esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Gravitating Bose-Einstein Condensates and the Thomas-Fermi Approximation
## 1 Introduction  
Self-gravitating Bose-Einstein condensates (BECs) have been proposed in various astrophysical contexts, including Bose-stars [1; 2; 3; 4] and BEC dark matter halos [5; 6; 7; 8; 9; 10; 11].  
Nonrelativistic, dilute BECs are well-described by the Gross-Pitaevskii equation (GPE). In its time-independent form, when kinetic energy can be neglected, the GPE has an approximate solution in the form of the Thomas-Fermi (TF) approximation.  
Self-gravitating systems can also be described using the GPE. The long-range gravitational interaction is represented by the external potential. Such systems, referred to in the literature as GPE-Poisson or GPN (Gross-Pitaevskii-Newton) systems, are extensively studied (a few recent examples of interest include [12; 13; 14; 15]).  
When applied to self-gravitating systems, the TF-approximation was found to describe an unstable system [16]. This also agrees with this author's experience with numerical simulation code, which failed to yield stable self-gravitating systems when the TF-approximation was used as an initial estimate of the condensate density.  
In the present paper, it is shown that this instability is the consequence of the TF-approximation, specifically the well-known issue of the divergence of its kinetic energy [7; 8; 17; 18]. To overcome this issue, we examine the time-independent GPE and attempt to solve it numerically without truncations. We develop a new approximation that has the desirable property that the system has negative total energy and it is stable. This approximation has since been incorporated into our simulation code for self-gravitating GPE-Poisson systems.
## 3 Materials and Methods
### Feature Selection for Malicious Traffic Classification
```
Input: DataFrame df, threshold value Output: Significant_features
1 Separate Data df_normal \(\leftarrow\) entries from df where 'is_attack'=0
2 df_attack \(\leftarrow\) entries from df where 'is_attack'==1
3 Define calculate_statistics(DataFrame)
4 Calculate \(\mu\), \(\sigma\), \(\gamma_{1}\), and \(\gamma_{2}\) for each feature
5 Return DataFrame of calculated statistics
6 Compute statistics (central moments)
7 stats_normal \(\leftarrow\) compute_stats(df_normal)
8 stats_attack \(\leftarrow\) compute_stats(df_attack)
9 Compute absolute differences
10 Initialize diff_stats as empty DataFrame
11 for each feature in DataFramedo
12 Calculate absolute differences for \(\mu\), \(\sigma\), \(\gamma_{1}\), and \(\gamma_{2}\)
13 sum_diff[feature] \(\leftarrow\) sum of all differences for the feature ;
14
15 Identify significant features
16 significant_features \(\leftarrow\) [] ;
17 for each feature in sum_diffdo
18 ifsum_diff[feature] > thresholdthen
19 append feature to significant_features ;
20
21 Return significant_features
```  
**Algorithm 1**Feature Selection
## 3 Results
### Data Pre-Processing  
The original EEG data are first common average re-referenced and then band-pass filtered with a passband from 0.5 Hz to 50 Hz using a fourth-order Chebyshev II filter. The filtered data are downsampled to 200 Hz. A toolbox is used to remove the EOG artifacts automatically. The 10 s EEG data recorded during the tasks are segmented into 2 s pieces for the next steps, which is shown in Figure 2. The data from baseline tasks is labeled as low engagement, while that from MA tasks is labeled as high engagement. The whole experiment is carried out under the environment of MATLAB 2020b. Results are obtained using a 10-fold cross-validation.
## 3 Results and Discussion
### Optimization of SPME  
#### 3.4.1 Effect of Extraction Temperature  
Extraction temperature made great impact on the mass transfer rates and partition coefficients during SPME process. Hence, extraction temperature has significant influence on the extraction efficiency of organics [14].  
Higher extraction temperature would promote the diffusion of volatile organic compounds and shorten the balance time between gas and liquid, especially during the SPME process [33]. However, higher extraction temperature could accelerate the desorption rate of low boiling point organics, which is unfavorable for detection. The effect of selected extraction temperatures (20, 30, 40, 50, 60 and 70 \({}^{\circ}\)C) on the extraction efficiency of these analytes is shown Figure 4a,b.  
As can be seen from Figure 4a,b, the extraction efficiency of TBA was greatly increased when the extraction temperature increased from 20 to 70 \({}^{\circ}\)C. For BTEX, the extraction efficiency exhibited  
Figure 4: Effect of extraction temperature on signal intensities of the twelve analytes (20 ug/L). The peak area at the extraction temperature of 20 \({}^{\circ}\)C is used as a reference (100%). **(a)** the peak area ratio of TBA, MTBE, DIPE, ETBE, TBF, and TAME; **(b)** the peak area ratio of benzene, toluene, ethylbenzene, m+p–xylene, and o–xylene.  
Figure 3: Effect of desorb time on signal intensities of the twelve analytes (20 ug/L). The peak area at the desorb time of 1 min is used as a reference (100%). **(a)** the peak area ratio of TBA, MTBE, DIPE, ETBE, TBF, and TAME; **(b)** the peak area ratio of benzene, toluene, ethylbenzene, m+p–xylene, and o–xylene.  
increasing trend when the temperature increased from 20 to 40 \({}^{\circ}\)C. It appears that higher extraction temperature was more favorable for TBA extraction. This phenomenon may cause by its relatively high melting point (25.7 \({}^{\circ}\)C) and boiling point (82.42 \({}^{\circ}\)C) compared to other selected analytes. However, for other analytes, the extraction efficiency increased in the temperature range of 20-40 \({}^{\circ}\)C. Therefore, 40 \({}^{\circ}\)C was selected for the extraction temperature during SPME process.
## 2 Related Work
### Threat Detection
Notably, they adapt the Upper Confidence Bound (UCB) algorithm by substituting the standard exploration term, typically the mean reward value, with a newly defined measure of malevolence. This metric is derived from historical temporal patterns modeled by a Long Short-Term Memory (LSTM) network. An elevated malevolence score increases the likelihood of selecting and, in turn, closely monitoring a particular process path.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Physical Strategies  
Physical strategies, such as radiation therapy (RT), hyperthermia (HT), and photodynamic therapy (PDT), have emerged as promising approaches to enhance the enhanced permeability and retention (EPR) effect in cancer treatment. These methods leverage external stimuli to modify the tumor microenvironment (TME), improving the delivery and efficacy of nanodrugs. Below, we discuss these strategies in detail, supported by recent research findings.
## 3 Methodology
### Analysis of the Variations
#### 3.2.2 Mercury Injection Porosimetry (MIP)  
Mercury injection porosimetry (MIP) was determined with a Micromeritics AutoPore IV 950. The pressures applied were from 0 to 345 kPa measuring the pores of access radii ranging between 1.8 and 180 \(\upmu\)m for the low-pressure stage, and up to 228 MPa for the quantification of pores between 0.003 and 3 \(\upmu\)m.
## 4 Simulation Results and Security Evaluation
### Statistical Attack
#### 4.4.2 Histogram Analysis  
An image histogram is a useful tool with which to visualize the statistical properties of an image based on the distribution of pixel values. Histogram shapes include the normal distribution, skewed distribution, double-peaked distribution, plateau distribution, flat  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & \multicolumn{2}{c}{**Original Image**} & \multicolumn{4}{c}{**Encrypted Image**} \\ \hline  & **Horizontal** & **Vertical** & **Diagonal** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline Lena & 0.9410 & 0.9143 & 0.9647 & 0.00553 & \(-\)0.00768 & \(-\)0.00578 \\ Cameraman & 0.9335 & 0.9084 & 0.9591 & 0.0070 & \(-\)0.0050 & \(-\)0.0146 \\ Peppers & 0.9696 & 0.9434 & 0.9733 & 0.0004 & \(-\)0.0018 & \(-\)0.0063 \\ Rice & 0.9262 & 0.8979 & 0.9434 & 0.0021 & 0.0020 & \(-\)0.0054 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relationships among variables before and after encryption.  
Figure 12: Correlation of adjacent pixels in the plain Baboon image and the cipher Baboon image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Image** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline  & Plain Lena & 0.9410 & 0.9143 & 0.9647 \\ Proposed & & 0.0055 & \(-\)0.0078 & \(-\)0.0058 \\ [PERSON] et al. [26] & Encrypted & 0.0150 & 0.0044 & \(-\)0.0036 \\ [PERSON] et al. [27] & & 0.0019 & 0.0038 & \(-\)0.0019 \\ [PERSON] et al. [28] & & \(-\)0.0237 & \(-\)0.0237 & \(-\)0.0283 \\  & Plain Pepper & 0.9696 & 0.9434 & 0.9733 \\ Proposed & & 0.0004 & -0.0063 & \(-\)0.0018 \\ [PERSON] et al. [28] & Encrypted Pepper & \(-\)0.0727 & \(-\)0.0225 & \(-\)0.0242 \\ [PERSON] et al. [29] & & 0.0016 & 0.0059 & 0.0034 \\  & Cameraman & 0.9335 & 0.9084 & 0.9591 \\ Proposed & & 0.0070 & \(-\)0.0146 & \(-\)0.0050 \\ [PERSON] et al. [29] & Encrypted & 0.0024 & 0.0013 & 0.0098 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of correlation coefficients.  
distribution, and edge peak distribution. A flat histogram is indicative of a secure and well-encrypted image technique. Figure 13 illustrates the histograms for the Lena, Baboon, and Pepper images for both the plain and encrypted versions. The flatness of the encrypted image histogram can be evaluated by determining its variance; this is conducted based on Equation (21) for an image of size \(m\times n\):  
\[Variance(h)=\frac{1}{2\times n^{2}}\times\sum_{i=1}^{n}\sum_{j=1}^{m}\bigl{(}x _{i}-x_{j}\bigr{)}^{2} \tag{21}\]  
where h is the histogram vector value and \(x\) denotes the number of gray pixel values at \((i,j)\) Table 4 displays the variance values for both the plain and encrypted images utilized in the experiments. A variance range averaging at 5000 indicates a more even distribution of pixel values, thus indicating a superior encryption mechanism.
## 3 Results and Analysis
### Effects of Stress Path on Sharing Strength  
The shearing strength characteristics of soil vary as a function of stress path experienced by soil element. In this work, the Mohr-Coulomb strength parameters (including cohesion and friction angle) are adopted to quantitatively evaluate the effect of stress path on the strength properties of the UGRS. Figure 11 shows failure lines for tests with different loading angles, and each failure line is plotted based on test results of three or four specimens with identical loading angles but different consolidation pressures. Using the method mentioned in Section 3.1., the Mohr-Coulomb strength parameters of the UGRS experiencing various stress paths were determined and a comparison among them is provided in Figure 12. It can be seen from Figure 12 that the variation trends of cohesion are contrary to those of friction angle: as loading angle increases, cohesion of the soil decreases monotonically, while friction angle continues to increase. Specifically, with the increase in loading angle from \(56.3^{\circ}\) to \(123.7^{\circ}\) (all on the compression side of \(p^{\prime}\)-\(q\) space, categorized as the compression test), cohesion of the UGRS decreases from 49.1 to 17.9 kPa and friction angle increases by as much as 23.3%, changing from \(25.8^{\circ}\) to \(31.8^{\circ}\). The Mohr-Coulomb strength parameters for triaxial extension tests with constant \(\sigma_{3}\) and decreasing \(\sigma_{1}\) are also included in Figure 12. There were significant differences between strength parameters obtained from the compression and extension tests. Soil specimens in extension tests showed a lower cohesion but a higher friction angle than those in compression tests. This result is slightly different from that obtained by [PERSON] et al. [50], probably due to their use of reconstituted soil, of which the natural structure was destroyed. Moreover, comparison between the Mohr-Coulomb strength parameters obtained from ICD and AL tests were made, to evaluate the effect of the consolidation state. Both cohesion and friction angles of the UGRS in AL tests were slightly smaller than those in ICD tests, which were the results of the anisotropy induced by the anisotropic consolidation stress. On the other hand, comparison between strength parameters under \(\alpha=123.7^{\circ}\) and \(251.6^{\circ}\) indicated the existence of inherent strength anisotropy of the UGRS.  
It has been mentioned in Section 3.2. that the relative magnitudes of distortion and compaction of soil fabric change as a function of loading angle. As a result, soil specimens sheared under different loading angles achieve different arrangement patterns and contact characteristics of soil particles at failure, which may be responsible for the effect of stress path on the strength properties of the UGRS.  
Figure 10: Variations of initial secant shear modulus with: (**a**) consolidation pressure; and (**b**) loading angle.
## 9 Seismic Isolation and Energy Dissipation Devices for Composite Structures
### Seismic Isolation and Energy Dissipation Devices for Composite Structures: Key Findings  
Seismic isolation and supplementary dampers enhance the performance of steel-concrete composite buildings during earthquakes. They reduce structural damage and improve occupant safety by decoupling the composite building's movement from ground motion and dissipating seismic energy. Base isolation systems elongate the composite building's natural period, decreasing inter-story drifts, floor accelerations, and structural demand. Supplementary dampers provide additional energy dissipation capacity. The combination of isolation and dampers offers synergistic benefits, leading to cost savings. Composite buildings with these systems exhibit improved ductility, energy dissipation, and seismic resilience. Design considerations include the interface between isolated and non-isolated portions and the proper integration of dampers. Analysis and design are crucial to maximize the benefits for each project.
## 3 Results
### FTIR Spectroscopy Spectra of Adipocyte Conditioned Media  
Comparing infrared spectra of mature white adipocyte conditioned medium (MWCM) (red line), white preadipocyte conditioned medium (WPCM) (green line), and white adipocyte medium (WAM) (blue line), MWCNT showed a different spectrum from WPCM and WAM (Figure 2A). Low amide I (1652 cm\({}^{-1}\))/II (1543 cm\({}^{-1}\)) bands and a unique ring C-C stretch of phenyl (1590 cm\({}^{-1}\)) were detected in the spectrum of MWCNT, but a high amide I/II and no peak related to ring C-C stretch of phenyl were detected in the spectra of WPCM and WAM (Figure 2A). In addition, a ring base (1559 cm\({}^{-1}\)) band was not found in the spectrum of MWCNT (Figure 2A). On the other hand, comparing infrared spectra of mature brown adipocyte conditioned medium (MBCM) (pink line), brown pre-adipocyte conditioned medium (BPCM) (green line), and brown adipocyte medium (BAM) (blue line), BPCM showed a different spectrum from MBCM and BAM. However, BPCM did not show absorbance band at 1590 cm\({}^{-1}\) (Figure 2B) corresponding to C=N, NH2, adenine molecular (1589 cm\({}^{-1}\)), or ring C-C stretch of phenyl (1590 cm\({}^{-1}\)). In the spectra of MWCNT and MBCM, both did not show a 1559 cm\({}^{-1}\) peak but a 1590 cm\({}^{-1}\) peak (Figure 2A). Regarding the amide I/II ratio, WAM (1.12), WPCM (1.29), and MWCNT (1.34) were similar, whereas a difference was detected among BAM (1.46), BPCM (3.96), and MBCM (2.42) (Figure 3A). Protein amount (amide I/I + II) showed WAM (0.53), WPCM (0.56), MWCNT (0.57) BAM (0.59), BPCM (0.79) and MBCM (0.71), respectively (Figure 3B).  
Figure 1: Characteristics of adipocytes assessed by Oil-Red-O and expression of adipocyte markers. (**A**) Expression of white adipocyte markers in white adipogenesis (white adipocyte differentiation) using RT-PCR analysis. (**B**) Expression of brown adipocyte markers in brown adipogenesis (brown adipocyte differentiation) using RT-PCR analysis. MWCNT: mixture white adipocyte conditioned medium; WPCM: white preadipocyte conditioned medium; WEM: white preadipocyte conditioned medium; WAM: white adipocyte medium; MBCM: mature brown adipocyte conditioned medium; BPCM: brown preadipocyte conditioned medium; BPCM: brown preadipocyte conditioned medium; BSAM: brown adipocyte medium. **\(p<0.01\).  
Figure 3: The difference of (**A**) amide I/amide II and (**B**) amide I/amide I + amide II of various adipocyte conditioned media. WAT: white adipocytes; WPA: white preadipocytes; BAT: brown adipocytes; BPA: brown preadipocytes. **_p_ \(<\) 0.01; _n.s._, not significance.  
Figure 2: FTIR spectra of various adipocyte-conditioned media. (**A**) MWCM (red), WPCM (green) and WAM (blue), (**B**) MBCM (pink), BPCM (green) and BAM (blue).
###### Abstract  
Adaptive streaming has become a key technology for various multimedia services, such as online learning, mobile streaming, Internet TV, etc. However, because of throughput fluctuations, video quality may be dramatically varying during a streaming session. In addition, stalling events may occur when segments do not reach the user device before their playback deadlines. It is well-known that quality variations and stalling events cause negative impacts on Quality of Experience (QoE). Therefore, a main challenge in adaptive streaming is how to evaluate the QoE of streaming sessions taking into account the influences of these factors. Thus far, many models have been proposed to tackle this issue. In addition, a lot of QoE databases have been publicly available. However, there have been no extensive evaluations of existing models using various databases. To fill this gap, in this study, we conduct an extensive evaluation of thirteen models on twelve databases with different characteristics of viewing devices, codecs, and session durations. Through experiment results, important findings are provided with regard to QoE prediction of streaming sessions. In addition, some suggestions on the effective employment of QoE models are presented. The findings and suggestions are expected to be useful for researchers and service providers to make QoE assessments and improvements of streaming solutions in adaptive streaming.  
q 2022 14 10.3990/11/05/11  
Adaptive streaming & Computing]QoE Models for Adaptive Streaming: A Comprehensive Evaluation  
[PERSON] et al.][PERSON] 1, [PERSON] 2 and [PERSON] 3,*
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 4 Differential Diagnosis and Disease Phenotyping and Staging in Breath Analysis
### Neurodegenerative Diseases  
Neurodegenerative diseases are characterized by gradually augmented occurrence, as a direct consequence of the increased lifespan of human population [250], with Alzheimer (AD) and Parkinson (PD) being the most frequent [114]. Concerning AD, early disease detection is of great importance for preventing, decelerating and terminating the disease [250] while diagnosis for both diseases is based on the assessment of clinical symptoms [114]. Remarkably, the analysis of exhaled VOCs has been investigated for precise AD diagnosis as well as for differential diagnosis between AD and PD [114; 250]. Recently, [PERSON] et al. attempted to discriminate mild cognitive impairment due to AD (MCI) from AD, using GC-MS, achieving 60% sensitivity and 84% specificity along with the detection of 6 potential discriminant VOCs [250]. [PERSON]'s group, on the other hand, achieved the discrimination of AD and PD as well as an overall discrimination of AD, PD, and HC, using an array of 20 nanomaterial-based chemeirsistors and GC-MS (Table 4) [114]. In a similar study, the ability of IMS and Cyranose 320 to differentiate AD, PD, and HC was demonstrated, achieving a high overall discriminant capability (Table 4) [233]. IMS analysis revealed five VOCs significantly different between the groups [233]. The discrimination of the same groups of subjects has been also attempted using different arrays of MOS sensors (TGS, MICS) with one of the combinations (8 MOS sensors) demonstrating the best discriminant ability [243].
## 4 CFD Validation and Discussion  
### Test Facility  
As previously described, the simulation model of this study was validated by comparing the results with experimental results. The engine test was conducted using the test rig shown in Figure 16 for ground conditions, and the Altitude Engine Test Facility of the Korea Aerospace Research Institute was used, as presented in Figure 17. The altitude-simulated conditions are demonstrated in Table 3. Through the tests, a series of data was acquired, including RPM, EGT, fuel pump voltage, thrust, fuel flow rate, air flow rate, and diffuser outlet pressure (pressure ratio). As in Figure 16, diffuser outlet pressure is measured by the pressure taps. So, the test value is static pressure, and the pressure ratio is calculated by the ratio of static pressure from the compressor outlet and total pressure from the compressor inlet.  
To exclude the influence of atmospheric pressure and temperature during the test, the corrected parameters were used for the performance comparison. Corrected RPM, corrected thrust, corrected fuel flow rate, and corrected TSFC were calculated by using Equations (3)-(6) and the dimensionless values of \(\theta\) and \(\delta\), which are the normalized values of atmospheric temperature and pressure, respectively, to standard atmospheric conditions.  
\[N^{*}=N/\sqrt{\theta} \tag{3}\]  
\[Fn^{*}=Fn/\delta \tag{4}\]  
\[W_{f}^{*}=W_{f}\sqrt{\delta}\ /\theta \tag{5}\]  
Figure 16: Micro Gas Turbine Ground Test Rig.  
Figure 17: Altitude Engine Test Facility (AETF) at KARI [26].  
\[TSFC^{*}=TSFC/\theta \tag{6}\] \[\theta=T/T_{SLS}\] (7) \[\delta=P/P_{SLS} \tag{8}\]
## 3 Results and Discussion
### Oxidation Index Based on FTIR Spectroscopy  
The characteristic absorption bands of each PLA functional group were found, corresponding to what is expected in the literature [45; 46; 47; 48]. As shown in Figure 6, every spectrum contains an absorption band just below 3000 cm\({}^{-1}\) corresponding to the C-H stretch and a weaker band at 1250 cm\({}^{-1}\) corresponding to C-H deformation. Spectra also contain an absorption band around 1750 cm\({}^{-1}\) for the C=O stretch, and weaker bands at 1380 cm\({}^{-1}\) and 770 cm\({}^{-1}\) correspond to the C=O bend. The absorption band near 1450 cm\({}^{-1}\) corresponds to the CH\({}_{3}\) bend, and the intense absorption bands in the 1180 and 1080 cm\({}^{-1}\) range are representative of the C-O stretch. The absorption band at 1040 corresponds to the OH bend. The absorption bands around 860 cm\({}^{-1}\) correspond to C-C stretches.  
FTIR spectroscopy was used primarily to determine the oxidation index (OI) in the irradiated samples (Table 1). OIs which include the formation of aldehydes, ketones, and carboxylic groups were found to increase with irradiation for all doses. Although these samples were irradiated in the absence of oxygen, they were exposed to the air after irradiation. Therefore, it is expected that peroxidation of the free radicals will take place.