esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Chang'E-5T Orbit Determination
### Orbit Determination  
#### 3.2.1 Residual Analysis  
For the entire 2-h arc OD results, the post-fit L1 and C1 residuals are firstly analyzed to evaluate the quality of estimated Chang'E-5T orbits. As seen in Table 2, most of the observation errors have been corrected during the OD process. Hence, large post-fit residuals often reflect poor observation modeling as well as poor orbit estimation. The L1 and C1 residuals are shown in Figure 9a,b respectively. The L1 residuals from several satellites show significant linear variations with respect to epochs, and can reach 60 cm, which may be due to carrier phase noise variations as a function of C/N0 [29] or imperfect observation models. The RMS errors of the post-fit L1 and C1 residuals are 74 mm and 4.6 m respectively, and the RMS of C1 residuals is consistent with that of C1 code errors presented in Section 2.3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Models** \\ \hline Estimator & Least-squares estimation \\ \hline Observations selection & GPS L1 and C1 \\ \hline Sampling rate & 3 s \\ \hline Phase-windup effect & Phase polarization effects applied \\ \hline GPS Satellite antenna phase center model & Corrected using GPS values \\ (PCO and PCV) & \\ \hline GPS Satellite orbit & Fixed in ICS final orbit \\ \hline GPS Satellite clock & Fixed in ICS final 30 s interval clock \\ \hline Chang’E-5T Receiver clock & Estimated as random walk process \\ \hline Precession and nutation & IAU 2000 precession and nutation model \\ \hline EOP parameters & Polar motions and UT1 from IERS C04 series aligned to ITRF 2008 \\ \hline Troposphere and Ionosphere & None \\ \hline Geopotential (static) & EIGEN\_GL04C up to 50 \(\times\) 50 \\ \hline Solid earth tide/Ocean tide/Solid earth & IERS Conventions 2003 [35] \\ pole tide/Relativistic effect & \\ \hline M-body gravity & Sun, Moon, Jupiter, Venus, Mars, Mercury, Uranus, Neptune, Saturn, Pluto. JPL DE405 ephemeris used \\ \hline Solar radiation pressure model & ECOM model 5-parameter with no initial value [36] \\ \hline Phase ambiguities & Real constant value for each ambiguity arc \\ \hline \hline \end{tabular}
\end{table}
Table 2: Observation models, dynamic models and estimated parameters for Chang’E-5T OD.  
Figure 9: (a) L1 observation residual series (unit: centimeter); (b) C1 observation residual series (unit: meter).
## 2 Materials and Methods
### System Architecture  
Multiple frailty detection methodologies were presented, each one being based on objectively and subjectively measurable parameters. The system presented in the current paper uses the following parameters, determined through objective methods:  
1. Physical activity;
2. Energy expenditure;
3. Unintentional weight loss;
4. Exhaustion or fatigue;
5. Daily sedentary time and steps history;
6. Heart rate values daily variation;
7. High body mass index.  
The parameters were chosen, considering their scientific relevance in frailty detection, objective measurability that ensures precision and reduces subjectivity, and the feasibility of continuous monitoring using current technologies. In what follows, the system architecture is described from a software and hardware perspective, with an emphasis on the functional components and modules.  
Figure 1 presents the high-level architecture of FIDS, the Frailty Insights Detection System. This architecture has a correspondence to previously published architectures, at a higher level, with a different use case [14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Number of Items** & **Scoring Mechanism** \\ \hline \multirow{3}{*}{Fried Frailty} & \multirow{3}{*}{5} & 0: Not frail \\  & & 1-2: Pre-frail \\  & & 3 or higher: Frail \\ \hline \multirow{3}{*}{Rockwood Index} & \multirow{3}{*}{N/A} & \textless{}0.1: Not frail \\  & & 0.1–0.2: Mildly frail \\  & & 0.2–0.3: Moderate frail \\  & & \textgreater{}0.3: Severely frail \\ \hline \multirow{3}{*}{Groningen Indicator} & \multirow{3}{*}{15} & 0–4: Not frail \\  & & 5–6: Mildly frail \\  & & 7–8: Moderately frail \\  & & 9 or higher: Severely frail \\ \hline \multirow{3}{*}{Tilburg Indicator} & \multirow{3}{*}{15} & 0–5: Not frail \\  & & 6–11: Mildly frail \\  & & 12–17: Moderately frail \\  & & 18 or higher: Severely frail \\ \hline \multirow{3}{*}{Edmonton Scale} & \multirow{3}{*}{17} & 0–5: Not frail \\  & & 6–7: Vulnerable \\  & & 8–9: Mildly frail \\  & & 10–11: Moderately frail \\  & & 12 or higher: Severely frail \\ \hline \multirow{3}{*}{FRAIL Scale} & \multirow{3}{*}{5} & 0: Not frail \\  & & 1–2: Pre-frail \\ \cline{1-1}  & & 3–5: Frail \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scoring mechanisms of frailty methodologies.  
The system uses the main structure of the system design shown as feasible for the project objectives in previous work [15], which is augmented with new algorithms and technologies to achieve a functional prototype. Each component of the system operates independently, consisting of modules that have the following functions and roles:
###### Abstract  
Plane wave imaging in Shear Wave Elastography (SWE) captures shear wave propagation in real-time at ultrafast frame rates. To assess the capability of this technique in accurately visualizing the underlying shear wave mechanics, this work presents a multiphysics modeling approach providing access to the true biomechanical wave propagation behind the virtual image. This methodology was applied to a pediatric ventricular model, a setting shown to induce complex shear wave propagation due to geometry. Phantom experiments are conducted in support of the simulations. The model revealed that plane wave imaging altered the visualization of the shear wave pattern in the time (broadened front and negatively biased velocity estimates) and frequency domain (shifted and/or decreased signal frequency content). Furthermore, coherent plane wave compounding (effective frame rate of 2.3 kHz) altered the visual appearance of shear wave dispersion in both the experiment and model. This mainly affected stiffness characterization based on group speed, whereas phase velocity analysis provided a more accurate and robust stiffness estimate independent of the use of the compounding technique. This paper thus presents a versatile and flexible simulation environment to identify potential pitfalls in accurately capturing shear wave propagation in dispersive settings.  
ultrafast imaging; shear wave elastography; multiphysics modeling +
Footnote †: journal: Applied Physics
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 1 Introduction
This design provides a structure-based performance evaluation mechanism for drift detection without introducing any additional model training. 3. An adaptive model update strategy guided by drift intensit  
The updating strategy is proposed, in which the update ratio is dynamically determined based on the intensity of the detected concept drift. The strategy automatically selects and replaces the base detectors whose structures are most significantly affected by the drift. This approach enables efficient incremental updates, making it well aligned with the computational constraints of edge computing applications. The remainder of this paper is organized as follows: Section 2 reviews related work on time series anomaly detection under concept drift, highlighting existing limitations in adaptive learning, drift detection strategies, and ensemble model design in non-stationary streaming settings. Section 3 provides an overview of the ADDAEIL framework and shows the details of the important modules. Section 4 presents the comprehensive experiments and ablation studies with systematic evaluations across multiple datasets. Finally, Section 5 concludes the paper, summarizing key contributions and future research directions.
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 4 Nanocatalysts
### Factors Affecting Nanocatalysts in EOR Applications
#### 4.2.3 Crude Oil Composition  
The results of viscosity reduction on different oils with nanocatalysts presented by different authors showed a significant difference in reduction values [137; 143]. This was theorized to be related with a chemical effect more than a strictly physical one. A possible reason for the viscosity reduction is the breakage of the C-S bonds in asphaltene/resin molecules. However, when the content of sulfur becomes negligible, other chemical bonds with higher dissociation energy may be broken so as to reduce the viscosity. Core mineralogy also plays a significant role in the generation of CO\({}_{2}\), and the amount of H\({}_{2}\)S produced depends on the oil composition, mineralogy, and time [159].
## 1 Introduction
We also study on the security parameters by exploiting the QKD for the fiber-connected mmWave nodes, key technological blocks for the converged optical-radio infrastructure of 5G networks. The rest of the paper is organized as follows: Section 2 describes the concept of the proposed quantum-secured eCPRI transport layer operating in both P2P and P2 MP optical segments. Different fiber assignments using either dedicated or shared links for the quantum transmission are thoroughly investigated. In addition, a Fiber-Wireless topology supporting the secured P2 MP distribution using mmWave nodes is proposed. The details about the physical connectivity implementation and the followed methodology for the DV-QKD layer are also discussed in this section. Section 3 presents the performance evaluation results and discusses the main outcomes and challenges for each deployment scenario. Section 4 briefly introduces a technological path in support of the QKD integration within existing deployed infrastructure in 5G/B5G-oriented topologies. Finally, Section 5 concludes this work.
# Modeling and Predicting Carbon and Water Fluxes Using Data-Driven Techniques in a Forest Ecosystem
## 2 Materials and Methods
### Data-Driven Techniques
#### 2.2.2 Support Vector Machine  
The SVM method is a popular computational intelligence technique and has been employed in many research fields [48; 49; 50]. The versatile functions and powerful generalization ability of SVM technique in dealing with complex nonlinear problems are mainly attributed to the theoretical foundation based on both the statistical learning theory and structural risk minimization principle. Its fundamental principle described in detail can be found in [PERSON] and Vapnik [51]. The kernel function is used to calculate the inner product through mapping the inputs into high-dimensional feature spaces and it is universally accepted as an innovative and crucial trick for the SVM method. To effectively capture useful information from training data, the choice of proper kernel function is particularly important. The radial basis function (RBF) is a simple, efficient, reliable and extensively adopted kernel and its role in the generalization ability of SVM method has been proven by previous studies [49; 52]. Therefore, in the present study, RBF algorithm was used for the developed SVM models to approximate the nonlinear processes of the input-output space. In addition, to obtain an optimal SVM model, its related parameters including regularization factor, insensitive error band width and kernel width should be taken into account. In this research, the values of both regularization factor and kernel width were determined through the grid search approach [53]. The value of insensitive error band width was set to 0.01 by default.
## 3 Results and Discussion
### Kinetics of Dye Sorption on MES and MMS
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Sorbent**} & \multirow{3}{*}{**Dye**} & \multirow{3}{*}{**Dye**} & \multicolumn{3}{c}{**Pseudo-First Order Model**} & \multicolumn{3}{c}{**Pseudo-Second-Order Model**} & \multicolumn{3}{c}{**Exp. Data**} \\ \cline{3-10}  & & & **k\({}_{\text{1}}\)** & **q\({}_{\text{e,cal.}}\)** & **R\({}^{2}\)** & **\(\chi^{2}\)** & **k\({}_{\text{2}}\)** & **q\({}_{\text{e,cal.}}\)** & **R\({}^{2}\)** & **\(\chi^{2}\)** & **q\({}_{\text{e,exp.}}\)** \\ \cline{3-10}  & & **[mg/L]** & **[1/min]** & **[mg/g]** & **-** & **-** & **[g/mg-min]** & **[mg/g]** & **-** & **-** & **[mg/g]** \\ \hline \multirow{3}{*}{**MES**} & **RB5** & 50 & 0.0829 & 20.78 & 0.9917 & 0.265 & 0.0059 & 22.57 & 0.9974 & 0.071 & 21.55 \\  & 250 & 0.0743 & 58.84 & 0.9581 & 3.469 & 0.0018 & 64.36 & 0.9914 & 0.635 & 61.97 \\ \cline{2-10}  & **RY84** & 50 & 0.0742 & 20.16 & 0.9758 & 0.680 & 0.0050 & 22.16 & 0.9961 & 0.085 & 21.38 \\  & 250 & 0.0606 & 51.08 & 0.9499 & 4.117 & 0.0016 & 56.53 & 0.9867 & 0.949 & 53.23 \\ \hline \multirow{3}{*}{**MMS**} & **RB5** & 50 & 0.0847 & 19.92 & 0.9989 & 0.029 & 0.0062 & 21.72 & 0.9991 & 0.027 & 20.29 \\  & 250 & 0.0706 & 43.72 & 0.9800 & 1.169 & 0.0022 & 48.02 & 0.9980 & 0.113 & 45.44 \\ \cline{1-1} \cline{2-10}  & **RY84** & 50 & 0.0675 & 16.00 & 0.9944 & 0.413 & 0.0053 & 17.85 & 0.9988 & 0.322 & 16.48 \\ \cline{1-1} \cline{2-10}  & **RY84** & 250 & 0.0553 & 34.92 & 0.9690 & 1.648 & 0.0022 & 38.16 & 0.9952 & 0.210 & 35.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Kinetic parameters of sorption of anionic dyes onto MES and MMS, determined from the pseudo-first order and pseudo-second order models (based on the average of three measurements). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Sorbent**} & \multirow{3}{*}{**Dye**} & \multirow{3}{*}{**Dye**} & \multicolumn{3}{c}{**Phase 1**} & \multicolumn{3}{c}{**Phase 2**} \\ \cline{3-10}  & & **Conc. ** & & **k\({}_{\text{41}}\)***** & **Duration** & **R\({}^{2}\)** & **k\({}_{\text{42}}\)** & **Duration** & **R\({}^{2}\)** \\ \cline{3-10}  & & **[mg/L]** & **[*]** & **[min]** & **-** & **[*]** & **[min]** & **-** \\ \hline \multirow{3}{*}{**MES**} & **RB5** & 50 & 3.807 & 20 & 0.9998 & 0.537 & 130 & 0.9252 \\  & & 250 & 11.993 & 10 & 0. (9) & 3.057 & 110 & 0.9775 \\ \cline{2-10}  & & 50 & 3.592 & 20 & 0.9997 & 0.689 & 130 & 0.9852 \\  & 250 & 9.687 & 10 & 0. (9) & 2.980 & 110 & 0.9843 \\ \hline \multirow{3}{*}{**MMS**} & **RB5** & 50  
As already mentioned, the shorter length of phase 1 in the experimental groups with higher initial dye concentrations is due to the faster saturation of the active sites and, thus, the faster transition of the process to phase 2 (Table 2). The MMS groups feature generally lower values of k\({}_{\rm d1}\) and a longer phase 1, suggesting that the sorbent surface had fewer sorption centers than MES (Table 2). The higher k\({}_{\rm d1}\) values determined for MES may also point to a higher sorption efficiency compared with MMS.
# Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine
###### Abstract  
In coherent optical communication systems, where multiple modulation formats are mixed and variable, the correct identification of signal modulation formats provides the foundation for subsequent performance improvement using digital algorithms. A modulation format identification (MFI) scheme based on signal constellation diagrams and support vector machine (SVM) is proposed. Firstly, the signal constellation diagrams are divided by the fractal dimension of the weighted linear least squares (WLS-FD) algorithm, and the fractal dimension (FD) in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the gray-level co-occurrence matrix (GLCM), and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, we compare it with the MFI scheme based on higher-order statistical (HOS) features, GLCM features, and FD features, respectively. Further, we built a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km, where the optical signal-to-noise ratio (OSNR) ranges from 0 dB to 30 dB. The proposed MFI scheme identifies seven modulation formats: QPSK, 8 QAM, 16 QAM, 32 QAM, 64 QAM, 128 QAM, and 256 QAM. The results show that compared with the other three schemes, our proposed scheme has a better identification accuracy at low OSNR. In addition, the identification accuracy of this scheme can reach 100% when the OSNR \(\geq\) 10 dB.  
Keywords:signal constellation diagrams; support vector machine (SVM); coherent optical communication; modulation format identification +
Footnote †: journal: _Photonics_
# _Penicillin_ spp. XK10, Fungi with Potential to Repair Cadmium and Antimony Pollution
## 4 Discussion
At this time, Sb in solution exists in the form of H\({}_{2}\)SbO\({}^{3-}\) or Sb(OH)\({}^{4-}\), and the negatively charged Sb and the negative charge on the surface of the biosorbent repel each other, and the adsorption rate of Sb decreases significantly. The adsorption time is one of the important factors of the biosorption process [46]. For Cd, the biomass of XK10 showed a predominantly increasing trend at first and then leveling off as the adsorption time increased. Due to the massive bioactive adsorption sites on the fungal cell surface at the beginning of adsorption, the biosorption rate of metal ions is rapid, and then saturation was reached. The maximum Cd removal by XK10 occurred at the adsorption time of day 4. At the adsorption time of 4-7 days, the heavy metal may \begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Minimum Growth** \\ **Inhibition Concentration** \\ \end{tabular} & **Adsorbent Material** & \
\begin{tabular}{c} **MIC (Solid Culture)** \\ **(mM)** \\ \end{tabular} &
\begin{tabular}{c} **MIC (Liquid Culture)** \\ **(mM)** \\ \end{tabular} & **Reference Literature** \\ \hline \multirow{6}{*}{Cd} & XK10 & \textgreater{}5 & 2 & \\  & Uncultured & \textgreater{}1.3 & & [10] \\  & _Westerdykella_ & & & [30] \\  & _Pseudomonas_ sp. & 4 & & [32] \\  & _Pseudomonas_ & 0.9 & & [32] \\  & _azotoformans_ & & 1.3 & [38] \\  & _Penicillium_ spp. & & \\ \hline \multirow{6}{*}{Sb} & XK10 & \textgreater{}7 & \textgreater{}20 & \\  & _Cupriavidus_ sp. & 6 & & [39] \\ \cline{1-1}  & _Bacillus_ sp. & 5.5 & & [39] \\ \cline{1-1}  & _Penicillium_ sp. & & 4.9 & [40] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The minimum inhibitory concentrations of Cd and Sb by different microorganisms. have had a toxic effect on XK10, causing the strain to die, resulting in a significant decrease in the fungal removal of Cd. For Sb, the removal of Sb by XX10 continued to increase as the adsorption time increased, indicating that XX10 has a strong adsorption capacity for Sb. The isothermal adsorption model can be developed to further understand the biosorption process [47]. In this study, the fits of Langmuir and Freundlich isotherms were used to simulate the biological processes of fungal adsorption of Cd and Sb. By comparing the linear correlation coefficients of the two models, it was found that the adsorption of Cd and Sb by XX10 is more consistent with the Freundlich model. The adsorption process is monolayer adsorption, the energy distribution on the active site is uneven and there is interaction between the adsorbed molecules.
# A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices
## 4 Results and Comparisons
### Ransomware Security Scanning Analysis  
As part of the security test, ransomware scanning analysis was performed utilizing the VirusTotal platform ([[https://www.virustotal.com/](https://www.virustotal.com/)]([https://www.virustotal.com/](https://www.virustotal.com/)) accessed on 1 August 2021). VirusTotal is an online detection system that provides scanning services for any data type. VirusTotal utilizes over 70 anti-virus engines to perform malware detection and classification services [59]. Moreover, to ease the scanning process for large-scale databases, VirusTotal provides an API service in which researchers can upload the entire dataset and perform the scanning process without using a web interface. As a result, business companies have widely used the VirusTotal platform to diagnose their products' false negatives and positives. Furthermore, its scanning services have been heavily used by researchers in the security field to detect several malware families [60].  
In our experiment, ransomware scanning analysis was implemented on three different files. Before the hiding process, the original ransomware APK file was fed as an input to VirusTotal engines. Subsequently, the scanning process was performed again on the stego frame (PNG file) resulting after applying the hiding process. Finally, the stego frame was merged with the original video frames and the entire infected video (AVI file) was scanned. The results of applying ransomware scanning analysis on the three files (APK, PNG, AVI) are illustrated in Figure 2. As Figure 2 demonstrates, performing the scanning on the original ransomware APK alerted 34 out of 64 antivirus engines about the malicious behavior of this APK. However, after applying the proposed approach, none of the antivirus engines were able to detect the concealed ransomware within the cover frame (Figure 2). Additionally, the hidden ransomware within the infected video was also undetectable by any of the VirusTotal engines (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **Approach** & **Balloon** & **Kimono** & **Shark** & **Microworld** & **GTHy** \\ \hline (AE) & This work & 0.5758 & 0.4649 & 0.5324 & 0.4992 & 0.3746 \\  & [58] & 0.6725 & 0.4922 & 0.5794 & 0.5375 & 0.4242 \\  & [38] & 0.6913 & 0.4937 & 0.6108 & 0.5798 & 0.5221 \\  & [39] & 0.6149 & 0.4706 & 0.6098 & 0.5578 & 0.497 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
Figure 2: VirusTotal scanning results. (a) Ransomware scan result. (b) Stego frame scan result. (c) Infected video scan result.
## 1 Introduction  
With the development of wireless communication technology, environmental protection and energy consumption issues have attracted widespread attention. The technology of green development and energy saving has become the mainstream technology of wireless communication development [1; 2]. Energy harvesting is a method of resource regeneration in wireless communication, which can realize the efficient use of renewable resources. In a scenario that the system has energy-harvesting transmitters, energy harvesters can harvest energy from the natural environment (such as wind energy, solar energy, tidal energy, etc.). Since the amount of harvested energy is limited and has large randomness, it is important to decide how to allocate the energy to transmit data. Although some research has been undertaken into power allocation in energy harvesting systems, algorithms are usually proposed which fail to take the energy losses into account or only think about one kind of energy loss. It is worth researching power allocation when considering that several kinds of energy losses exist.