matrix as a subfield of the backscattering field, and the position of the echo matrix cell is traversed to obtain the target backscattering field. The target simulated echo is processed by the range Doppler (RD) imaging algorithm to obtain the SAR-simulated image. The simulation results show that compared with a CPU single-thread simulation, the simulation speed of the proposed method is significantly improved, and the SAR simulation image has high structural similarity with the real image, which fully verifies the effectiveness of the proposed method.  
SAR image simulation; ray tracing; multiple backscatter; echo simulation; bounding volume hierarchies; CUDA 2024143340. [[https://doi.org/10.3390/app14083340](https://doi.org/10.3390/app14083340)]([https://doi.org/10.3390/app14083340](https://doi.org/10.3390/app14083340))
# P-Wave Reflection Approximation of a Thin Bed and Its Application
## 2 Theory
### Inversion of Thin-Bed Elastic Parameters and Thickness
#### 2.2.2 AVA Inversion of Thin-Bed Properties  
According to a least-squares approach, the objective function \(Q\) for the inversion of the thin-bed P-wave reflection coefficient is defined as follows:  
\[Q(\mathbf{m})=\left\|\mathbf{R}^{obs}-\mathbf{R}\right\|^{2}, \tag{11}\]  
where \(\mathbf{m}=(\mathbf{v}_{\mathrm{P}}\) vs. \(\rho\) h)\({}^{\mathrm{T}}\) are the thin-bed elastic parameters and thickness. \(\mathbf{R}\) is the vector of the calculated P-wave reflection coefficients by the linear analytic approximation for the given model parameter vector \(\mathbf{m}\). The update model vector \(\Delta\mathbf{m}=(\Delta\mathbf{v}_{\mathrm{P}}\ \Delta\mathbf{v}_{\mathrm{S}}\ \Delta\rho\ \Delta\mathbf{h})^{\mathrm{T}}\) can be calculated through the Gauss-Newton formula [29; 30; 31; 32] as:  
\[\Delta\mathbf{m}=\left[\mathbf{J}^{\mathrm{T}}(\mathbf{m})\mathbf{J}(\mathbf{ m})+\chi\mathbf{J}\right]^{-1}\mathbf{J}^{\mathrm{T}}(\mathbf{m})[\mathbf{R}( \mathbf{m}_{0}+\Delta\mathbf{m})-\mathbf{R}(\mathbf{m}_{0})], \tag{12}\]  
where \(\mathbf{m}_{0}\) is the initial parameter vector, \(\chi\) is a Lagrange multiplier, \(\mathbf{I}\) is an identity matrix and \(\mathbf{J}\) is the Jacobian matrix as  
\[\mathbf{J}=\left[\frac{\partial\mathbf{R}_{i}(\mathbf{m})}{\partial m_{k}} \right]\text{ with }i=1,\dots,NA\times NF;k=1,\dots,NM, \tag{13}\]  
where the subscripts \(i\), \(k\) indicate indices for reflection coefficients and model parameters, respectively; NA, NF and NM are the numbers of angle, frequency and model parameters, respectively.  
Considering that \(\mathbf{m}\) has many parameters, including P-wave velocities, S-wave velocities, densities of three layers and the thickness of middle layer for a thin bed, \(t\) is more prone to obtaining local minimum solutions than conventional AVA inversion. In order to obtain reasonable inversion results, priori information is adopted to constraint the inverted thin-bed model.
# Thai Rice Vinegars: Production and Biological Properties
## 3 Results and Discussion
### Biological Properties of Rice Vinegars
#### 3.3.2 Determination of Antibacterial Activity
aureus_ (DIZ = 17.7 \(\pm\) 0.6 mm), while showing medium inhibitory effect on _S. epidermidis_ (DIZ = 13 mm). This result was similar as detected in commercial distilled vinegar. Moreover, glutinous rice vinegar showed the higher inhibitory effect on _E. coli_ and _S. aureus_ than commercial fermented rice vinegar significantly. The antibacterial activity of black glutinous rice vinegar on _E. coli_ was similar to glutinous rice vinegar, accepted for the inhibition against _S. aureus_ (DIZ = 16 mm) and _S. epidermidis_ (DIZ = 12 mm) showing the lower activity significantly. Black fragment rice vinegar demonstrated a medium inhibition against all tested bacteria (DIZ = 11-14.3 \(\pm\) 0.6 mm). For polished rice vinegar, medium inhibition was detected on _E. coli_ and _S. aureus_ with DIZ values of 13.7 \(\pm\) 0.6 mm, while weak inhibition was observed on _S. epidermidis_ (DIZ = 10 \(\pm\) 1 mm). However, Tetracycline-HCl (positive control) showed the strongest inhibition against all strains of bacteria tested significantly. There were no statistically significant differences of antimicrobial activity between glutinous rice vinegar and black glutinous rice vinegar. In contrast, acetic acid (4%) showed medium inhibition against all strains of bacterial tested. Vinegars mainly consist of acetic acid have been known to possess strong antimicrobial activity against bacteria [28]. A previous study has shown that antibacterial activities of vinegars could be partly related to both their acetic acid contents and pH values, in addition to their phenolic contents. The inhibition of microbial growth or survival of bacterial cells depend on the acidity level of vinegar. Acetic acid in rice vinegar shows antimicrobial activity by traversing the cell membrane in the undissociated form and dissociating in accordance with the intracellular pH to release a proton (H\({}^{+}\)) in the cytoplasm. The resulting enhancement of acidity in the cytoplasm leads to cell damage. Moreover, proteins and enzymes are denatured, inhibiting DNA and RNA synthesis. The energy is diminished because ATP is used to eliminate the excess protons, resulting in the growth inhibition and death of the cells [31,32]. The results of the present study indicated that all Thai rice vinegars contained antibacterial activity against both Gram-negative and Gram-positive bacteria. The antibacterial activity was related to the total acid and acetic acid contents of vinegars, which were more effective than the total phenolic content.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 2 Room Acoustic Solver Using Conventional Explicit TD-FEM
### Fourth-Order Accurate Dispersion Reduced Scheme  
Conventional explicit TD-FEM uses dispersion-reducing four-node square or eight-node cubic FEs, respectively, for 2D or 3D analysis to achieve fourth-order accuracy in both space and time. The dispersion-reducing FEs are constructed using modified integration rules [31], which are two points Gauss-Legendre quadrature with modified integration points. In general, element matrices \(\mathbf{M}_{\text{e}}\) and \(\mathbf{K}_{\text{e}}\) of four-node quadrilateral FEs are constructed using Gauss-Legendre quadrature with two integration points in each direction as explained below.  
\[\mathbf{K}_{\text{e}}=\sum_{i=1}^{2}\sum_{j=1}^{2}\
abla\mathbf{N}(\alpha_{k,i},\alpha _{k,j})^{\intercal}\
abla\mathbf{N}(\alpha_{k,i},\alpha_{k,j})\det(\mathbf{J}), \tag{9}\]  
\[\mathbf{M}_{\text{e}}=\sum_{i=1}^{2}\sum_{j=1}^{2}\mathbf{N}(\alpha_{m,i},\alpha_{m,j })^{\intercal}\mathbf{N}(\alpha_{m,i},\alpha_{m,j})\det(\mathbf{J}). \tag{10}\]  
Therein, \(\alpha_{k}\) and \(\alpha_{m}\) represent local coordinates of integration points for the computation of \(\mathbf{K}_{\text{e}}\) and \(\mathbf{M}_{\text{e}}\). For this discussion, \(\mathbf{J}\) represents the Jacobian matrix. For the two points rule, local coordinates of integration points are usually set as \(\alpha_{k}=\alpha_{m}=\pm\sqrt{1/3}\). However, time-domain dispersion error analysis [31] revealed that the use of modified integration points produces fourth-order accurate scheme instead of a conventional second-order accurate scheme. The modified integration points are given as [31]  
\[\alpha_{k}=\pm\sqrt{\frac{2}{3}}\,\ \ \ \ \alpha_{m}=\pm\sqrt{\frac{1}{3}(4- \tau^{2})}, \tag{11}\]  
where \(\tau\) represents the Courant number defined as \({c_{0}}\Delta t/h\). Therein, \(h\) denotes the length of square FEs. The dispersion error analysis yields a useful expression to evaluate the dispersion error of the fourth-order accurate explicit scheme. The numerical sound speed \(c^{h}\) can be evaluated as  
\[c^{h}{\approx}{c_{0}}(1-\frac{(kh)^{4}}{1440}((8-10\tau^{2}+2\tau^{4})-(19-1 0\tau^{2}+5\tau^{4})\cos^{2}\!\theta\sin^{2}\!\theta)), \tag{12}\]  
where \(k\) denotes the wavenumber and \(\theta\) represents the propagation direction of a plane wave in a polar coordinate system. This conventional dispersion-reduced explicit TD-FEM is computationally efficient by virtue of its use of linear elements and low-order finite difference approximation in time. However, the scheme can achieve fourth-order accuracy in both space and time for the case using square elements because the modified integration point in \(\alpha_{m}\) requires the length of square elements \(h\). This requirement poses an important hindrance to room acoustic modeling in that the staircase approximation is introduced into the room boundary model. In a later section, we present a means of solving this difficulty.