esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Isolation and Quantification of Rhizosphere Bacteria  
The rhizosphere bacteria were isolated from the newly harvested fine roots. The procedure involved first preparing 5 g of fresh roots from the seedlings of maize (CC150) and quinoa (CC300) groups. One hundred mL of sterile saline solution (9.0 g:L\({}^{-1}\) NaCl) were added to the 5 g of maize or quinoa fine roots in tubes. The tubes containing the fine roots and saline solution were then left to stir in an orbital shaker for a couple of hours at room temperature. The solutions, rich in bacteria, were recovered, transferred to a new sterile tube and then centrifuged at 30 RCF at 4 \({}^{\circ}\)C for two minutes to eliminate soil residues. The supernatants were recovered and centrifuged again at 550 RCF for 20 min always at 4 \({}^{\circ}\)C. Forty milliliters of saline solution were added to each bacteria pellet deposited on the bottom of the tube to remove residual debris and wash the bacteria; then the bacterial solutions were centrifuged at 550 RCF for other 20 min. Finally, after supernatant removal, the bacterial pellet was resuspended again in a mL of saline solution and then serially diluted up to 10\({}^{-6}\). Five hundred mL of ND (not diluted) and of each dilution, from both maize and quinoa, were plated on 14 cm diameter Petri dishes containing 100 mL sterile PCA culture medium (plate count agar--5% peptone, 0.25% yeast extract, 0.1% glucose, 1.5% agar and deionized water q.b. to 1 L), or on PCA supplemented with 500 mM of NaCl (PCA + 500 mM of NaCl). Aliquot of undiluted bacterial pellet solutions (two mL) of both maize and quinoa rhizosphere were undergone to a temperature of 85 \({}^{\circ}\)C for 15 min and then 500 \(\mu\)L plated on 14 cm diameter Petri dishes containing 100 mL sterile YSA (yeast starch agar--1% starch, 0.2% yeast extract, 1.5% agar and deionized water q.b. to 1 L), or YSA medium added with 500 mM of NaCl (YSA + 500 mM NaCl) to isolate spore-forming bacterial colonies.  
Petri dishes of each bacterial dilutions and ND, of both analyzed plant species, were incubated at 27 \({}^{\circ}\)C for one week and inspected daily. Bacteria population was quantified by determination of colony forming units (CFU) with agar plate method. During the incubation time morphologically different colonies (e.g., shape, size, and color) were collected with a sterile loop and streaked on a sterile 9 cm diameter Petri dish containing 20 mL of 1.0 M NaCl PCA solid medium.
## 2 Experimental Setup
### Shadowgraphy Configuration  
Flow visualization inside the jet is performed using a shadowgraphy setup. A lens is used to direct light from an intense source through the supersonic jet normal to the direction of the flow and then into a high-speed camera (Phantom v710). The light source and camera are at a sufficiently long distance from the flow such that light can be considered collimated. This configuration is illustrated in the upper left portion of Figure 3. Nonuniformities in transparent air, such as shock waves, display large variations in refractive index; those regions appear dark (shadows) on a bright illuminated background. The camera acquires images at a rate of 9000 frames per second with a \(512\times 384\) pixel field of view (FOV) and a magnification of 0.11 mm/pixel. The images in Figure 4 are cropped to \(6.8\times 20\) mm\({}^{2}\) and averaged over 90 instantaneous frames (10 ms).  
Figure 3: Overview of the MTV and shadowgraphy setup.
# Weak Edge Target Segmentation Network Based on Dual Attention Mechanism
## 3 Model Design and Optimization
### Multi-Scale Information Fusion Module
Following the pointwise convolution, the global descriptor is converted into a compressed guidance vector and then restored to the same number of channels as the input feature map through another 1 \(\times\) 1 pointwise convolution, yielding a reweighted guidance vector. In the feature fusion stage, the guidance vector undergoes element-wise multiplica  
Figure 5: EIFM structure diagram. tion with the output of the shortcut branch, adaptively adjusting the weight of each channel to achieve selective feature enhancement. Finally, the enhanced feature maps of different scales are concatenated along the channel dimension to form the final fused features, which are then passed to the subsequent layer. By introducing a lightweight channel attention mechanism and a cross-scale feature fusion strategy, MIFM can efficiently extract and integrate multi-scale contextual information, enhancing the quality and robustness of feature representation. This structural design effectively integrates the channel attention mechanism, improving the model's performance and efficiency while maintaining accurate capture and effective utilization of feature information. In WETS-Net, each skip connection layer incorporates a MIFM module before the decoders at each level. Through this design, the network can fully utilize the rich semantic information extracted by the encoder and the high-resolution spatial details restored by the decoder, thereby better capturing the multi-scale features of weak and small targets. Applying MIFM at each skip connection enables WETS-Net to optimize feature representation layer by layer and achieve cross-scale fusion. The introduction of this structure effectively enhances segmentation accuracy and efficiency while maintaining the stability and interpretability of the overall network architecture. The improved network can more effectively learn and utilize the importance of different channels, thereby increasing focus on key features and reducing noise interference in the final feature representation. This layer-wise optimization and cross-scale fusion method enables WETS-Net to perform well in processing complex scenes, not only improving the accuracy of segmentation tasks but also enhancing overall computational efficiency. Consequently, the network becomes more suitable for practical application scenarios.
## Dynamic Key Sharing and Fuzzy Adaptive Route Selection
### Fuzzy Adaptive Selection of Data Forwarding Routes
#### 3.6.1 En-Route Filtering  
When an intermediate CH receive a report, it will inspect whether all the MACs belong to the same cluster by comparing C\({}_{id}\) (cluster ID) with \(floor\Big{(}\frac{i}{L}\Big{)}\) for each (\(i\), MAC\({}_{i}\)) pair in the report. The intermediate node will verify the report if it holds the corresponding key at index \(i\) in its memory.  
Verification CHs that possess a verification key corresponding to a report use two binary verification sequences to record the verification result, i.e., \(\mathit{vote}_{v}\) and \(\mathit{vote}_{t}\). \(\mathit{vote}_{v}\) records the number of verified votes and \(\mathit{vote}_{t}\) records the number of verified true votes. When the value of \(\mathit{vote}_{t}\) reaches the threshold, the flag Acceptr is set to 1. The report with the flag bit Acceptr set to 1 is not verified again on the path. This saves the remaining verification nodes from investing energy in computing MACs for the verification.  
In the worst case, a false report travels all the way to the BS whereas in the best case, the false report is immediately dropped at a node \(T_{f}\) hops away from the e-CH. For a true report, the best case is when it arrives at the BS station with the minimum verifications performed on it. In the worst case, a true report gets dropped just before the BS receives it, with the maximum verification operations performed on it. Equations (3) and (4) give the total amount of energy invested in forwarding the false and the true reports, respectively:  
\[E_{false\_report}=\left\{\begin{array}{c}(n+1)\big{(}E_{transmit}+E_{receive }\big{)}+s\cdot E_{verify}\ \mathit{worst\ case}\\ T_{f}\Big{(}E_{transmit}+E_{receive}+E_{verify}\Big{)}\ \mathit{best\ case}\end{array}\right., \tag{3}\]  
\[E_{true\_report}=\left\{\begin{array}{c}n\cdot\big{(}E_{transmit}+E_{receive }\big{)}+s\cdot E_{verify}\ \mathit{worst\ case}\\ (n+1)\Big{(}E_{transmit}+E_{receive}\big{)}+T_{t}\cdot E_{verify}\Big{)}\ \mathit{best\ case}\end{array}\right., \tag{4}\]  
where, \(n\) = number of intermediate nodes on the path, \(E_{transmit},\ \ E_{receiver},\ \ E_{verify}\) are energies invested in transmitting, receiving and verifying a report, respectively, \(T_{t}\) and \(T_{f}\) are true and false vote threshold values respectively, and \(s\) is the number of votes attached to a report.
# Microsphere Polymers in Molecular Imprinting: Current and Future Perspectives
## 2 Microsphere Polymers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application** & **Template** & **Monomer and Crosslinker** & **Polymerisation Technique** & **References** \\ \hline \multirow{5}{*}{Propranolol} & Thymopentin & p-CMS, VI, EDDMA & ILRPP & [68] \\ \cline{2-5}  & Vanillin & MAA, EDSMA & RAFTPP & [69] \\ \cline{2-5}  & & MAA, DVB & PP & [70] \\ \cline{2-5}  & & MFABA, NIPAAm, ECDMA & & [71] \\ \cline{2-5}  & Propranolol & 4-VP, NIPAAm, MPABA, DMAEMA, ECDMA & & [72] \\ \cline{2-5}  & & MAA, ECDMA & & [73] \\ \cline{2-5}  & MAA, HEMA, ECDMA & & [74] \\ \hline \multirow{5}{*}{Tetracycline} & MAA, HEMA, AnHEMA, ECDMA & & [75] \\ \cline{2-5}  & MAA, ECDMA & & [76] \\ \cline{2-5}  & Quercetin & 2-VP, ECDMA & RAFTPP & [77] \\ \cline{2-5}  & Aristolololchic Acid I & AA, ECDMA & RAFTPP & [78] \\ \cline{2-5}  & Isopropylaminopropanediol & MAA, ECDMA, AIBN & PE & [79] \\ \cline{2-5}  & Erythromycin & MAA, ECDMA & SP & [80] \\ \cline{2-5}  & YPLG & MAA, ECDMA & SP & [81] \\ \hline \multirow{5}{*}{Controlled Drug Release} & Paclitaxel & MAA, ECDMA & RAFTPP & [82] \\ \cline{2-5}  & Adhenosine 5\({}^{\prime}\)-monophosphate & DMAEM, NIPAAm, MBAM & PE & [79] \\ \cline{2-5}  & Vanomycin & HEMA, DEAEMA & PP & [83] \\ \cline{2-5}  & Smith & MAA & PP & [84] \\ \cline{2-5}  & 1,4-dimethyl-6-hydroxy-9H-carbazole & & & [85] \\ \cline{2-5}  & (CABI) & & & [85] \\ \cline{2-5}  & 1-(1-naphthyl)ethylamine & Macrocyclic & SP & [86] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline
**Application** & **Template** & **Monomer and Crosslinker** & **Polymerisation Technique** & **References** \\ \hline  & Tebuconazole & 4-VP, ECDMA & PP & [52] \\ \cline{3-5}  & & PP & [52] \\ \cline{3-5}  & & PP & [52] \\ \cline{3-5}  & & 4-VP, ECDMA & ATRPP & [1] \\ \cline{3-5}  & & PE & [87] \\ \cline{3-5}  & & 4-VP, MAA, ECDMA, TRIM & PP & [88] \\ \cline{3-5}  & & 4-VP, DVB & PE & [89] \\ \cline{3-5}  & Moncorotophos & MAA, ECDMA & PP & [90] \\ \cline{3-5}  & Difenoconazole & HFMA, ECDMA & PP & [91] \\ \cline{3-5}  & & \(\rm{Azoxystrobin}\) & HFMA, ECDMA & PP & [92] \\ \cline{3-5} Environmental Contaminants & Carbaryl & MAA, ECDMA & PP & [93] \\ \cline{3-5} Environmental Contaminants & Di(2-ethylhexyl)phthalate & MAA, ECDMA, TRIM & PP & [94] \\ \cline{3-5}  & Simertyne & ABA, DVB & PP & [95] \\ \cline{3-5}  & Cyhalothin & AM, ECDMA & PP & [96] \\ \cline{3-5}  & Polystyrene & MAA, DVB & PP & [97] \\ \cline{3-5}  & \(p\)-nitroaniline & IL, ECDMA & PP & [98] \\ \cline{3-5}  & Dxlofenac & 2-VP, ECDMA & PP & [99] \\ \cline{3-5}  & & MAzoPy, ECDMA & ATRPP & [100] \\ \cline{3-5}  & & 4-VP, NIPAAm, ECDMA & ILRPP & [101] \\ \cline{3-5}  & & 4-VP, ECDMA & & [5,8] \\ \cline{3-5}  & & 4-VP, NIPAAm, ECDMA & RAFTPP & [102] \\ \cline{3-5}  & & 4-VP, HEMA, ECDMA & & [103,104] \\ \hline Pyrazosulfuron- ethyl & MAA, 4-VP, AA, ECDMA, DVB & & [105] \\ \cline{3-5}  & & \(\rm{MAA}\), ECDMA & RAFTPP & [106] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 3 Results
### Color Values of SSEF at Different Conditions
#### 3.2.2 Color Values at Different Soaking Times in Different Conditions  
The SSEF with different SWE concentrations was also subjected to different soaking times under various conditions (Tables 3-5). Based on the color measurement of the SSEF under different soaking times in acidic conditions (Table 3), the L* of the SSEF with 0.15% SWE exhibited a significantly (\(p<0.05\)) higher value (73.09-73.41) than the films with 0.25% (67.56-68.74) and 0.35% (59.75-60.62) SWE. Conversely, the a* and b* values of the SSEF containing the highest concentration of SWE showed significantly (\(p<0.05\)) higher values (15.31-19.96 and 39.42-45.07, respectively) than the SSEFs with 0.25% (10.22-10.81 and 28.95-34.79, respectively) and 0.15% (5.65-6.29 and 19.61-21.15, respectively). The total color difference (\(\Delta\)E) of the SSEF with the addition of 0.15%, 0.25%, and 0.35% SWE increased significantly (0.29-1.69, 0.26-5.99, and 1.49-7.37, respectively). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Treatment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} & \multicolumn{1}{c}{**Totalment**} \\ \hline \hline \multirow{8}{*}{0.25\%} & L* & 73.6\% & 73.0\% & 72.8\% & 72.2\% & 71.5\% & 71.5\% & 71.1\% & 72.8\% & 70.5\% & 70.1\% & 60.5\% & 64.9\% & 64.7\% \\  & 0.03\% & 0.06\% & 0.02\% & 0.02\% & 0.01\% & 0.02\% & 0.01\% & 0.02\% & 0.03\% & 0.08\% & 0.02\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.03\% \\  & \(\sigma\) & 76.8\% & 84.1\% & 85.3\% & 10.8\% & 11.8\% & 11.5\% & 12.3\% & 12.7\% & 12.5\% & 13.8\% & 12.2\% & 13.8\% & 0.01\% & 0.01\% & 0.01\% \\  & \(\sigma\) & 0.01\% & 0.01\% & 0.01\% & 0.02\% & 0.02\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% \\  & \(\sigma\) & 0.01\% & 0.04\% & 0.01\% & 0.02\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% \\  & \(\sigma\) & 0.01\% & 0.04\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% \\  & \(\sigma\) & 0.01\% & 0.04\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% \\  & \(\sigma\) & 0.01\% & 0.03\% & 0.03\% & 0.03\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% & 0.01\% \\ \hline \hline \end{tabular}
\end{table}
## 3 Experiments and Analysis
### Ablation Study
#### 3.3.1 LS Module: UDDRes Depth Analysis  
This ablation study (Table 4) investigated the impact of varying the number of UDDRes repetitions (\(m\)) within the LS module, while keeping all other hyperparameters fixed. We aimed to determine if the LS module benefits from similarity measurements at higher semantic levels. Results indicate that directly measuring the similarity between encoder and decoder features is optimal (\(m=1\)), eliminating the need for extracting high-dimensional semantic representations. This is because the module's primary function is to capture local spatial feature similarity between target and background, effectively achieved through large convolutional kernels. While increasing UDDRes depth might lead to further performance gains, it would incur excessive computational costs during training, making it impractical for our current setup.
## 4 Results
### Diagenetic Events  
#### 4.6.1 Compaction and Cementation  
During the early burial stage, due to the mechanical compaction, the primary porosity is significantly decreased, suggested by the twisted clay flakes (Figure 11A,B). The SEM images suggest that clay flakes were frequently banded and oriented under the compaction process. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Lithofacies**} & \multirow{2}{*}{**Well Name**} & \multirow{2}{*}{**Depth (m)**} & \multirow{2}{*}{**Pore Volume (10\({}^{-2}\) cm\({}^{3}\)/g)**} & \multicolumn{3}{c}{**Pore Volume (10\({}^{-2}\) cm\({}^{3}\)/g)**} & \multicolumn{3}{c}{**Surface Area (m\({}^{2}\)/g)**} \\ \cline{4-9}  & & & & **Area** & & **(m\({}^{2}\)/g)** & & **Micropore** & **Mesopore** & **Macropore** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Silicous- \\ argillaceous \\ mixed shale \\ \end{tabular} } & DB & 3783.9 & 1.63 & 22.25 & 0.48 & 0.91 & 0.24 & 15.73 & 6.42 & 0.1 \\ \cline{2-9}  & DA & 2247.09 & 2.51 & 31.91 & 0.72 & 1.36 & 0.43 & 23.19 & 8.54 & 0.18 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Silicous \\ mixed shale \\ \end{tabular} } & DA & 2262.2 & 2.44 & 35.33 & 0.84 & 1.28 & 0.32 & 26.83 & 8.36 & 0.14 \\ \cline{2-9}  & DA & 2267.7 & 3.19 & 47.46 & 1.1 & 1.83 & 0.26 & 35.93 & 11.41 & 0.12 \\ \cline{2-9}  & DA & 2269.74 & 3.3 & 48.56 & 1.13 & 1.94 & 0.23 & 36.12 & 12.34 & 0.1 \\ \cline{2-9}  & DB & 3799.28 & 2.37 & 33.94 & 0.76 & 1.28 & 0.33 & 25.29 & 8.51 & 0.14 \\ \cline{2-9}  & DB & 3809.24 & 2.9 & 44.57 & 1.05 & 1.52 & 0.33 & 34.71 & 9.72 & 0.14 \\ \cline{2-9}  & DB & 3812.4 & 3.09 & 45.87 & 1.08 & 1.77 & 0.24 & 34.79 & 10.97 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)Figure 8: Pore size distribution calculated from CO\({}_{2}\) and N\({}_{2}\) adsorption isotherms for (**A**) argillaceous shales, (**B**) siliceous-argillaceous mixed shales, and (**C**) siliceous shales. Cementation refers to the formation of authigenic minerals in the pores and fractures. By examining the WF-LMX shale reservoirs using high-resolution SEM-EDS-CL techniques, it was found that there are mainly two types of silica cements, namely quartz overgrowth and microcrystalline quartz (called microquartz). Most of the microarray filled in primary intergranular pores and coexisted either in the form of a single crystal closely associated with clay flakes or in the form of aggregates (Figure 11C,D). Migrated organic matter were frequently observed to coexist with microarray aggregates (Figure 11C). The quartz overgrowth is mainly distributed around detrital quartz that is bright in the luminescence
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 3 Proposed Methodology
### Training Approach  
There are two different training approaches used for the P300 speller, subject-specific training (SST) and generic training (GT). SST assumes training for each user separately, and it is used in the majority of the classical P300 spellers. The training dataset in this case was collected from a single subject. SST usually provides better results, however, it is not an option for the subject-independent P300 speller, as it usually requires training and testing the classifier using the same user's data. This approach is not user-friendly, as it usually takes at least 30 min to train the speller for each user.  
The GT approach merges the data from different subjects into a single training dataset. The generically trained model can further be used for new subjects without retraining. GT training has been applied for subject-independent adaptive EEG classification in the P300 speller [12]. Sometimes both intersubject features resulting from GT training and intrasubject features extracted during SST training are used for designing an adaptive classifier [33]. The GT approach represented better results in our previous experiments as reported in [34]. Therefore, the GT approach was employed for the proposed ensemble voting classifiers training, as discussed in this paper.
## 2 Materials and Methods
### Internal Load Indicators  
From the results for the entire match, no differences were observed between positions in either of the two internal load variables (% HRmax: F = 1.196, \(p\) = 2.327, \(\eta^{2}\) = 0.104; TRIMP\({}_{\rm Edw}\): F = 0.175, \(p\) = 0.950, \(\eta^{2}\) = 0.017) (Figure 6). Furthermore, no differences were observed between positions in each half separately (first half: (% HRmax: F = 1.835, \(p\) = 0.141, \(\eta^{2}\) = 0.152; TRIMP\({}_{\rm Edw}\): F = 0.191, \(p\) = 0.329, \(\eta^{2}\) = 0.104; second half: (% HRmax: F = 0.569, \(p\) = 0.686, \(\eta^{2}\) = 0.053; TRIMP\({}_{\rm Edw}\): F = 0.701, \(p\) = 0.596, \(\eta^{2}\) = 0.064)). The internal load indicators for the entire match are presented in Figure 5B. From the results, it was evident that % HRmax differed significantly between the two halves in all positions. Specifically, in all positions, the % HR decreased (CD: t = 6.101, \(p\) < 0.001, d = 1.848; SD: t = 4.269, \(p\) = 0.003, d = 1.423; CM: t = 5.604, \(p\) < 0.001, d = 1.772; SM: t = 3.145, \(p\) = 0.012, d = 0.994; F: t = 2.792, \(p\) = 0.032, d = 1.055) (Figure 5A). The internal load evaluated using the TRIMP\({}_{\rm Edw}\) method decreased for CM (t = 3.886, \(p\) = 0.004, d = 1.229) and F (t = 2.443, \(p\) = 0.049, d = 0.923).
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 5 Performance Evaluation
### Simulation
#### 5.1.2 Report Interval  
This section evaluates the impact of the time interval in which a mobile node reports its values. The report interval varies from 5 to 50 s. The moving speed, the missing neighbor rate and the elevator waiting time are set to 1.1 m/s, 0% and 0 s, respectively. Figures 5a and 6a show the results. The figures show that the success rate decreases when the report interval is greater than 10 s. These results indicate that some left nodes are classified as a failed node incorrectly because they reported their last values within the monitoring area. Therefore, most of the left nodes with an incorrect state do not satisfy the condition \(Q_{w}<T_{nc}\). On the other hand, all failed nodes within the area are correctly considered as failed nodes, and they are divided into three groups. First, the failed nodes, which move within the area, do not commonly satisfy the two conditions of \(Q_{w}<T_{nc}\) and \(R_{w}>T_{r}\) in the classification rule. Second, the failed nodes that are continually located near the door do not commonly satisfy the two conditions \(S(Q)<0\) and \(S(R)>0\). Finally, the failed nodes, which are continually located far from the door, do not satisfy the four conditions. However, the proposed method correctly classifies all failed nodes and all left nodes when the report interval is less than or equal to 5 s. In this case, the moving distance of a mobile node between two consecutive reports is less than the communication range. Therefore, the left nodes can report their last values from outside of the monitoring area at least once before being disconnected from the sensor network, and they satisfy the classification rule.
## 5 Experimental Verification  
To validate the proposed multiple Rx coil WPT system, an experimental measurement is performed under the static, dynamic environments and the experimental setup is presented in Figure 15. The Tx, Rx and load coils are designed using 30 AWG Litz wire to minimize the ohmic drop in the coil. National Instruments (NI) virtual bench (VB-8012) (National Instruments, Bengaluru, India) is used to give the trigger signal to the power amplifier, and NI myRIO is used for the switching between different capacitive elements to select the different resonant frequency at the Tx coils. Two channel  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Tx** & **RxI/Load1** & **Rx2/Load2** & **Rx3/Load3** & **Rx4/Load4** \\ \hline Diameter (c.m) & 12.5 & 4.5/4.5 & 4/4 & 3.5/3.5 & 3/3 \\ Number of turns & 08 & 10 & 8 & 8 & 6 \\ Compensation capacitance (pF) & - & 6.71 & 8.42 & 11.23 & 14.11 \\ Wire AWG & 30 & 30 & 30 & 30 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design parameters of coil.  
Figure 11: Magnetic field Intensity of Selective Power Transfer with compensation.  
Figure 12: Magnetic field intensity of two identical frequency Rx with compensation.  
Figure 13: Magnetic field Intensity of Selective Power Transfer with lateral separation of 3 cm.  
KEYSIGHT digital storage oscilloscope is used for the measurement of the voltage across each load coils. The dimensions and circuit parameters of the coils are same as the simulation parameters as given in Table 4. Measurements are performed for three different cases as follows.