esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Silicon Dioxide Nanoparticles  
Unfortunately, nanomaterials based on silica or nano quartz show very poor biocompatibility and are toxic, which makes their use in tissue engineering impossible. However, mesoporous silica nanomaterials have been prepared by low-temperature synthesis, which has proven to be suitable for bone tissue engineering. Due to the low temperature during the preparation, potential toxic two- and three-membered siloxane rings are substituted by vicinal silanols. Decomposition studies of mesoporous silica-based nanomaterials in SBF at 37 \({}^{\circ}\)C have shown their gradual decomposition. In addition, studies in mice have shown their biocompatibility and biodegradability. Another advantage is that these mesoporous nanomaterials can be injected [211].  
At present, hydrogels with osteogenic potential, which can be injected into a bone defect, are attracting attention. Such a hydrogel fills the defect irregularity very well, and its application is completely non-invasive [212]. An injectable material composed of silica nanopowder and CS showed an improvement in osteogenic properties of mouse pre-osteoblasts OB-6. The presence of SiO\({}_{2}\) nanoparticles (SiO\({}_{2}\)-NPs) did not affect the surface of prepared microparticles. Furthermore, it was found that the presence of SiO\({}_{2}\)-NPs improved the differentiation of OB-6 cells and their proliferation along the surface [213]. Another injectable hydrogel was prepared by [PERSON] et al. It was a composite material of gelatin methacryloyl with bioactive silicate nanoplatelets (SN) supplemented by stromal cell-derived factor alpha 1. SN induces osteogenic differentiation without the need for any other osteoinductive factors. This composite showed enhanced cell proliferation, migration, expression of osteogenic biomarkers, and matrix mineralization. The big advantage was the possibility of injecting it with a 17-G needle at room temperature and crosslinking by UV light in situ [214].  
[PERSON] et al. prepared scaffolds consisting of silica-silk fibroin. The cellular experiments showed that this material is biocompatible and promotes osteoblast attachment and growth. Due to the osteeconductive nature of silica nanoparticles in this scaffold, the sedimentation of bone-type HAp minerals was higher. The high potential of this material for the formation of new bone tissue and the healing of bone defects has been confirmed not only by in vitro but also by in vivo studies [215].  
Nano silica has been found to be a promoter of bone regeneration and hierarchical bone structure through an ossification process and has an important place in the field of bone tissue engineering [216].
## 3 Results and Discussion
### System Performance and Resilience  
The integration of a solar PV-battery storage hybrid system, along with a backup generator created a resilient energy system that adapts to concerns in power demand and solar unavailability. The EMS plays a crucial role in optimizing the energy distribution system. Further, Figure 11 illustrates the typical summer and winter weekdays grid consumption pattern, where net grid consumption, as proposed in this study, is reduced significantly in all seasons. The lithium-ion battery system performed as expected, providing consistent energy storage and discharge during peak demand periods and at nighttime. The advanced thermal management system-maintained healthy battery performance despite the high temperatures of Merredin WA site. Overall, the proposed approach outperformed initial projections, particularly in reducing grid dependence and ensuring operational continuity.
## 1 Introduction
This approach represents a significant departure from traditional EMD methods, which typically embed about 1.10 bits of data per pixel in dual-pixel pairs. Our innovation does not only match the data embedding efficiency of irreversible EMD methods, but also significantly enhances the reversibility of RDH. One of the most compelling features of our technique is its refined error minimization, which significantly reduces distortions in the marked images. This optimization ensures the visual quality of the output images remains pristine, establishing a new benchmark for RDH practices. Moreover, our method stands out by enabling the complete recovery of the original images without the need for embedding additional payload data--a breakthrough that addresses one of the major limitations faced by previous RDH methods. Empirical evaluations have shown that our RDH method surpasses existing state-of-the-art techniques in several critical aspects. Notably, it achieves a high embedding rate (ER) while preserving the visual integrity of the stego images. With these advancements, our study presents a novel RDH solution that enhances security and efficiency for data hiding applications, thereby opening new avenues for future research and development in the field of secure digital communication. The rest of this paper is structured as follows: Section 2 provides the foundational knowledge for our work. In Section 3, we explain the proposed dual RDH method that utilizes EMD and revised LSB matching. Section 4 presents our experimental results and compares them with the established methods. Finally, in Section 5, we draw conclusions and discuss avenues for future research.
In our previous study [11], patients with apnea hypopnea index (AHI) \(\geq\) 30 events/hour showed higher values of maximal inspiratory pressure compared to those <30 events/hour due to intermittent breath-holding during hypoxia-reoxygenation, which probably increases intrathoracic pressure with successive changes in transmural pressure of the cardiac cavities (inspiratory and expiratory pressures provide key insights into respiratory mechanics, e.g., stiff lungs require higher inspiratory pressures, airway obstruction increases expiratory pressures, increased pressures indicate greater work of breathing, and both help to assess gas exchange and alveolar stability), while inspiratory muscle training alleviates accompanying SDB hypertension [11]. Despite efforts to develop interventions and exercise protocols for patients with OSA, there are currently no studies investigating the efficacy of respiratory muscle training and its impact on cardiopulmonary parameters in combination with cognitive function. The effectiveness of exercise programs for patients with OSA is well documented [12], but there are no studies that suggest alternative training methods for patients with OSA who are unable to exercise due to limitations (e.g., musculoskeletal disability). The types of exercise programs in patients with OSA contribute to counteracting the disease-related syndromes due to the low adherence to continuous positive airway pressure (CPAP) treatment in a percentage that in adults reaches the 46 to 83% of patients with OSA [13]. This randomized controlled trial aims to evaluate the impact of a 4-week respiratory muscle training (RMT) intervention on cardiorespiratory parameters and cognitive function in patients with obstructive sleep apnea (OSA). Additionally, it seeks to compare outcomes between patients undergoing RMT and those receiving standard CPAP treatment.
## 4 Results and Discussion
### RQ1: What NAS Approaches Are Applied to GANs in the Literature?  
Our literature review uncovered a variety of strategies for implementing NAS-GAN approaches. We grouped the studies based on their core algorithms and common traits. This resulted in three main categories: Evolutionary Algorithms, Reinforcement Learning, and Gradient-based Algorithms. As shown in Figure 2, Evolutionary Algorithms dominate the literature with 11 works, while both Reinforcement Learning and Gradient-Based Approaches each account for 5 works. We explore each category in depth, detailing the specific approaches and discussing their respective strengths and limitations.
## 6 Discussion
### Backscatter Structure  
Many backscatter systems, such as RFID systems, are constrained as they require a powerful ACS (ambient carrier signal) emitter (few Ws). TunnelScatter, which uses tunnel diodes in the backscatter structure, can improve such scenarios by eliminating the need for power hungry ACS emitters [18]. Meanwhile, TuniScatter [19] can achieve fine-grained amplitude and frequency controls by merely using a tunnel diode on the backscatter tag. We believe that integrating novel backscatter structures, such as tunnel diodes, into the tag design can significantly enhance the large-scale deployment of our backscatter system.
## 3 Simulation of Typhoon Lekima
### Simulated Wind Field and Precipitation  
The evolution of the typhoon wind field from 0000 UTC 09 Aug to 0060 UTC 10 Aug 2019 at 30 m above the ground level in WRF d02 domain is plotted with a time interval of 6 h in Figure 7 (Zhoushan Island is marked with a red dot). It shows that the ARW model successfully captured the process of Lekima gradually approaching Zhejiang and reducing its intensity immediately when reaching the coastal area before making landfall due to the mechanical dragging. During this process, the local wind velocity in Zhoushan Island was greatly influenced by the mesoscale spiral structure of Lekima. Figure 8 marks the location of six ground meteorological stations near the east coast of China and Figure 9 compares the simulated 6 h averaged rainfall intensity at these stations with the observation data obtained from China Meteorological Data Service Centre ([[https://data.cma.cn/](https://data.cma.cn/)]([https://data.cma.cn/](https://data.cma.cn/)), accessed on 12 May 2024). It shows that extreme heavy rainfall occurs from 0000 UTC 09 Aug to 1200 UTC 10 Aug and the ARW model reproduced the rainfall event successfully. The simulated rainfall intensity at coastal stations (Shangyu, Hongjia, and Yuhuan) match  
Figure 5: Simulated track of Lekima (red) and CMA best track dataset (black).  
Figure 6: Maximum sustained wind speed and minimum sea-level pressure of Lekima.  
the observation more closely, compared to the inland areas (Huzhou, Linan, Hangzhou), because the complex surface condition increases the uncertainty of precipitation simulation. The simulated results indicate that the proposed WRF method could regenerate the whole process of typhoon events and reproduce the strong wind fields and local extreme heavy rainfall with a reasonable accuracy.  
Figure 8: Location of six meteorological stations in Zhejiang Province.  
Figure 7: Simulated wind field at 30 m above ground level in WRF d02 domain. From 0000 UTC 09 Aug to 0060 UTC 10 Aug 2019, at 6-h intervals, the wind velocity contours and vector diagrams are arranged in chronological order as (**a**–**f**).
# A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data
## 1 Introduction
Unlike the questionnaire and social media data, air pollution complaint data form the real dissatisfaction (negative emotion) expressed by the public, which can most directly reflect the public's actual air pollution perception. Only a few studies have utilized the complaint data to investigate how people perceive and respond to environmental or air pollution [23; 24; 25]. These studies mainly analyzed the potential pollution problems implied in the complaint data or identified potential pollution sources through the public complaint data on environment and odor [24; 25]. However, the above studies mostly analyzed the content of complaint data from the perspective of statistics and time and space and rarely investigate the subjective feelings of the contents of public complaints. It is imperative to understand the personal emotion of public complaint data, because the change and intensity of public sentiment in complaint data can reflect the severity of air pollution and the effectiveness of governance to a certain extent. Therefore, this paper proposed a new index to evaluate public perception of air pollution, namely air pollution perception index (APPI), by using the air pollution complaint data and emotion analysis from subjective public emotion. Firstly, we obtained public complaints about air pollution through the network and constructed an air pollution's emotional dictionary. Then we calculated the emotional intensity of public complaints by using the constructed emotional dictionary and the emotional analysis method of text mining. Secondly, we used the address-matching method to match the geographical location of complaint data. We analyzed the spatial distribution of complaint density and emotion intensity of air pollution by the Kernel density analysis and Kriging interpolation method, and we constructed the air pollution perception index (APPI) through the piecewise function of the normalized complaint density and emotion intensity. Finally, the proposed APPI was applied to the air pollution complaint data of Shandong Province from 2012 to 2017. This paper's proposed APPI is a supplement to the existing research on public perception of air pollution. It can provide strong support for air quality management and decision-making. The remainder of this paper is organized as follows: Section 2 shows the datasets used in this study and the proposed index APPI. The results of the application study are presented in Section 3. Section 4 discusses and verifies the application results. Section 5 presents the major conclusions, recommendations, and further research.
## 1 Introduction
In the time domain, a noise power spectrum conversion algorithm based on IIR is proposed, which converts the noise power spectrum into the time domain noise sequence. The effect of phase noise on the performance of the digital communication system is simulated and analyzed in the time domain sequence. Several commonly used parameter indexes are given, which can be used as a reference for system parameter design.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 4 Experiment
### Quantitative Evaluation  
We quantitatively compare SGPC to the state-of-the-art low-light image enhancement methods regarding the no-reference metrics on the MEF [37], LIME [10], and DICM [38] datasets, along with the full-reference metrics on the LOL [35] and SICE [30] datasets, respectively. Referring to Table 1, analyzing the scores from the no-reference metrics such as NIQE [41] and NR-CDIQA [42], it becomes evident that our method significantly outperforms state-of-the-art LLIE methods. In terms of full-reference metrics, for the SSIM [40] score, DRBN [36] has the highest score on the LOL [35] dataset, while PairLIE [17] has the highest score on the SICE [30] dataset. As for the PSNR metric, SGPC performs better than other methods on both datasets. This outcome demonstrates that our proposed method can consistently deliver competitive results, even without a reference image.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Type**} & \multicolumn{3}{c}{**NIQE \(\downarrow\)**} & \multicolumn{3}{c}{**NR-CDIQA \(\uparrow\)**} & \multicolumn{3}{c}{**PSNR \(\uparrow\)**} & \multicolumn{3}{c}{**SSIM \(\uparrow\)**} \\ \cline{3-11}  & & **MEF** & **LIME** & **DICM** & **MEF** & **LIME** & **DICM** & **LOL** & **SICE** & **LOL** & **SICE** \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} LECARM [31] \\ SDD [32] \\ GCP [33] \\ \end{tabular} } & T & 3.1990 & 3.8584 & 2.9401 & 3.0557 & 3.0965 & 3.0773 & 16.1561 & 18.2328 & 0.7241 & 0.5784 \\ \cline{1-1}  & T & 3.4432 & 3.7465 & 3.1302 & 3.0367 & 2.9723 & 3.0962 & 15.1448 & 16.5577 & 0.7573 & 0.5515 \\ \cline{1-1}  & T & 3.5551 & 4.1018 & 3.8215 & 3.0796 & 3.1083 & 3.0714 & 18.9534 & 17.6448 & 0.5281 & 0.5791 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparisons in terms of no-reference image quality assessment metrics (NIQE and NR-CDIQA) on the MEF, LIME, and DICM datasets, along with full-reference metrics (PSNR and SSIM) on the LOL and SICE datasets. A lower NIQE score indicates better human subjective visual quality, while a higher NR-CDIQA, PSNR, and SSIM value indicates better perceptual quality. “T”, “S”, and “U” represent “Traditional”, “Supervised”, and “Unsupervised” methods, respectively. The best results are highlighted in **bold**.  
Figure 4: Visual comparisons on a testing example from LIME [10] dataset among various LLIE methods, including traditional methods like LECARM [31], SDD [32], and GCP [33], supervised methods like RetinexDIP [34], RetinexNet [35], DRBN [36], and unsupervised methods like Zero-DCE [20], RUAS [18], SCI [19], and PairLIE [17], as well as our proposed method.
## 4 Analysis and Findings
### iPhone Analysis  
iOS 14.1 by Apple saves a variety of artifacts associated with apps. These artifacts can provide insight into how a user interacts with an app. As discussed in [47], it may be possible to determine how frequently and for how long a user interacts with an app by looking at information such as the user's screen time and activity time within the application. Furthermore, the iPhone has the ability to take a screenshot of the application window so that it can display it when the user uses the app switcher functionality (i.e., app moved to the background). These screenshots are stored as individual photos in the folder <AppUUID>\Library\SplashBoard\Snapshots\sceneID:<AppPackageName>[47]. For example, we found that the Metamask app does not use any empty or blurred image; however, it uses a screenshot taken from the main screen of the app. Figure 7 shows the screenshot taken by the app that shows the main page of wallet 1, and we were able to recover this screenshot from Library\SplashBoard\Snapshots\sceneID:io.metamask.MetaMask-default\OF8 FDBB0-B5 DB-4B36-8381-BB8666 [EMAIL_ADDRESS]. Using the timestamp of the screenshot, we determine the amount of the first wallet at that time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device/OS** & **App Name** & **Wallet Address** \\ \hline \multirow{4}{*}{Android} & Metamask & Wallet1:0x012469 cE84E7c64A94368C7D74F0e0D4a0671617 \\  & & Wallet2:0 xF99 dd4568D459e30E439D966C6 deb9686F88A5A \\  & Trust Wallet & Wallet1:0x98b256497 Ab16884 cD8a6363A70 cbC5E67 AD917E \\  & & Wallet2:0 xC261645380F30224e820254736D0 acE38311 BF08 \\ \hline \multirow{4}{*}{iOS} & Metamask & Wallet1:0x5b54 Fe7E3289E9635 Ab3361 fe3 BfA21 cB926 \\  & & Wallet2:0x3 dd70 db097 cddc26 fad833134548134e7F5a87d \\ \cline{1-1}  & Trust Wallet & Wallet1:0x9E2C9A41C9c0661 AeDEA9b494b20604a7 dF62 AD7 \\ \cline{1-1}  & & Wallet2:0 xE689B84497c4560596a5f888a053093 DcDAEBd1D \\ \hline \hline \end{tabular}
\end{table}
Table 6: Wallet addresses created in the study.  
Figure 7: Screenshot snip-it that was taken by the iOS.  
Furthermore, another identifier, used as deviceUID, was recovered for both apps within \private\var\mobile\Library\AggregateDictionary inside a file named session_bundles.log. Figure 8 shows the name of the app, version number and DeviceUID CC8 DC2B3-C872-4594-8665-AEA27 ACBE346 for Trust Wallet and 70 AA222F-76E3 -4E0D-A91C-E5A20E13F340 Metamask.
## 3 Dimension Synthesis and Kinematic Analysis
### Stephenson\(-\)II Mechanism  
Figure 8 illustrates the kinematic skeleton and vector coordinate system of the Stephenson\(-\)II mechanism.  
As shown in Figure 7(b), the Stephenson\(-\)II mechanism contains two independent vector loops (Loop I and Loop II), such as Equations (20) and (21).  
\[\overset{\rightarrow}{r}_{2}+\overset{\rightarrow}{r}_{3a}-\overset{ \rightarrow}{r}_{4}=0 \tag{20}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Max. Velocity (\(1\,\mathbf{V}_{max}\,\mathbf{l}\))** & **Max. Acc. (\(1\,\mathbf{A}_{max}\,\mathbf{l}\))** & **Force Ratio (\(F_{int}\)/\(F_{out}\))\({}_{max}\)** \\ \hline Stephenson\(-\)I & 819.28 mm/s & 4478.05 mm/s\({}^{2}\) & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The peak values of the Stephenson\(-\)I mechanism.  
Figure 8: Mold clamping mechanism of the Stephenson\(-\)II mechanism.  
Figure 7: The kinematic characteristics and force ratio of the Stephenson\(-\)I mechanism.  
\[\stackrel{{\rightarrow}}{{r}}_{4}+\stackrel{{\rightarrow}}{{r}}_{3c} +\stackrel{{\rightarrow}}{{r}}_{5}-\stackrel{{\rightarrow}}{{r}}_{6}=0 \tag{21}\]  
Here, Loop I is represented by Equation (20), and Loop II is represented by Equation (21), where the vectors \(\stackrel{{\rightarrow}}{{r}}_{2}\), \(\stackrel{{\rightarrow}}{{r}}_{3d}\), \(\stackrel{{\rightarrow}}{{r}}_{3c}\), \(\stackrel{{\rightarrow}}{{r}}_{4}\), \(\stackrel{{\rightarrow}}{{r}}_{5}\), and \(\stackrel{{\rightarrow}}{{r}}_{6}\) represent the links of the mechanism.  
The horizontal component X and vertical component Y equations are  
\[r_{2x}+r_{3d}\cos\theta_{3d}-r_{4}\cos\theta_{4}=0 \tag{22}\]  
\[r_{2y}+r_{3a}\sin\theta_{3d}-r_{4}\sin\theta_{4}=0 \tag{23}\]  
\[r_{4}\cos\theta_{4}+r_{3c}\cos\theta_{3c}+r_{5}\cos\theta_{5}-r_{6}=0 \tag{24}\]  
\[r_{4}\sin\theta_{4}+r_{3c}\sin\theta_{3c}+r_{5}\sin\theta_{5}=0 \tag{25}\]  
Equations (22) and (23) represent the horizontal and vertical components of Loop I, respectively. Equations (24) and (25) represent the horizontal and vertical components of Loop II, respectively. Additionally, \(\theta_{3d}\), \(\theta_{3c}\), \(\theta_{4}\), and \(\theta_{5}\) correspond to the angles between each link and the positive X-axis.  
According to the design parameters shown in Table 7, the input (\(S_{in}\)) is driven in 1 s using an MS curve. The output (\(S_{out}\)) displacement can be calculated based on Equations (22)-(25). Then, the velocity and acceleration can be calculated using the finite difference method, and Equation (7) can calculate the driving force. The analysis results are shown in Figure 9. The peak values for the Stephenson-II mechanism are summarized in Table 8.  
Figure 9: Kinematic characteristics and force ratio of the Stephenson-II mechanism.
# DSCope: Development of Automatic Program for Detecting Fractures and Measuring Dip Angles
## 4 Discussion  
The DSCope program measured the dip angles of the fractures by analyzing photographs of drilling cores. The fracture angles could be measured more precisely and accurately if the existing televiewers were used. However, televiewer exploration is disadvantageous because accurate data cannot be obtained unless the survey is performed simultaneously with the drilling or immediately after drilling. The advantage of DSCope is that it is not limited by time, because the core photographs are taken and analyzed after drilling. Moreover, the fractures can be analyzed by additionally shooting photographs if the drilling core is stored.  
However, the results obtained can vary depending on the camera settings and environmental conditions under which the photographs were taken. Even if all the photographs are captured under the same environmental conditions, the results may still vary due to different camera settings such as the ISO, aperture value (f value), shutter speed and white balance. To avoid this, the photographs were analyzed after converting them into grayscale. This minimized the discrepancies related to color.  
The DSCope program developed in this study was designed to estimate the dip angles of the fractures (discontinuities) through linear fitting. Therefore, an incorrect result was produced when it was applied to nonlinear fractures. When there were two or more fractures in one scanbox or multiple fractures were generated in a fracture zone, they could be detected, but their dip angles could not be measured. To address this problem, we developed a GUI to select only those results that had a good linear fit, because the existence or absence of a fracture zone could be easily distinguished with the naked eye. The selected results could be organized separately.  
Lastly, the resolution (size) of the photographs also affected the analysis of DSCope. A resolution of 4000 \(\times\) 3000 pixels was considered appropriate for effective analysis using DSCope.