esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image-Guided Endoscopic Sinus Surgery System with 3D Volumetric Visualization  
We performed a prospective cohort study by enrolling eleven chronic sinusitis patients who needed the ESS with an IGS system from 14 May 2019 to 23 July 2019. The CT images of the patients were acquired using a CT scanner (SOMATOM definition flash, Siemens, Berlin, Germany) under 120 kVp and 80 mAs with a slice thickness of 1 mm. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of Gachon University Gil Medical Center (GCIRB2018-428)  
The overall intraoperative procedures were as follows. (1) Two IGESS systems were installed side by side for comparative evaluation (Figure 4). A field generator of the EM tracking system was installed on the back of the patient's head. The reference tracking tool was tightly fixed onto the patient's forehead to prevent head movement during surgery. (2) The patient's CT image was loaded into the two systems and virtually visualized on the screen using a volume-rendering technique. We then selected six landmarks for registration on the patient's virtual face surface. (3) The physical positions of the corresponding landmarks on the patient's face were measured with a tracked pointing tool with respect to the reference tracking tool, and these were sequentially registered by point-to-point matching with corresponding positions selected on the patient's 3D volume rendered face. (4) We calculated the image loading time, registration time, and registration error (FRE) for comparison. (5) After performing the surgery, the surgeon indicated five anatomical landmarks (middle turbinate, maxillary sinus, posterior ethmoid, sphenoid sinus, and frontal sinus) using the pointing tools of the two systems (Figure 5). (6) Finally, the degree of accuracy was evaluated by two surgeons using a visual analogue scale ranging from 1 to 10 points.  
Figure 4: Performing image-guided endoscopic sinus surgery (IGESS) using a developed navigation system.  
We used SPSS ver. 20 (SPSS Inc., Chicago, IL, USA) for all statistical analyses. We used a Mann-Whitney test to compare the values of the two systems. All data were expressed as mean \(\pm\) standard deviation, and the \(p\)-value < 0.05 was considered as statistically significant.
# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
###### Abstract  
In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomplete 3D models with blurred geometric structures and textures. To address these challenges, an implicit-explicit coupling enhancement for a UAV large-scale scene modeling framework is proposed. Benefiting from the mutual promotion of implicit and explicit models, we initially address the issue of missing co-visibility clusters caused by environmental noise through large-scale implicit modeling with UAVs. This enhances the inter-frame photometric and geometric consistency. Subsequently, we enhance the multi-view point cloud reconstruction density via synthetic co-visibility clusters, effectively recovering missing spatial information and constructing a more complete dense point cloud. Finally, during the mesh modeling phase, high-quality 3D modeling of large-scale UAV scenes is achieved by inversely radiating and mapping additional texture details into 3D voxels. The experimental results demonstrate that our method achieves state-of-the-art modeling accuracy across various scenarios, outperforming existing commercial UAV aerial photography software (COLMAP 3.9, Context Capture 2023, PhotoScan 2023, Pix4D 4.5.6) and related algorithms.  
UAV large-scale scene; implicit-explicit coupling; dense point cloud reconstruction; 3D mesh modeling +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
2024
## 4 Conclusions  
This article investigates the correlation between the microstructure and electrical properties of Si\({}_{3}\)N\({}_{4}\) ceramics, exploring the potential of using Si\({}_{3}\)N\({}_{4}\) ceramics to enhance the surface insulation performance of SF6 high-voltage direct current electrical equipment. The following conclusions are mainly drawn: (1) Y\({}_{2}\)O\({}_{3}\)-Al\({}_{2}\)O\({}_{3}\) was used as a sintering aid to prepare high-density (>99%) Si\({}_{3}\)N\({}_{4}\) ceramics through two-step pressureless liquid-phase sintering. With the increase in sintering aid content, the grain size of Si\({}_{3}\)N\({}_{4}\) ceramics increased; when the aid addition amount was 10%, the grain size of Si\({}_{3}\)N\({}_{4}\) ceramics gradually decreased with the increase in Y\({}_{2}\)O\({}_{3}\) proportion. (2) The room temperature resistivity of sample 1825-5Y-92 SN reached a maximum of 7.33 \(\times\) 10\({}^{14}\)\(\Omega\cdot\)m; the increase in sintering aid content decreased the volume resistivity of Si\({}_{3}\)N\({}_{4}\) ceramics but improved the temperature stability of volume resistivity; when the aid content was 12%, the volume resistivity was the most stable, with a remaining rate of 48.4% at 80 \({}^{\circ}\)C. (3) Under a DC electric field, the carrier migration type of Si\({}_{3}\)N\({}_{4}\) ceramics was mainly dominated by ionic conductivity, mainly along the grain boundaries; by doping with Y\({}^{3+}\) functional ions to reduce the potential barrier conductivity level and refine the grain size, the temperature stability of the resistivity of Si\({}_{3}\)N\({}_{4}\) ceramics could be improved. (4) With the increase in sintering aid content, the dielectric constant and dielectric loss of Si\({}_{3}\)N\({}_{4}\) ceramics gradually increased, mainly influenced by interface polarization, at a frequency of 50 Hz; when the sintering aid content was 12%, the maximum dielectric constant was 8.50.  
Conceptualization, D.H.; methodology, D.H. and Y.Z.; validation, D.H., investigation Y.H., J.N., Y.Z. and L.C.; resources, D.H. and J.N.; writing--original draft preparation, D.H., Y.H. and J.N.; writing--review and editing, D.H., Y.Z. and L.C.; supervision, Y.H. and L.C. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (NSFC; grant No. U23B20134).  
Data Availability Statement: Data are contained within the article.  
This research was supported by State Grid Smart Grid Research Institute Co., Ltd.  
Conflicts of Interest: Author [PERSON], [PERSON], [PERSON] and [PERSON] was employed by the company State Grid Smart Grid Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 8: Relationship between dielectric loss and temperature of Si\({}_{3}\)N\({}_{4}\) ceramics at 50 Hz.
## 3 Results and Discussion
### UV-Vis Spectroscopy  
Figure 4 shows the UV-Vis spectra of irradiated and non-irradiated samples. The spectra had a maximum at approx. 260 nm and a second maximum at 220-230 nm for all samples. The absorbance increased along the entire spectrum as a function of irradiation, especially when irradiating for 300 s. The ratio 260/230 was below 1 for all samples and increased with irradiation compared to the control. The ratio 260/280 slightly decreased at 60 s irradiation compared to the control and increased at 300 s irradiation compared to both the control and 60 s irradiation. The spectra most probably represent the overlapping of proteins, polyphenols, polysaccharides, DNA, and metabolites from rice husk and/or _Trichoderma_, in various proportions, which seem to be partially affected by irradiation. The irradiation for 300 s significantly increased the absorption in the UV region, implying an intense liberation and production of compounds absorbing UV. The average refractive indices of the samples were approximately 1.3336 and 1.3337 for non-irradiated and irradiated samples, respectively. The dry mass contents of undiluted samples, as determined via refracometry were 0.47 \(\pm\) 0.05% (control, 0 s irradiation), 0.57 \(\pm\) 0.08% (3 \(\times\) 60 s total irradiation), and 0.57 \(\pm\) 0.06% (3 \(\times\) 300 s total irradiation). The total dry mass slightly increased as a result of irradiation compared to the control, but the higher irradiation dose seemed to increase the proportions of only certain compounds that mainly absorb UV compared to other compounds, and not the total dry mass. Further, more in-depth investigations are necessary to confirm this observation and identify the compounds responsible for it.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Source of Information
#### 4.4.3 Hybrid Personalized Summarization Techniques  
Hybrid personalized summarization techniques analyze and use information that is both external and internal to the content of the video stream. From the life cycle of the video, at each stage, its information is analyzed by external summarization techniques. Any combination of outer and inner summarization techniques can form hybrid summarization techniques. Each approach tries to capitalize on its strengths while minimizing its weaknesses, to make video summaries as effective as possible [11]. Combining text metadata with the capabilities of image-level video frames can help improve summary performance [47]. Furthermore, for non-domain-specific techniques, hybrid approaches have proven useful [11].
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
### Scope and Contributions  
This review is intended for readers who are inclined to read about UAV technology. This work is a comprehensive review on UAV's critical existing challenges, security concerns, applications and future research directions. This review aims to assist the readers get an overview of the state-of-the-art UAV technology in several aspects. The readers will have a good overview of UAV types, charging, autonomous swarms, and different application scenarios. It provides a complete review of anticipated attacks on UAVs. It also investigates realistic recommendations to improve UAV architecture to ensure secure communication. Extensive analyses are carried out for various viable solutions to empower the utility of UAVs in different applications. It provides an assessment of existing challenges, security issues, and future research directions to develop secure UAV communication.
###### Abstract  
Leak detection and location in a gas distribution network are significant issues. The acoustic emission (AE) technique can be used to locate a pipeline leak. The time delay between two sensor signals can be determined by the cross-correlation function (CCF), which is a measure of the similarity of two signals as a function of the time delay between them. Due to the energy attenuation, dispersion effect and reverberation of the leakage-induced signals in the pipelines, the CCF location method performs poorly. To improve the leakage location accuracy, this paper proposes a modified leakage location method based on the AE signal, and combines the modified generalized cross-correlation location method and the attenuation-based location method using multilayer perceptron neural networks (MLPNN). In addition, the wave speed was estimated more accurately by the peak frequency of the leakage-induced AE signal in combination with the group speed dispersive curve of the fundamental flexural mode. To verify the reliability of the proposed location method, many tests were performed over a range of leak-sensor distances. The location results show that compared to using the CCF location method, the MLPNN locator reduces the average of the relative location errors by 14%, therefore, this proposed method is better than the CCF method for locating a gas pipe leak.  
acoustic emission; nondestructive testing; leakage location; fault diagnosis +
Footnote †: journal: Applied  
15 March 2019; Accepted: 8 May 2019; Published: 13 May 2019
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 2 Materials and Methods
### Study Area and Data  
The study area in this study was a metal mine located in Gijang-gun, Busan, South Korea (35\({}^{\circ}\)18\({}^{\prime}\)31.36\({}^{\prime\prime}\) N, 129\({}^{\circ}\)13\({}^{\prime}\)25.56\({}^{\prime\prime}\) E). The mine was a major Cu producer from the 1930s to the 1940s that extracted various other mineral resources and closed around 1990. However, the site was not properly remediated or treated after the mine was abandoned,  
Figure 1: Flowchart of the geostatistics- and machine learning-based interpolation modeling processes.  
resulting in heavy metal-laden mine drainage and large quantities of waste rock. High concentrations of Cu were observed in the areas surrounding the abandoned mine [27].  
Figure 2 shows the distribution of the soil sampling locations near the metal mine used in this study. Data for these locations were collected using a portable X-ray fluorescence spectrometer. A total of 40 samples were collected.
## 3 Materials and Methods
### Volatility Calculation  
Volatility is the rate at which the price of a market index increases or decreases for a given set of returns [24]. It is a measurement of the risk of security. If the daily price of a particular security fluctuates very rapidly over a long period, that causes high volatility; on the other hand, if the daily price of a particular security fluctuates very slowly over a long time, then that causes low volatility. Volatility is measured by calculating the standard deviation of the daily returns over a given period of time. The period which we have picked to calculate the volatility can be varied with different purposes or events such as dividends, splits and financial reports. Some companies may report their events after a specific period of time [25]. Therefore, one can choose any specific or random periods based on days, weeks or months. Since the companies we have chosen do not share a common window, we have therefore taken 21 days of the rolling window for each dataset to maintain consistency. The events can be taken into account while calculating the volatility. In that case, the value of the rolling window needs to follow the event period [26; 27]. However, volatility has two types: historical volatility and implied volatility. Historical volatility measures the fluctuations in the security's prices in the past. However, historical volatility is mostly used for predicting future trends based on the previous trends. On the other hand, implied volatility measures the expected magnitude of a stock's future price changes. Unlike the historical volatility, it provides a progressive direction on possible future price fluctuations. We will use historical volatility for predicting future fluctuations based on the previous trends in our work. The frontier market lacks the \"volatility\"parameter, so the \"close price\" parameter has been used to calculate the \"volatility\" parameter. First, we derived the daily returns from the \"close price\" parameter. The daily return has been calculated from the percentage of dollar change in the previous day's closing price. Lastly, the \"volatility\" parameter has been estimated from the standard deviation of daily returns over a given period of time. The formula for the volatility calculation is shown below [2].  
\[\sigma=\sqrt{\frac{\sum_{i=1}^{n}(x_{i}-\mu)^{2}}{n-1}} \tag{1}\]  
where \(\sigma\) refers to standard deviation/volatility, \(x\) refers to daily returns, \(\mu\) refers to the mean of stock observations and n refers to the number of observations in the dataset.
## 5 Experimental Results
### Preprocessing: Time Series Representation
#### 5.2.2 Preprocessing of Twitter Data (Tweets)  
Concerning time series representation of tweets for each company, the relative tweets were collected by using the cashtag ($), a symbol that is commonly used when searching for tweets that are related to stocks. For example, in order to collect relative tweets about Microsoft, we searched tweets with the symbol $MSFT. Furthermore, we removed the tweets from the retweets set. After the collection of the relative tweets and removing duplicates, we calculated the total number of tweets per day. Table S4 of the Supplementary Material file tabulates the total number of tweets per day from 20 April to 30 October for the year 2015. Therefore, the time series of tweets, named as numTweets, represents the number of tweets per day for each stock.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 4 The Use of EPR in Passive Tumor Targeting with Nanomedicines
### Cellular Barriers for Nucleic-Acid Therapies  
Nucleic-acid therapies, including small interfering RNA (siRNA), messenger RNA (mRNA), and plasmid DNA (pDNA), face multiple cellular barriers that impact their delivery, uptake, and therapeutic efficacy. The negatively charged and hydrophilic nature of nucleic acids prevents passive diffusion across the cell membrane, making them effectively rely on carrier systems, such as lipid NPs, polymeric NPs, or viral vectors, which facilitate cellular entry via endocytosis [164]. Once internalized, nucleic acids are often trapped in endosomes and degraded before reaching their intracellular targets. Strategies such as pH-responsive polymers, ionizable lipids, and fusogenic peptides are employed to enhance endosomal escape, improving cytosolic delivery [165].  
Additionally, nucleic acids are susceptible to enzymatic degradation by extracellular and intracellular nucleases, which can hinder their therapeutic potential. To address this, chemical modifications like 2'-O-methylation and phosphorothioate backbones are used to enhance stability and prolong circulation half-life, ensuring better therapeutic outcomes [166].
# Spectrum Allocation Based on an Improved Gravitational Search Algorithm
## 3 Technologies  
Spectrum allocation is essentially a binary multiobjective optimization problem with constraints, which is NP-hard. Heuristic algorithms, in accordance with intuitive or empirical, are extensively used in combinatorial optimization problems and express outstanding performance. GSA is a novel heuristic algorithm, which has been successfully applied to various real optimization problems, e.g., neural network training, mechanical engineering, image processing, and telecommunication. Then, we try to employ GSA to solve the spectrum assignment issue in CRNs. However, similar to other swarm intelligence algorithms, GSA has such problems as being prone to falling into local optimum and having low precision of solving. Consequently, PSA, a derivative-free evolutionary algorithm with strong fine search capability, is adopted to this paper to enhance the accuracy of GSA.
## 4 Numerical Work
### Material Calibration
#### 4.2.2 Foam Cores
A range of values for the rate sensitivity parameter, \(c_{\mathrm{DF}}\) in Equation (19), between 0 and 0.1 in 0.01 intervals were applied to the low-velocity impact model, and a value of \(c_{\mathrm{DF}}=0.02\) for all three foam densities was found to give acceptable agreement with the experimental data. Numerical and experimental results \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\sigma_{p}\) & \(\gamma\) & \(\alpha_{2}\) & \(1/\beta\) \\  & [MPa] & [–] & [–] & [–] \\ \hline \(C_{0}\) [–] & 0 & 0 & 10.01 & 0.17 \\ \hline \(C_{1}\) [–] & 700 & \(1.017\times 10^{-4}\) & 1.618 & 54.81 \\ \hline \(n\) [–] & 2.27 & \(-2.615\) & \(-0.3874\) & 2.114 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Density-dependent hardening constants for the XPS foams. Figure 14: Comparison between experimental and numerical results for (**a**) quasi-static and (**b**) low-velocity impact loading of foam cubes. The maximum displacement obtained numerically is denoted by a cross for legibility. for the low-velocity impact tests are compared in Figure 14b. Note that the force-displacement curves were truncated when the impactor reached zero velocity and that elastic unloading was omitted. It is confirmed that the viscoplastic model can capture the rate sensitivity observed in the foams reasonably well. For XPS-250 and XPS-400, the viscous increase in plateau stress is in good agreement with the experiments, while the increase in plateau stress is slightly underestimated for XPS-700. For both XPS-400 and XPS-700, the maximum displacement is in good agreement with the experimental results, while the maximum displacement is lower in the numerical simulation than in the experimental test for XPS-250.
# Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval
## 2 Materials  
### GF-5-02 Data  
The GF-5-02 satellite is China's first comprehensive hyperspectral observation operational satellite designed for ecological and environmental purposes. It was successfully launched on 7 September 2021. The GF-5-02 satellite is a polar orbit satellite operating on a sun-synchronous orbit with an inclination angle of 98.203 degrees and an altitude of 705 km. The local time of the descending node of the GF-5-02 satellite is 10:30 a.m. The DPC and POSP are the two optical payloads of the GF-5-02 satellite. The DPC has eight bands within the spectral range of 0.4-1 \(\upmu\)m. The eight bands all have multi-angle observation capabilities. Among them, the three bands at 490, 670, and 865 nm have the capability to observe both intensity and polarization data, while the remaining five bands (443, 565, 763, 765, and 910 nm) can only observe intensity data. The POSP has nine bands in the spectral range of 0.3-2.3 \(\upmu\)m. The nine bands of the POSP all have the capability to observe both intensity and polarization data, while they only have single-angle observation capabilities. Since the POSP has an onboard calibration system and the DPC lacks one, calibrating the DPC is one of the important tasks of the POSP [13], in addition to aerosol detection [2]. The onboard radiometric calibration system of the POSP adopts the solar calibration method based on the solar diffuser to achieve the radiometric calibration of the observations of the POSP. The radiance standard is determined by the solar spectral irradiance and the diffuser bidirectional reflectance distribution function in near real time. The solar diffuser stability monitor is utilized to monitor and correct changes in the solar diffuser. These devices ensure that the radiometric calibration of the visible and near-infrared bands of the POSP have an uncertainty of 3% and a systematic error of 0 [13]. The spectral response functions (SRFs) of the bands that the DPC and POSP use are shown in Figure 1. The DPC and POSP data for a total of ten days from 21 to 30 November 2022 are used in this study.