esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Basic Experiments
### Runtime Experiment
For the full search strategy, we see an almost linear relationship between the dimensionality \(K\) and the runtime. This strategy is independent of the underlying data distribution. For that reason, we do not distinguish between PCA- and DNN-based dimensionality reduction for the full search strategy. This is different for the tree-based search strategies (Db-Tree and Doc-Trees), where the data distributions of the PCA- and DNN-based embeddings lead to different search times. We also see an almost linear relationship for the Doc-Trees search strategy for both PCA- and DNN-based retrieval approaches. In general, for this strategy, the runtime is higher than for the full search. In our case, the data size of a single document is too small for the Doc-Trees strategy to give any benefit over the full search strategy. For the Db-Tree strategy, we see a slightly exponential growth \begin{table}
\begin{tabular}{l r r} \hline \hline
**Approach** & **Full Search** & **Index (Db-Tree)** \\ \hline Brute Force, SDTW (Chroma, 10 Hz) & 23,190.1 & - \\ Brute Force, SDTW (CENS, 1 Hz) & 351.5 & - \\ Brute Force, Shingles (\(K=240\)) & 23.0 & 76.9 \\ DNN (\(K=30\)) & 3.3 & 5.0 \\ DNN (\(K=12\)) & 1.8 & 1.1 \\ DNN (\(K=6\)) & 1.2 & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Time (in seconds) needed for searching 3300 queries, for selected approaches. of runtime with growing \(K\). When the dimensionality falls below 15, the Db-Tree strategy starts to give benefits for the fast nearest neighbor search. We want to emphasize again that the absolute runtimes are implementation-dependent. Therefore, we want to highlight some general tendencies: The shingle-based approaches are significantly faster than the alignment-based approaches. In general, the experiments confirm that lower dimensionalities accelerate the nearest neighbor search. In particular, when using small dimensions (below 15 in our setting), we can further speed up the search by standard multidimensional indexing strategies.
# Influence of Load Dynamics on Converter-Dominated Isolated Power Systems
###### Abstract  
The operation of isolated power systems with 100% converter-based generation requires the integration of battery energy storage systems (BESS) using grid-forming-type power converters. Under these operating conditions, load dynamics influences the network frequency and voltage following large voltage disturbances. In this sense, the inclusion of induction motor (IM) load models is required to be properly considered in BESS power converter sizing. Thus, this paper presents an extensive sensitivity analysis, demonstrating how load modeling affects the BESS power converter capacity when adopting conventional control strategies while aiming to assure the successful recovery of all IM loads following a network fault. Furthermore, this work highlights that generators with converter interfaces can actively contribute to mitigate the negative impacts resulting from IM loads following a network fault. Thereby, two distinct control strategies are proposed to be integrated in the power electronic interfaces of the available converter-based generators: one to be adopted in grid-following converters and another one suitable for grid-forming converters. The proposed control strategies provide an important contribution to consolidating insular grid codes, aiming to achieve operational scenarios accommodating 100% penetration of converter-based generation with a significative percentage of the IM load composition without resorting to a significative increase in BESS power converter sizing.  
grid-forming converter; grid-following converter; induction motor; isolated power systems; load modeling; virtual synchronous machine +
Footnote †: journal: Applied Sciences  
1 February 2021  
Accepted: 2 March 2021  
Published: 6 March 2021
## 1 Introduction
### Article Aims  
This systematic review of the scientific literature on the use of sensor technologies to monitor structural health and safety management aspects in mine TSFs reviews the publications available on the Web of Science (WoS) and Scopus databases. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used and scientific maps were generated with VOSviewer software version 1.6.20. A methodological procedure of three phases was developed: (i) a quantitative bibliometric analysis, (ii) a qualitative systematic review of the literature, and (iii) a mixed review to integrate the findings. The following research questions (RQs) guided the investigation of structural health and safety management monitoring in mine TSFs:  
* RQ1: How can the sensor concepts applied to TSF be clustered and how have they evolved?
* RQ2: What are the main engineering disciplines in which sensors are applied?
* RQ3: What timescale is used to record measurements with sensor technologies?
* RQ4: Are the data measurements conducted remotely or on-site with sensor technologies?
* RQ5: What types of sensor technology are applied?
* RQ6: What connectivity technology is applied in sensors?
* RQ7: Where are sensors applied within a mine TSF?
* RQ8: What technologies related to Industry 4.0 are currently being linked to sensors?  
Finally, this article is structured as follows: (i) The introduction presents the context and scope of the research; (ii) the materials and methods define the resources and materials considered in this review and the methodological procedure adopted; (iii) the results are presented in the text, graphs, figures, and tables; (iv) the discussion reviews the findings obtained; and (v) the conclusion shares the lessons learned and recommendations.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Performance Analysis of Tbrl with Dbr and R-ERP\({}^{2}\)R  
For taking the comparison of our proposed routing protocol, we have taken DBR protocol and R-ER\(P^{2}\)R. DBR uses a greedy approach against nodes depth and finds the next node. A node having less depth as compared to other nodes has been selected as next forwarder. Its greedy nature makes it a favorable choice for managing dynamic topology. R-ER\(P^{2}\)R uses greedy approach to select the next one, but it also considers the physical distance and residual energy value of next node. It also ensures the quality of a link. Therefore, we have evaluated our results against DBR and R-ER\(P^{2}\)R. The same datasets of nodes and simulation settings are placed against them and collected results in the form of delivery ratio, time, and energy consumption.
## 2 Methodology
### Equation of Motion and System Matrices  
Assuming linear structure properties and representing the displacements relative to the ground as  
\[\mathrm{x}\ =\ [x_{b}\ x_{1}\ x_{2}\ x_{3}\ x_{4}\ x_{5}] \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Storey** & **Mass (kg)** & **Stiffness (kN/m)** & **Damping (kN.s/m)** \\ \hline
1 & 5897 & 33,732 & 67 \\
2 & 5897 & 29,093 & 58 \\
3 & 5897 & 28,621 & 57 \\
4 & 5897 & 24,954 & 50 \\
5 & 5897 & 19,059 & 38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fixed Base**} & \multicolumn{2}{c}{**Passive BIS**} \\
**Mode** & **Frequency (Hz)** & **Time Period (s)** & **Frequency (Hz)** & **Time Period (s)** \\ \hline
1 & 3.20 & 0.31 & 0.40 & 2.5 \\
2 & 8.72 & 0.11 & 5.47 & 0.2 \\
3 & 13.62 & 0.07 & 10.30 & 0.1 \\
4 & 17.61 & 0.06 & 14.73 & 0.07 \\
5 & 20.92 & 0.05 & 18.41 & 0.05 \\
6 & - & - & 21.32 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Structure vibration characteristics.  
Figure 2: Building structure idealized representation.  
the equation of motion for structure reduced into matrix form can be expressed as:  
\[[M]\big{\{}\ddot{x}(t)\big{\}}\ +\ [C]\big{\{}\dot{x}(t)\big{\}}\ +\ [K]\big{\{}x(t) \big{\}}\ =\ [\gamma]\big{\{}u(t)\big{\}}\ +\ [\delta]\ddot{x}_{g} \tag{2}\]  
The mass, stiffness, and damping matrices for the fixed base system are \(5\times 5\) matrices, and for the base-isolated structure they are 6x6 matrices and are widely expressed as generalized in Equations (3), (4), and (5), respectively.  
\[[M]\ =\ \textit{Mass}\ \textit{Matrix}=\left[\begin{array}{ccc}mb&\cdots&0\\ \vdots&\ddots&\vdots\\ 0&\cdots&m5\end{array}\right] \tag{3}\]  
\[[C]\ =\ \textit{Damping}\ \textit{Matrix}=\left[\begin{array}{ccc}cb+c1&-c1 &\cdots&0\\ -c1&c1+c2&\cdots&0\\ \vdots&&\ddots&\vdots\\ 0&&\cdots&c5\end{array}\right] \tag{4}\]  
\[[K]\ =\ \textit{Stiffness}\ \textit{Matrix}=\left[\begin{array}{ccc}kb+k1&-k1 &\cdots&0\\ -k1&k1+k2&\cdots&0\\ \vdots&&\ddots&\vdots\\ 0&&\cdots&k5\end{array}\right] \tag{5}\]  
\(\gamma\) is the force location matrix to be used only for MRE based isolated structure model. For fixed base and passive isolated models, \(\gamma\) would be a null matrix. Similarly, \(u(t)\) is the applied force and applicable only to the MRE isolated structure model.  
\[\gamma\ =\ n\ \times\ r\ \text{force}\ \text{location}\ \text{matrix}\ =\ \big{[}\ 1\ \ \ 0\ \ \ 0\ \ \ 0\ \ \ 0\ \ \ 0\ \ \big{]}^{T} \tag{6}\]  
\[u(t)\ =\ \big{[}\ u(t)_{b}\ \ \ u(t)_{1}\ \ \ u(t)_{2}\ \ \ u(t)_{3}\ \ \ u(t)_{4}\ \ \ u(t)_{5}\ \big{]}^{T} \tag{7}\]  
\(\delta\) is the coefficient vector for earthquake ground acceleration, \(\ddot{x}_{g}\). \(\delta\) will be \(5\times 1\) for the fixed base structure model and \(6\times 1\) for isolated structure models.  
\[\delta\ =\ \big{[}\ m_{1}\ \ \ m_{2}\ \ \ m_{3}\ \ \ m_{4}\ \ \ m_{5}\ \big{]}\ \text{for}\ \text{fixed base structure} \tag{8}\]  
\[\delta\ =\ \big{[}\ m_{b}\ \ \ m_{1}\ \ \ m_{2}\ \ \ m_{3}\ \ \ m_{4}\ \ \ m_{5}\ \big{]}\ \text{for}\ \text{isolated structure} \tag{9}\]
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(R^{2}\) & **RMSE** & **MAE** \\ \hline Stacking & 0.9702 & 0.0389 & 0.0304 \\ \hline Blending & 0.9369 & 0.0567 & 0.0441 \\ \hline SVR & 0.8883 & 0.0788 & 0.0639 \\ \hline Linear Regression & 0.8883 & 0.0799 & 0.0619 \\ \hline ExtraTrees & 0.8616 & 0.0886 & 0.0730 \\ \hline GradientBoost & 0.8601 & 0.0890 & 0.0695 \\ \hline XGboost & 0.8091 & 0.1043 & 0.0755 \\ \hline CatBoost & 0.8083 & 0.1025 & 0.0819 \\ \hline RandomForest & 0.7996 & 0.1068 & 0.0855 \\ \hline KNN & 0.6593 & 0.1392 & 0.1162 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of single model, blending ensemble model, and stacking ensemble model.
# Research Progress of Self-Powered Gait Monitoring Sensor Based on Triboelectric Nanogenerator
## 3 Applications of Self-Powered Triboelectric Nanogenerator in Gait Monitoring
### Machine Learning-Assisted Identity Recognition
The individual information is identified according to the characteristics of the gait signals of different participants (Figure 6(di)). In terms of machine learning technology, researchers proposed a Residual Dense-BiLSTM deep learning model evolved from the classic long short-term memory (LSTM) algorithm (Figure 6(dii)). The accuracy recognition rate of this model was 99.4% (Figure 6(diii)). The identity recognition technology based on TENGs is becoming increasingly mature, providing technical references and theoretical guidance for more convenient and intelligent identity recognition systems. In general, the identity recognition achieved by the gait sensors based on TENGs mainly relies on the sensors to obtain signals and machine learning technology to identify these signals. Among them, the CNN machine learning technology, with its advantages such as high efficiency in feature extraction and strong robustness, has become an important tool and technical means in the field of gait monitoring. Figure 6: Applications of self-powered sensors based on TENG in the field of identity recognition. (**a**) Wearable triboelectric sensors with a PVA/acrylic fluorescent layer for identity recognition. (**ai**) Charge distribution when the upper and lower triboelectric layers are in contact. (**aii**) Resistance to mechanical deformation and the distribution of the sensing array in the insole. (**aiii**) Signals with different characteristics generated by different participants. (**aiv**) Confusion matrix for personal identification. (**b**) Identity recognition system based on the Internet of Things and artificial intelligence. (**bi**) The process of the identity recognition system collecting signal characteristics from different participants and completing identity recognition. (**bii**) CNN model for personnel identification and activity pattern recognition. (**c**) Identity recognition system realized by a replaceable triboelectric sensor. (**ci**) The process of identity recognition using a ring-shaped triboelectric sensor. (**cii**) Working mechanism of the ring-shaped triboelectric sensor. (**cili**) Stretchability of the ring-shaped band. (**d**) Triboelectric sensors embedded in a carpet for identity recognition. (**di**) The schematic diagram of the gait sensors embedded in the carpet and the signal output characteristics of 8 users on the carpet. (**dii**) Architecture of the sensor system and the identity recognition process. (**diii**) Confusion matrix of the test dataset for 8 users.
## 3 Results
### Analysis on the Area of Average Draining Formula  
Substituting the parameters of the empirical formula into Formula (4), the F\({}_{\rm A}\) under different rainfall durations and different return periods was calculated. The results are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**1-Day Rainfall**} & \multicolumn{3}{c}{**3-Day Rainfall**} \\ \cline{2-7}  & **10-Year** & **5-Year** & **3-Year** & **10-Year** & **5-Year** & **3-Year** \\ \hline
1991 & 2.75 & 2.11 & 1.83 & 3.08 & 2.68 & 2.36 \\
1995 & 2.75 & 2.12 & 1.83 & 3.09 & 2.68 & 2.36 \\
2009 & 2.76 & 2.12 & 1.84 & 3.08 & 2.67 & 2.31 \\
2010 & 2.76 & 2.12 & 1.84 & 3.08 & 2.67 & 2.31 \\
2014 & 2.76 & 2.13 & 1.85 & 3.07 & 2.62 & 2.13 \\
2015 & 2.76 & 2.13 & 1.85 & 3.07 & 2.62 & 2.09 \\
2016 & 2.77 & 2.14 & 1.85 & 3.07 & 2.62 & 2.05 \\
2018 & 2.82 & 2.19 & 1.87 & 3.07 & 2.57 & 2.04 \\ Average & 2.77 & 2.13 & 1.85 & 3.07 & 2.64 & 2.21 \\ Annual change rate & 0.26\% & 0.30\% & 0.15\% & \(-\)0.04\% & \(-\)0.41\% & \(-\)4.52\% \\ \hline \end{tabular}
\end{table}
Table 5: Drainage duration corresponding to the empirical formula under 1-day and 3-day rainfall with different return periods (\(T_{E}\), in d).  
As shown in Table 6, \(F_{A}\) ranged from 173 to 2029 km\({}^{2}\) for 1-day rainfall duration and from 5353 to 55,883 km\({}^{2}\) for 3-day rainfall duration. For the same rainfall duration, \(F_{A}\) kept rising as the return period decreased. \(F_{A}\) increased with the increase in rainfall duration under the same return period. Therefore, the smaller the rainfall duration and the greater the return period, the smaller \(F_{A}\). On the contrary, the larger the rainfall duration and the smaller the return period, the larger F\({}_{\rm A}\). F\({}_{\rm A}\) decreased with time under the 1-day rainfall duration and increased with time under the 3-day rainfall duration. According to the absolute value of the annual \(F_{A}\) change rate, the annual \(F_{A}\) change rate of 1-day rainfall duration was smaller than that of the 3-day rainfall duration, and both of them increased with the decrease in the return period.  
\(F_{A}\) was small under 1-day rainfall duration and relatively large under the 3-day rainfall duration. Under 1-day rainfall duration with a 10-year return period, \(F_{A}\) was the lowest, varying from 173 to 203 km\({}^{2}\). Under 3-day rainfall duration with a 3-year return period, F\({}_{\rm A}\) was the highest, varying from 24,407 to 55,883 km\({}^{2}\). It was evident that the average draining formula gave the small results for long duration of rainfall and drainage, making it potentially unsafe for application in engineering design.
## 6 Conclusions  
The image restoration model via structured light projection is a novel approach for virtual periscopes. Different from previous methods, we do not require special natural illumination [15; 16; 23; 25], multiple viewpoints [24] or image accumulation processes [17; 18; 19; 20; 21; 22]; we only require a simple projection setup and an image of the distorted scene. This means that our method can be applicable to more scenarios--for instance, monitoring the habits of seabirds, path planning and obstacle avoidance for underwater vehicles, airborne target detection, recognition and tracking and seafloor mapping, etc.  
In Section 5, we coded our image recovering scheme described above, which can simulate the process of WAI reconstruction and image restoration according to the system parameters. Compared with the state-of-the-art method, our method can overcome the influence of changes in natural illumination conditions for WAI reconstruction and improve the accuracy of WAI restoration. Furthermore, the results show that our method can significantly reduce the distortion and performs better in the recovery of distortions. In the future, we plan to conduct further tests in the laboratory and at sea to verify the effectiveness of our approach.  
Similar to other instantaneous distorted image restoration algorithms [11; 15; 17; 23; 25], the proposed method still cannot eliminate the problem of loss of image details caused by random refraction of the WAI (marked in red in Figure 16). However, this problem can later be handled by image fusion algorithms [48; 49; 50; 51] after images are corrected by our process. Rather than addressing the full-blown distortions in raw images, such video post-processing methods may handle more easily images whose distortions are residual.  
In Section 4, the limitation of the algorithm is analyzed, and it is found that errors stemming from aliasing and correspondence will occur in structured light when the spatial frequency of the WAI is large. In the future, it is necessary to further explore a solution to this problem, such as structured light encoding [52], optical flow method [53], etc.  
Figure 16: Results of images recovered by the estimated wavy WAI with the two methods [23].  
**Author Contributions:** All authors have made significant contributions to this paper. [PERSON] designed the research, conducted simulation analysis and wrote the manuscript; [PERSON] provided guidance; [PERSON] conducted data curation; [PERSON] conducted validation; [PERSON] provided supervision and funding. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi National Science Foundation, grant number 2018 GXNSFAA294056, and the Guangxi Young and Middle-Aged Teachers' Basic Research Ability Improvement Project (2022 KY0703).  
**Data Availability Statement:** All data or code used to support the findings of this study are available from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Laser Induced Aggregation of Light Absorbing Particles by Marangoni Convection
## 2 Experimental Methods  
Figure 1a illustrates the experiment setup. The setup is based on a homemade microscope. A 1064-nm laser (CNI, Changchun, China, MIL-N-1064, TEM\({}_{00}\), cw) with the beam waist 3 mm was used as the laser heating source. The beam is expanded to fulfill the pupil of the objective (8 mm) with the beam expander, constructed with two lenses (L1 and L2, f\({}_{L1}\) = 30 mm, f\({}_{L2}\) = 100 mm). The beam is reflected by mirrors M1 and M2 into the homemade microscope, and reflects upward by a dichroic mirror to a microscope objective (40\(\times\), N.A. = 0.7) and focuses into a sample cell. The sample cell is mounted at a three-axial stage. The images were recorded by a CMOS camera, and acquired with movie capture software. The laser power (P) can be changed with an adjustable attenuation and measured at the pupil of the objective.  
The samples were diluted APs in a thin suspension layer. In the suspension layer, a thermal gradient was created while keeping the local temperature rising by focusing the laser on the APs. The APs used in the experiments were core-shell magnetic microspheres (polystyrene@Fe\({}_{3}\)O\({}_{4}\), 4.0-5.0 \(\upmu\)m in diameter, density with 1.463 g/cm\({}^{3}\), BaseLine Company, Tianjin, China). The initial concentration was 4.1 \(\times\) 10\({}^{18}\) cm\({}^{-3}\) in the solution. The particles were diluted to 2.1 \(\times\) 10\({}^{17}\) cm\({}^{-3}\) for the experiments.  
The sample cell was prepared using a homemade circular trough with an inner diameter of 10 mm. The sample cell is composed of two glass slides and a rubber ring, and its diameter is 10 mm. The sample cells with two different thicknesses were used in the experiments. The thickness of the first sample cell was 1 mm, and the thickness of the second sample cell was 70 \(\upmu\)m. Both the sample cells were sealed with coverglass to decrease evaporation during the experiments. A diluted suspension drop with volume of 6 \(\upmu\)L of was injected into the sample cell for observation. When the suspension was injected into the sample cell with a thickness of 1 mm, a liquid-air interface was formed in the sample cell; the thickness of the suspension layer was about 70 \(\upmu\)m. When the suspension was injected into the sample cell with a thickness of 70 \(\upmu\)m, the thickness of the liquid phase was equal to the thickness of the sample cell; there was not a gas phase in this sample cell. The experiments were performed at room temperature.  
Figure 1: **(a)** Schematic of the experimental setup. PA, power attenuation; BE, beam expander; M1–M2, mirrors; DM, dichroic mirror; L, lens; MO, microscope objective; CCD, CCD camera. **(b)** Schematic illustration of Marangoni convection-induced particles accumulation inside the sample cell.