esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Receding Horizon Trajectory Planning  
On most occasions, paths which are obtained by planning techniques are suboptimal. Hence, the initial trajectory that is generated based on the initial path is to be refined, ensuring dynamic feasibility for controlling the MAV. Various approaches can be applied for trajectory refinement. However, enabling recursive feasibility and incorporating terminal constraints and convergence to the desired state are the utmost importance considerations to be contemplated throughout the process. LQR and MPC are the two most popular approaches that are being used for receding horizon planning. LQR is applied for linear systems, whereas iLQR and differential dynamic programming (DDP) are applied for nonlinear system. Both in LQR or iLQR, OCP is defined as an open-loop control problem. On the other hand, MPC is designed as a close-loop OCP. In other words, OCP is seeking actions knowing the behavior of the surrounding environment.
## 4 Optimization Process Using NSGA-II
### NSGA-II Optimization Method  
The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is a multi-objective optimization algorithm based on Pareto optimal solution [20]. The flowchart of NSGA-II is shown in Figure 13. The steps of NSGA-II are given as follows:  
Step 1: Randomly generate the initial population; the size of the population is N;  
Step 2: The initial population is sorted by the non-dominated method, followed by selection, crossover and mutation to obtain the first generation offspring population;  
Step 3: Merge the new population with the parent population, and perform fast non-dominated sorting on the merged population. Then calculate the crowding degree of the individual, select the appropriate individual according to the non-dominant relationship and the crowding degree of the individual, and form a new parent population;  
Step 4: A new population is obtained through the above steps, and the above steps are repeated until the termination condition of the algorithm is satisfied.
## 4 Results and Analysis
### Discussion  
Through the analysis and comparison of the above results, we found that the YOLOv3-Tiny network model, which is representative of the lightweight convolutional neural network, performed poorly in the above evaluation and test results. YOLOv3-Tiny has excellent detection speed, but its network structure has fewer layers than YOLOv5S, B-YOLOv5S and BV-YOLOv5S networks, so the detection speed is fast, but YOLOv3-Tiny has fewer layers of convolutional neural network. In the face of deep and complex feature information such as bridge defects, the ability to extract is obviously insufficient, resulting in poor performance of the model in bridge defect detection, and it has difficulty meeting the reliability requirements of bridge damage detection engineering. The original YOLOv5S network model has a low score in the evaluation indicators in detection, especially the key evaluation indicator mAP@.5, and also exposes that PANet has a weak feature extraction ability for bridge damage target images. The B-YOLOv5S bridge damage detection model after enhanced feature extraction has a significant improvement in the mAP@.5 index. The introduction of the BiFPN network structure has a slight adverse impact on the accuracy of bridge damage target detection and other indicators. However, overall performance still improved. The BE-YOLOv5S bridge damage detection model proposed in this paper obtains significant advantages in the evaluation index and shows good robustness in detection under complex conditions. Comparing the BE-YOLOv5S network model with the B-YOLOv5S network model, we can find that using EFL to deal with the sample imbalance problem is more suitable than the traditional Focal Loss; it still has good robustness for the detection of multiple small targets and complex conditions with strong lighting. In summary, the BE-YOLOv5 network model proposed in this paper has certain advanced and reliable properties in the field of bridge damage detection.
# Using Double Convolution Neural Network for Lung Cancer Stage Detection
## 5 Comparison of the Regular Against Double CDNN  
We tested these images with standard Convolution Deep Neural Network, used by the authors of [28], against our double Convolution pre-clustered Deep Neural Network with edge sharpening filters. We used this test set of images to determine the threshold or Tx stage in which both networks can detect possibility of cancer. The networks output a decimal value from 0.0 to 1.0, where 1.0 is  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Regular DNN** & **Double CDNN** \\ \hline Accuracy & 0.8769 & 0.99621 \\ Sensitivity & 0.97460 & 0.99912 \\ Specificity & 0.66957 & 0.98664 \\ threshold classification & 0.70 & 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measurements of accuracy, sensitivity, specificity and positive predictive against regular and double CDNN.  
Figure 7: Stages 2, 3 and 4 of lung cancer.  
cancer and 0.0 is cancer-free. We converted this value as a percentage of certainty and multiplied this by 100. The results of the two networks are shown in Figure 8. The drawback here is that we had to decide the minimal value of certainty we would accept as being satisfactory. To fairly compare both networks (regular and double CDNN), we took a mean of their best accuracy value. In Table 2, we can see that the best accuracy of the regular CDNN is 70%, and the best accuracy of the 76%, thus we used 73% as the minimal threshold value for certainty for cancer detection. Taking 73% as threshold for cancerous for both topologies and using this value as a threshold for cancerous, we can see in Figure 8 that our double CDNN detected cancer in stage 3, whereas the regular DNN from [28] did not detect cancer even in stage 4 (late stage). Taking the lower threshold value of 70% (Table 2), the regular CDNN detected possibility of cancer in late stage 4.  
Our results were discussed and analyzed with medical personnel from the oncology department, of the hospital in Tetovo, Macedonia. The results were marked as satisfactory, since expert oncologists cannot determine possibility of cancer from a CT scan up to stage 2 or 3. Experts can have doubts of a possibility of cancer from stage 0, but will not schedule a biopsy of the tissue until late stage 2 or 3. The threshold is expected at this stage, since most of the cancerous images used (Section 3.1) for training and testing (Section 3.2) of the algorithms (both standard and double convolution DNN) were mostly from phase T3 or above.
## 4 Conclusions  
A novel fluxgate sensor with a bilayer Fe-Co-B-based amorphous ribbon core was designed, fabricated, and tested in the current study. A simple melt-spinning technique was carried out to obtain a Fe-Co-B-based amorphous ribbon composite of Fe\({}_{58.1}\)Co\({}_{24.9}\)B\({}_{16}\)Si\({}_{1}\). Then, the obtained as-spun ribbon was annealed at 595 K for 1 h to achieve soft magnetic properties. The prepared material showed excellent soft magnetic performance, with a high saturated magnetic intensity of 1.74 T and a coercivity of less than 0.2 Oe. Afterward, a micro-fluxgate sensor based on the prepared amorphous ribbon was fabricated via MEMS technology combined with chemical wet etching. The resulting sensor exhibited a sensitivity of 1985 V/T, a wide linearity range of \(\pm\)1.05 mT, and a perming error below 0.4 uT with a 70-mA excitation current and a 500-kHz frequency. The minimum magnetic field noise was about 36 pT/Hz\({}^{1/2}\) at 1 Hz under the same excitation conditions, and a superior resolution of 5 nT was also achieved in the fabricated sensor. To the best of our knowledge, a compact micro-fluxgate sensor with such a high-resolution capability has not been reported anywhere else. When compared to similar magnetic sensors previously reported [7; 31; 32; 33; 34], our sensor not only exhibited a relatively high sensitivity but also provided a wide measuring linearity range. Moreover, because the current fluxgate sensor can be easily fabricated via the MEMS technique and is compatible with lab-on-chip technology, it can be easily integrated into an electronic microchip for modern information technology and portable electronic equipment applications. In summary, the microsensor presented here with such improved characteristics may considerably enhance the development of micro-fluxgate sensors and is promising for more application fields.  
**Supplementary Materials:** The Figure S1 is available online at www.mdpi.com/2072-666X/8/12/352/s1.  
**Acknowledgments:** This work is supported by The National Natural Science Foundation of China (No. 61273065), National Science and Technology Support Program (2012 BAK08B05), Natural Science Foundation of Shanghai (13 ZR1420800), Support fund of Shanghai Jiao Tong University (AgriX2015005), Support fund of Joint research center for advanced aerospace technology of Shanghai Academy of Spaceflight Technology-Shanghai Jiao Tong University (USCAST2015-2), Support fund of aerospace technology (15 GFZ-J02-05), the Analytical and Testing Center in Shanghai Jiao Tong University, and the Center for Advanced Electronic Materials and Devices in Shanghai Jiao Tong University.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion  
### Evaluation of Regression Performance  
The reliability of the results estimated from the regression model needs to be evaluated before interpretation of the results. According to the significance test, the \(p\)-value of the regression for each site is less than 0.001 on a monthly scale whereas almost all of the P value is less than 0.001 on ca weekly scale except for the 39 th week at Sidaoqiao and Daman. This indicates that the association between the dependent and the independent variables is statistically significant at the level of 0.001.  
\(R^{2}\) is a statistical value to measure how close the dependent and the independent variables are fitted on the regression line. Figure 5 shows the frequency distribution of \(R^{2}\) for each site on the monthly and weekly scale. Overall, the value of \(R^{2}\) on the weekly scale is higher than that of the monthly scale for each site because the minimum value of \(R^{2}\) of each site on the monthly scale is lower than that of the weekly scale and the range of \(R^{2}\) for each site is much narrower on the weekly scale. This indicates that there is a strong linear relationship of ET and GPP VPD\({}^{0.5}\) on a weekly scale, and therefore the weekly \(u\)_WUE\({}_{a}\)_ results are utilized for analysis in the present study.  
Figure 5: Frequency distribution of \(R^{2}\) value. (**a**) is regression \(R^{2}\) using the half-hourly data on monthly scale, (**b**) is regression \(R^{2}\) using the half-hourly data on weekly scale.
## 4 Tests and Results
### Results, Indicators, and Discussion
Moreover, the number of cycles in the GA is slightly lower on sunny and cloudy days, thus yielding a less stressful operation of EV batteries than in the MILP case, whereas on rainy days the number is very limited due to lower availability of the PV source. From a more general point of view, it can be remarked that the proposed indicators give synthetic, immediate, and effective information on EV performances. Therefore, they can be usefully exploited for different subjects (microgrid operator, EV aggregator, and owners) in order to improve the awareness of EVs contributions to energy management tasks, of their exploitation levels, and of relevant economic effects. In addition to the previous comparison, global microgrid performances are compared by means of overall economic figures, such as total objective function value, as well as total cost \(C_{g}^{in}\) and total revenue \(R_{g}^{out}\) for energy exchange with the external network, defined as follows:  
\[C_{g}^{in}=\sum_{t=1}^{N_{t}}c_{g}(t)\cdot P_{g}^{in}(t)\cdot\Delta T, \tag{36}\]  
\[R_{g}^{out}=\sum_{t=1}^{N_{t}}r_{g}(t)\cdot P_{g}^{out}(t)\cdot\Delta T. \tag{37}\]  
Results are reported in Table 4, where it can be noted that the operation of the microgrid always involves global costs, that are reduced on the sunny day due to energy delivery to the network, whereas cloudy and rainy days show similar global economic efforts, since the wearing cost due to intense EV exploitation counterbalances the reduction of costs for energy purchase. The difference of reported indices between the two procedures is usually below 2% and reaches 9% on the sunny day due to different optimal EV usage values. As for computational performances, total simulation time and iteration number are reported in Table 5. It can be noted that the reference non-linear problem takes roughly 2 hours to reach the solution, whereas the proposed iterative MILP approach takes less than 1 minute in each case. Therefore, it can be remarked that the proposed MILP iterative procedure allows one to obtain affordable optimal solutions with reduced computational effort and in a reasonable time. Moreover, the number of iterations increases with the worsening of the weather, because only more expensive energy sources are available in this situation. Finally, it can also be noted that the number of generations (in the GA approach) is higher than the iteratio \begin{table}
\begin{tabular}{c c c c} \hline  & & **Proposed Iterative MILP** & **Reference Non-Linear with GA** \\ \hline \multirow{2}{*}{\(R_{g}^{out}\)} & **Sunny** & 6.77 & 6.66 \\  & **Cloudy** & 1.95 & 1.92 \\
**[€]** & **Rainy** & 0.00 & 0.00 \\ \hline \multirow{2}{*}{\(C_{g}^{in}\)} & **Sunny** & 0.00 & 0.00 \\  & **Cloudy** & 4.20 & 4.28 \\
**[€]** & **Rainy** & 4.82 & 4.90 \\ \hline \multirow{2}{*}{\(f(\mathbf{x})\)} & **Sunny** & 8.66 & 9.53 \\  & **Cloudy** & 16.40 & 17.06 \\ \cline{1-1}  & **Rainy** & 15.88 & 16.08 \\ \hline \end{tabular}
\end{table} 
Table 4: Techno-economic performances of the microgrid for the three typical days.
# Corner Reflector Plasmonic Nanoantennas for Enhanced Single-Photon Emission
###### Abstract  
The emission rate of atom-like photon sources can be significantly improved by coupling them to plasmonic resonant nanostructures. These arrangements function as nanoantennas, serving the dual purpose of enhancing light-matter interactions and decoupling the emitted photons. However, there is a contradiction between the requirements for these two tasks. A small resonator volume is necessary for maximizing interaction efficiency, while a large antenna mode volume is essential to achieve high emission directivity. In this work, we analyze a hybrid structure composed of a noble metal plasmonic resonant nanoparticle coupled to the atom-like emitter, which is designed to enhance the emission rate, alongside a corner reflector aimed at optimizing the angular distribution of the emitted photons. A comprehensive numerical study of silver and gold corner reflector nanoantennas, employing the finite difference time domain method, is presented. The results demonstrate that a well-designed corner reflector can significantly enhance photon emission directivity while also substantially boosting the emission rate.  
single-photon emitters; nanoantennas; plasmonics; corner reflectors +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Statistical Analysis  
Continuous variables are displayed as means and deviations from the mean, while categorical variables are depicted as proportions and occurrence frequencies. The normal distribution of all variables was evaluated using the Kolmogorov-Smirnov examination. Bivariate correlation analysis was utilized to explore potential individual associations between a range of factors (independent variables, such as balance confidence, gait speed, handgrip strength, and dynamic balance, alongside other covariates, such as BMI, sex, age, and educational level) and global cognitive function, language, and semantic and phonologic fluency. To investigate the independent associations between the present variables, both a multivariate linear regression model and a step-by-step method were implemented to incorporate variables into the model. Cognitive status was documented as the dependent variable in separate models, considering the significance in bivariate correlation (\(p<0.05\)), and was integrated into the multivariate linear regression. The adjusted R\({}^{2}\) was used to calculate the effect size coefficient of multiple determinations in linear models. An R\({}^{2}\) less than 0.02 was considered insignificant, moderate if ranging between 0.02 and 0.15, and substantial if it surpassed 0.35. A confidence level of 95% was adopted (\(p<0.05\)). The analysis of data was carried out utilizing the SPSS statistical package for Windows (SPSS Inc., Chicago, IL, USA).
## 3 Research Progress on Adsorption and Separation of Hydrocarbon Molecules
### Aromatic Hydrocarbons
#### 3.2.4 Metal-Organic Frameworks (MOFs)  
Attributing to ultra-high specific surface area (up to 9000 m\({}^{2}\)/g), high porosity [69], and controlled functional groups, metal-organic frameworks (MOFs) have been widely applied to hydrocarbon adsorption [70]. [PERSON] [71] used mesoporous carboxylate with a different topological structure to adsorb benzothiophene and dibenzothiophene. They found that octahedral trimer MOFs with Lewis acid sites had high selectivity for N-containing aromatics. These MOFs showed high performance and good reproducibility even under the detrimental influence of solvent coadsorption.  
Porous Zn\({}_{4}\)O(BDC)\({}_{3}\), also known as MOF-5, which is a typical isoreticular metal-organic framework (IRMOF), is also widely used in the adsorption of aromatic hydrocarbons. [PERSON] [72] studied the adsorption behavior of different hydrocarbon molecules, such as n-alkane, isooctane, benzene, toluene, and xylene. They found an unusual thermal desorption profile, which was different from the five standard isotherm when the adsorbed molecules were large. Further investigation should be made to explain its particular adsorption characteristics.
## 4 Proposed Solution
### Routing Constraints  
The route allocation is modeled by binary path variable given as follows.  
\[r[k,i,j]=\left\{\begin{array}{ll}1,&if\ f_{k}\ \text{is\ routed\ over\ link}\ (v_{i},v_{j}),\\ 0,&otherwise.\end{array}\right. \tag{7}\]  
The route for a flow \(f_{i}\) starts at its source \(f_{i}\)_src_ and ends at its destination(s) \(f_{i}\)_._dest_, hence the number of the outgoing links is exactly one more than the incoming link of \(f_{i}\)_src_ has to be active, additionally, the number of destinations over the incoming links of the \(f_{i}\)_._dest_ is one.  
\(if\ i==f_{k}\)_src_ :  
\[\sum_{(i,j)\in E}r[k,i,j]=1,\sum_{(j,i)\in E}r[k,j,i]=0 \tag{8}\]  
\(if\ i==f_{k}\)_dest_ :  
\[\sum_{(j,i)\in E}r[k,j,i]=1,\sum_{(i,j)\in E}r[k,i,j]=0 \tag{9}\]For the intermediate nodes \(v_{s}\) eligible for forwarding \(f_{k}\), the flow conservation constraint needs to be satisfied because of the unicast model.  
\[\forall v_{s}\in V\setminus(f_{k}.src\ \cup f_{k}.dest): \tag{10}\] \[\sum_{\begin{subarray}{c}l_{1}\in\varv_{s}\\ \rightarrow\end{subarray}}r[k,l_{1}]-\sum_{\begin{subarray}{c}l_{2}\in \varv_{s}\\ \leftarrow\end{subarray}}r[k,l_{2}]=0.\]  
Here, \(\varv_{s}\) and \(\varv_{s}\) are the ingoing link and outgoing link of node \(v_{s}\) eligible for routing \(f_{k}\).  
To prevent routing loops in the same flow, the flow \(f_{k}\) can be forwarded by any node at most once, i.e., each switch receives flows from at most one port.  
\[\forall f_{k}\in F: \tag{11}\] \[\sum_{(v_{i},p_{j})\in E}r[k,i,j]\leq 1\]
## 3 Case Studies
### Air Transportation Between World Regions and Communication Space
Communication flows change dynamically depending on the economic, political, social, and technological factors. An analysis of the communication space allows us to assess the state of the world system from the standpoint of its sustainable development.
## 4 Experimental Evaluation
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
To comprehensively assess the performance of the model, we used the following evaluation metrics: Accuracy (AC), Precision (PC), Recall (RC), and F1 Score (F1).  
* Accuracy (AC): The proportion of correctly classified samples out of the total number of samples.
* Precision (PC): The proportion of correctly classified positive samples out of all predicted-positive samples.
* Recall (RC): The proportion of correctly classified positive samples out of all actual positive samples.
* F1 Score (F1): The harmonic mean of precision and recall.  
The specific formulas for each metric are as follows:  
\[\text{AC }=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[\text{PC }=\frac{TP}{TP+FP} \tag{15}\]  
\[\text{RC }=\frac{TP}{TP+TN} \tag{16}\]  
\[\text{F1 }=\frac{2\times PC\times RC}{PC+RC} \tag{17}\]  
TP (True Positive): The number of samples that are correctly predicted as positive and are actually positive.  
FP (False Positive): The number of samples that are incorrectly predicted as positive but are actually negative.  
FN (False Negative): The number of samples that are incorrectly predicted as negative but are actually positive.  
TN (True Negative): The number of samples that are correctly predicted as negative and are actually negative.
## Experiments
### Feature-Level Fusion of I-Vector and Latent Variable
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ine** & **Class. Err. (\%)** & **EER (\%)** & **DCF08** & **DCF10** \\ \hline _i-vector(400)_ & 7.67 & 2.68 & 1.54 & 0.06 \\ _LM + LV_ & 6.94 & 2.03 & 1.23 & 0.05 \\ _i-vector(200) + LM_ & 5.36 & 1.78 & 0.97 & 0.05 \\ _i-vector(200) + LV_ & **4.99** & **1.65** & **0.94** & **0.04** \\ _i-vector(600)_ & 5.07 & 2.17 & 1.29 & 0.05 \\ _i-vector(200) + LM + LV_ & **2.75** & **0.97** & **0.61** & **0.03** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results between various feature-level fusions of the conventional i-vector and mean and log-variance of the latent variable extracted from the proposed VAE. Figure 6: DET curves of the speaker verification experiments using a 400-dimensional i-vector and combinations of two features out of the 200-dimensional i-vector, latent variable mean (LM), and the log-variance of the latent variable (LV). standard i-vector with the same dimension (i.e., _i-vector(600)_). Figure 7 shows the DET curves obtained when _i-vector(200)+LM+LV_ and _i-vector(600)_ were applied.