esa-sceva/satcom-chunk-collection huggingface dataset:
# A Practical Group Symbol and Group Name Approach (PSNA) according to EN ISO 14688-2:2018
## 1 Introduction
The results show that only 11 samples were not correctly identified among the 88 total data. After EN ISO 14688-2:2018 [21] came into force, national classification systems (e.g., TS 1500 [24]) were withdrawn in CEN countries and EN ISO 14688-2:2018 [21] became practically unusable in countries without a compatible national standard. Therefore, there is no approach capable of providing a group name and group symbol to define soil in detail in geotechnical engineering applications in these countries. Thus, it is intended to make this standard practically usable. In this study, a practical and useful group symbol and group name marking approach is proposed. This approach is not a classification method or a standard. The objective of this study is to utilize EN ISO 14688-2:2018 [21] in practical engineering applications. The method considers EN ISO 14688-2:2018 [21] and it is supported by ASTM D2487-17e1 [25]. EN ISO 14688-2:2018 [21] gives a general identification for soils. To overcome this generalized identification for geotechnical engineering purposes, the study presented here proposes a novel approach. Because identifying and classifying soil is the first step in designing projects, EN ISO 14688-2:2018 [21] and ASTM D2487-17e1 [25] were assessed separately and used to develop this new marking method. There are very limited studies in the literature [15; 19] that include approaches the standards that cannot be used in practice. The study presented herein proposes an approach where the grain boundary values are clarified, and it is made clear what they are based on, as opposed to the general applicability allowed by EN ISO 14688-2:2018 [21].
# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 4 Proposed Methods
### GMM-kNN Fusion with a Reject Option
Additionally, for the one that is higher than the threshold, the corresponding class label will be assigned to it. Otherwise, the prediction will be rejected. * GMM-R (GMM with a reject option). Since the proposed classifier is built on a GMM-R classifier, the effectiveness of the proposed algorithm will be compared with the GMM-R classifier. * KNN-R (kNN with a reject option). Similar to LDA-R and GMM-R, kNN-R can reject a low-reliability decision. Although high computational requirements may prevent real-time application when the number of classes is large, its high classification accuracy is still attractive. Figure 2: Workflow of proposed classifier. * GK-R (GMM and kNN fusion with a reject option). There are two parameters in the GK-R classifier; the parameters are tuned, and the performance of GK-R for different parameters will be presented.
# A ROTI-Aided Equatorial Plasma Bubbles Detection Method
## 5 Conclusions and Perspectives  
In this study, we present a ROTI-aided method to detect the EPBs based on the GNSS TEC data. This method develops the previous IBS method using a low-order polynomial  
Figure 8: Above two cases with different polynomial orders. The blue line represents the observed TEC, the green and red lines present the partially fitted TEC using third-order and sixth-order polynomial, respectively.  
fitting instead of a linear fitting to extract the detrended ionospheric TEC during the period of EPB. The EPBs over the Hong Kong area during 2014 were detected using this technique with a third-order polynomial fitting strategy. The main results and conclusions are listed as follows:  
1. EPBs generally occur after 20:00 local time and are observed throughout the nighttime and the occurrence during the spring and autumn equinoxes is significantly greater than during the summer and winter solstices, which is also reported in previous studies. In addition, most of the TEC depletion error is smaller than 1.5 TECU and the mean error is 0.63 TECU. These findings suggest that the detection method is feasible and highly accurate.
2. For short-duration EPB, the fitted TEC series are similar between the third-order polynomial and linear fitting; for long-duration EPB, the fitted TEC series via the third-order polynomial are smoother and more fitting than those via a linear fitting. This suggests that the third-order polynomial fitting is more suitable to fit the background TEC data, and thus adequately extract the TEC depletion by the EPB.  
Overall, this method can acquire high-precision EPBs signals which can help to understand the fine characteristics of EPBs and evaluate their effect on GNSS positioning accurately. Here, only the EPBs over the Hong Kong area are tested. In the future, more cases should be investigated to further check this method.  
[PERSON] contributed to data analysis and led the manuscript writing. [PERSON] supervised this study. [PERSON]-P.L. contributed to data processing. [PERSON] contributed to manuscript editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Grant number 41804021, 41974014).  
GNSS data are freely available here: [[ftp://ftp.geodetic.gov.hk](ftp://ftp.geodetic.gov.hk)]([ftp://ftp.geodetic.gov.hk](ftp://ftp.geodetic.gov.hk)) (accessed on 22 September 2021).  
The authors thank the Geodetic Survey Section of Survey and Mapping Office in Hong Kong for providing the GNSS data.  
The authors declare no conflict of interest.
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology
### Traffic Data Prediction Model of the Intersection Entrance Section
#### 3.4.3 Model Evaluation Index  
Mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the prediction effect of the model, as in (17) and (18).  
\[MAPE=\frac{100\%}{n}\sum_{t=1}^{n}\left|\frac{\hat{x}^{t}-x^{t}}{x^{t}}\right| \tag{17}\]  
\[RMSE=\sqrt{\frac{1}{n}\frac{1}{t=1}^{n}(\hat{x}^{t}-x^{t})^{2}} \tag{18}\]  
where \(n\) is the number of test set samples, \(\hat{x}^{t}\) is the traffic data value in the predicted time period \(t\), and \(x^{t}\) is the actual traffic data value corresponding to the time period \(t\).
## 4 Results and Discussions
Figure 2a) considering that: (i) node 1 is adjacent to the exit 1; (ii) the exit 1 is closed; and (iii) the people around node 1 have to cross the whole park while avoiding all the emergencies (i.e., the nodes \"A\") to reach the exit 2. In each of the six abovementioned cases, the system's response times, using a desktop PC equipped with Intel(r) Core(tm) i5-7400 CPU @ 3.00 GHz, 8.00 GB of RAM, were calculated by averaging the results of 50 simulations for each case. The results of the scalability analysis described above are reported in Table 6.  
The results in Table 6 were used to define the law that better describes how the response time of the algorithm 3 decays if the number of nodes increases (Equation (2) was derived). Figure 6.0 \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **\# of Node That Make** & **\# Nodes in Alarm (i.e.,** & **Average Time of Response** \\  & **Up the System** & **20\% of All the Nodes)** & **of the Algorithm 3 (s)** \\ \hline
1 & 6 & 1 & 0.5513 \\ \hline
2 & 60 & 12 & 0.9253 \\ \hline
3 & 240 & 48 & 1.7147 \\ \hline
4 & 480 & 96 & 2.9778 \\ \hline
5 & 960 & 192 & 5.9606 \\ \hline
6 & 1200 & 240 & 7.3738 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the scalability analysis. Figure 18: Example of the safest and fastest paths provided by the Algorithm 3 in case of an environmental/structural alarm at nodes 1, 3, and 5 (N1, N3, and N5), for the people at (**a**) nodes 5 (N5) and (**b**) 6 (N6), and (**c**) is a table that shows the overall condition. where \(t\) is the response time of the algorithm 3 (seconds) as a function of the number of sensing nodes \(N\). Finally, all the algorithms have been developed and tested in a desktop PC and in a Raspberry PI, showing a computational time compatible with the level of risk associated with the safety application (<3 sec) in the case of actual nodes. The possibility to run a Raspberry PI on a Wireless Local Area Network (WLAN) is also powerful in the case of a network communication interruption, as the algorithms can be implemented in low computational and standalone devices. Future works include (1) the implementation on low computational devices (e.g., microcontrollers), (2) the comparison with other simple neural network classifiers, such as a K-Means algorithm or a tree-based method (e.g., random forest or decision tree), or Convolutional Neural Networks and (3) the usage of a \"flow graph\" created by means of a min-cut max flow algorithm in order to maximize the number of people per unit of time who can move out of the park (e.g., using the number of people per unit of time who can use the path as capacities, and the actual number of people who are expected to use it at any given time based on a decision for evacuation as a flow value. In this way, crowds can be redirected from all the nodes simultaneously, and the flow is maximized).
# A Method to Accurately Estimate Fish Abundance in Offshore Cages
## 4 Experimental Results and Discussion
The reason for the consistent result is that during the dark hours, due to lack of visibility, the fish move on their own without forming any school. This behavior results in uniform distribution of fish inside the cage, thereby giving consistent and stable estimation. One interesting pattern observed is that the fish count is the most accurate during slack water. The reason for relatively high accuracy is because at the slack tide the walls of the cage are relatively even, and the fish are uniformly distributed in the cage. Another noticeable observation of fish count variability between nights is that the average estimation decreases slightly as we move from the first night to the last night. Since the data collection started from the lowest current day, as time progressed, the water current increased day by day, resulting in higher turbulence on the floating transducer. This is the main reason for the decrease in the fish estimation, as evidenced by the decrease in the received signal strength. The result from the net bottom analysis experiment is shown in Figure 6. The echograms and their corresponding fish counts are stacked over each other, and their x-axes are aligned. In Figure 6c,d, the pink graph represents fish count per ping and the red dash line denotes the true count. On the left echogram, it can be seen that the cage bottom is flat, which corresponds to absence or weak current. The echogram showed that the net bottom was stable at 6.5 m. The corresponding fish count results are also comparatively stable. The two spikes in the fish count are the result of cage pulling. The times of spikes exactly match with the times of the cage pulling. Figure 6b illustrates a high movement of cage bottom during high water current. The cage bottom fluctuated between 4.5 and 5.5 m most of the time and in only some instances attended its true depth at 6.5 m. The cage wall was also observed to be deformed washed toward the direction of current, resembling a parallelogram (side view). The sharp changes in fish counts verify that the fish count is affected by the position of the cage bottom. The highly volatile fish count graph suggests that fish counting is not suitable during a high current. The fish count result from the short experiments is shown in Table 6. The table shows that the fish count estimation of the algorithm has a maximum error of \(\pm\)9.5%. Even though the number of experiments performed was limited, the results matched well with our established findings. Accuracy of echo-integration can be affected by TS estimates, TS used for scaling, fish behavior, spatial sampling error, and analysis techniques and parameters used among other factors [23]. However, the results suggest that the proposed algorithm estimates fish abundance in an offshore cage with high accuracy if experiments are performed at night during slack water on the lowest current day.
# Mapping How Artificial Intelligence Blends with Healthcare: Insights from a Bibliometric Analysis
## 3 Results
### Key Influential Factors through Bibliographic-Coupling Analysis
#### 3.1.3 Distribution of Publications across Various Types and Publishers
Focusing on the sources of publication with the most significant contributions outside of academic journals, we find that both books and conference papers feature different publishing houses playing a pivotal role. Springer dominates in book publications, particularly with its \"Lecture Notes in Computer Science\" series, which boasts 173 publications and significantly enriches the reviewed bibliography. In the realm of books, IOS Press stands out with \"Studies in Health Technology and Informatics\", contributing 75 publications. For conference proceedings, the prominent publisher ACM leads with its \"ACM International Conference Proceeding Series\" and the \"Conference on Human Factors in Computing Systems\", both of which are influential in this domain. Additionally, with the aim of distinguishing the networks formed among different sources, we utilized VOSviewer again. In total, out of 1108 distinct source titles, 797 comprised the largest set of connected items. As depicted in Figure 3, dense yellow clusters are identified among the most significant sources. The following elements are notable as their density color closely resembles yellow. \"Lecture Notes in computer science\" is part of a cluster with Total Link Length (TLS) 1319. \"Artificial Intelligence in Medicine\" ranks second with TLS 1123. \"Journal of Biomedical Informatics\" holds the third position with TLS 707. \"International Journal of Medical Informatics\" secures fourth place with TLS 634. Lastly, \"Studies in Health Technology and Informatics\" ranks fifth with TLS 468.
## 3 Results and Discussion
### Electrodes Characterisation in Biotic Conditions  
Following the abiotic tests, the electrodes were inoculated and operated according the procedure described in Section 2.3. Current started to increase right after inoculation in the DFM and acetone electrodes. A period of 60 days was allowed to favor the development of a mature electroactive biofilm, at the end of which the current profiles were fairly repeatable across successive cycles (see Figure S1 in the Supplementary Materials), and the averaged maximum current densities for the acetone and the DMF electrodes were 5.2 and 4.8 Am\({}^{-2}\), respectively. The electrochemically activated electrode failed to produce any current which, together with the poor performance it displayed in the abiotic test, led us to dismiss this electrode. Therefore, it was not subjected to further analyses in biotic conditions. Regarding the control electrode, although it produced some current, it was almost negligible compared to that of the DMF and acetone electrodes (see Figure S1 in the Supplementary Materials).  
The bioelectrochemical performance of the electrodes was assessed by means of CV tests. The voltammograms recorded under non-turnover conditions (no substrate was available) showed the characteristic redox systems of a typical bioanode [18], with half-wave potentials at \(-0.310\) V, \(-0.364\) V, and \(-0.376\) V (Figure 4), in accordance with the redox system associated to _Geobacter_ sp. [19, 20, 21] and the mixed anodic consortia [19, 22].  
The results of the CV tests performed under turn-over conditions (33 mM acetate [23]) are shown in Figure 5. The current of the control cell was negligible (not included in the figure), compared to that of acetone and DMF. The voltammogram of the acetone electrode displayed a typical sigmoid-shaped profile, presenting a flat activation region (\(-1.000\) to \(-0.500\) V), a linear zone (\(-0.500\) to \(+0.035\) V), and an incipient \"plateau\", caused by the mass transfer limitation. In contrast to this, the DMF electrode showed a peak at \(+0.244\) V instead of the expected plateau region. This could be caused by a poor diffusion of either acetate or oxidation products (probably protons) through the biofilm [24]. This, together with the greater \"thickness\" of the voltammogram in the non-faradaic region (\(-1.000\) to \(-0.500\) V), suggests that the DMF activation generates an electrode/biofilm structure with a larger electrical capacity that could hinder the diffusion of species.  
Figure 4: Cyclic voltammograms obtained under non-turnover conditions (scan rate: 0.1 mV\(\cdot\)s\({}^{-1}\)).
## 4 Results and Discussion  
### Well Location  
Various factors have been studied to understand their impact on well productivity, including well depth, lithotypes, geological structures, topographic settings, and weathering thickness. When analyzing the flow rates of wells, it was found that they ranged from 0 to 15.50 L per second, as shown in Figure 6a. A majority of the wells (56.83%) fell in the very-low-to-low-rate category (0-3 L/s), while 18.4% were in the moderate category (3-5 L/s), 15.3% in the high category (5-10 L/s), and 9.47% in the very high category (>10 L/s). These results suggest that half of the wells had a flow rate below 3 L/s. Higher flows were observed in wells located close to perennial streams (<300 m) (Figure 3b), where the deposits were thick, permeable, and ideal for water infiltration. These geomorphological locations were suitable for shallow wells, especially for irrigation purposes, as noted in previous studies [33; 34; 44; 45].  
Figure 6: (**a**) Frequency histograms of wells yield (L/s) for the Tata watershed; (**b**) Percentage of wells exploiting different kinds of geological formations; and (**c**) Correlation between yield and depth of wells.
## 2 Literature Review and Research Design
### Research Questions and Design  
4.1 Q1: What Is the Predictive and Explanatory Power of Psychographic Segmentation in E-Commerce User Preferences?  
Marketers and scholars have highlighted the need to account for customer perceptions and expectations in a specific market segment. In the offline context, relationships between psychographic segmentation and user behaviors and preferences have been asserted in various scenarios, and results have demonstrated that psychographic variables are effective in understanding and predicting user behaviors and preferences [26; 54]. However, in the e-commerce context, the potential predictive and explanatory power between segmentations--particularly psychographic variables and the clustering method--in predicting e-commerce consumer preferences has not been comprehensively studied.  
4.2 Q2: What Are the Differences between SVS and BFF Segmentation in Predicting and Explaining E-Commerce User Preferences?  
Researchers argue that values are a useful basis for segmentation or psychographics because, compared with AIO, values are less numerous, more central and, compared with personality, more immediately related to motivations and consumer behaviors [21]. The comparison between personality and values mostly shares the context of brand preference. For example, [PERSON] (2009; 2011) compared the predictive power of personality and values on brand preferences within a fashion context and found that values are indeed better predictors of brand preferences [55; 56]. Although scholars have studied the predictive and explanatory power of psychological segmentation on consumer behavior, few studies have examined the relative importance of different psychographic variables, that is, personality and values, in understanding e-commerce consumer behavior.  
4.3 Q3: What Is the Adaptability of Psychographic Measurements in Predicting and Explaining E-Commerce User Preferences?  
As two of the psychographic measures, the SVS and BFF models have been proven to improve the understanding of consumer purchase behaviors and preferences. However, [PERSON] (2012) highlighted that not all value dimensions can effectively predict individual behaviors [27]. In an online context, it is reasonable to partition the value items into more or less fine-tuned distinct values according to the needs and objectives of the analysis.
## 3 Results and Discussion
To investigate the magnetic phase transitions in GdFe\({}_{2}\)/Rh and GdFe\({}_{2}\)/Ir under the external magnetic field at 0 K, we have performed atomistic spin-dynamics simulations using the model described by Equation (1). Using the parameters obtained from DFT, the magnetic phase diagrams is displayed in Figure 4a,b. For both films capped by Rh and Ir, the ground magnetic state is a spin spiral consistent with the energy minimum at zero applied magnetic field. However, for the film capped by Rh, the skyrmion lattice is energetically stable at a critical field value of \(\sim\)1.12 T, and this skyrmion lattice phase is changed to the ferromagnetic phase by a larger critical field value of \(\sim\)2.25 T. For the film capped by Ir, the skyrmion lattice emerges at relatively weak field of 0.75 T, and disappears for a large filed of \(\sim\)1.74 T.  
In our simulation, the spin structure is relaxed using spin dynamics. As shown in Figure 4c, skyrmions with a diameter of \(\sim\)2-4 nm emerge under external magnetic fields of 1-2 T for both Rh- and Ir-capped GdFe\({}_{2}\). The size of skyrmions decreases rapidly with the increasing value of applied magnetic field. For deeper insights into the skyrmion size, the diameter has been computed for isolated single skyrmions in two different ways: (i) Using the fixed MCA energy and exchange constants obtained from DFT calculation but varying the DMI value; (ii) using fixed DMI obtained from DFT  
Figure 4: Phase diagrams for the (**a**) GdFe\({}_{2}\)/Rh and the (**b**) GdFe\({}_{2}\)/Ir films at zero temperature. The relative energies of the spin spiral states, skyrmion lattice, and ferromagnetic state are shown. The red, green, and blue colors represent the regime of the spin spiral states, skyrmion lattice, and ferromagnetic state, respectively. (**c**) Radii of skyrmions in the films of GdFe\({}_{2}\)/Rh and GdFe\({}_{2}\)/Ir as a function of the applied magnetic field. (**d**) Schematic representation of possible spin configurations in a magnetic material with Dzyaloshinsky–Moriya interaction for different values of an external field. but varying the MCA. From these calculations we confirmed that the skyrmion size decreases with reduced DMI but it expands with reduced MCA.