esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Transmission Characteristics
Note that the transmission eigenstates of 3.5 A nanowire for the AP are delocalized in the whole device, and the shape of the transmission eigenstates are similar to that for the P, the spin-up and spin-down electronic states of the left and right electrodes match, resulting in the phenomenon that the AP configuration conductance does not decrease, which indicates that the spin reversal of the right electrode has limited influence on the matching degree of the wave function. Figure 6e shows that the metal could be regarded as a bridge, which opens the channel between porphyrin and Cp in the transport, and \"Left electrode-N4-TM-Cp-Right electrode\" is the main transmission pathway. To further verify that the electrodes and the contacts can significantly affect the MR of the PVCp device, we use the same method as [PERSON] et al. [10] to add 1-4 Ni atoms successively at the contact between the 3.5 A Ni nanowire electrode and the molecule. The transmission spectra are plotted in Figure 8, in which the device structure is shown in the insets. It is found that in the case of only one additional atom, the peak values of transmission spectra for the AP spin-up and spin-down channels are exactly at the Fermi level, and the sum is slightly larger than that for the P, giving rise to a negative MR of the device. When the number of adatoms \(n\) increases to be 2-4, the transmission value at Fermi level for the  
Figure 6: Transmission spectra of PVCp for P and AP configurations of (**a**) Ni monoatomic wire electrode, (**b**) 2 Å Ni nanowire electrode, (**c**) 3.5 Å Ni nanowire electrode, (**d**) 4.5 Å Ni nanowire electrode. (**e**) Transmission eigenstates of two largest eigenvalues at Fermi level of four devices, isovalue = 0.3. AP becomes very small, while it is not less than 0.5 in the case of the P, which suggests excellent MR effects. Figure 8e shows the increasing trend of MR with varying number of adatoms \(n\). The MR of the device surpasses 75% while \(n\) is 4, which is quite close to the value of the device with electrodes of one-dimensional atomic wire, indicating that the contact has a significant effect on the MR. Due to the experimental challenges in realizing a one-dimensional infinite monoatomic chain electrode, we can achieve a comparable outcome by introducing multiple adatoms. Figure 8: Transmission spectra of PVCp for P- and AP-configurations of 3.5 Å Ni nanowire electrode through adding (**a**) one adatom, (**b**) two adatoms, (**c**) three adatoms, and (**d**) four adatoms. Insets show central region of device. (**e**) Dependence of MR on number of adatoms. White sphere: H atom; gray sphere: C atom; silver sphere: V atom; blue sphere: N atom. Figure 7: (**a**) LUMO of spin-down channel for PVCp(triplet), (**b**) band structure of 2 Å Ni nanowire, inset is blochstate of highest point of red line indicated by arrow, (**c**) band structure of 3.5 Å Ni nanowire. White sphere: H atom; gray sphere: C atom; silver sphere: V atom; blue sphere: N atom.
# A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection
## 4 Numerical Simulations
### Structural Damage Detection of Two-Story Rigid Frame
In contrast, the identification results of the ChOA have four misjudgments in element 2, element 3, element 7, and element 13. As shown in Figure 7b, if the damage percentage is 10% and the noise level is 10%, the ChOA results have nine misjudgments. The identification results of the W-ChOA are much better with only one misjudgment at element 11. Figure 5: Single damage detection results of two-story rigid frame by two chimp-based optimization algorithms. (**a**) 5% noise level. (**b**) 10% noise level. Figure 6: Double damage detection results of two-story rigid frame by two chimp-based optimization algorithms. (**a**) 5% noise level. (**b**) 10% noise level. Figure 7: Triple damages detection results of two-story rigid frame by two chimp-based optimization algorithms. (**a**) 5% noise level. (**b**) 10% noise level. With the increase in noise level, the identification accuracy of the ChOA reduces significantly. Compared with the ChOA, the W-ChOA shows remarkable robustness. In addition, with the increase of damage elements, the misjudgments of the ChOA increase significantly. As shown in Figure 7b, the number of misjudgment damage elements of the ChOA even reaches nine. Compared with the ChOA, the W-ChOA shows perfect case stability. With three different damage cases, the maximum number of misjudgment damage elements of the W-ChOA is only one. The mathematical model of multi-damage detection has multiple global optimal values, which causes the optimization algorithm to be easily disturbed during the iterative optimization process. By introducing the Sobol population initialization strategy, the bubble-net hunting mechanism, and the random search mechanism of the whale optimization algorithm, the W-ChOA shows significantly better performance than the ChOA algorithm in multi-damage detection cases. Simulation results of the two-story rigid frame show that both the ChOA and the W-ChOA have good performance in the single-damage detection case with a low noise level. When the noise level is high or multi-damage exists in the structure, the W-ChOA shows its superiority over the ChOA algorithm regarding damage detection accuracy and robustness.
# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
## 3 Methods
### Object Detector Model  
We know that the detection of small targets is challenging for the fast RCNN, because the fast RCNN is based on the last convolution layer conv5_3 for object detection. The receptive field in the feature map is quite large, which is insufficient to encode object information. In addition, the deeper the convolutional layer, the more information each pixel on the feature map contains that is not the object. In this paper, we adopt the layers of base network 2 conv3_3, conv4_3 and base network 1 conv5_3 to extract features where the high-resolution information of the lower-level layer will not be lost in terms of small-scale objects. The region proposals and input feature maps can be collected through an region of interest (ROI) pooling layer. ROI pooling layer is characterized by the non-fixed size of feature maps. The input of three network connections is the feature map of base network 2 conv3_3, conv4_3 and base network 1 conv5_3. The base network 2 feature map of conv3_3 and conv4_3 is used to obtain relevant information about small and medium objects, and the base network 1 feature map of conv5_3 is used to obtain relevant information about large objects. In addition, by connecting the middle-level and low-level layers of base network 2 and the high-level layers of base network 1, the characteristics of infrared and optical images can be fully utilized.  
According to the Inside-Outside NET [23], as more features are connected, we must consider issues of dimensionality and amplitude. We know that the final feature's shape must be \(512\times 7\times 7\) so that it can be processed by the first final fully connected layer. The three ROIs corresponding to object proposal are fed into three corresponding ROI pooling layers, the number and scale of channels differ on each layer of convolution base, with a higher scale on a lower layer. To satisfy the shape constraint, we concatenate three pooled features along the channel axis and reduce the dimension by a \(1\times 1\) convolution. Besides, the L2 normalization is applied to normalize amplitudes for each tensor ahead of concatenation [24], and then to scale each tensor independently. For a d-dimensional input vector \(x=(x_{1},x_{2},\cdots,x_{d})\), L2 normalization is expressed by the following equation:  
\[\|x\|_{2}=\sqrt{\left(\sum_{i=1}^{d}\lvert x_{i}\rvert^{2}\right)} \tag{6}\]  
\[\hat{x}=\frac{x}{\|x\|_{2}} \tag{7}\]  
where \(x\) is the input pixel vector, \(d\) is the dimension of each ROI polling tensor and \(\hat{x}\) is the normalized pixel vector.  
In the last step of the object detector model, the \(7\times 7\) feature vector is input into two fully connected layers that branch into two sibling layers and, finally, the softmax layers for object classification and the regression function for bounding box regression results.
# A Scene Graph Similarity-Based Remote Sensing Image Retrieval Algorithm
## 4 Experimental Evaluation
### Evaluation of the One-Shot Object Detection Algorithm for Remote Sensing Images
#### 4.2.2 Performance Comparison Result
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **CoAE [17]** & **AIT [18]** & **BHRL [19]** & **SiamACDet** \\ \hline \multirow{8}{*}{Visible classes} & oil tank & 33.6 & 35.2 & 38.4 & **41.7** \\ \cline{2-5}  & boat & 7.9 & 8.5 & 8.3 & **9.1** \\ \cline{2-5}  & plane & 43.8 & 44.2 & 45.6 & **48** \\ \cline{2-5}  & house & 36.3 & 36.2 & 39.5 & **39.3** \\ \cline{2-5}  & dam & 8.2 & 9.9 & 10.3 & **12.5** \\ \cline{2-5}  & highway service areas & 10.6 & 13.2 & 11.5 & **16.8** \\ \cline{2-5}  & gymasium & 30.9 & 28.7 & 30.6 & **31** \\ \cline{2-5}  & football & 51.2 & 54.6 & 55.2 & **58.1** \\ \cline{2-5}  & overpass & 13.9 & 14.1 & 19.3 & **14.3** \\ \cline{2-5}  & wind generator & 9.6 & 13.2 & 11 & **18.1** \\ \cline{2-5}  & bridge & 3.8 & 4.5 & 6.5 & **4.6** \\ \cline{2-5}  & basketball court & 37.2 & 36.9 & 35.4 & **39.7** \\ \cline{2-5}  & **mAP** & **23.9** & **24.9** & **26** & **27.8** \\ \hline \multirow{8}{*}{Invisible classes} & golf course & 2.4 & 4.2 & 5.5 & **5.5** \\ \cline{2-5}  & vehicle & 5.8 & 5.9 & 6.2 & **8** \\ \cline{2-5}  & railway station & 14.8 & 15.1 & 15.9 & **17.4** \\ \cline{2-5}  & chimney & 16.5 & 17.2 & 17.6 & **18.7** \\ \cline{2-5}  & **mAP** & **9.9** & **10.6** & **11.3** & **12.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the comparison experiment on the DIOR dataset (%). fuses the features of the query image with those of the image being retrieved, reducing the interference from anchors of different classes from the query image and improving the accuracy of the position regression task, without a significant decrease in detection speed compared to the RPN network. Additionally, this study designed a dual-branch detector to replace the traditional similarity calculation function in the Siamese network for the classification task, which better captures the relationship between query image features and support image features. Both the FC-head and Conv-head improve the detection accuracy of the SiamACDet algorithm, and combining the two allows for a better learning of the relationships between the complex feature vectors, enhancing the classification ability of the algorithm.
## 3 Measurement Results
### System Functionality  
Isolated solutions for protocols such as USB3 and HMDI require data rates in the range of Gbps. Moreover, isolated high-speed clocks and data links for industrial sensor interfaces operate at the same range. For this purpose, we have performed the system measurements at high frequencies. Figure 12 demonstrates the input-output waveforms of the fabricated inductive-based digital isolation system at (a) 250 MHz and (b) 300 MHz frequencies. We have compared the input with outputs from both systems based on the two fabricated customized transformers. OUT1 is related to the system using the metal1-metal2 transformer, while the system's output denoted by OUT2 utilizing the metal1-metal3 transformer is denoted by OUT2. The measured typical propagation delay from the generated Tx input IN and the recovered Rx output OUT1 is 800 ps at both rising and falling edges for 250 MHz, as shown in Figure 12a. Compared to OUT1, the propagation delay is increased to 3.25 times, and the amplitude is reduced by 59% for  
Figure 10: Experimental measurements: (**a**) testbench setup with the equipment to measure the performance of the fabricated digital isolator chip; (**b**) front and bottom view of PCB prototype; (**c**) temperature chamber.  
OUT2 at 250 MHz. This is because the separation between the coils maximizes the isolation voltage at the cost of reducing the energy of the detected signal at the Rx input. Figure 12b shows the measured outputs at 300 MHz, where the propagation delay for OUT1 and OUT2 is increased from 2.6 ns to 4.8 ns, respectively. Despite the increased propagation delay, the achieved results support fast transmission for high data rate applications.
## 5 Analysis of the VIV Model Test Data of Flexible Riser with Staggered Buoyancy Elements
### Improved Prediction for other Cases of the 2/2 Configuration  
The optimized parameters are used to predict the responses of the 2/2 configuration for U = 0.5-1.2 m/s. Figure 13 presents the comparison of the prediction with the optimal parameters and the measured response frequencies. It can be seen that the predicted response frequency is in good agreement with the measured values.  
Table 12 presents the measured responses from the experiment by [PERSON] et al. [16] and the predicted responses from an optimal dataset of excitation coefficient parameters. The results show that the mode has been correctly predicted and the error in the frequency prediction is within 10% for the chosen case (U = 1.0 m/s). The error in the frequency prediction for the other cases of the same configuration L\({}_{\text{C}}\)/L\({}_{\text{B}}\) = 2/2 is within 10% and 20% for the bare riser section and the buoyancy element, respectively.  
Figure 14 presents the comparison of the predicted RMS curvatures with the default parameters and the optimized parameters as well as the RMS curvatures obtained from the measured responses as a function of flow speed (U = 0.5-1.2 m/s). The prediction with optimized parameters shows a good agreement with the measured rms curvature when compared with the prediction with the default excitation parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Flow Speed**} & \multicolumn{4}{c}{**Bare Part**} & \multicolumn{4}{c}{**Buoyancy Part**} \\ \cline{2-9}  & **Measured Response** & **Predicted Response** & **Measured Response** & **Predicted Response** & **Predicted Response** \\ \hline  & **M*** & **F*** & **M*** & **F*** & **M*** & **F*** & **M*** & **F*** \\ \hline
0.5 & - & 2.99 & 7 & 2.78 & - & 0.86 & 2 & 1.01 \\
0.6 & - & 3.48 & 8 & 3.09 & - & 0.
## 5 Results and Discussion
### Analysis of the Thermal Performance of the Storage Tank during Discharge with a c-h Ratio of 0
#### 5.2.2 Thermocline Thickness  
Figure 14 shows the temperature contour distribution inside the storage tank for E4, E5, and E6 during energy discharge at a flow rate of 0.3 m/s. It can be observed that Type c has the largest thermocline thickness and the most uneven temperature distribution. In contrast, Type e and Type f experience less disturbance, resulting in better water temperature stratification. Type f, which includes an additional porous obstacle below the hot water inlet and outlet, further inhibits the growth of the thermocline, resulting in the smallest thermocline thickness.  
The position of the porous structure directly affects the degree of mixing between water layers. At a flow rate of 0.2 m/s, the thermocline thickness of Type e is 106% higher than that of Type f. At a flow rate of 0.3 m/s, the thermocline thicknesses of Type c and Type e are 133% and 17% higher than that of Type f, respectively.  
Figure 13: Temperature distribution during discharge at different flow rates.
# Experimental Study on the Shear Strength of Silt Treated by Xanthan Gum during the Wetting Process
## 3 Results and Discussion
### Mercury Intrusion Test  
The pore size distribution of the treated specimens with different XG contents was investigated, which is helpful in analyzing the interaction between the silt and XG. Figure 11(a),b show the cumulative pore size and differential pore size, respectively.  
[PERSON] et al. [45] found that there are aggregates in the soil, which leads to different pore sizes. The pore size distribution of soil is therefore generally bimodal and manifested as micropore and macropore peaks. Figure 11(a) shows that the cumulative pore intrusion of the mercury intrusion test specimens decreases from 0.1815 mL/g for XG-free silt to 0.1697 mL/g for silt with 2% XG, which implies that the pore size distribution decreases with increasing XG content. Figure 11(b) shows when 1% XG is added to pure silt, the large pores (10-80 \(\upmu\)m) are gradually connected by XG hydrogels and reduced to medium pores (2-10 \(\upmu\)m). Small pores (0.1-2 \(\upmu\)m) in the silt are then filled with XG, and their number gradually decreases. The number of large and small pores in silt, therefore, strongly decreases, while the number of medium pores significantly increases. The pore size distribution of silt also turns into unimodal pore size distribution. Upon increasing the XG content from 1% to 2%, the medium pores in the silt are gradually connected by the filling of XG and hydrogels, resulting in a significant reduction in the number of medium pores and a large increase in the number of small pores.
## 5 Results
### Scalability in Node Density  
In wireless sensor networks, the battery energy of nodes is limited and generally there is no energy supplement. Therefore, the primary goal in designing routing protocols is to let nodes work longer and to efficiently collect and forward more collected data from the source to the base station through the network.  
In some practical applications, this will change the network topology through the addition, movement, or failure of nodes from the topology and the failure of the communication link due to environmental factors. At the same time, the increasing of node density will cause the increment of network scale. This requires routing protocols to respond immediately to the change of topology and react to the change of network scale. Therefore, a routing protocol not only needs to have a low energy consumption, but also certain scalability.  
In Sections 5.2-5.4, we design three scenarios in which the number of nodes keeps accumulating. We compare five algorithms based on three aspects in experiments, respectively. As the number of network nodes increases, as shown in Figure 3, LEACH and K-LEACH algorithms generate the maximum clusters. In contrast, the DCK-LEACH algorithm still obtains a relatively good clustering result.  
As shown in Figure 6a-c, with the number of nodes increasing, DCK-LEACH keeps a higher performance in terms of energy dissipation compared with the other algorithms. Therefore, the DCK-LEACH algorithm is salable and can adapt to changes in the node's density of networks.
## 2 Lunar Surface Propagation Path Loss Models
The study yielded promising results, highlighting the impact of antenna heights, frequency, and lunar ground material on antenna performance and path loss. Notably, it was observed that path loss increased with higher frequencies. There is an ongoing need for improved path loss models, especially at higher frequencies. This area of research remains an open question, with numerous researchers striving to introduce innovative approaches for crafting a more realistic model that encompasses the real terrain effects found in the lunar environment. Table 1 summarizes the propagation models reported so far.
## 3 Results
### Extract the Feature Wavelengths by PLSR  
To further extract the feature wavelengths for improving AGB estimation accuracy, the variable importance in projection (VIP) scores from PLSR was applied to evaluate the performance of the feature bands with the combination of four different grown stages data. The VIP could indicate the contribution of each wavelength (x, independent variable) to the value of AGB (y, dependent variable); it is calculated by equations (10).  
\[\text{VIP}_{\text{k}}(\text{a})=\text{K}\underset{\text{a}}{\sum}\text{W}^{2} \text{ak}(\frac{\text{SSY}_{\text{a}}}{\text{SSY}_{\text{t}}}) \tag{10}\]  
Where VIP\({}_{\text{k}}\)(a) is the importance of the wavelength variable based on a model with a factors (PLSR-components); Wak is the PLSR-weights of wavelength variable in an ath PLSR-factor; \(\text{SSY}_{\text{a}}\) represents the explained sum of squares of Y; and, \(\text{SSY}_{\text{t}}\) is the total sum of squares of Y. The threshold of the VIP score is 1.0 and a large VIP value shows that the wavelength is more important for AGB estimation.  
Figure 6 showed that the VIP scores of visible wavelengths (400-750 nm) were all below 1.0 (the threshold of the VIP score is 1.0), and large VIP values are located at 800 nm and 1200 nm; it indicated that the NIR wavelengths were more important than the visible wavelengths for WOR AGB estimation. It was possible to construct new vegetation indices only from NIR wavelengths to improve WOR AGB estimation accuracy and the wavelengths (800 and 1200 nm) could be selected as the feature wavelengths.  
Figure 5: The coefficient of determination (R\({}^{2}\)) between AGB and NDVI-like indices at four different stages: (**a**) seeding stage; (**b**) bolting stage; (**c**) flowering stage; and, (**d**) pod stage.
## 7 Design Applications
### Hybrid Modelling--Genetic Algorithm Regression
A multi-dimensional search space based on two defined variables and HeuristicLab software (based on genetic algorithm symbolic regression technique) were used to derive appropriate design functions. A scenario table was defined for the maximum pressure wave calculations based on varying vehicle speeds and gap distance between the vehicle and gantry structure. Table 1 shows the scenario table for the numerical simulations, where different scenarios based on speeds of vehicle passages under the gantry and the minimum distance between the top of the vehicle and road gantry structure are defined. For the data handling and postprocessing  
The use of genetic algorithm symbolic regression has opened up opportunities for generic development of design curves for cyclic loading of gantry structures in main roads. It would also help to design a better configuration for commercial large vehicles like trucks and buses based on their aerodynamic performance, which was a subject of the large European project \"Configurable & Adaptable Trucks and Trailer for Optimal Transport Efficiency (TRANSFORMERS)\" [41]. A more comprehensive discussion about the regression technique and genetic algorithm can be found in [40].