esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In our experiments, we obtained more accurate results on PSSs using a feature-enhanced anchor-free method. The score threshold and _IoU_ in FENet were set to 0.01 and 0.5, and the proposed model can achieve 94.4% recall rate and 79.5% precision rate.  
The model outputs the corresponding classification confidence (0.1-1.0) for each predicted box when the model is detecting objects. Precision rate and recall rate are affected by the score threshold. High score threshold can filter out negative samples and obtain a high precision. However, the recall rate is lower and the number of positive samples detected will decrease correspondingly by setting a high score threshold. Compared with the anchor-based method, our proposed method generates fewer negative samples while predicting. It means that our method needs to set a relatively low score threshold for obtaining a high recall rate and has more positive samples. In future research, composite object detection could pay more attention to the setting of thresholds to better evaluate the model performance.  
Due to the complex environment in RSIs, the appearance of PSSs in different regions is variable. Based on the experiments above, we also applied the trained model on the dataset of Huadong region to verify the robustness and generalization ability of FENet. We collected 705 images of Huadong region from the same GF dataset in our study, whose size was 512 \(\times\) 512 pixels. Figure 9 shows the detection results of our proposed method on the Huadong dataset. It can be seen from the detection results that our method can detect PSSs with small size, unclear features, and complex backgrounds.  
Figure 9: Visualization of the detection results of Huadong region. The ground truth boxes are plotted in green and the detection results of the proposed method are plotted in red.  
The experiments results demonstrate that the FENet can effectively improve the discriminative ability and successfully weaken the influence of complex background information. Meanwhile, our proposed method can effectively balance the positive samples and negative samples. Compared with anchor-based methods, FENet can achieve a significant performance without pre-designed anchors.  
Our study indicates that the anchor-free method can tackle the problems of complex objects in RSIs. It provides a new idea for object detection in RSIs. Feature enhancement of CNN and precise boundary detection are important for PSSs detection. However, there still are some problems with PSSs detection. Figure 10 shows some failure detection of FENet. The ground truth, detection results, false positives, and false negatives are marked in green, red, blue, and orange rectangles, respectively. It is still challenging for our method to deal with some small objects with unclear features in the PSSs dataset. In addition, some ground objects have similar characteristics with PSSs which may cause false positives. For future work, it is important to use higher-resolution remote sensing images and further improve the distinguishing ability of the model.
## 3 Results and Discussion
### Analysis of Baijiu Samples
The Fenjiu sample showed the presence of 34 volatile compounds that had an OAV >1, the Wuliangye sample showed 51 volatile compounds with an OAV >1, and the Moutai sample showed 50 volatile compounds with an OAV >1, which indicated that the odors in these media could be perceived by the human nose [38]. The ethyl esters are the most important group of yeast-synthesized aroma substances in Baijiu, which mainly produce the pleasant fruit odors. Because their concentrations are much higher than aroma thresholds, they make an important contribution to the flavor of Baijiu [39]. In addition to the reported compounds, this study also quantified some esters with a lower content in Baijiu for the first time. The content of ethyl 3-methylpentanoate and ethyl cyclohexanoate in the three Baijiu samples was only tens of \(\upmu\)g/L. The contents of ethyl 4-methylpentanoate in Fenjiu, Wuliangye, and Moutai samples were 62.42, 622.67, and 263.38 \(\upmu\)g/L, respectively. The aroma threshold of ethyl 4-methylpentanoate in water is 0.01 \(\upmu\)g/L [40], and our group has measured the threshold value of 21.4 \(\upmu\)g/L in 50% aqueous alcohol. This meant that the contents of these compounds were low, but above the aroma thresholds, so these compounds may make some contribution to the flavor of Baijiu. Sulfur compounds also play an important role in the flavor of Baijiu, which mainly present unpleasant odors of onion and rotten cabbage. This study quantified 11 types of sulfur compounds in different Baijiu samples. The highest concentration was 3-methylthio propanol in the Moutai sample, which was 732.65 \(\upmu\)g/L, and the lowest concentration was methyl (2-methyl-3-furanyl) disulfide in the Fenjiu sample, which was only 0.44 \(\upmu\)g/L. Among them, although the content of furfuryl mercaptan in soy sauce aroma type Baijiu was only 35.20 \(\upmu\)g/L, its aroma threshold was 0.1 \(\upmu\)g/L in 46% ethanol/water solution [10], and the calculated OAV was as high as 352, which made an important contribution to the aroma of soy sauce aroma type Baijiu.
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 7 Evaluation of _T-Wing_
### Accuracy of Wi-Fi Connectivity Prediction  
We evaluate the accuracy of our Wi-Fi connectivity prediction algorithm as described in Section 4.3. We measure the prediction accuracy at 16 different locations. Figure 8 shows the accuracy for different values of \(X\) for Metric \(A\) (i.e., the number of base stations reported to the proxy server) as well as Metric \(B\). The best results are achieved for Metric \(A\) with X = 3 when the accuracy reaches 97.2%. We verified that this result is consistent across different regions and at different time.
## 4 Results and Discussion
### Results of the Initial Grouping of Source Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{R}^{2}\) & \multicolumn{1}{c}{-} & \multicolumn{1}{c}{**Std Error**} & \multicolumn{1}{c}{**Observation**} \\ \hline
**0.947** & **0.926** & **2603.251** & **29** & \\ \hline  & **Coefficients** & **Std Error** & \(t\) **Stat** & \(p\) **Value** \\ \hline Intercept & 24,459.688 & 16,525.257 & 1.480 & 0.154 \\ X\({}_{1}\) & 4.778 & 12.076 & 0.396 & 0.697 \\ X\({}_{2}\) & 40.385 & 19.317 & 2.091 & 0.049 \\ X\({}_{3}\) & \(-\)7.390 & 19.666 & \(-\)0.376 & 0.711 \\ X\({}_{4}\) & 7.867 & 38.877 & 0.202 & 0.842 \\ X\({}_{5}\) & 211.851 & 74.906 & 2.828 & 0.010 \\ X\({}_{6}\) & 37.612 & 32.499 & 1.157 & 0.261 \\ X\({}_{7}\) & 8.061 & 13.108 & 0.615 & 0.546 \\ X\({}_{8}\) & \(-\)13.710 & 9.380 & \(-\)1.462 & 0.159 \\ \hline ANOVA & df & SS & MS & F & Significance F \\ \hline Regression & 8 & 2,415,852,024 & 301,981,503 & 44.56 & \(4.413\times 10^{-11}\) \\ Residual & 20 & 135,538,320.4 & 6,776,916.021 & \\ Total & 28 & 2,551,390,345 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multivariate regression results for the linear model. are significantly different from zero. The current structure of the model is correct and is represented by the relation (21) and geometric interpretation in Figure 2:  
\[\hat{y}_{1}=44.004\mathrm{x}_{2}+234.194\mathrm{x}_{5}+19,472.544 \tag{21}\]
###### Abstract  
Karst tunnels commonly pass through large karst caves during their construction and operation. Although treatment technologies are relatively mature, a systematic treatment method to guide the selection of treatment technologies is lacking. To solve this problem, a spatial decomposition method (SDM) of large karst caves is proposed that is based on analyzing the spatial relationship between tunnels and karst caves and summarizing the relevant treatment techniques. In this method, the space between the tunnel and the cavern is divided into eight parts using a space dividing line (SDL), which makes the spatial position relationship between the tunnel and cavern more intuitive. A geometric model of the SDM is established, and the numerical values of each geometric parameter are determined by field surveys and drawings. Constructing a three-dimensional spatial diagram by applying relevant parameters to the geometric model provides a reference for selecting a treatment technology. The SDM of the arch top, arch bottom, and two wings matching the treatment technology is proposed. Seven principles of technical selection--namely, safety, convenience, scientificity, sustainability, economy, feasibility, and openness--are mentioned in order to overcome the difficulty of technology selection due to such factors as technological diversity, materials, equipment, and environment. Finally, the SDM is used to solve the problem of the Shangyuan tunnel passing through a large karst cave. The implementation of the SDM in tunnel construction would represent a significant breakthrough and has important engineering value in solving the problem of tunnel passes through large karst caves.  
spatial decomposition method; spatial decomposition line; huge karst cave; geometric model; treatment technology +
Footnote †: journal: Applied Sciences  
_Appl. Sci._**2020**, _10_, 7204; doi:10.3390/app10207204
# Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach
## 1 Introduction
### Contribution and Paper Organization  
This work presents an alternative to wind turbine simulation tools, to save time and effort. To do so, extensive simulations were run on the QBlade simulation tool while performing parametric analysis. Design parameters of the blade and inflow wind speed were changed one at a time, resulting in a dataset with over 5000 entries. The dataset is analyzed, cleaned, and processed for further training by the machine learning models.  
Machine learning models are required to effectively predict key outputs of a wind turbine, including aerodynamic loads and power performance characteristics. This novel methodology of wind turbine design is accurate, time-saving, and can be further used for optimizing the turbine design, without diving into sophisticated governing equations that require a time-consuming iterative process.  
The paper is organized into four main sections. In Section 1, the introduction and key literature findings are presented. Section 2 shows the methodology followed for the wind turbine simulations and parametric analysis that leads to data acquisition, the key findings of data processing and analysis, and the different machine learning models and their key quality metrics. Section 3 shows the main findings of this work and the prediction accuracies of different models for the four key outputs of the wind turbine simulation. And finally, Section 4 concludes the work and shows the suggestions for future work.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 5 Discussion and Conclusions  
The integration of the YOLO algorithm for pose estimation in elderly care systems offers significant benefits in enhancing fall detection capabilities. The real-time processing and accuracy of the YOLO algorithm, combined with the detailed movement analysis provided by pose estimation, create a powerful and reliable monitoring solution.  
CUDA Python was specifically chosen to be used due to its ability to significantly improve computational efficiency. The GPU, a specialized processor designed to accelerate graphics rendering, features a highly parallel structure that is ideal for executing multiple computations simultaneously.  
This integrative approach not only ensures prompt and accurate detection of falls but also supports care and personalized monitoring of the elderly. By integrating an alert system through Telegram, we can ensure a quick reaction in case of incidents, thus improving the quality of life of those who need constant surveillance. The flexible architecture and high performance of YOLO for pose estimation make this solution particularly valuable in the fields of health monitoring and care for the elderly. Also, our system adheres to privacy standards, guaranteeing that processed information is not saved. This data protection measure is essential to ensure the security and privacy of users, avoiding unauthorized storage and access to sensitive data.  
In conclusion, the application of the YOLO algorithm for pose estimation in elderly care represents significant advancements. This approach not only improves the efficiency and effectiveness of care but also ensures that elderly individuals receive the attention and support they need in a densectful manner. As technology continues to evolve, the integration of such cutting-edge solutions will be essential in addressing the growing demands of elderly care.
# Design of Encoding Algorithm for Underwater Wireless Optical Communication Based on Spinal Code
## 6 Conclusions  
This paper proposes a novel improved encoding scheme based on Spinal codes, aimed at effectively transmitting information of varying importance and reducing decoding computational overhead. The new Superposition UEP-Spinal Code structure fully leverages the characteristics of unequal error protection, adjusting the encoding method of Spinal codes to assign different encoding priorities based on the importance of the information. Compared to traditional mapping encoding methods, this improvement simplifies the encoding structure and enhances decoding efficiency. The Superposition UEP-Spinal Codes divide the signal into two parts, important information and secondary information, applying different encoding strategies for superposition processing. Compared to Spinal codes, the encoded Superposition UEP-Spinal Code achieves an average BLER reduction of 23.62% for the LIB tail. During the encoding of MIB information, check bits are introduced, increasing the Euclidean distance between nodes and thereby reducing the number of searches. By increasing the Euclidean distance between nodes, the demodulation algorithm's ability to differentiate between nodes can be effectively improved. As the geometric distance between adjacent nodes in the signal space increases, the isolation of their corresponding decision regions is enhanced, thereby reducing the probability of misjudgment caused by confusion between neighboring nodes. This geometric feature optimization fundamentally reduces the generation of erroneous branches in the decoding tree, preventing redundant searches along incorrect paths and significantly decreasing the number of searches. Compared to the RNG-mapped Spinal code with the same spectral efficiency, the Superposition UEP-Spinal Code achieves an average BLER reduction of 5.04% for MIBs while reducing decoding complexity by 13.3%, saving computational resources. This provides a more stable, reliable, and efficient communication method for underwater wireless optical communication, where channel conditions are relatively random.  
X.Y.: conceptualization, methodology, and writing--original draft; M.Y.: formal analysis and writing--original draft; Y.Z.: writing--review and editing; T.C.: software. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the General Project of the National Natural Science Foundation of China, grant number 52271349.  
Not applicable.  
Some codes generated or used during this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 5 Experiment and Analysis
### Dataset  
The dataset was collected in a hilly area (as shown in Figure 25), which was about 200 km\({}^{2}\) (28.08\({}^{\circ}\) N, 112.69\({}^{\circ}\) E-28.22\({}^{\circ}\) N, 112.83\({}^{\circ}\) E), located in the suburb of Changsha City, China. In this region of interest (ROI), the skylines change greatly, and the vegetation is dense. However, the manmade buildings are few and have little interference on the trend of the skylines.  
The DEM dataset with an accuracy of 10 m was used to establish the DEM skyline database. Meanwhile, more than 200 photos were captured with a camera (focal length was 8 mm, FoV was 24\({}^{\circ}\), and the resolution was 1920 \(\times\) 1080), which was fixed on the platform about 180 cm above the ground. Figure 26 shows an example photo and Table 5 shows the longitude and latitude, attitude angle, and other information during the photoshoot.  
Figure 24: Coarse matching (**a**) and refined matching (**b**) The blue dots indicate the candidate points for localization, and the dark color means that the coarse matching error was small. lapel points can greatly improve the localization accuracy.  
Figure 25: The region of interest (ROI) considered is denoted by the semitransparent polygon.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## Appendix A  
The filter order was selected based on the FEM data. A brute force search was applied to maximize the SNR of Defect-1, calculated by Equation (6). Therefore, the best case of each weight is considered. The generated results are shown in Table 11. The SNR of the unfiltered signal is 5.3 dB. Therefore, this signal is more difficult to detect as it is almost buried in the noise level. As can be seen, by increasing the filter order, the SNR is reduced. When the size of filter order grows above 8, the SNRs of filtered and unfiltered signals are approximately the same. This is because by growing the number of samples, the differences between a torsional and flexural wave become limited as they are in the same frequency band. However, in smaller steps, the changes in flexural waves are more time-varying. Therefore, fromthese results, 8 can be the considered as the safest number for filter order where no change will be applied to the signal and 2 can be considered as the best filter order for maximizing the SNR.  
On the other hand, the best filter order can be dependent on other factors, such as frequency and the existing flexurals of the iteration. Since the main aim of this paper is to test the workability of the adaption algorithm to the guided-wave scenario, the filter order is fixed to 5. This number is a compromise between the safest choice, 8, and the best achieved results, 2, based on FEM with 30 kHz excitation sequence. Furthermore, a filter order of 5 can be considered as a good compromise, as a drop of only 1 dB is observed from the maximum achieved SNR, which is 8.31 dB.
## 5 Discussion of Experimental Results
### Second-Stage Experimental Results
In Figure 5b, the data diagram of the SVM classifier shows that when the number of features was reduced, there was no significant improvement in the Probe recognition accuracy, but there was a decrease in the accuracy. As shown in Figure 6a, in the case of the random forest classifier, when the number of features was reduced to 22, 21, 20, 19, 18, and 15, the Probe recognition accuracy rate increased to a maximum of 63%. For the XGBoost classifier in Figure 6b, a maximum of 53% improvement in the Probe recognition accuracy rate was observed when the number of features decreased to 18. The MLP classifier in Figure 7a revealed that when the number of features decreased to 21, the Probe recognition accuracy rate increased to a maximum of 76%. Similarly, the CNN classifier in Figure 7b shows that when the number of features dropped to 23, the Probe recognition accuracy increased again, rising to a maximum of 68%. Table 14 summarizes the analysis results of the optimal number of features for each classification. As can be seen from this table, feature selection could effectively improve the accuracy of the Probe attack recognition. Moreover, when the number of features decreased, the time required for model training and recognition could be reduced. For the MLP classifier in this table, we found that the Probe recognition accuracy increased to a maximum of 76% when fine-tuning the feature number, which was higher than 74% without fine tuning in Table 10 of the first-stage experimental results.
## 1 Introduction  
A passivated emitter and rear cell (PERC) concept with rear surface passivation and local aluminum (Al) rear contacts was first proposed in 1989 [1]. In recent years, PERC solar cells have undergone rapid development and become one of the most important high-efficiency photovoltaic products. Different from the conventional crystalline silicon (c-Si) solar cells, PERC solar cells demand that the rear SiN\({}_{\times}\)/Al\({}_{2}\)O\({}_{3}\) passivation layer not be corroded by silver (Ag) paste during the sintering process. However, meeting this requirement results in the emergence of other issues. Specifically, the much greater contact resistance between rear Ag and Al electrodes often becomes a critical problem, impairing cell efficiency [2; 3].  
In general, the metallization Ag paste for c-Si cells is composed of Ag powder, glass frit, and an organic vehicle. The glass frit serves as a solder to bond the fired Ag electrode to the cells, and thus it influences the contact resistance between them. On the front-side of the cells, the Ag/Si contact Ag paste is used, and the firing of this Ag paste results in etching of the SiN\({}_{\times}\) passivation layer and formation of Ag/Si ohmic contact by a glass-phase medium [4; 5]. In this case, the key role of the glass frit involved and the relevant mechanism of the current transmission through the Ag/Si contact have been extensively studied [6; 7; 8]. However, in the rear-side metallization of PERC solar cells, the Ag/Si non-contact Ag paste is employed to form a Si/Al/Ag interconnection for current transmission [2; 3]. So far, little attention has been paid to the influence of the glass frit in Ag paste on the contact resistance between the Ag and Al electrode. According to our investigation, this influence would be quite marked in terms of the glass frit composition, and thus produces an important effect on the cell efficiency.  
The PbO-containing glass frit has been widely used in commercial Ag paste for c-Si solar cells due to its desired property of forming an Ag/Si contact [4; 5]. Generally, the lead glass frit used in the front-side Ag paste demonstrates good performance with regard to corroding the SiN\({}_{\mathrm{x}}\) passivation layer. However, the glass frit used in rear Ag paste for PERC solar cells is expected to not corrode the SiN\({}_{\mathrm{x}}\) passivation layer. Although up to now, the PbO-containing glass frit has been employed in the majority of rear Ag pastes, there is a potential risk of degrading the cell efficiency due to the corrosion effect of PbO on the SiN\({}_{\mathrm{x}}\) passivation layer. Accordingly, we investigated the application of boro-vanadate glass frit in the rear Ag paste for PERC solar cells.  
Herein, we present an investigation on the effect of the glass phase on the formation of the Ag/Al contact in rear Ag paste metallization on PERC solar cells. In this article, the Ag/Al contact resistance is discussed with respect to the dependence of the contact interface structure on the glass frit composition.