esa-sceva/satcom-chunk-collection huggingface dataset:
# An Image Processing Method for Measuring the Surface Area of Rapeseed Pods
## 3 Results and Analysis
### Analysis of the Results of Measurement for Multiple Pod Surface Areas  
Multi-pod measurements were conducted on 83 rape plant samples in the pod stage using the image processing method. The results were corrected by the correction model established during replicate 2 of the accuracy test experiment (Figure 9). Five people were selected to take photos of the pods during the early period of the experiment, and four were responsible for image processing during the later period. A total of 1203 pod photos were processed; this took approximately 20 h. The measurement of 39,501 pods from 83 rape plant samples provided a total surface area of 330,189 cm\({}^{2}\); the average surface area of pod per plant was 3978 cm\({}^{2}\). The average surface area of the pods from each plant (A1, A2, A3, B1, B2, and B3) were 3403 cm\({}^{2}\), 3943 cm\({}^{2}\), and 4567 cm\({}^{2}\), and 3435 cm\({}^{2}\), 4127 cm\({}^{2}\), and 4451 cm\({}^{2}\), respectively. The measurement results of the pod surface areas of the samples from different cultivars and fertility treatments were significantly different, consistent with our experimental design concept.  
Figure 9: Correction model established by image processing method and the 3-D method. The range of the pod surface area was measured as 6.03–16.89 cm\({}^{2}\).
# Contributions of DNA Sequencing Technologies to the Integrative Monitoring of Karstic Caves
## 5 Taxonomic and Functional Assessment of Cave Microorganisms by Metagenomics
### Cave Metagenomics Tells Us Who Is There
A third sequence analysis method consists in reconstructing MAGs (Metagenome-Assembled Genomes). It relies on an assembly that merges collinear metagenomic reads from the same genome into a single contiguous and longer sequence (i.e., contig) [102]. In some instances, complete or nearly complete genomes can be assembled, which provides insight into the genomic composition of uncultured organisms found in a community. The major challenge associated with assembly is the possible generation of chimeras, wherein sequences from two distinct genomes are spuriously assembled into a contig due to shared sequence similarity. This can lead to MAGs that aggregate sequence information from different strains of the same species, or from different species or even genera. In Weebubbie Cave (Australia), a high proportion of reads were taxonomically assigned to the phylum _Thuamarchaeota_. From the assembly of the metagenomic data, a set of 20,233 contigs with a minimum of \(\times\)4 coverage, ranging in size from 100 bp to 67 kb,was generated. The subset of contigs that binned to _Thaumarchaeota_ was used to generate alignments, and the global alignment showed that the greatest degree of synteny of the Weebubbie _Thaumarchaeota_ contigs was with _Nitrosopumilus maritimus_ SCM11 [117]. While several studies have investigated microbial cave communities using assembly methods, the reconstruction of genomes is limited to the most abundant taxa in the community. Without extensive sequencing, it may be difficult to assemble genomes of rare microbiota. Also, the variation in repeat copy number between assemblies should be carefully evaluated. Finally, the assembly can be computationally intensive, especially in its requirements for RAM, but binning sequences prior to assembly can be a good way to cut down on computational complexity.
## 3 Computational Modeling and Simulation
### Secondary Injection Analysis
#### 3.2.4 Configuration C3 and C4: Effect of Mach Number  
The Mach number profile of C3 in Figures 10a and 12 dropped but remained supersonic. The rise in the Mach number of C4 in Figures 10c and 12 was not due to combustion but to unconsumed GOX exiting the nozzle from the four tangential injectors.  
Configuration C1 gave sustained combustion and a Mach number. It was found that injecting GOX normally at 300 m/s with a mass flow rate of 0.006 kg/s before the flow separation point gave a stable flame, with the temperature reaching up to a maximum of 1200 K. The detached flow re-energized, accelerated, and filled the void space with combusted gases. The nozzle became highly supersonic from the center to the outer region of the nozzle, i.e., from 0 to 21 mm. The flow remained between Mach 1.5 and Mach 3.0.  
Injecting GOX normally allowed GOX to penetrate and mix with the core flow efficiently, while the symmetry of the flow was also maintained, unlike other configurations. This Mach enhancement led to an increase in shaft power of the turbine, which increases propellant flow rate in the main chamber correspondingly with pump power. Hence, the thrust of the main engine increases.  
Based on configuration C1, the prototype was manufactured with normal secondary injection ports, and an experiment was conducted to validate CFD results.
## 1 Introduction
### FRT Capability and DRES Reaction During Grid Faults
#### 3.5.3 Costs and Benefits  
The costs and benefits are presented here in a qualitative way only. Their quantitative evaluation is pending research to be carried out in the near future. The costs that need to be considered are: _(1)_ the additional investment cost of the required FSS according to (18); (2) in very weak parts of the distribution grid or in islanded microgrids the injected currents in (17) may need to be larger than 1 pu, [104]. In such cases the converter of the DRES needs to be somewhat oversized. The oversizing is not in direct proportion to the injected RMS current, if the short-term overloading capability of the power-electronic switches is also considered; (3) part of the cost of the DC/DC converter required for the control of the FSS. It is noted that the one DC/DC converter is required to control one physical FSS, which is actually used to provide three AS- inertial response, ramp-rate control and FRT. The provision of FRT is only rarely needed, therefore the DC/DC converter is rarely used for this AS. The unit costs of the FSS, the DC-DC converter and the inverter oversizing are mentioned above in previous sections.  
The benefits associated with this AS are the deferral of investments that would be otherwise needed in upgrades of the distribution grid protection system (see Table 1) in order to accommodate larger DRES penetrations.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 5 Offline Anomaly Clustering
### Sensitivity to Algorithm Parameters  
To assess the sensitivity of the 'optimal' clustering methodology--fuzzy c-means clustering with ECDF using Euclidean distance--we conduct further tests on the simulated time series with an assumption of nine clusters. Figure 7 illustrates the results on 10 days of the series. We see that the three main categories of collective anomalies--split, interference, and shift--are appropriately distinguished. This suggests that the combination of fuzzy c-means clustering and ECDF is an effective way to cluster anomalous time series segments.  
The maximum anomaly length of SCAPA is another key tunable parameter, so we experiment with it reduced to 60. This is less than the true maximum length and thus collective anomalies in the simulated time series may be sliced into multiple segments or shortened by SCAPA missing out the tails. Figure 8 illustrates the results with the  
Figure 7: Fuzzy c-means clustering with ECDF for 9 clusters. (**a**) Time series. (**b**) Residual series.  
reduced maximum anomaly length and its resultant fragmented anomalies. We see a more pronounced detrimental effect as a result of the fragmentation. Principally, this is because tails of decaying anomalies were ignored or assigned to different anomaly segments by SCAPA, and points from the same anomaly type can seem to belong to very different patterns. Nevertheless, fuzzy c-means clustering with ECDF is able to cluster the most segments with similar structures together correctly, except for the region around 3250 min, where there is a change of nonlinear pattern for the time series.  
Generally, we see that fuzzy c-means clustering and ECDF is an effective clustering method that can perform well with fragmented anomalous time series segments. It is more sensitive to the structural information rather than the general intensity of data distributions. Consequently, its performance was robust to some misspecification of the number of clusters but is slightly compromised with the misspecification of segment length.
## 2 Data Set
### GOCI Data  
The GOCI data used in this study cover a total of eight bands of visible and near-infrared wavelengths from 402 to 885 nm. The satellite can perform hourly observation from 8:30 a.m. to 3:30 p.m. (Beijing time). Its temporal resolution (1 h) and spatial resolution (500 m) are the advantages that can be used for coastal marine environmental monitoring. GOCI level-1B (L1B) data can be downloaded from the Korea Ocean Satellite Center ([[http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/)]([http://kosc.kiost.ac.kr/](http://kosc.kiost.ac.kr/)), accessed on 13 July 2021) [36]. Eight groups of data can be obtained every day. There are many GOCI products, such as TSM, Chl-a, and normalized water-leaving radiance, which can be taken as tracers. Among them, in coastal areas, the current inversion using TSM as a tracer can capture the changes in tidal phases [10], and are used to compare with other bio-optical remote sensing products. The inversion results using suspended matter products are more accurate and reliable [4; 29]. We use the GOCI-derived TSM as the tracer in this study.
## 5 Obstacles and Challenges in Evolving V2X Communication
### Positioning  
In the safety-critical vehicular applications, as depicted in Section 2.4, the relative position of the traffic participants is the most interesting. The most obvious one could be the usage of various sensory data, like LiDARs or radars. The detected information can also be shared via standardized messages. There are several emerging technologies as well, like UWB [137], to obtain relative position data via radio communication. There are various efforts in 5G [21] to obtain positioning data. The following techniques support these efforts:  
* Cell-identity-based;
* Angle-based;
* Range-based;
* Fingerprinting-based;
* 5G-network-based positioning;
* Assisted positioning in 5G networks;
* Machine learning techniques.  
The accuracy of these methods is promising. However, all of the mentioned techniques depend on 5G cells to calculate the position values, and we cannot depend on cell towers in the safety-relevant, direct communication use cases. If an ITS station is equipped with both 5G Vu and PC5, then these methods could be used for enhancing the quality of the ego position information, but, in order to obtain the relative positions among the stations, further developments should be made.
## 3 Experimental Results
### Use of Drone Survey for Areas without Ground Targets  
Eight points were selected where no surface settlements are installed near K-SK-10 and K-SK-11, and the sites are marked as blue squares in Figure 12. The settlement analysis was then carried out using the drone-surveyed data.  
Figure 11: Regression of cumulative settlement. (**a**) K-SK-10, (**b**) K-SK-11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{5}{c}{**Drone Survey**} & \multicolumn{3}{c}{**Surface Settlements**} & \multicolumn{2}{c}{**Difference**} \\ \hline  & **Predicted** & **Predicted** & **Predicted** & **Predicted** & **Predicted Settlement** & **Consolidation** \\  & **Settlement** & **Consolidation** & **Costination** & **Difference** & **Rate Difference** \\  & **(m)** & **(\%)** & **(m)** & **(\%)** & **(mm)** & **(\%)** \\ \hline K-SK-10 & 3.154 & 62.17 & 3.162 & 67.06 & \(-\)8 & \(-\)4.88 \\ K-SK-11 & 2.485 & 57.15 & 2.293 & 62.71 & 192 & \(-\)5.56 \\ K-SK-14 & 0.476 & 71.77 & 1.382 & 63.33 & \(-\)906 & 8.44 \\ K-SK-19 & 1.085 & 58.73 & 1.420 & 53.29 & \(-\)335 & 5.43 \\ \hline \hline \multicolumn{5}{c}{RMSE} & \multicolumn{3}{c}{492} & 6.23 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The predicted settlements and the rates of consolidation from the drone survey and surface settlement.  
The measured settlements from the drone survey were plotted with the surface settlements at K-SK-10 and K-SK-11 in Figure 13. The drone-derived settlement of these points shows a similar pattern to the near-surface settlement, though the settlement was slightly different point-by-point.  
Figure 12: Sites where no surface settlements are installed (blue squares).  
Figure 13: _Cont._  
The long-term settlement and consolidation for these points were then predicted using the drone survey, as shown in Table 6. The regression was carried out with a high R-square. We should note that the reliability of the predicted settlement and the consolidation are within 492 mm and 6.23% from the analysis in Table 5.
# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
## 4 Analysis of Predicted Results  
To evaluate the superiority of the proposed EMD-BiGRU algorithm, six algorithms, including EMD-GRU, BiGRU, GRU, GBR, RFR, and SVR, were selected for analysis. BiGRU, GRU, GBR, RFR, and SVR models are single-predicted methods that use raw acceleration data to predict missing data. EMD-BiGRU, EMD-GRU are hybrid predicted methods based on EMD decomposition. The decomposed subseries and raw acceleration data are inputted into hybrid predicted methods together to forecast missing data. To verify the effectiveness of different algorithms, four acceleration data are located on different locations of a three-story structure are tested by using different methods, such as EMD-BiGRU, EMD-GRU, BiGRU, GRU, GBR, RFR, SVR algorithms in Table 2. In addition, point 5 represents that 5 points are missing for raw data sets. Table 2 represents the prediction results for four data sets (A_1, A_2, A_3, and A_4) using different methods. The bold number indicates that the method has the best performance in the corresponding data sets. It can be seen from Table 3 that the EMD-BiGRU has the minimum values of MSE, RMSE, MAE, and the maximum value of R\({}^{2}\), compared with the other six methods. To be specific, for A_1 data sets, compared with the EMD-GRU algorithm, the EMD-BiGRU reduces 0.0151, 0.0499, and 0.0389 in MSE, RMSE, and MAE, respectively, and increases 0.0536 in R\({}^{2}\). EMD-BiGRU reduces 0.0151, 0.0499, and 0.0389 in MSE, RMSE, and MAE, respectively, and an increase of 0.9433 in the R\({}^{2}\), compared with the BiGRU in A_1 data sets. Moreover, the EMD-BiGRU has lower MSE, RMSE, MAE, and higher R\({}^{2}\) than the machine learning models GBR, RFR, SVR in A_1 data sets. In addition, The MSE values of the EMD-BiGRU for A_1, A_2, A_3, and A_4 are 0.0160, 0.0065, 0.0040, and 0.0041, respectively. Compared to the comparative model's minimum MSE, it reduces by 0.0151, 0.0032, 0.0015, and 0.0123 using the EMD-BiGRU method. Compared to the comparative model's minimum RMSE, it reduces by 0.0499, 0.0179, 0.0107, and 0.0637, respectively. Compared to comparative model's minimum MAE, it increased by 0.0389, 0.0130, 0.0079, 0.0442, respectively. It increased by 0.0536, 0.0155, 0.0067, 0.0812, compared to its comparative model's maximum R\({}^{2}\), respectively. In summary, GRU, BiGRU, etc., deep learning algorithms outperform GBR, RFR, SVR machine learning methods on predicted capability, which indicates the superiority of deep learning in dealing with temporal sequences. EMD-based hybrid models such as EMD-GRU, EMD-BiGRU have lower errors in MSE, RMSE, MAE compared with single models such as GRU, BiGRU, which indicates the effectiveness of the EMD method.
## 2 Algorithms
### Backward (Nondeterministic) DAWG Matching, B(N)DM  
The main idea of the BDM algorithm is to build a deterministic finite automaton (in this case, a suffix automaton, which is a directed acyclic word graph or DAWG) that recognizes all substrings of the reversed pattern, accepts all suffixes of the reversed pattern (including the empty suffix), and enters a FAIL state if a string has been read that is not a substring of the reversed pattern.  
The suffix automaton processes the window right-to-left. As long as the FAIL state has not been reached, we have read a substring of the reversed pattern. If we are in an accepting state, we have even found a suffix of the reversed pattern (_i.e._, a prefix of \(p\)). Whenever this happens before we have read \(m\) characters, the last such event marks the next potential window start that may contain a match with \(p\), and hence determines the shift. When we are in an accepting state after reading \(m\) characters, we have found a match, but this does not influence the shift.  
So, \(\xi^{p}(w)\) is the number of characters read when entering FAIL (including the FAIL-inducing character), or \(m\) if \(p=w\). Let \(I^{p}(w)\subseteq\{0,\ldots,m-1\}\) be the set defined by \(i\in I^{p}(w)\) if and only if the suffix automaton of \(\overset{\leftarrow}{p}\) is in an accepting state after reading \(i\) characters of \(w\). Then  
\[\textit{shift}^{p}(w)=\min\big{\{}m-i\,\big{|}\,i\in I^{p}(w)\big{\}}.\]  
Note that \(I^{p}(w)\) is never empty as the suffix automaton accepts the empty string and, thus, \(0\in I^{p}(w)\) for all windows \(w\).  
The advantage of BDM is that it makes long shifts, but its main disadvantage is the necessary construction of the suffix automaton, which is possible in \(O(m)\) time via the suffix tree of the reversed pattern, but too expensive in practice to compete with other algorithms unless the search text is extremely long.  
Constructing a nondeterministic finite automaton (NFA) instead of the deterministic suffix automaton is much simpler. However, processing a text character then does not take constant, but \(O(m)\) time. However, the NFA can be efficiently simulated with bit-parallel operations such that processing a text character takes \(O(m/W)\) time, where \(W\) is the machine word size. For many patterns in practice, this is as good as \(O(1)\). The resulting algorithm is then called BNDM.  
From the \"text character accesses\" analysis point of view, BDM and BNDM are equivalent, as they have the same shift and cost functions.
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 3 Performance Analysis for Downlink WPCN
### Coverage Probability
It is reasonable that if the receive threshold increases, some nodes at the edge of covered region will be inactive and harvest energy. Meanwhile,the adjustment of user association probability will impact on the coverage probability of the typical node. This is because more nodes receive information from BSs when the bias of BSs increases.
###### Abstract  
Anomaly detection of liquid levels in molds is an important task in continuous casting. Data that consists of a series of liquid levels in mold during a continuous casting process can be viewed as a time series, on which Time Series Anomaly Detection (TSAD) methods can be applied. However, the abnormal and normal data in the liquid data in the mold sequence share similar features. And due to manual control limitations, the anomaly sequence in liquid level in mold data lasts longer. Therefore, using existing TSAD methods based on AutoEncoders (AEs) often results in high false positive rates. In this paper, a novel framework is proposed for anomaly detection of liquid level in mold by using unsupervised deep-learning-based TSAD. The framework decomposes a time series into normal and error sequences. A forecasting network reconstructs the normal sequence to solve the first issue, which allows the proposed method to consider the context. An error extraction network generates errors from the original sequence to solve the second issue. It removes anomalies from the original sequence during training to prevent anomaly pollution and allows the forecasting network's training to be free from anomaly pollution. A new dynamic threshold method is proposed to identify anomalies. The proposed method is evaluated on the actual casting dataset by comparing it with baseline methods. The experiment results indicate that the proposed framework outperforms some of the best anomaly detection methods in terms of accuracy, precision, and F1 score.  
anomaly detection of liquid level in mold; time series anomaly detection; continuous casting; error generation; autoencoder; dynamic threshold Article  
Citation: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Anomaly Detection of Liquid Level in Mold during Continuous Casting by Using Forecasting and Error Generation  
_Appl. Sci._**2023**, _13_, 7457. [[https://doi.org/10.3390/app13137457](https://doi.org/10.3390/app13137457)]([https://doi.org/10.3390/app13137457](https://doi.org/10.3390/app13137457))
# An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
## 4 Conclusions  
The RSSI-based robotic system navigates in a diverse indoor environment with the help of available wireless data links. The RSSI values are changes as the object appears in between wireless node and a receiver module. In this case, the data-link quality plays a crucial role in identifying accurate mapping position. Dense area sampling with adequate sample points in diverse conditions improves accuracy. The accuracy of the classification models depends on the data distribution and training: testing ratio. It is necessary to calculate the classifier accuracy before fixing any model. For sampled dataset, the random forest classification method provides a promising result with the least MAE, MSE, and RMS error compare to other prediction models. Path optimization algorithm chooses  
Figure 13: Results of: (**A**) object detection and recognition (**B**) facial detection and recognition algorithms.  
the lowest hoping node path as the shortest path. In the proposed method, the RF classification method achieved a location accuracy which was 5.7%, 7.4%, and 9.4% higher than RBF SVM, KNN, and MLP in a 3.2 m span. The designed robotic platform can be further modified into a transport laboratory equipment from one room to another on the same floor. The system can sense the surroundings and make immediate decisions using the proposed computer vision. In future, it is possible to deploy this system on aerial vehicles inside buildings for surveillance purposes.  
Conceptualization, A.R., D.D.; Data acquisition and processing, J.U.; Results analysis, A.R., D.D.; Writing - original draft, J.U., D.D., A.R.; Writing - review & editing, V.M., M.-L.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.