esa-sceva/satcom-chunk-collection huggingface dataset:
# A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms
## 5 Experimental Results and Discussion
### Support Vector Machine Classifier  
In the case of the SVM classifier, it is necessary to select a kernel function to train and test the model. In this work, we have used four different kernel functions to make comparisons, i.e., _linear_, _sigmoid_, _rbf_, and _polynomial_[76], providing the results presented in Table 4.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Activation Function** & **Training-Testing** & **Accuracy \%** & **Average Precision** & **F-Score (Macro)** & **F-Score (Micro)** & **F-Score (Weighted)** \\ \hline Logistic- & 60–40 & 65.9 & 0.594 & 0.616 & 0.660 & 0.615 \\ lbfgs & 70–30 & 65.9 & 0.594 & 0.615 & 0.659 & 0.615 \\  & 80–20 & 68.2 & 0.612 & 0.628 & 0.682 & 0.628 \\ Identity- & 60–40 & 63.4 & 0.580 & 0.619 & 0.634 & 0.619 \\ adam & 70–30 & 63.6 & 0.580 & 0.620 & 0.636 & 0.62 \\  & 80–20 & 66.0 & 0.601 & 0.617 & 0.660 & 0.616 \\ Identity- & 60–40 & 63.3 & 0.578 & 0.621 & 0.633 & 0.621 \\ lbfgs & 70–30 & 63.4 & 0.580 & 0.620 & 0.634 & 0.621 \\  & 80–20 & 65.8 & 0.590 & 0.640 & 0.658 & 0.640 \\  & 60–40 & 65.7 & 0.590 & 0.615 & 0.660 & 0.614 \\ Relu-adam & 70–30 & 65.7 & 0.590 & 0.615 & 0.660 & 0.614 \\  & 80–20 & 68.0 & 0.600 & 0.627 & 0.680 & 0.627 \\  & 60–40 & 65.9 & 0.594 & 0.615 & 0.659 & 0.615 \\ Relu-lbfgs & 70–30 & 65.9 & 0.594 & 0.615 & 0.659 & 0.615 \\  & 80–20 & 68.2 & 0.611 & 0.628 & 0.682 & 0.627 \\ Tanh- & 60–40 & 65.8 & 0.590 & 0.615 & 0.660 & 0.614 \\ adam & 70–30 & 65.8 & 0.590 & 0.615 & 0.660 & 0.614 \\  & 80–20 & 67.9 & 0.600 & 0.626 & 0.680 & 0.626 \\  & 60–40 & 65.9 & 0.594 & 0.616 & 0.659 & 0.615 \\ Tanh-lbfgs & 70–30 & 65.9 & 0.594 & 0.615 & 0.659 & 0.615 \\  & 80–20 & 68.1 & 0.601 & 0.628 & 0.681 & 0.628 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._There, we can observe that both the rbf and linear kernel functions achieve similar performance, with the best classification accuracy of around 67% for the 80-20 split in the training and testing sets. Next, using the polynomial function for the kernel, we obtain an accuracy of 64%, and, finally, the sigmoid kernel function provides a 56.4%. In the case of the 70-30 split, the rbf function leads to an accuracy of 65.7%, while the linear, polynomial, and sigmoid achieve accuracies of 65.4%, 62.3%, and 56%, respectively. Finally, the split consisting of 60-40 partitions takes us to a similar performance of accuracy for both rbf and linear kernel functions (65.5% and 65.3%, respectively), followed by the polynomial function with an accuracy of 62.2%, and last, the sigmoid kernel with 55.8% accuracy. As in the case of the NN classifier, the average precision obtained shows that the prediction models are pretty well balanced.
## 3 Platform Design Criteria
### Gateway Design Criteria
#### 3.2.1 Gateway Communication  
The gateways typically have two types of communication capabilities: long range and short range. The long-range communications are used to connect with the application server either directly or through the Internet. The short-range communications are mainly used to connect with the field nodes, either sensor nodes or peer gateways.  
Long-range communication can use several technologies. A cellular data network is commonly used in outdoor fields that are covered by mobile data services [83]. Alternatively, dedicated communication links on either a private or public radio spectrum can be used, e.g., the 868-MHz short range devices (SRD) band allows ranges up to 40 km [84], and long-range Wi-Fi links can exceed 100 km [85]. Satellite links may also be used [86], especially for remote, isolated fields.  
The constraints for the short-range in-field communications are less stringent for the gateways than for the sensor nodes (which were discussed in Section 3.1.2). The gateways have more energy reserves and computing resources, thus they can support most communication protocols: from simple low power listening (LPL) for random access MACs in star topologies and up to complex routing and synchronization schemes.  
Thus, the gateways can help reduce the overall cost of the WSN application by supporting the short-range communication protocol that optimizes the cost of the sensor nodes, which is a significant component of the overall cost of the solution.
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
###### Abstract  
In this paper, a radial gradient seismic metamaterial (RGSM) is proposed. The structural unit cell is composed of an external square soil embedded with a triangular-cross-sectioned steel ring, which is filled at different angles of multiple steel rings to form a supercell. The dispersion curve and attenuation spectrum of the unit cell are calculated by the finite element method, and the opening mechanism of the band gap is explained by analyzing the modes at the band gap boundary. The influence of geometric parameters and material parameters on the band gap is further studied, and the optimized supercell radial gradient seismic metamaterial (OS-RGSM) structure is designed through structure and parameter optimization. The ultra-low broadband excellent band gap in the range of 2.35-20 Hz for seismic Lamb waves is realized, and its three-dimensional frequency response and displacement field diagram are calculated. In addition, the attenuation characteristics of the optimized supercell seismic metamaterial on the seismic surface wave are calculated and analyzed. It is found that the attenuation can reach more than 50% in the ultra-low frequency range of 3.5-9 Hz. The seismic wave barrier is verified by the vibration transmission characteristics of RGSM under finite period and dynamic time history analysis. The results show that RGSM can effectively shield from seismic Lamb waves in the ultra-wideband with the starting frequency of 2.35 Hz and can also effectively attenuate the seismic surface wave in semi-infinite space.  
radial seismic metamaterials; supercell method; ultra-low frequency broadband; graded metamaterial +
Footnote †: journal: Applied Sciences
# Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals
## 5 Conclusions  
In this paper, NDFA was proposed, which utilizes the idea of LWLPA to fuse 12-lead ECG signals. Meanwhile, two fuzzy inference systems were designed for effectively inheriting the characteristics of original signals. In this study, Synthetic ECG signals, noisy synthetic ECG signals, and realistic ECG signals were employed to test the validity of the algorithm. Due to the limitation of papers length, two 12-lead ECG signals were adopted randomly from Set A of PhysioNet/Computing in Cardiology Challenge 2011, which contains 773 acceptable qualities of ECG records. Analogously, two 12-lead ECG signals were adopted randomly from the data set, which was tagged as unacceptable quality. By the analysis of the remaining data in Set A, the quality characteristics of ECG signals can be exhibited by the reconstructed trajectories of the fused signals clearly. The experimental results indicate that NDFA can effectively compress the 12-lead ECG signals, and well fuse the quality characteristics of the original signal.  
There are still many problems that require solutions. If the fused signal needs to be analyzed further, how to obtain the quantified characteristic parameters is still a crucial problem in the quality estimation of ECG signals, although the quality characteristics of the fused signal can be observed easily. The recurrence quantification analysis (RQA) method particularly suits for handling biological  
Figure 8: Performance comparison of NDFA, MDF, Algorithms 1 and 2 by DTW. (**a**) Is the comparison for the fused signals by four algorithms and the lead _MLII_. (**b**) Is the comparison for the fused signals by four algorithms and the lead _V5_.  
signals. Hence, RQA should be used to extract the quality characteristics of the fused signal in the further research. Additionally, how to design the optimized FISs is also to be dealt with in future work.  
[PERSON] wrote the paper and analyzed the data; [PERSON] and [PERSON] participated in writing the paper.  
This research was funded by National Natural Science Foundation (NNSF) of China (Grants 11805156, 61867005), Natural Science Foundation of Gansu Province (Grant 17 JR5 RA278), Natural Science Foundation of Qinghai Province (Grant 2019-ZJ-948Q), and Fundamental Research Funds for the Central Universities (Grants 31920180121, 31920170015, 31920160003, and 31920170141).  
The authors gratefully acknowledge the insightful comments and suggestions from the reviewers and editors, which have helped improve the presentation.  
The authors declare no conflicts of interest.
## 3 Stability Analysis of Roadways Under High Horizontal Stress Conditions
### Numerical Simulation Scheme  
Based on the borehole log, a FLAC3D numerical model for the 6A# Left 2 nd Roadway was established. The model dimensions are as follows: in the X direction, it is 120 m long; in the Y direction, it corresponds to the roadway's mining face strike, extending over 40 m; and in the Z direction, it represents the rock stratum's vertical extent, with a height of 80 m. The total number of zone units in the model amounts to 348,950. Figure 5 presents a schematic diagram of the model. The Mohr-Coulomb model is used for this numerical simulation. The parameters are shown in Table 2. The measured in situ stress field is applied to the model, that is, a uniform load of 20.3 MPa is applied to the model top. The displacement constraints are applied to the surrounding and bottom boundaries of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stress Component** & **No.** & **1** & **2** & **3** \\ \cline{2-5}  & **H/m** & **743.6** & **839.3** & **844.2** \\ \hline Minimum principal & Magnitude/MPa & 8.8 & 14.9 & 21.2 \\ stress (\(\sigma_{3}\)) & Dip Direction/\({}^{\circ}\) & 171.6 & 196.6 & 188.1 \\  & Dip Angle/\({}^{\circ}\) & 23.6 & 2.1 & \(-\)38.3 \\ \hline Vertical stress (\(\sigma_{\rm v}\)) & Magnitude/MPa & 13.3 & 22.4 & 25.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
In combination with the geological conditions of Ronghua No. 1 Mine, the design of the support scheme needs to comprehensively take into account the influences of factors such as burial depth, the angles between the direction of the \(\sigma_{\rm H}\) and the roadway orientation, the lateral pressure coefficient, the strength of the rock, and the excavation-induced disturbance. However, following substantial roadway deformation, an analysis of the stress field in the surrounding rock reveals that both the angle and the lateral pressure coefficient significantly affect the 6A# Left 2 nd Roadway. Therefore, this work focuses on discussing the influences of the angle and the lateral pressure coefficient on the stability of the surrounding rock. We discuss the influence patterns of the two factors on roadway stability using the controlling variable method. The specific research plan is outlined as follows:  
1. The angles between the direction of the \(\sigma_{\rm H}\) and the roadway orientation: Seven groups of schemes are set at \(0^{\circ}\), \(15^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(60^{\circ}\), \(75^{\circ}\), and \(90^{\circ}\), respectively. The displacement and the horizontal stress are analyzed to elucidate the influence law.
2. The lateral pressure coefficient: Four groups of schemes are set at 1.8, 1.9, 2.0, and 2.1, respectively. The displacement and the horizontal stress are analyzed to elucidate the influence law.
## 4 Material and Methods
### Organelle Markers  
For the identification of the subcellular compartments that accumulate the fusion proteins, a set of organelle markers was used in cotransformation experiments with the MEP pathway enzyme constructs. The \"plastid\"-mCherry (CD3-1000), \"plastid\"-CFP (CD3-994), \"mitochondria\"-mCherry (CD3-992), \"mitochondria\"-CFP (CD3-986), and \"peroxisome\"-CFP markers, described by [66] were obtained from the ABRC ([[http://www.arabidopsis.org](http://www.arabidopsis.org)]([http://www.arabidopsis.org](http://www.arabidopsis.org))). The mCherry-GUS cytosolic marker and the mCherry nucleocytosolic marker were described previously [59]. For plastid subcompartment markers, the CAB1 (chlorophyll a/b binding protein 1, At1g29930), TIC40 (translocation at the inner envelope of chloroplasts, At5g16620) and CCD4 (carotenoid cleavage dioxygenase 4, At4g19170) proteins of _A. thaliana_ were used as thylakoid, inner membrane of plastid and plastoglobulin markers, respectively. The plastoglobule localization of CCD4 was previously demonstrated by [67] and [52]. The coding sequence of the three proteins was amplified using specific primers (Table S7) and retrotranshed from RNA extracted from _A. thaliana_ as a matrix, and subsequently cloned into the pSCA-cassette-CFPi plasmid [59].
## 4 Discussion
### Future Applications for Agriculture Practice in Salt-Affected Soils  
The accumulation of sodium chloride is a vital reason for salinization in coastal agricultural region [33]. Due to the filling of fine sand, the artificial macropore structure maintained effectively, and the salt redistribution for topsoil could be detected under 25-day observation. Irrigation experiments showed that total desalinization in top 30 cm soil profile with \(\geq\)10 cm macropore depth treatments for AMSF method were improved by more than 50% as compared to control group without artificial macropore.  
In this study, we also focused on validating the feasibility of sandy materials in agricultural farming. In salt-affected soils, fertilizers and amendments are the most common agronomic resources, which would be applied into plough layer in the form of physical particles. Based on the proven hypothesis, we have reasons to infer that water and salt environment can be further improved by optimizing particle composition ratio and application strategy for amendment, and thereby stimulate crop performance.  
In general terms, it is essential to establish an optimized management method for salt-affected farmland, considering the interaction between water input, plant tolerance and agronomic measures. There are two unanswered questions that need to be completed in subsequent research. Firstly, soil evaporation makes a vital role in water movement, and long-term monitoring is required to explain the role of evaporation in soil salt accumulation. Secondly, the study of AMSF method affecting soil properties and crop growth was not involved. It is necessary to apply the hypothesis to agriculture practice to deepen the research on soil amendment utilization. For example, whether farmers can obtain better rhizosphere environment by artificial macropores with amendment incorporation can be further discussed. Deeper insights into the effect of amendment's spatial distribution on response to the ionic migration and the potential values for particles size redesign in field crop experiments will probably contribute to the successful farming practice of our findings.  
Importantly, the short-term observations described here about how AMSF method can affect salt condition provide an exploration demonstration to pursue these studies with the ultimate goal of incorporating new application approaches for amendment into coastal salt-affected lands of northern China.
### Research Objectives  
The open research questions, and the difficulties inherent in addressing them, should be detailed before actually diving into the system design of the proposed dNAS. It will then be followed by an outline of a preliminary design of the system model and an effective blockchain-based system architecture running on a cloud-production environment. Given the variety of advantages, such as the prevention of single-point failure, as well as the better resilience and availability of its being applied to the supply chain participants, that are brought by the blockchain technology and the concept of decentralized application, which lead to a more secured and sophisticated supply chain system against counterfeiting attacks, the main research question should be:  
_\"How is it feasible to decentralize an anti-counterfeiting application, already developed and implemented in the supply chain industry, to better combat the rampant counterfeiting attacks?\"_  
In order to progress the research with a prototype of decentralizing the aforementioned legacy NAS, the main question is then addressed by answering the following sub-questions throughout the research:  
1. How are the chosen blockchain and its consensus protocol being implemented in the proposed dNAS?  
Figure 1: Comparison of Decentralized and Centralized Architecture.  
How are the current operations of wine record management and product provenance verification reengineered to be a more decentralized, secured, autonomous and economical alternative?
* How are wine record data being stored in the proposed decentralized and distributed storage architecture?  
Given the main research question and the identified set of derived sub-questions, we aim to find an organized way of exploring them step-wise in this research. The best approach is to follow research techniques in computing, namely _proof-by-demonstration_ and _implementation-driven_ research techniques, under which a working prototype of dNAS will be introduced and validated with the idea of the decentralized supply chain solution, addressing the main research question and the derived sub-questions. It is important that, during development, the next best steps are always taken into account and ultimately offer a better analysis of the research questions with detailed documentation of the development of such an innovative and decentralized solution, using blockchain technology and other useful technical concepts, to improve the current situation of product counterfeits in the supply chain industry.
# Polypphenols: Natural Antioxidants to Be Used as a Quality Tool in Wine Authenticity
## 4 Current Analytical Approaches to Wine Authenticity
### Official Wine Analytical Methods  
Traditional wine analytical methods involve multidisciplinary approaches in order to assess wine quality and authenticity. Only the official methods published in the OIV's Compendium of International Methods of Analysis of Wines and Musts are applicable for verification purposes and settling disputes. Official wine analytical methods include the determination of: alcoholic strength (ethanol) [125], reducing substances [126], total and volatile acidity [127; 128], total and free sulfur dioxide [129], volatile compounds by gas chromatography (GC) [130], principal organic acid concentration by high-pressure capillary electrophoresis [131], mineral elements by inductive plasma atomic emission spectrometry [132], ethanol origin by isotope ratio mass spectrometry [133] and ethanol deuterium distribution by NMR [134]. Regulatory authorities also assess wine for the presence of artificial sweeteners [135] or colorants [136], preservatives as well as fermentation inhibitors [137].  
Regarding the study of polyphenols in wine authenticity studies, the Folin-Ciocalteau index is the OIV reference method for the determination of all compounds with a phenolic structure (total phenols) and it is used in the European Union (EU) as the official method of analysis [138]. The official methods of analysis also include the determination of the five most important non-acylated anthocyanins and the four major acylated anthocyanins by reversed-phase liquid chromatography (RP-LC) HPLC [139] as well as the determination of the possible presence of malvidin diglycoside by fluorescence spectroscopy [140]. In any case, OIV encourages member states to continue research in the areas of interest to avoid any non-scientific evaluation of results.
## 6 Performance Evaluation
### Performance Analysis of CL-APCC
#### 6.1.4 Communication radius \(r\) on the impact for CL-APCC protocol  
The size and number of the grids are decided by communication radius \(r\), which affects the control effect of CL-APCC on network congestion. Hence, it needs to make stimulations on the impact for the RPR of CL-APCC with the different network densities and communication radius. We respectively set the communication radius to \(r=40\) m and 50 m, and the number of nodes is 50, 75 and 100.  
Figure 14 shows that the RPR of the network declines when the communication radius is increased. The reason is that 1 the bigger the communication radius is, the greater the number of the neighbor nodes is, so the possibilities of data sending collisions between the nodes are correspondingly increased. 2 The communication radius of the node has an impact on the size of the grid. That is to say, if the communication radius of the node increases, the number of grids decreases, and then the local control capacity of CL-APCC on the network will accordingly decline. Therefore, the overall RPR of the network also declines.  
Figure 13: The average energy consumption of each data packets received by BS.
## 5 Experimental Results
### The Datasets and the Evaluation Metrics
#### 5.1.3 Evaluation Metrics  
To evaluate the semantic segmentation algorithms, four metrics are used: the accuracy, the recall, the precision and the F1 score. These metrics are calculated using \(TN\) (True Negatives), \(TP\) (True Positives), \(FN\) (False Negatives) and \(FP\) (False Positives). Considering a semantic segmentation class \(C\), \(TP\) is the number of pixels of class \(C\) classified successfully as \(C\). \(TN\) corresponds to the number of pixels that are not \(C\) and the algorithm did not classify them as \(C\). \(FN\) corresponds to the number of pixels that belong to the class \(C\) but the algorithm did not classify them as \(C\). \(FP\) is the count of pixels that are classified incorrectly as \(C\) while they do not belong really to \(C\). These metrics are expressed below:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=Sensitivity=\frac{TP}{TP+FN} \tag{11}\]  
\[F1=Dice=\frac{2\times TP}{2\times TP+FP+FN} \tag{12}\]  
We used also a fifth metric, which is the Intersection over Union (IoU) to evaluate the global segmentation efficiency. IoU is computed for every class separately before deducing the mean IoU for all the classes. Below is the expression of the IoU for two different sets of data \(A\) and \(B\):  
\[IoU(A,B)=\frac{size(A\cap B)}{size(A\cup B)}=\frac{TP}{TP+FP+FN} \tag{13}\]