esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of the Orthogonal Experiment Results
#### 3.1.1 Range Analysis of the Erosion Depth
Third, when the nozzle diameter increases, the erosion depth initially increases and then decreases with no significant variation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Values**} & \multicolumn{3}{c}{**Factors**} & \multirow{2}{*}{**Empty**} \\ \cline{2-2} \cline{4-5}  & \(V\) **(m/s)** & \(L\) **(cm)** & \(D\) **(mm)** & \\ \hline \(K_{1}\) & 3.304 & 9.735 & 8.580 & 8.580 \\ \(K_{2}\) & 8.687 & 8.681 & 8.689 & 8.586 \\ \(K_{3}\) & 13.876 & 7.454 & 8.598 & 8.701 \\ \(\overline{K_{1}}\) & 1.101 & 3.245 & 2.860 & 2.860 \\ \(\overline{K_{2}}\) & 2.896 & 2.894 & 2.896 & 2.862 \\ \(\overline{K_{3}}\) & 4.625 & 2.485 & 2.866 & 2.90 \\ \(R_{i}\) & 3.524 & 0.76 & 0.036 & 0.04 \\ \hline \multicolumn{5}{c}{Significance} & \multicolumn{3}{c}{\(V>L>D\)} \\ Optimal parameter combination & \multicolumn{3}{c}{\(V3L1D2\)} \\ \hline \hline \end{tabular} \(K_{ij}\) is the sum of the test values corresponding to the \(j\) level of the \(i\) factor, and \(\overline{K_{ij}}\) is the average value of \(K_{ij}\). \(V\), \(L\), and \(D\) represents jet velocity, standoff distance, and nozzle diameter, respectively.  
\end{table} 
Table 4: Range analysis of the erosion depth. Figure 5: Trend chart of the erosion depth variation of HBS. (**a**) Water jet velocity; (**b**) standoff distance; (**c**) nozzle diameter.
# Estimation of Structural Deformed Configuration for Bridges Using Multi-Response Measurement Data
## 5 Conclusions  
In this study, an algorithm that can estimate SDS using displacement data combined with an additional slope and strain data was proposed, and its effectiveness was verified using an FEM model. In the validation process, three issues that can affect the estimation results were analyzed. The main findings derived from the validation process are as follows.  
* Comparison of results by MSF and SSF has shown that SSF is more useful for estimating structural deformed shape. In addition, EI-DPR-distance, the sensor placement method proposed in this study, can minimize the interference effect between adjacent sensors and estimate the deformed shape with more stable accuracy than the EI and EI-DPR methods.
* Finally, an estimation algorithm using multi-response data has shown better performance compared with previous work. The addition of slope and strain data can improve estimation accuracy or reduce required displacement data to estimate rational SDS. Therefore, it is expected that cost-effective SHM can be established using the proposed estimation method.
* However, the target model verified in this study is limited to the 2D FEM model. Verification with a 3D FEM model, including transverse response and field tests, is required to be performed as a further study in order to apply the developed algorithm to the SHM of an actual structure.  
**Author Contributions:** Supervision, Y.-J.K.; Writing original draft, N.B.; Writing review & editing, J.L. and K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MIST) (No. 2020R1A2C201445012).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 8 Emerging Mycoremediation Processes  
### Myco-Nanotechnology  
Myco-nanotechnology, which combines \"mycology\" and \"nanotechnology\", offers much potential, in part because of how diverse and widely distributed fungi are [241]. It was discovered that nanoparticles with good monodispersed and consistent dimensions could be synthesized by focusing on the preparation of nanoparticles using fungi. Fungi have been found to produce significant amounts of protein, which may contribute to the mass productivity of nanoparticles. Many biomedical scientists are becoming interested in nanomaterials, one of the most recent and promising applied science fields [241].  
Plant extracts, bacteria, and fungi can be used to synthesize nanomaterials. Applying mycology to the biosynthesis of nanoparticles has several benefits over using bulk plants because it eliminates the need to purify the nanoparticles after synthesis thoroughly [240]. They can also be grown quickly in laboratories and extensively in industrial settings. Fungi also create nanoparticles with precise dimensions and excellent mono-dispersion [242; 243]. Numerous studies have shown the significance of fungi in the environmentally friendly synthesis of gold nanomaterials. Evidence indicates that certain fungi, including _Fusarium_, _Aspergillus_, and _Penicillium_ sp., have been applied to synthesise nanoparticles like palladium, silica, platinum, gold, silver, etc. [244].  
Certain bacteria, yeasts, and fungi are crucial for removing toxic metals by reducing the metal ions. For example, bio-friendly microorganisms could reduce the toxicity of metallic nanoparticles by reducing the metal ions or by creating colloidal particles containing insoluble complexes with metal ions (such as metal sulfides) [244]. It is proposed that the removal of heavy metals from wastewater and soils is achieved through biosorption-based biosynthesis of nanoparticles. Additionally, this process could support the production of heavy metal nanoparticles with potential technological applications [245]. An innovative method to fabricate metal nanoparticles has emerged: using the highly structured physical and biosynthetic activities of microbial cells (NPs) [246; 247].  
Mycoremediation is a cost-effective and ecologically dependable strategy for combating the escalating terrestrial and aquatic pollution crisis. The benefits of fungi are primarily attributable to their robust growth, resistance to complex pollutants, increased surface area to volume ratio, production of multifunctional extracellular enzymes, vast hyphal network, and ability to adapt to changes in temperature, metal-binding proteins, and pH [171; 248; 249; 250; 251].
# Single Camera-Based Remote Physical Therapy: Verification on a Large Video Dataset
## 1 Introduction
### Related Work  
Studies [6; 16] show how OpenPose and similar camera-based systems could be used in telerehabilitation, but they are not dealing with the practical telerehabilitation applicability. Studies that evaluate the accuracy of motion detection typically study only one specific type of exercise motion. [PERSON] [17] concludes that OpenPose is an adequate library for evaluating patient performance in rehabilitation programs that involve the following exercises: left elbow side flexion, shoulder abduction, and shoulder extension based on comparison with Kinect. [PERSON] [8] verifies the reliability and validity of OpenPose-based motion analysis during the bilateral squat based on comparison with Vicon MoCap system. Studies [18; 19] show that OpenPose can be used to capture and analyze both normal gait and abnormal gait. [PERSON] [20] compares the accuracy of 3D OpenPose with multiple cameras to the Vicon marker system. This study considers common human body movements such as walking, jumping, and ball throwing.  
All the aforementioned studies use a system fixed in relation to the moving body and thus represent a one-sided task of interest, i.e., measuring body motion in only one anatomical plane. In general, when it comes to gait or run analysis, the setup is always the same, i.e., the camera is positioned to record only the sagittal plane of the body as accurately as possible. When it comes to measuring range of motion or measuring angles between joints, the setup is such that the person stands in a precisely defined position to the camera and only selected angles in a single anatomical plane are measured. Thus, none of the studies presented here considers the application of a camera based approach in distance rehabilitation and NN based software to recognize anatomical points, where the precise alignment of the camera system perpendicular to the anatomical plane being measured would not be necessary prior to measurement.
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 5 Results
### Vacation Leave Prediction  
#### 5.1.1 Short-Term Predictive Performance (Single-Target Prediction)  
To assess the effectiveness of predicting vacation leave one week in advance based on our data representation, we compared the performance of the baseline method and regression trees with ensemble methods across four evaluation metrics. For this purpose, we used single-target prediction methods. The results are presented in Figure 4 (detailed results are available in Table S1 in the Supplementary Materials). Based on these metrics, the Bagging method delivers the best results for predicting one-week-ahead vacation leave, outperforming the other models. In contrast, the baseline method produced the poorest results in our evaluation, demonstrating the importance of more advanced techniques for accurate forecasting.
## 2 Materials and Methods
### Data Sets  
#### 2.2.1 Satellite NDVI Data Set  
The NDVI has been widely used as an important proxy of photosynthetic activity to reflect the seasonality of vegetation. In this study, we used the 15-day GIMS3g (third-generation Global Inventory Modeling and Mapping Studies) NDVI time series ranging from 1982 to 2015 to extract the dates of the SOS and EOS, and we subsequently identified the growing season length for each pixel. The GIMS NDVI3g version1 was provided by the NASA Ames Ecological Forecasting Lab ([[http://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1](http://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1)]([http://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1](http://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1))). Such NDVI3g data have a spatial resolution of 1/12\({}^{\circ}\) and have undergone rigorous correction, preprocessing, and validation, which has allowed the data to be widely used in previous climate change-related research [24; 34].
## 3 Results
### Time Synchronization  
This system has three timing boards, which are responsible for the time synchronization of laser point cloud, visible light image, and thermal infrared image acquisition, respectively, and are fundamental for ensuring that the system supports the fast mobile crop phenotype platform with a high acquisition frequency. By testing the following three types of synchronization, the root mean square value of the timing board synchronization time error is obtained.  
After a 72 h test experiment, the clock errors of the three timing boards were recorded every 1 s, and a total of 2,073,600 error values were obtained. As shown in Figure 5, the data curves of 1 h were randomly intercepted (the time difference between the PTP master and slave clocks, the time difference between the system clock of each timing board and its own PTP clock, and the time difference between the system clock of each timing board and the PTP clock of timing board 1).  
To further analyze and calculate the synchronization time differences in the different clocks of the three timing boards, a synchronization error bar chart was drawn (Figure 5). It can be seen from Figure 6 that the time errors between the PTP slave clocks of timing boards 2 and 3 and the PTP master clock of timing board 1, the time errors between the system real-time clocks of timing boards 1, 2, and 3 and their own PTP clocks, and the time errors between the system clocks of  
the system real-time clocks of each timing board and the PTP clock of timing board 1 (the master clock) were 129.17, 128.87, and 120.47 ns, respectively, all of which are less than 130 ns. This system exhibits high-speed time synchronization.
## 5 Discussion of the Case Study--Rwanda
### Deployment Architecture of the IoT Sensor Nodes  
Figure 5 depicts the deployment design architecture of the proposed building monitoring IoT system. The system components concerning the Edge/Gateway/Fog/Cloud architecture are also shown.  
The system consisted of multiple nodes deployed in different rooms or locations to monitor selected environmental parameters, including temperature, humidity, CO\({}_{2}\), motion, and occupants. The end nodes were equipped with various sensors (such as humidity, temperature, CO\({}_{2}\), motion, and RIF sensors) and connected to the manager (centralized controller) to ensure connectivity with deactivation in case of poor function.  
The controller was configured as a Linux computer with all management capabilities. An orchestrator was installed there to manage all microservices, including the services to connect, update, and disconnect any newly connected sensor node. The evaluation of running, pending swarm of services, was monitored through the configured services Docker Swarm visualizer. The end nodes sent the data to the cloud database using publish/subscribe services. However, the management of sensor nodes' gateways was managed by the manager through REST services. The data analytics were done in the cloud to aggregate the data and make predictions.
## 3 Results
### Prediction Results of High-Order Soliton Compression
(**a**) Temporal intensity evolution as a function of wavelength for NLSE and the neural network. (**b**) Spectral intensity evolution over time for NLSE and the neural network. The close match between NLSE and DeepONet results demonstrates the high accuracy of the neural network in predicting the temporal and spectral characteristics of pulse propagation. (**c**) Time-domain evolution of high-order soliton compression as a function of propagation distance.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 4 Results and Discussion
### Network Configuration  
Taking into account the final network layout (Figure 11), and to achieve an optimal operation, the lowest possible power consumption and a sufficient sensitivity to accomplish the detection, the values selected for the network configuration parameters are described below. There are some LoRa parameters that remained fixed such as CF, TP, BW, and CR, and others such as SF or the preamble length that are adapted depending on which elements are communicating:  
* CF = 433 MHz, the license-free band (Europe) with the greatest range
* TP = 10 dBm (10 mW effectiv radiated power), the maximum transmission power in order to ensure that messages reach the receiver
* CR = 4/5 because there is not too much interference, thus guaranteeing a shorter time on air (see Section 2.1)
* BW = 125 kHz to achieve sufficient sensitivity for the detection
* For SF, the lowest possible value that allows a correct operation of the network has been chosen. A low SF allows for less time on air and therefore less consumption (see Section 2.1). There are several SF configurations within the network: SF = 7 for the repeater-thermometer link, and SF = 8 for the gateway-repeater link because it is longer than the other
* The preamble length is adapted to two different situations. As previously mentioned, at least 4.25 symbols are needed for the detection. In cases where the receiver is not in deep sleep mode, a short preamble will be enough, as in the case of the gateway-repeater communication in both directions, and also when the thermometers talk to the repeater (see Section 3.2.1 and 3.2.2). In this case, the chosen preamble length is 12 symbols. When the repeater communicates with the thermometers, the latest are in low power mode (see Section 3.2.3). Taking into account that one cycle of the low power mode lasts 1000 milliseconds (980 ms in deep sleep mode and 20 ms listening) and the following equation [74]: \[\text{Tsymbol}=\frac{2^{SF}}{BW}\] (1)  
Figure 12: Temperature maps of the areas A and B provided by MultiTeide campaigns (not published). The location of each thermometer within both areas is highlighted.  
where Tsymbol stands for the symbol duration in seconds or the time taken to send 2 SFchips and BW stands for the bandwidth. For SF = 7 and BW = 125 kHz, Tsymbol = 1.024 milliseconds. Therefore, a preamble length of 1100 symbols has been chosen so that even in the most unfavorable case (preamble transmission starts when the thermometer goes to deep sleep mode), the detection occurs.
## 4 Conclusions  
To better handle MSW and achieve resource recycling, an integrated CO\({}_{2}\)/O\({}_{2}\) co-gasification system of MSW and BC with CO\({}_{2}\) capture was developed and simulated by the Aspen plus. The established model in this work showed better reliability and accuracy. It was concluded that the gasification performance of BC was much different from that of MSW, and the addition of BC was beneficial to the gasification of MSW. Besides, the sensitivity analysis of the main operating parameters was investigated in detail. With increasing gasification temperature, the system exhibited better gasification performance, and 900 \({}^{\circ}\)C was a demarcation point of the reaction equilibrium at constant \(R_{\rm o}\) and \(R_{\rm c}\). The oxygen equivalence ratio, \(R_{\rm o}\), had a sizable impact on the CGE and OEE of system, and indicated a relatively good performance when \(R_{\rm o}\) was equal to 0.2. Moreover, the increase in the \(R_{\rm c}\) ratio led to the decrease in H\({}_{2}\) mole fraction due to the enhancement of reverse R9. With increasing MSW blends ratio, \(R_{\rm M}\), the mole fraction of CO, CGE, and OEE of the system all decreased gradually. The relatively optimal operating conditions outlined by the analyses were set as \(T=900\)\({}^{\circ}\)C, \(R_{\rm o}=0.2\), \(R_{\rm c}=0.5\), and \(R_{\rm M}=0.6\). Using these operating conditions, it was possible to achieve the large processing capacity of MSW at the cost of efficiency; the corresponding OEE of system was 0.57.  
Conceptualization, G.D. and B.H.; methodology, G.D.; software, B.H.; validation, G.D. and B.H.; writing--review and editing, G.D. and B.H. All authors discussed the results. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China, Grant No. 51576014.  
The authors declare no conflicts of interest.  
Figure 6: Effect of \(R_{\rm M}\) on raw syngas composition and system performance (\(T=900\)\({}^{\circ}\)C; \(R_{\rm o}=0.2\); and \(R_{\rm c}=0.5\)).
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 6 Conclusions and Recommendations
It is recommended to pursue research into mechanical properties of sea ice in the Antarctic and to develop a regulatory ship navigation system, similar to the Arctic Ice Regime Shipping System, by combining the vessel's ice-strengthened class with the ice thickness and concentration. This work was supported by Chinese Arctic and Antarctic Administration, National Natural Science Foundation of China (41676176, 41676182 and 41428603), Chinese Polar Environment Comprehensive Investigation and Assessment Program. PH was supported by Australian Antarctic Science grants 4072 and 4301 as well as by the Antarctic Climate and Ecosystems CRC program. [PERSON]_'s nautical data were accessed via the \"_XUELONG_ Online\" website ([[http://Xuelong.chinate.cn/Xuelong/](http://Xuelong.chinate.cn/Xuelong/)]([http://Xuelong.chinate.cn/Xuelong/](http://Xuelong.chinate.cn/Xuelong/))). We are grateful to the European Space Agency for providing ASAR data, the Institute of Environmental Physics at University of Bremen of Germany for providing daily sea ice concentration data, NASA for providing daily MODIS mosaic imagery of Antarctica and the Polar Meteorology Group at Byrd Polar Research Center of The Ohio State University for providing AMPS forecast data. The 1:500,000 bathymmetric data are courtesy of the Australian Antarctic Data Centre. Last but not least, the authors wish to thank the anonymous reviewers who did a thorough critical review of the manuscript and provided valuable suggestions to improve it. [PERSON] conceived and designed the experiments, and wrote the manuscript; [PERSON] and [PERSON] processed and analyzed the data; [PERSON] and [PERSON] collected field data and discussed the results; [PERSON] and [PERSON] investigated the results and revised the manuscript; [PERSON] investigated the results, revised the manuscript and supervised this study. The authors declare no conflict of interest.
# Novel Artificial Neural Network Aided Structural Topology Optimization
## 5 Results and Discussion
### Proportion of FEA Time Consumption  
Next, the proportion of FEA time consumption in the entire process of SIMP and ANN methods is investigated. The main source of consumption in finite element calculation is the sparse matrix equation solver. The computational cost in other steps is represented by the density updating of the optimization solver. The test results are shown in Figure 13.  
It is demonstrated that the finite element computation takes more than 90% of the total operation time of the SIMP method. This result is in good agreement with one obtained by [PERSON] et al. [5]. Also, the proportion represented by running time in finite element computations via neural network methods with different partitions and different design variables is explored, as shown in Figure 14.  
Figure 13: The proportion of time consumption for each module of the SIMP method with (**a**) 60 \(\times\) 60, (**b**) 120 \(\times\) 120, (**c**) 240 \(\times\) 240, and (**d**) 480 \(\times\) 480 meshes.  
It is observed that the computational cost and ratio of finite element method are lower than those of traditional SIMP method. The larger the dimension of partitioning is, the smaller the computational cost and proportion of finite element analysis are. It is indicated that the proposed technique effectively alleviates excessive works of finite element computation during topology optimization. In addition, as the number of structural grids increases, the time proportion of neural network inference in the entire process gradually decreases. It is inferred that under the condition of millions of elements, as are seen in actual application scenarios, the additional computational consumption of neural network prediction can be ignored. That is, using neural network methods to enhance large-scale structural topology optimization is feasible.