esa-sceva/satcom-chunk-collection huggingface dataset:
_Appl. Sci._**2015**, \(5\), 1992-2008; doi:10.3390/app5041992  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Improved Permeate Flux of PVDF Ultrafiltration Membrane Containing PVDF-g-PHEA Synthesized via ATRP**  
**[PERSON] \({}^{\dagger}\), [PERSON] \({}^{\ddagger}\), [PERSON], [PERSON], [PERSON] and [PERSON] \({}^{\star}\)**  
Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro,  
Seodaemun-Gu, 120-749 Seoul, Korea; E-Mails: [EMAIL_ADDRESS] (K.-M.K.);  
[EMAIL_ADDRESS] (S.H.W.); [EMAIL_ADDRESS] (J.S.L.);  
[EMAIL_ADDRESS] (H.S.P.); [EMAIL_ADDRESS] (J.P.)  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-2-2123-2757; Fax: +82-2-312-6401.  
Academic Editor: [PERSON]  
_Received: 10 September 2015 / Accepted: 14 December 2015 / Published: 21 December 2015_  
**Abstract:** Polyvinylidene fluoride (PVDF) ultrafiltration (UF) membrane combined with polyvinylidene fluoride-graft-2-hydroxyethyl acrylate (PVDF-g-PHEA) was fabricated via non-solvent induced phase separation (NIPS). In this study, PVDF-g-PHEA was synthesized via atom transfer radical polymerization (ATRP) method, and then synthesized graft copolymer was characterized using Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR) and thermogravimetry analysis (TGA). Moreover, PVDF membranes containing graft copolymer (PVDF-g-PHEA) showed lower water contact angle value than pristine PVDF membranes. Macrovoid holes were also observed in cross sectional scanning electron microscope (SEM) image of PVDF membrane containing PVDF-g-PHEA. Accordingly, it was confirmed that these characteristics led PVDF membrane blended with graft copolymer has high final permeate flux and normalized flux compared to pristine PVDF membrane.  
**Keywords:** polyvinylidene fluoride (PVDF); 2-hydroxyethyl acrylate (HEA); atomic transfer radical polymerization (ATRP); ultrafiltration (UF) membrane; phase inversion
## 2 Methods
### Data Preprocessing  
The time series is first obtained from different sensors, as shown in Figure 1. To avoid interference from outliers, the series is subjected to Hampel filtering. Subsequently, the filtered data are separately processed to construct the causality matrix and the DTW [14] matrix. The construction process of the causality matrix includes EEMD decomposition, the merging of Intrinsic Mode Functions (IMFs), an Augmented Dickey-Fuller (ADF) stationarity testing, and Granger causality testing [19], resulting in the final causality matrix. Similarly, DTW is applied to compute the similarity matrix for the data. Combining these matrices forms a water demand sensors causal graph, which serves as the output of the data preprocessing process and is fed into the model for computation. The details of this chapter will describe the data preprocessing process.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 1 Introduction
utilized a deep neural network architecture and interpreted the results of geological hazard susceptibility models through the spatial patterns of SHAP values [25]. [PERSON] et al. combined infinite slope stability analysis with classic statistical algorithms such as logistic regression and CNN to create a hybrid model that enhances the practicality, interpretability, and cross-regional generalization capabilities of regional landslide susceptibility models [26]. Hong Kong is a region that frequently experiences landslide disasters. Numerous studies have assessed its landslide susceptibility. These studies rely on historical landslide points as samples and collect multi-source geospatial data as landslide assessment factors [27; 28; 29]. However, most of these studies employ relatively static LSA models, resulting in a loss of spatial and temporal correlations in landslide susceptibility predictions and insufficient understanding of landslide evolution patterns. Therefore, it is crucial to dynamically quantify regional landslide susceptibility across multiple time periods. A robust landslide susceptibility model can provide decision-making support for landslide disaster prevention and control in the region [30], while also enabling people to clearly understand the development patterns and future trends of landslides [31; 32; 33]. To address the aforementioned issues, this study takes Hong Kong, China, as the research area and proposes a dynamic LSA model that takes into account spatial and temporal consistency. The aim is to address the lack of spatial and temporal correlation in landslide susceptibility results and the insufficient understanding of landslide evolution patterns. The main contributions of this study are as follows: 1 The results of LSA in Hong Kong from 2000 to 2019 were calculated with a time resolution of 5 years. This approach enables the model to capture temporal variations in landslide occurrence, allowing for a more accurate representation of the dynamic nature of landslide susceptibility. 2 This study analyzes the 20-year evolution patterns of landslides in the region of Hong Kong. By examining the spatial distribution and temporal trends of landslide occurrences, this study provides insights into the factors that influence landslide susceptibility and how they change over time. This analysis not only enhances the understanding of landslide mechanisms but also contributes to the development of more effective landslide prevention and mitigation strategies. Overall, the proposed dynamic LSA model offers a comprehensive approach to assessing landslide risk in the region of Hong Kong. By incorporating spatial and temporal consistency, this model provides a more accurate representation of landslide susceptibility and enables a deeper understanding of landslide evolution patterns. This information can be used to inform decision-making in landslide disaster prevention and control, ultimately contributing to the safety and resilience of the region [34; 35].
###### Abstract  
Radio interference in the uplink makes communication to satellites in the UHF amateur radio band (430-440 MHz) challenging for any satellite application. Interference measurements and characterisation can improve the robustness and reliability of the communication system design. Most published results focus on average power spectrum measurements and heatmaps. We apply a low complexity estimator on an SDR (Software-Defined Radio) to study the interference's dispersion and temporal variation on-board a small satellite as an alternative. Measuring the Local Mean Envelope (LME) variability with different averaging window lengths enables the estimation of time variability of the interference. The coefficient of variation for the LME indicates how much the signals vary in time and the spread in magnitudes. In this article, theoretical analysis, simulations, and laboratory results were used to validate this measurement method. In-orbit measurements were performed on-board the LUME-1 satellite. Band-limited interference with pulsed temporal behaviour and a high coefficient of variation was detected over North America, Europe, and the Arctic, where space-tracking radars are located. Wide-band pulsed interference with high time variability was also detected over Europe. These measurements show why operators that use a communication system designed for Additive White Gaussian Noise (AWGN) at power levels obtained from heatmaps struggle to command their satellites.  
amateur radio band; interference statistics; in-orbit measurements; radio interference; SDR (Software-Defined Radio); small satellite; stationarity; time variability; UHF Article  
In-Orbit Measurements and Analysis of Radio Interference in the UHF Amateur Radio Band from the LUME-1 Satellite  
29 June 2021  
12 August 2021  
17 August 2021
# Personal Exposure to Radio Frequency Electromagnetic Fields among Australian Adults
## 1 Introduction  
In recent decades, people are being increasingly exposed to man-made radiofrequency-electromagnetic fields (RF-EMFs). These fields range between 0.1 and 6 GHz frequency bands and are classified into two broad categories: near-field (mobile phones, iPads, tablets, laptops, and so on),and far-field sources (mobile phone base stations, Wi-Fi routers, radio/television broadcasting towers, mobile phones in the surroundings, and so on) [1]. Near-field sources operate in close proximity to the body and result in non-uniform RF-EMF exposure, while far-field sources, which operate from far greater distances from the body and typically result in a much lower but more uniform level of RF-EMF exposure [2; 3].  
Personal RF-EMF exposure assessment has been a challenging task in human epidemiological studies. However, personal RF-EMF exposure to a wide range of frequency bands, including mobile phone base stations, Wi-Fi networks, FM radio, etc. can be measured using personal exposimeters [4; 5]. The World Health Organization (WHO) prioritized research into understanding the health effects of RF-EMF emphasizing the need to measure personal exposures [6]. Subsequently, measurement protocols have been suggested and implemented by researchers [7; 8; 9].  
Previous studies in Europe have reported personal RF-EMF exposures using exposimeters [9; 10; 11] although most were limited to predefined micro-environment measurements over a short time or did not address the issue of personal RF-EMF exposures [7; 8; 12; 13]. Such measurements are known to introduce significant variability according to the type and location of the selected micro-environments, the RF-EMF frequency band considered, and the time of day measurements were performed [12; 13]. Although some studies found that exposure to RF-EMF can depend on many complex factors, exposimeters can help capture different sources of personal RF-EMF exposure, and evaluate how this exposure varies over time [4; 9; 14; 15]. It is therefore important to generate more local data on personal RF-EMF exposures from common sources of RF in the environment.  
In this study, we conducted RF-EMF exposure measurements among Australian adults using personal exposure measurement meters (ExpoM-RF) for approximately 24 consecutive hours. The purposes of this study were to: (1) evaluate personal RF-EMF exposure levels without limiting them to specific micro-environments, so as to assess an individual's level of exposure in a routine 24 h period, and (2) describe the RF-EMF exposure from each frequency band over the whole measurement period by occupation category, time of the day, and day of the week.
# Real-Time Detection of At-Risk Movements Using Smartwatch IMU Sensors
## 5 Discussion  
We conducted an experiment to detect at-risk situations using IMU sensors for personal safety. Based on the experimental results, we developed a real-time motion detection application and demonstrated its motion recognition accuracy and real-time response capabilities.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Behavior Classification**} & \multicolumn{2}{c}{**Accuracy**} \\ \cline{2-3}  & **Time Domain** & **Frequency Domain** \\ \hline Walking & 99\% & 100\% \\ Running & 100\% & 93\% \\ Desk Work & 84\% & 88\% \\ At-risk movements & 74\% & 95\% \\ Total & 89.25\% & 94\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Action accuracy comparison across time and frequency domains in the real-time test.  
Figure 8: Testing various actions while wearing the wearable watch. (**a**) Walking, (**b**) running, (**c**) desk work, (**d**) at-risk movements.  
Figure 9 presents an example of a real-time biomonitoring dashboard that combines IMU-based at-risk motion detection with PPG-based health monitoring, which will be developed for future research, although the PPG study is not included in this research. The web-based dashboard visually demonstrates the potential applications of this hazard detection technology. This dashboard can be used to analyze the wearer's movements in real time and alert police administrators to at-risk situations. This is significant as it allows administrators to monitor the status of the personal safety wearer and take necessary actions, thereby expanding the application scope of personal safety technologies. This study analyzes the raw signals from IMUs by converting them into SVM (signal vector magnitude) and considers using a variety of data sources such as GPS and biometric signals for detecting public safety hazards. The issue of motion misclassification discussed could be addressed by integrating these diverse data sources.  
However, the limitations of this study are also clear. In real wearable environments, unexpected large movements caused errors in the model, leading to discrepancies with the simulation results. This issue became more pronounced as the diversity of continuously input data increased. Future research should involve data collection from a variety of environments and situations to overcome these problems. Furthermore, continual optimization of the algorithms is necessary to enhance accuracy, and technology development should prioritize user convenience. Through these efforts, we expect to increase the practical utility of personal safety technologies.
# Review of sEMG for Robot Control: Techniques and Applications
## 2 Methods Summary in sEMG-Based Robot Control  
Various methods have been used to process sEMG signals. In general, the processing steps are roughly the same: The signals are first filtered and amplified after collection. This step is crucial for subsequent research, especially for patient data where residual muscle strength may be weaker, meaning that their sEMG amplitude may be smaller by an order of magnitude or greater than that of healthy individuals. Therefore, reasonable filtering and amplification processes can ensure the signal-to-noise ratio of the signal, making it easier to extract sufficient motion information from sEMG. Feature extraction was then performed. Most studies focus on muscle activation levels; therefore, time-domain features are often selected for extraction. Studies related to muscle fatigue levels tend to focus on the frequency-domain features. In addition, there are studies based on time-frequency feature sets. Finally, sEMG signal recognition was performed, and various machine-learning methods were used to decode the sEMG signals into limb movement information. This step is critical and is currently a research hotspot. Some scholars do not perform recognition; instead, they combine the muscle model proposed by [PERSON] [29; 30] to acquire muscle activation levels through sEMG and then perform subsequent research based on muscle activation. The main contents of this article are summarized in Figure 1.
## 2 Description and Challenges of a Customized Biomedical Implantable Device
### Research Challenges in Implantable Devices
#### 2.2.1 The Powering Module
Nevertheless, there are other approaches to power an implantable device without the use of a battery. One approach is based on the vibration energy harvesting point of view and the use of MEMS [53]. In [PERSON] _et al._[70], a MEMS piezoelectric generator is used to harvest energy from vibrations; it also uses supercapacitors as storage elements. An example of a MEMS designed for implantable devices is given in [PERSON] and [PERSON] [71], where it is stated that the micro-generator is able to generate more energy per unit volume than conventional batteries; that is, an RMS power of 390 \(\upmu\)W for 1 mm\({}^{2}\) of footprint area and a thickness of 500 \(\upmu\)m, which is smaller than the volume of a typical battery in a pacemaker. Another approach is based on the use of fuel cells. The conception of a fuel cell as a bioreactor for implantable devices has emerged, with interesting results like in [PERSON] _et al._[72], where a primary glucose fuel cell is derived for an implantable device. In some ways, the basic concept is the use of fluids in the body as a fuel source for the fuel cell, which would be an inexhaustible energy source. An interesting approach is the use of glucose as a fuel source, or the oxygen dissolved in blood [73,74]. Advanced approaches also explore a shift to the use of white blood cell capacities in biofuel cells [75]; or approaches such as that in [PERSON] and [PERSON] [76], where the fuel cell is based on the use of a microorganism to convert the chemical energy of glucose into electrical energy, in a PDMS structure.
## A Novel Massive Deployment Solution Based on the Peer-to-Peer Protocol
###### Abstract  
The BitTorrent (BT) is a peer-to-peer (P2P) file sharing protocol that was developed approximately 20 years ago, is becoming increasingly popular, and has been widely accepted. The BT-based mass deployment system can be used to improve performance and scalability that cannot be achieved by the unicasting, broadcasting, and multicasting protocols. However, when the BT-based system is applied in massive deployments, a major issue related to insufficient temporary storage space to store the whole system image before deploying needs to be resolved. Such problems arose because the system is deployed to the disk space, meaning that it cannot be used for temporary storage. Therefore, a novel BT-based solution that can remove the limitations caused by the insufficient temporary storage issue is proposed. The BT-based mass deployment system was designed by using the file system blocks transferring (FSBT) mechanism. The receiver of the FSBT mechanism can obtain the blocks of the file system from other peers. Then, those blocks will be written directly to raw disks or partitions. The sender of the FSBT mechanism can read the blocks of file systems directly from raw disks or partitions. Then, the blocks can be sent to other peers. This approach solves the insufficient temporary storage issue. The novel BT-based mass deployment system was tested and verified for the configuration consisting of at most 32 personal computers (PCs). To demonstrate the achievable performance of the novel BT-based system, comparisons were made between the novel program and the traditional multicast solutions, as well as other solutions for mass deployment. The proposed BT solution can be much faster than the multicast solution when deploying 11 machines or more. The experimental results demonstrated the feasibility and superior performance of the proposed system. Furthermore, performance comparisons of the proposed BT-based mass deployment system versus other solutions demonstrated the feasibility and efficiency of the proposed solution. In the future, the BT parameters can be further optimized, and the simultaneous read and write features can be implemented to improve the deployment performance. In addition, the BT-based mass deployment system can serve as the basis for the development of other mass deployment systems.  
massive deployment; system deployment; bare-metal provisioning; open source; file system imaging; peer-to-peer (P2P); BitTorrent (BT)
## 5 Results and Discussions
### Experimental Results
#### 5.1.1 Relation of Loading Behavior and Heat Temperature  
Figure 5 shows the complete loading curve with different maximum temperatures obtained in this study, including the pre- and post-peak loading processes. As expected, the stiffness, strength,  
Figure 4: Complete stress-strain curve: (a) definition of stiffness (E), strength (q\({}_{\text{u}}\)), and pre-peak toughness (T\({}_{\text{pre}}\)); (b) definition of post-peak toughness (T\({}_{\text{post}}\)).  
and toughness decreased with an increase in maximum temperature. The reduced stiffness, strength, and pre-peak toughness ratios are shown in Figure 6. At T\({}_{\text{max}}\) = 200 \({}^{\circ}\)C, the stiffness, strength, and pre-peak toughness were not reduced significantly. However, when T\({}_{\text{max}}\) reached to 400 \({}^{\circ}\)C, the stiffness, strength, and pre-peak toughness were reduced significantly. The reduced stiffness, strength, and pre-peak toughness ratios are 51%, 48%, and 36%, respectively. At T\({}_{\text{max}}\) = 600 \({}^{\circ}\)C, the reduced stiffness, strength, and pre-peak toughness ratios reached 79%, 71%, and 61%, respectively. When T\({}_{\text{max}}\) = 800 \({}^{\circ}\)C, the material lost its strength.  
As the heat temperature increases, cracks are formed at the particle and cementitious material interface and also the C-S-H (calcium-silicate-hydrate) colloid, provided that the cementitious material strength is initially decomposed at a 200 \({}^{\circ}\)C temperature. When the elevated temperature reaches 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C, calcium hydroxide (Ca(OH)\({}_{2}\)) and calcium carbonate (CaCO\({}_{3}\)) decomposition occurs [16]. This is why the stiffness and strength decrease as the heat temperature is increased. As the stiffness and strength decrease, the toughness with respect to the area under the stress-strain curve is also decreased, as shown in Figure 5.  
For the post-peak behavior, two classified post-peak behavior fracture types for rock: snap through (Class I) and snap back (Class II), were defined using uniaxial compressive tests, as shown in Figure 7 ([PERSON] 1968). In Class I (stable fracture type), the post-peak cumulative strain energy cannot sustain crack propagation. The specimen carrying capacity decreases with the increase in strain. In Class II (unstable fracture type), the post-peak strain or degree of specimen displacement decreases; however,  
Figure 5: Completed uniaxial compressive curves for concrete subjected to various temperatures.  
Figure 6: Maximum temperature effect on stiffness, strength, and pre-peak toughness.  
the energy stored in the specimen can sustain crack propagation, even without external pressure. Figure 5 shows that concrete suffers from heat-driven damage, causing the post-peak stability from snap back (Class II) convert to snap through (Class I), and the transition temperature is between 200 to 400 \({}^{\circ}\)C. Therefore, the post-peak toughness increased at Tmax = 200\(-\)500 \({}^{\circ}\)C (Figure 8).