esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### RNA Isolation and Reverse Transcriptase (RT)-PCR Analysis  
_P. quinquefolium_ hairy root cultures after 6, 12, and 24 h of carvacrol elicitation were used for total RNA isolation. The Total RNA Prep Plus Minicolumn Kit (A&A Biotechnology, Gdansk, Poland) was used for RNA isolation according to manufacturers' protocol, and details were provided in a previous article [34]. The High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems; Thermo FisherScientific, Inc., Waltham, MA, USA) was used to process the obtained RNA into cDNA. The composition of reaction mixture was as follows: dNTPs (final concentration 1 mM), anchored oligo (dT)23 (final concentration 3.5 \(\upmu\)M), 2 \(\upmu\)L of 10\(\times\) buffer, RNase inhibitor (20 U/\(\upmu\)L), and MultiScribe Reverse Transcriptase (RT; 50 U/\(\upmu\)L). DNase digestion was performed on all RNA samples, and this was verified by quantitative real-time PCR reaction with control samples of RNA without the standard reverse-transcription step. The cDNA was immediately used or stored at \(-20\)\({}^{\circ}\)C.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 7 Multi-User Multiple-Input Multiple-Output (MU-MIMO)
### Related Works
#### 7.2.3 Performance Evaluation  
The authors of [171] observed the behavior of random access considering different MU MIMO sizes and frame aggregations over different channel widths. They concluded that the collision probability is lower with a large contention window. Fewer retransmissions are required and it saves energy. The counterpart is a reduction of the network capacity when the number of contending stations is insufficient to saturate the medium.  
[PERSON] et al. [15] considered indoor/outdoor and single/multiple BSSs. The simulations revealed that using MU MIMO increases the throughput in OFDMA. They concluded that Wi-Fi 6 gains more when the bandwidth increases. Downlink transmissions also impose a lower overhead than uplink transmissions. The authors of [102] developed a light simulator to evaluate MU MIMO. Uplink MU MIMO performance improvement has an inverse relationship with the number of contending stations. Therefore, transmitting data in a scheduled manner avoids degrading the throughput.  
[PERSON] et al. [172] compared SU MIMO and MU MIMO in terms of latency and throughput. They reported that MU MIMO improves the latency, throughput, and collision probability. They also expressed that MU MIMO improvements in WLAN are not as effective as in cellular networks.  
[PERSON] [173] concluded that dynamic scheduling helps to improve the throughput. He analyzed the channel sounding for uplink and downlink MU MIMO. The analysis included the impact of in-phase and quadrature (IQ) imbalances, channel-sounding compression modes with phase noise, and carrier frequency offset.
## 2 System Model and Problem Formulation
### MUSIC Algorithm  
The DoA information is estimated by MUSIC spectral estimation on the filtered version of the received signal, as shown in Figure 2, [19; 25; 26]. The MUSIC algorithm developed by [PERSON] [16] is an eigenstructure-based DOA-finding method and, similar to other parametric algorithms such as ESPRT, has demonstrated a superior resolution with respect to the non-parametric methods.  
By considering a partially connected hybrid mmWave beamforming, after the analog beamforming and the analog-to-digital conversion, the baseband signal is [19; 28]:  
\[\mathbf{y}=\mathbf{A^{H}}\mathbf{Hs}+\mathbf{A^{H}}\mathbf{w} \tag{8}\]  
By performing eigenvalue decomposition on the covariance matrix \(\mathbf{R_{yy}}\) of the output vector \(\mathbf{y}\) of the virtual array, we have:  
\[\mathbf{R_{yy}}=E_{S}\Lambda_{S}E_{S}^{H}+\sigma_{w}^{2}E_{w}E_{w}^{H}\]  
where \(\mathbf{E_{s}}\) is the \((N_{RF}\times K)\) matrix of the signal eigenvectors corresponding to the \(K\) largest eigenvalues, \(\mathbf{E_{w}}\) is the \((N_{RF}\times(N_{RF}-K))\) noise subspace matrix with eigenvectors corresponding to the smallest \((N_{RF}-K)\) singular values, and \(\Lambda_{\mathbf{s}}\) is the \((K\times K)\) diagonal matrix containing the \(K\) largest eigenvalues \(\lambda_{1}>\lambda_{2}\),... \(>\lambda_{K}\) of \(\mathbf{R_{yy}}\). It is clear that the signal subspace \(E_{s}\) and the noise subspace \(E_{w}\) are orthogonal.  
The MUSIC algorithm utilizes the orthogonality between the two complementary spaces to estimate the spatial signal. Therefore, the DoA estimation \(\hat{\theta_{k}}\) consists of finding the values of \(\theta\), whereby the filtered vector \(Aa_{D}(\theta)\) is related to the signal subspace of \(\mathbf{R_{yy}}\), where  
\[\mathbf{a_{D}}(\theta)=\frac{1}{\sqrt{N_{R}}}[1,e^{j\frac{2\pi}{\lambda}Isin( \theta)},...e^{j\frac{2\pi}{\lambda}(N_{R}-1)Isin(\theta)}]\]  
is the array manifold vector of the virtual array.  
By using the definition of a pseudo-spectrum of the MUSIC algorithm, the estimated DoA of the emitter direction can be calculated by maximizing the function  
\[P_{MU}(\hat{\theta})=\frac{1}{\mathbf{a_{D}^{H}}(\theta)\mathbf{ AE_{w}}\mathbf{E_{w}^{H}}\mathbf{A^{H}}\mathbf{a_{D}}(\theta)}, \tag{9}\]  
which provides high resolution of angle separation.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### AIDA Method
#### 4.4.6 Step 6--Multihop Analysis  
The bottom-up and top-down approaches establish the one-hop feasible connection between candidate positions and smart meters. Smart meters not connected by the described approaches can then connect to a cluster of SMs using multihop connections.  
The MST computed with the SMs positions is considered for the theoretical multihop connection analysis. First, smart meters belonging to SMs clusters (i.e., SMs connected to candidate positions) and their links with MST edges are identified. Then, for each SM that already belongs to a cluster, a search is conducted for adjacent MST neighbors (MST vertices) that are not yet connected but have enough received power to establish the connection (i.e., MST blue edges, with \(LRP\geq l_{min}\)). The search is made up to the maximum hops (\(h_{max}\)) limit established by the method. Neighbors with \(LRP\geq l_{min}\) are connected to the SM cluster. When finding a neighbor with a received power value lower than the minimum required, such a node is discarded. It remains unconnected because there are no technical conditions for the connection.  
```
1:Input:\(\mathcal{V}\), \(\mathcal{C}\), \(\mathcal{L}\), \(l_{min}\), \(n_{max}\)
2:Output:\(\mathcal{V}_{con}\)
3:\(\mathcal{V}_{con}\leftarrow\{\}\)
4:for all\(v_{i}\in\mathcal{V}\)do
5:\(\mathcal{K}\leftarrow\) Select all \(c_{j}\in\{\mathcal{C}\mid(c_{j}^{n}<n_{max})\wedge(dist(v_{i},c_{j})\leq c_{j}^ {r})\}\)
6:\(l_{max}\gets l_{min}\)
7:\(c_{s}\leftarrow\{\}\)
8:for all\(c_{k}\in\mathcal{K}\)do
9:\(l_{con}\leftarrow\{l_{v_{i}\in\mathcal{L}}\mid(l_{v_{i},c_{k}}\in\mathcal{L})\}\)
10:if\(l_{con}\geq l_{max}\)then
11:\(l_{max}\gets l_{con}\)
12:\(c_{s}\gets c_{k}\)
13:endif
14:endfor
15:if\(c_{s}\
eq\{\}\)then
16:\(\mathcal{V}_{con}\leftarrow\mathcal{V}_{con}\cup\{<v_{i},c_{s},l_{max}>\}\)
17:\(c_{s}^{n}\gets c_{s}^{n}+1\)
18:endif
19:endfor
```  
**Algorithm 1** Bottom-Up Approach (BU)
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 5 Fairness Cooperation with Power Allocation (FCPA)
#### FCPA Protocol  
The Fairness Cooperation with Power Allocation (FCPA) protocol assigns weights to the cooperating nodes to distribute the load. The allocation of weights considers the energy cost of reception and retransmission associated with cooperation.  
The lifetime of the network is maximized using the following modifications and criteria:  
* Modifications in the network, such as the appearance of a jammer, its mobility or transmit power change, demand that each isolated node chooses a CH as its sink  
Figure 2: Different routes that the nodes can use to reach the sink node in the network using direct routes or cooperation routes.  
node using the Euclidean distance that separates it from the CH and the CH from the jammer. Likewise, it chooses the closest neighbors associated with the cluster of the chosen CH.
* Since the association of nodes to a CH is subject to minimizing the energy cost associated with communication, when the location of the jammer changes the nodes that remain isolated may conveniently associate with a different CH. Consequently, the number of nodes associated with each cluster may change.
* The selection of each relay node is conditioned by the number of hops between the optimum route chosen in the cooperation algorithm. If any route exceeds the preset number of hops limit \(N_{hops}\), then it is discarded. This maximum allowed number of hops is preset by the network manager.
* The weight associated with the relay selection is inversely proportional to the energy cost of the cooperation and is determined by the number of alternative valid routes to support the same communication.  
The weights used to distribute the load between the cooperating nodes are estimated as  
\[w_{i,j}=\frac{1/e_{cop_{i,j}}}{\sum_{k=1}^{K}\frac{1}{e_{cop_{i,k}}}}, \tag{9}\]  
where \(e_{cop_{i,j}}\) is the cooperation energy between the \(i\)th node that needs cooperation and the \(j\)th node (relay) that provides the cooperation. Then, \(e_{cop_{i,k}}\) is the energy of the \(k\)th node that can provide cooperation to the \(i\)th node in the routing chain, where \(K\) is the number of alternative valid routes of the \(i\)th node. Finally, this approach generates a tree-based clustering given the distribution of the load as shown in Figure 3.  
Figure 3: The arrows show the different routes from the isolated node to the cluster head. A wider arrow represents a higher transmit power than a thinner arrow. In (**a**), the direct communication with fixed transmit power is executed by FP, while in (**b**), the power control is used by PA. The inclusion of cooperation occurs for (**c**) by CP, where different routes are considered with the same transmit power. In (**d**), the transmit power control and cooperation algorithms are integrated.
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
###### Abstract  
Model interpretability is essential in machine learning, particularly for applications in critical fields like healthcare, where understanding model decisions is paramount. While SHAP (SHapley Additive exPlanations) has proven to be a robust tool for explaining machine learning predictions, its high computational cost limits its practicality for real-time use. To address this, we introduce C-SHAP (Clustering-Boosted SHAP), a hybrid method that combines SHAP with K-means clustering to reduce execution times significantly while preserving interpretability. C-SHAP excels across various datasets and machine learning methods, matching SHAP's accuracy in selected features while maintaining an accuracy of 0.73 for Random Forest with substantially faster performance. Notably, in the Diabetes dataset collected by the National Institute of Diabetes and Digestive and Kidney Diseases, C-SHAP reduces the execution time from nearly 2000 s to just 0.21 s, underscoring its potential for scalable, efficient interpretability in time-sensitive applications. Such advancements in interpretability and efficiency may hold value for enhancing decision-making within software-intensive systems, aligning with evolving engineering approaches.  
Keywords:interpretability in machine learning; interpretability; SHAP; LIME; C-SHAP; computational efficiency; K-means clustering; software engineering +
Footnote †: journal: Applied sciences
# Optical and Thermal Remote Sensing for Monitoring Agricultural Drought
###### Abstract
Based on the understanding of existing reviews (e.g., [6; 17; 30; 31]), as well as an extensive collection of the latest research results, this paper is aimed at reviewing and summarizing developments in optical and thermal remote sensing-based drought monitoring in the context of agriculture and food security. Section 2 discusses optical remote sensing, highlighting the promising utilization of solar-induced fluorescence as an early indicator, while Section 3 is about thermal remote sensing, and Section 4 is on the combination of optical and thermal remote sensing, with a detailed analysis of the temperature-vegetation space. Section 5 illustrates methods and techniques that utilize multi-source data or data assimilation. Finally, in Section 6, we discuss future development directions and give perspectives on the more effective monitoring of agricultural drought.
## 3 Numerical Simulation of Tunnel Excavation
### Computational Model and Material Parameters  
The computational model is established based on the design parameters from the construction drawings. The transverse dimension is 205 m (greater than six times the tunnel diameter) to reduce boundary effects, and the longitudinal length is 63 m, corresponding to the engineering stationing from K1+838 to K1+901. The height is designed based on the actual depth, as shown in Figure 7. The model uses hexahedral solid elements for meshing. The mesh density is increased around the tunnel section by controlling the element size, with a minimum size of 0.5 m and a maximum size of 5 m. The boundary conditions of the rock mass are set with the ground surface as a free surface and the remaining boundaries as constrained boundaries. The load considered during calculations is the stress field due to self-weight. Using the software's tunnel construction phase assistant, the excavation sequences for the bifurcation tunnel are designed with 3 m excavation progress per cycle. For the RTM, the computational model consists of 126 construction steps, and for the ACM, it consists of 119 construction steps. The models are shown in Figure 8a,b, respectively. The rock and soil materials in the computational model adopt the Mohr-Coulomb elastoplastic constitutive model. The material mechanical parameters are obtained from the engineering geological survey report, as shown in Table 1.  
Figure 7: Computational model and grid division.
# Rotating Machinery Fault Detection Using Support Vector Machine via Feature Ranking
## 2 Basic Rotation Machinery Faults
### Basic of Bearing and Its Typical Fault  
The bearing is a mechanical part that assists with rotations for three primary purposes. First, it helps maintain the correct position for the rotating shafts. Second, it also helps reduce friction and make rotations smoother. Last but not least, it protects the part that supports the rotation. Because it directly relates to the rotation, its failure can significantly impact the whole system. A bearing typically comprises four parts: outer ring, cage, balls, and inner rings as shown in Figure 2  
Bearings typically exhibit three issues. As shown in Figure 3, first, the inner race fault occurs when the inner ring is defective. Second, the outer race fault occurs when the outer ring has some issues. Third, a ball fault occurs when one or more internal steel balls are  
Figure 1: Diagram displaying the workflow of the study.  
Figure 2: Bearings and its parts: outer ring, cage, steel balls, and inner rings [25].  
defective. Depending on the location of the bearing, it could be classified as an overhang or underhang fault. Underhang is when a bearing is located between a rotor and a motor. Overhang is when a rotor is between a bearing and a motor.
## 2 Materials and Methods
### Quality Parameters  
#### 2.3.1 Color Measurement  
Color preserving is crucial when processing food and herbs. Indeed, the first quality feature that a consumer notices when deciding to buy is product color. The machine vision was used to capture fresh and dried _E. amoenum_ petals. The visual system consists of a black wooden box with dimensions of \(50\times 50\times 50\) cm, four fluorescent lamps of 18 W located on the wall of the box at 45\({}^{\circ}\) for illumination, and a Sony Cyber-shot DSC-W370 digital camera with 14 Mpc of the resolution, which was placed vertically at a distance of 22.5 cm from the samples. Digital images were processed by MATLAB software in Lab color model to evaluate color changes. The color difference (CD) was calculated based on the following optimized formula by the CIE committee  
\[\text{CD}=\sqrt{\left(\frac{\Delta L}{K_{L}S_{L}}\right)^{2}+\left(\frac{ \Delta C}{K_{C}S_{C}}\right)^{2}+\left(\frac{\Delta H}{K_{H}S_{H}}\right)^{2}} \tag{1}\]  
where \(\Delta L\), \(\Delta C\), \(\Delta H\) are the differences in brightness, chroma, and hue angle of the dried sample from the reference (fresh) sample, respectively, \(K_{L}\), \(K_{C}\), \(K_{H}\) are the parameter factors that describe the effect of the change from reference conditions (for reference conditions, they are all in 1), and \(S_{L}\), \(S_{C}\), \(S_{H}\) are the weighting functions (\(S_{L}\) = 1, \(S_{C}\) = 1 + 0.045C, and \(S_{H}\) = 1 = 0.015C).
## 2 Numerical Model for Grout Permeability in Backfilling Layers  
### Modeling Strategy and Computational Methods  
To systematically investigate the grout diffusion process within the pea gravel layer of a double-shield TBM tunnel, a heterogeneous porous media infiltration model was established in this study. The simulation methodology and workflow are presented in Figure 1. The modeling process integrates two software platforms: PFC 5.0 and COMSOL 6.2. The objectives of employing PFC are twofold: (1) to approximate the porosity of the pea gravel layer by simulating its consolidation process; and (2) to generate discrete particles for the construction of a porous media model. On this basis, a porosity model of the pea gravel layer is developed in COMSOL.  
The grouting process is treated as a two-phase flow problem involving grout and air. The interface between the two phases is tracked using the level set method, which enables the precise capture of the evolving grout-air boundary. A transient solver is employed to simulate the time-dependent behavior of interface propagation and grout diffusion within the complex pore structure.  
The motion of the grout is governed by the Navier-Stokes equations, which express the fundamental laws of momentum conservation. The incompressible form of the Navier-Stokes equations used to describe the two-phase flow is as follows:  
\[\rho\Big{[}\frac{\partial u}{\partial t}+(u\cdot\
abla)u\Big{]}=\
abla\Big{[} -pI+\mu(\
abla u+\
abla u^{\top})\Big{]}+F \tag{1}\]  
Figure 1: Research roadmap.  
\[\
abla u=0 \tag{2}\]  
where \(\rho\) represents the fluid density, \(u\) denotes the velocity field of the fluid, \(p\) is the pressure, \(\mu\) is the dynamic viscosity, \(I\) is the identity tensor, and \(F\) accounts for external forces acting on the fluid.  
To model the dynamic interface between two phases (grout and air), the level set method is utilized. This method captures the movement of the interface by solving the following equation:  
\[\frac{\partial\phi}{\partial t}+u\cdot\
abla\phi=\
abla\cdot\left[\varepsilon \
abla\phi-\phi(1-\phi)\frac{\
abla\phi}{|\
abla\phi|}\right] \tag{3}\]  
where \(\phi\) represents the level set function, and \(\varepsilon\) is the thickness control parameter. The value of \(\phi\) varies between 0 and 1 and can represent the volume fraction of the two phases. When \(\phi=0\), the fluid is in the gas phase; when \(\phi=0.5\), the fluid is in the liquid phase. When \(0<\phi<1\), \(\phi\) represents the phase interface with a certain thickness. In post-processing, the iso-surface of \(\phi=0.5\) is typically taken as the phase interface.
## 3 Results
### 3.3 Requested Features
#### 3.3.1 EMR Team: Conversation Download  
The EMR development team requested the ability to download conversation transcripts as.txt files for upload to their clinical data warehouse (CDW), along with the functionality to delete specific messages. Feedback was requested inside of JAVIS, leveraging Vue.js's flexible component design. These features were developed, containerized, updated, and tested within approximately five hours.
# Feature-Based Image Watermarking Algorithm Using SVD and APBT for Copyright Protection
## 4 Experimental Results and Comparative Analyses
### Performance Evaluation Indexes  
After the watermark embedding, compared with the original image, the quality of the watermarked image will be reduced. Peak signal-to-noise ratio (PSNR) is an important objective quality evaluation index adopted in most watermarking methods to evaluate the watermark's imperceptibility. For an image with size of \(M\times N\), PSNR is defined as:  
\[\text{PSNR}=10\log_{10}\frac{255^{2}MN}{\sum\limits_{i=0}^{M-1}\sum\limits_{j= 0}^{N-1}\left[I(i,j)-I_{\text{w}}(i,j)\right]^{2}}(\text{dB}), \tag{18}\]  
where \(I(i,j)\) and \(I_{\text{w}}(i,j)\) express pixel values in row \(i\) and column \(j\) of the host image and the watermarked image separately.  
The bit error rate (BER) is used to detect the difference between the extracted and original watermarks, which is shown in Equation (19):  
\[\text{BER}=\frac{1}{l}\sum\limits_{i=0}^{m-1}\sum\limits_{j=0}^{n-1}\left|W_{ \text{e}}\left(i,j\right)-W(i,j)\right|, \tag{19}\]  
where \(W(i,j)\) and \(W_{\text{e}}(i,j)\) show pixel values at a specific location \((i,j)\) in the original watermark and the recovered watermark respectively. \(l\) expresses the length of the watermark information derived from the watermark one-dimensional vector, and \(m\times n\) is the size of watermark.  
The similarity of two images can be evaluated by normalized correlation (NC), which is defined as:  
\[\text{NC}=\frac{\sum\limits_{i=0}^{m-1}\sum\limits_{j=0}^{n-1}\left[W(i,j) \times W_{\text{e}}(i,j)\right]}{\sum\limits_{i=0}^{m-1}\sum\limits_{j=0}^{n- 1}\left[W(i,j)\times W(i,j)\right]}. \tag{20}\]  
Higher NC values represent a better quality of the extracted watermark.
# Atmospheric Density Inversion Based on Swarm-C Satellite Accelerometer
## 2 Materials and Methods
### Method and Model
#### 2.2.3 Comparison of Swarm-C Cone Shadow and Column Shadow Modeling  
In order to evaluate the effect of the simplified conical shadow model and ensure the accuracy of the satellite surface light radiation pressure modeling, the satellite light radiation pressure acceleration along the orbital direction through the penumbra in the conical shadow model is modeled using the column shadow model. Then, the modeling results are compared using the simplified conical shadow model. The results obtained by modeling the satellite along the orbital direction using the cone-shadow model and the column-shadow model are shown in Figure 8.  
As can be seen from Figure 8, the transition time from the full-shadow region to the shadow-free region is longer for the simplified cone-shadow model than for the column-shadow model. In the simplified conical shadow model, the Swarm-C satellite is in the full-shadow region during the 0-20 s, the Swarm-C satellite is in the penumbra region during 20-80 s, and the Swarm-C satellite enters the no-shadow region after the 80 s. Moreover, in the column shadow model, the Swarm-C satellite is in the full-shadow region during 0-42 s. After 62 s, the Swarm-C satellite enters the no-shadow region. For modeling the satellite surface light radiation pressure in the full-shadow and shadowless regions, the difference between the improved cone-shadow model and the column-shadow model is not significant. However, because the Sun's rays are parallel rays in the column-shadow model, the model does not have the penumbra region, so the transition time of the Swarm-C satellite from the present shadow region to the shadowless region only lasts for about 20 s. In contrast, the transition time of the Swarm-C satellite from the penumbra to the no-shadow region in the conical shadow model lasts about 60 s.