esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 6 Discussion
### Comparison with State-of-the-Art Methods
#### 6.1.3 Computational Efficiency  
While AnomRank and DecayRank execute faster on small datasets (taking **4.03 s** and **5.46 s**, respectively, on DARPA), their lower precision limits their effectiveness. Adaptive-DecayRank, in contrast, maintains efficiency, even at large scales. As seen in Table 4 and Figure 6, our model processes **1 million edges in just 14.23 s**, outperforming both SedanSpot and DynAnom, which require substantially more computation.  
The model's efficiency stems from its **incremental Bayesian updating and adaptive decay factor**, which avoids full-graph recomputation by updating rankings dynamically. Unlike traditional PageRank-based methods that recompute anomaly scores from scratch, Adaptive-DecayRank maintains a dynamic node importance measure that adapts to graph changes in real time, significantly reducing redundant computations.
## 5 Challenges and Future Research Directions
### Network Analysis in the UDN  
The deployment of dense small-cell networks increases network complexity, making real-time analysis crucial for identifying bottlenecks and inefficiencies. However, issues such as shadow fading and interference complicate performance monitoring. Future studies should explore AI-driven analytics and simulation-based methodologies to enhance network evaluation and autonomous optimization [7].
## 3 Numerical Model and Analysis Method
### Valuation of Numerical Model  
In order to verify the effectiveness of numerical simulation, the numerical results of the axial strain of the top slab were compared with those observed in the field investigation of several cases (Cases 2-4) in asymmetrical construction, as shown in Figure 6. The numerical simulation captured the basic changing regulation of the axial strain of the top slab, and it showed a decreasing tendency along its width. Meanwhile, the value of axial strain in numerical simulation is quite close to that in field investigation. Consequently, it is regarded that the results determined by numerical simulation are consistent with those obtained by field investigation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer** & **Unit Weight/\(\gamma\)** & **Elastic Modulus/E** & **Poisson** & **Internal Friction** & **Cohesive** \\
**ID** & **Soil Layer** & **(kN\(\cdot\)m\({}^{-3}\))** & **(MPa)** & **Ratio/\(\
u\)** & **Angle/\(\phi\) (\({}^{\circ}\))** & **(kPa)** \\ \hline \hline
1 & Miscellaneous fill & 18.5 & 21 & 0.3 & 10.0 & 5.0 \\
2 & Clay & 19.2 & 33 & 0.3 & 15.0 & 81.2 \\
3 & Clay & 19.2 & 42 & 0.28 & 15.6 & 88.9 \\
4 & Silty clay & 19.1 & 36 & 0.27 & 18.0 & 26.7 \\
5 & Clay & 19.3 & 48 & 0.28 & 15.8 & 97.0 \\
6 & Silty clay with silt & 20.0 & 36 & 0.3 & 25.0 & 20.0 \\
7 & Argillaceous sandstone & & & & & \\ strongly weathered & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of soil layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit Weight/\(\gamma\) (kN\(\cdot\)m\({}^{-3}\))** & **Elastic Modulus/E (MPa)** & **Poisson Ratio/\(\
u\)** \\ \hline
26.0 & 3.15 \(\times\) 10\({}^{4}\) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of concrete material.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit Weight/\(\gamma\) (kN\(\cdot\)m\({}^{-3}\))** & **Elastic Modulus/E (MPa)** & **Poisson Ratio/\(\
u\)** \\ \hline
26.0 & 3.15 \(\times\) 10\({}^{4}\) & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Procedure of asymmetrical and symmetrical construction.
## 3 Selected Applications of GBMs in Key Miniaturized Techniques
### Magnetic Solid-Phase Extraction
The recovery values for water, saffron, and soft drink samples ranged from 93% to 107%. The proposed graphene-based magnetic sorbent demonstrated exceptional efficacy in extracting anionic food dyes from water samples, offering a versatile and cost-effective analytical approach. However, to enhance the affinity of graphene sorbents modified with magnetite particles, other materials can anchor and/or functionalize these sorbents, creating hybrid materials. This process improves material performance during the MSPE extraction step, resulting in a more homogeneous dispersion in the aqueous medium and enhanced selectivity for the compounds of interest, achieving a quick and efficient equilibrium. In this line of research, [PERSON] et al. [117] anchored magnetite particles modified with TEOS and APTES (Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)-NH\({}_{2}\)) onto graphene oxide, followed by successive in-situ functionalization with \(\beta\)-cyclodextrintrin and an ionic liquid composed of a cation with an imidazole ring and an anion anthraquinone (VOIm+AQSO\({}_{3}\)\({}^{-}\)), resulting in Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/GO/\(\beta\)-CD/IL. This composite was employed in an MSPE method for extracting seven plant regulators from vegetable samples with determination by LC-MS/MS. Utilizing 60 mg of sorbent, equilibrium extraction was achieved within 5 min, leveraging a range of interaction mechanisms inherent in its structure, including \(\pi\)-\(\pi\) stacking, hydrogen bonding, stronger hydrophobic interactions, host-guest inclusion complex formation, and electrostatic interactions. Furthermore, recovery percentages exceeding 80% were attained, along with excellent precision and low relative standard deviation values (\(\leq\)10.4). The method exhibited outstanding performance, with LOQs ranging from 0.03 to 0.58 ug kg\({}^{-1}\). Recently, [PERSON] and [PERSON] [118] conducted a multi-step synthesis using glutaraldehyde as a crosslinker to modify silica-coated Fe\({}_{3}\)O\({}_{4}\) with GO. They then functionalized it with chi tosan and \(\beta\)-CD for extracting four bisphenols from environmental water and food samples, followed by analysis by LC-FLD. This process resulted in a high surface area (91.83 m\({}^{2}\) g\({}^{-1}\)), facilitating compound extraction. Adsorption mechanisms involved hydrogen bonding, electrostatic interactions, \(\pi\)-\(\pi\) stacking, and hydrophobic interactions mediated by \(\beta\)-CD. Employing 50 mg of the adsorbent, the method achieved low LOQ levels (0.03 ug L\({}^{-1}\)), with recovery percentages above 80% for the investigated bisphenols and possibly recycling the sorbent up to five times. As discussed, combining MSPE methods with magnetic sorbents containing GBMs, when combined with other materials, enables obtaining more selective phases and more efficient extraction methods. This integration enhances the selectivity and efficiency of extraction processes, reducing the need for large amounts of extraction phase and, consequently, eluent, significantly improving the quality of the results.
## 2 Methods
### Sea Outfall Turbid Plume Detection  
Following the cloud's free data processing to L2 on ACOLITE, the imagery was analyzed on the Sentinel Application Platform (SNAP), an open-source platform developed by ESA (European Space Agency) that is ideal for Earth data observation processing and analysis [37], for the marine outfall's turbid plume detection (Figure 3).  
If no patch was identified through ocean color data, the spectral analysis was carried out, aiming to find the spectral signature of the wastewater plume around the Sao Jacinto sea outfall's mouth coordinates. Nevertheless, if there was no clear spectral signature of this water mass, we checked the results generated by the water clarity algorithm on ACOLITE. Finally, if no sign of the wastewater plume was detected, the no-data day was documented, and the image was discarded.  
On the other hand, the spectral analysis was carried out if the turbid plume was detected. Some measurements were taken with SNAP tools, such as extent and catchment area; for each case, a polygon was manually drawn around the patch and exported for later analysis. Additionally, the predominant dispersion direction and relevant characteristics were documented. An annual and seasonal analysis was carried out on MATLAB to  
Figure 2: Sentinel-2 satellite data retrieval and processing flowchart.  
determine if any temporal dispersion patterns of the Sao Jacinto sea outfall's turbid plume could be identified.  
Following turbid plume identification and documentation, the polygons were imported into GIS software, such as ArcMap, to create a global catchment area for each year of analysis, following [PERSON] et al. [11]. In addition, the daily area under the influence of the sea outfall plume has been studied to understand whether there was any seasonal tendency in the dataset. For that reason, an area anomaly analysis was carried out by subtracting the sea outfall daily area of the respective month from the monthly sea outfall average area.  
Nevertheless, to comprehend the influence of the local meteo-oceanographic conditions on the outfall turbid plume's dispersion, sea surface elevation data were acquired for the Aveiro lagoon mouth from an existing hydrodynamic model [38], and hourly wind speed and direction were acquired from a WRF model (the Weather Research and Forecasting (WRF) Model is a mesoscale numerical weather prediction system used for atmospheric research and operational forecasting applications), with a spatial resolution of 12 km [39]. For this reason, one example of the wastewater plume dispersing inward toward the Ria de Aveiro coastal lagoon, observed through ocean color data, will be addressed.
## 6 Results and Discussion
### Observer Simulation under Ideal Condition without Cable Disturbing Force  
The state variables adopt \(q=[u_{r},v_{r},w_{r},r,u_{c},v_{c}]^{T}\), the observer variables adopt \(\eta=[\dot{X},\dot{Y},\dot{Z},\dot{\psi}]^{T}\), and the observer parameters adopt \(L=diag\{5,5,5,5,5\}\), \(\rho=diag\{0.5,0.5,0.5,5,5\}\). When there is no cable disturbing force, \(\sigma\) is zero in (44). The parameters of the slowly varying current flow field model are \(\mu_{cx}=1,\ \mu_{cy}=1.\ \omega_{cx}\) and \(\omega_{cy}\) are composed of the Gaussian white noise signal and the step signal. The amplitudes of the step signals are \(-0.4\) and \(-0.3\), respectively, and the power spectrum density of the white noise is \(10^{-5}\). The current velocity generated by (43) is regarded as the true value of the current velocity, and the current velocity averages (\(-0.4\) m/s, \(-0.3\) m/s, 0 m/s). The effects of the measurement noise are ignored, and the propeller thrust acting on the ROV is \(F_{T}=(2000N,500N,0N)\). The simulation time is 100 s, and the simulation step size is 0.001 s.  
From the above figures, we can infer that if the effect of the sensor noise is not considered, when the system model does not have the current velocity and the cable disturbing force, the observer can rapidly track the observation state variables, including the ROV's position, attitude angle, velocity, and current velocity (Figures 9-11). The tracking error is small, as the velocity observation error is less than 0.02 m/s, and the displacement observation error is less than 0.03 m (Figure 12). This shows that the designed state observer has good observation performance without divergence in the ideal circumstance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cable Length (m)** & **Working Depth (m)** & \(u_{c}\) **(m/s)** & \(u_{r}\) **(m/s)** & \(u\) **(m/s)** & \(F_{cablex}\) **(N)** & \(F_{cablez}\) **(N)** \\ \hline \multirow{4}{*}{150} & \multirow{4}{*}{100} & \(-1.5\) & \(1.5\) & \(0\) & \(-1617\) & \(-741\) \\  & & \(-1.5\) & \(1.6\) & \(0.1\) & \(-1731\) & \(-790\) \\  & & \(-1.5\) & \(1.8\) & \(0.3\) & \(-1970\) & \(-895\) \\  & & \(-1.5\) & \(2\) & \(0.5\) & \(-2230\) & \(-1000\) \\  & & \(-1.5\) & \(2.2\) & \(0.7\) & \(-2500\) & \(-1125\) \\  & & \(-1.5\) & \(2.4\) & \(0.9\) & \(-2800\) & \(-1250\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tension on the ROV under different relative velocity conditions.  
Figure 11: Observed results of position and heading of ROV: (**a**) \(X\); (**b**) \(Y\); (**c**) heading; (**d**) trajectory of ROV in the XY plane.  
Figure 12: Observed errors of velocity and position: (**a**) velocity error; (**b**) position error; (**c**) angular velocity error.  
Figure 14: Cable disturbing force (LPM): (**a**) cable force in the X direction; (**b**) cable force in the Z direction.  
Figure 13: Observed result of velocity: (**a**) current velocity in the X direction; (**b**) relative velocity of ROV in the x direction.  
Figure 15: Observed result of ROV position: (**a**) X; (**b**) Z.
# Optimization of Chinese Chive Juice as a Functional Feed Additive
## 2 Materials and Methods
### UHPLC-LTQ-Orbitrap-MS/MS Analysis  
Freeze-dried fresh and heated CC juice samples (5 g) derived from the optimal heating conditions (60 \({}^{\circ}\)C for 8 h, CC60 and 80 \({}^{\circ}\)C for 3 h, CC80) were extracted with 50 mL of 80% (_v/v_) methanol followed by the sonication for 20 min. Then the mixture was centrifuged (10,000\(\times\)\(g\), 4 \({}^{\circ}\)C, 10 min) and the collected supernatants were dried using a rotary vacuum evaporator at 45 \({}^{\circ}\)C followed by freeze-drying. The extracts were re-dissolved in 80% methanol at a concentration of 10 mg/mL (_w/v_) and chloramphenicol (2.5 mg/mL) was used as an internal standard (IS) for ultra-high-performance liquid chromatography coupled to a linear trap quadrupole-orbitrap-tandem mass spectrometry (UHPLC-LTQ-Orbitrap-MS/MS) analysis. Five microliters of sample were injected into a UHPLC system equipped with a Vanquish binary pump H system (Thermo Fisher Scientific, Waltham, MA, USA), auto-sampler, and column compartment. Chromatographic separation was performed on a Phenomenex KINETEX(r) C18 column (100 mm \(\times\) 2.1 mm, 1.7 \(\upmu\)m particle size; Torrance, CA, USA). The column temperature was 40 \({}^{\circ}\)C and the flow rate was 0.3 mL/min. The mobile phase consisted of 0.1% formic acid in water (_v/v_, solvent A) and 0.1% formic acid in acetonitrile (_v/v_, solvent B). The 14 min gradient run was carried out by 5% solvent B for 1 min followed by a linear increase to 100% solvent B over 9 min and maintained for the next 1 min, and then re-equilibrated to the initial condition (5% solvent B) over 3 min. The total run time was 14 min. The MS data were collected by using an Orbitrap Velos Pro(tm) system, which was equipped with an ion-trap mass spectrometer and HESI-II probe. Mass spectra were acquired over the range of 100-2000 m/z. The MS parameters were: probe heater at 300 \({}^{\circ}\)C, capillary temperatures at 350 \({}^{\circ}\)C, and capillary voltage of 2.5 KV in negative mode.
## 1 Introduction  
Although crystalline silicon (c-Si) solar cells are a mature technology with competitive energy prices in several markets, efforts toward higher efficiencies, lower costs and lesser environmental impacts continue to lead the photovoltaic community. State of the art technology already allows for low-temperature deposition of dopants (T \(<\) 200 \({}^{\circ}\)C) in amorphous silicon a-Si:H/c-Si heterojunction devices, but thermally-diffused doping (T \(>\) 900 \({}^{\circ}\)C) is still the industry standard, covering \(\sim\)12% of the module fabrication energy input [1]. As advanced solar cell architectures become closer to the theoretical efficiency limits for single-junction devices, further cost reduction must then come from ambient-temperature or solution-based processes and materials.  
In parallel, the development of thin-film and dye-sensitized/organic photovoltaics has introduced novel materials with excellent optoelectronic properties that could substitute standard silicon dopants. Such materials have recently been reported in conjunction with p- and n-type c-Si, including organic polymers (PEDOT:PSS [2], P3 HT [3]), transparent conductive oxides (ZnO [4]), transition metal oxides (TiO\({}_{2}\)[5], MoO\({}_{3}\)[6], WO\({}_{3}\)[7]) or their combination [8,9], reaching power conversion efficiencies as high as 18.8%[10] for MoO\({}_{3}\)/n-type c-Si heterojunctions. A distinctive attribute of these materials is their preferential conductivity for one kind of charge carrier (_i.e._, holes) while blocking the other kind (electrons), aiding in the separation of photogenerated carriers [11]. They are usually wide bandgap semiconductors (highly transparent) with low contact resistivities, although the primary benefit is their processability by low-temperature techniques (vacuum thermal evaporation, atomic layer deposition) or by thin-film coating methods.  
This paper explores the use of four transition metal oxides (V\({}_{2}\)O\({}_{5}\), MoO\({}_{3}\), WO\({}_{3}\), and ReO\({}_{3}\)) as front p-type contacts in planar n-type crystalline silicon (n-Si) solar cells. The compositional, optical and electrical properties of these oxides will be discussed in terms of the solar cell design, making emphasis on their advantages (optical gains) and disadvantages (post-annealing degradation) when compared to conventional a-Si:H layers.
###### Abstract  
Life sign detection is important in many applications, such as locating disaster victims. This can be difficult in low signal to noise ratio (SNR) and through-wall conditions. This paper considers life sign detection using an impulse ultra-wideband (UWB) bio-radar with an improved sensing algorithm for clutter elimination, harmonic suppression and random-noise de-noising. To improve detection performance, two filters are used to improve SNR of these life signs. The automatic gain method is performed in fast time to improve the respiration signals. The spectral kurtosis analysis (SKA)-based windowed Fourier transform (WFT) method and an accumulator in the frequency domain are used to provide two distance estimates between the radar and human subject. Further, the accumulator can also provide the frequency estimate of the respiration signals. These estimates are used to determine if a human is present in the detection environment. Results are presented which show that the range and respiration frequency can be estimated accurately in low signal to noise and clutter ratio (SNCR) environments. In addition, the performance is better than with other techniques given in the literature.  
Keywords: respiration; victim detection; ultra-wideband (UWB) bio-radar; windowed Fourier transform (WFT); kurtosis; frequency accumulator +
Footnote †: journal: Applied Sciences
# An Active Indoor Noise Control System Based on CS Algorithm
## 2 The Indoor ANC System Based on CS Algorithm  
### System Framework  
Feedback ANC is now a well-known technique [21], and it was a reasonably simple feedforward control system for a long, narrow duct. Before passing via a loudspeaker, an input microphone near the noise source detects a reference signal \(x(n)\). The noise canceler generates a signal \(y(n)\) with the same amplitude but that is 180\({}^{\circ}\) out of phase using the reference input signal. This anti-noise signal is utilized to drive the loudspeaker, which produces a canceling sound that reduces the duct's primary acoustic noise. The noise in [21] is not complicated, and the distance between the source and canceling speaker is short;thus, its implementation is not complex. However, when ANC technology is applied to indoor environments, it is a bit more complicated.  
The indoor ANC system design proposed in this paper can be seen in Figure 1. Outside noise can be produced by a variety of sources, including air conditioner noise, construction noise, traffic noise, and the voices of passersby. These noises can be unpredictable in addition to being periodic. In order to identify outside noise sources, the system discussed in this study incorporates input microphones placed at each window. The noise canceler makes use of the incoming signal. The noise canceler generates a signal \(y(n)\) with the same amplitude but that is 180\({}^{\circ}\) out of phase with the input signal \(x(n)\). This noise is sent to a loudspeaker to cancel out the undesirable noise. The error microphone detects the error (or residual) signal e(n), which is then utilized to optimize the filter coefficients. Since the error signal is not compared to the reference input, using a downstream error signal to alter the adaptive filter coefficients does not constitute feedback. Actual implementations need additional considerations with respect to the ability to engage with acoustic issues in the duct. In the suggested system, we utilize a house with four windows as an example, installing four input microphones and error microphones in each office position near the human ear to improve the noise reduction performance as much as possible. When the primary noise is periodic (or nearly so) and produced by rotating or reciprocating machinery, the input microphone can be substituted with a non-acoustic sensor such as a tachometer, accelerometer, or optical sensor. This alternative solves the acoustic feedback problem. However, in the proposed system, such sensors are not as versatile; thus, they are not employed.
# Hybrid Fibres as Shear Reinforcement in High-Performance Concrete Beams with and without Openings
## 3 Experimental Results and Discussion
### Load-Deflection Curves and Cracking Behaviour
#### 3.1.5 Cracking Patterns and Failure Modes
It can be observed that the flexural failure mode is characterized by cracks that occur gradually, allowing the beam to reach significant ductility. The beam with the lowest fibre content (B2) failed soon after the first diagonal crack appeared. Several diagonal cracks were observed for beams with a relatively high percentage of fibres (B3, BO2, BO3), thus indicating stress redistribution beyond cracking. In particular, the steel fibres became effective in transferring the tensile stresses after diagonal cracks formed, and bridging them until the fibres were pulled out or broken off at crack failure. This was also observed by other researchers [20; 29; 64]. The effectiveness of the fibres, the addition of 1% steel fibres and 0.05% polypropylene fibres to full beam B3 and the fibre content of 1.5% steel fibres and 0.1% polypropylene fibres to beam BO3 with openings transformed the failure mode from flexure-shear to mainly flexure (Figure 7). This confirms the beneficial effect of hybrid fibres in resisting shear failure. Beam B2 with a 0.5% steel fibre and 0.025% polypropylene fibre content failed in shear and exhibited rapid failure at the ultimate stage. Brittle failure occurs for the B1 and BO1 beams without fibres. The cracks are more localized above and along the inclined line joining the loading point with the support. The tests showed that stirrups are subjected to large deformations, which can indicate attainment of the yield limit. Rupture of the stirrup crossing the cracked section appears when the spacing between the stirrups is 130 mm for beam BO1 or 185 mm as in case of beam B1. This phenomenon might be eliminated by the addition of fibres.