esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
In lemon balm and lavender, the higher level of rosmarinic acid was measured in higher amounts in both types of extract. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Extracts**} & \multicolumn{4}{c}{**Herbs**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-3} \cline{5-6}  & & **Peppermint** & **Lemon Balm** & & **Lavender** \\ \hline \multirow{4}{*}{ABTS} & Infusion & 0.35 \(\pm\) 0.03 b & 0.45 \(\pm\) 0.04 c & 0.25 \(\pm\) 0.02 a & 0.000 \\ \cline{2-6}  & Metanol & 0.33 \(\pm\) 0.07 ab & 0.57 \(\pm\) 0.21 b & 0.25 \(\pm\) 0.02 a & 0.049 \\ \cline{2-6}  & Acidified Methanol & 0.18 \(\pm\) 0.00 b & 0.38 \(\pm\) 0.06 c & 0.03 \(\pm\) 0.01 a & 0.000 \\ \hline \multirow{4}{*}{FRAP} & Infusion & 6117.76 \(\pm\) 94.13 c & 2770.60 \(\pm\) 48.96 b & 1795.47 \(\pm\) 88.39 a & 0.000 \\ \cline{2-6}  & Methanol & 1541.20 \(\pm\) 409.88 b & 1279.88 \(\pm\) 214.69 b & 490.15 \(\pm\) 17.50 a & 0.007 \\ \cline{2-6}  & Acidified Metanol & 1506.27 \(\pm\) 112.24 a & 1588.32 \(\pm\) 84.60 a & 2096.26 \(\pm\) 1625.30 a & 0.721 \\ \hline \multirow{4}{*}{Total Polypphenols} & Infusion & 23,470 \(\pm\) 29 g & 18,102 \(\pm\) 83 f & 4530 \(\pm\) 39 bc & 0.000 \\ \cline{2-6}  & Methanol & 5282 \(\pm\) 196 c & 6648 \(\pm\) 36 bd & 3538 \(\pm\) 883 b & 0.063 \\ \cline{1-1} \cline{2-6}  & Acidified Methanol & 11,815 \(\pm\) 196 e & 11,629 \(\pm\) 657 e & 2146 \(\pm\) 269 a & 0.000 \\ \hline \hline \end{tabular} Results are expressed as mean \(\pm\) SD; mean values with different letters (a-g) within the individual rows (other than the last column) are statistically different (\(p\)\(\leq\) 0.05).  
\end{table} 
Table 2: Antioxidant activity (umol Trolox/1 g DM) and total polyphenols (mg/100 g DM) content in the tested herbs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**Peppermint**} & \multicolumn{2}{c}{**Lemon Balm**} & \multicolumn{2}{c}{**Lavender**} \\ \cline{2-6}  & **Infusion** & **Acidified Methanol** & **Infusion** & **Acidified Methanol** & **Infusion** & **Acidified Methanol** \\ \hline Gallic acid & 2.90 \(\pm\) 0.03 b & 3.05 \(\pm\) 0.01 b & 2.60 \(\pm\) 0.12 c & 0.00 \(\pm\) 0.00 a & 3.4 \(\pm\) 0.14 d & 0.00 \(\pm\) 0.00 a \\ Catechin & 38.59 \(\pm\) 1.97 e & 18.33 \(\pm\) 0.04 b & 11.37 \(\pm\) 0.08 a & 23.57 \(\pm\) 0.27 c & 35.56 \(\pm\) 0.03 d & 43.59 \(\pm\) 1.06 f \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Polyphenolic profile of peppermint, lemon balm and lavender (mg/100 g DM).
## 1 Introduction
However, 3D shape reconstruction based on deep learning techniques must still address these challenges. These difficulties highlight the existing research gaps, namely the need to develop robust algorithms to effectively improve the quality of 3D shape reconstruction [16]. Early methods had predefined rules to infer local geometric features and generate 3D shapes. However, these methods often limited the diversity of 3D reconstructions. Figure 1: FDS using digital sketching applications converted to D3 DM (developed by the authors). The advent of deep learning has revolutionized this field. With large datasets of synthetic sketches to train deep neural networks, algorithms can predict 3D geometries from free-hand sketches. For example, conditional Generative Adversarial Networks (cGANs) can process free-form sketches without strict structural requirements. Multi-view integration methods reduce ambiguity in single-view sketches, enhancing depth perception and structural coherence. Direct shape optimization methods reconstruct 3D shapes from multiple sketches, providing greater detail and alignment with artistic intent. In our work, we use the marching cubes algorithm to convert implicit functions into surface meshes and render any view of the reconstructed surface through rasterization. This method supports iterative model optimization based on user input, making it ideal for design and prototyping applications. This study selected eyeglass frame design as the subject for 3D shape reconstruction from FDS due to the distinct modeling characteristics of its rims, bridges, and temples. These components define the product's design and feature absolute symmetry and smooth surfaces, which are important criteria for the D3 DM outcomes. The transformation from an eyeglass frame's FDS to its D3 DM requires 3D shape reconstruction to solve challenges such as surface fitting, edge detection, and geometric shape matching. Therefore, this research explores the essential technological application of 3D reconstruction algorithms to capture the modeling characteristics of the target product and ensure symmetry and smoothness standards. Innovative solutions in this field can help promote the broad application prospects of 3D shape reconstruction from FDS based on deep learning techniques. The research results of the essential technologies can directly drive the progress of computer-aided design and digital manufacturing, opening up new possibilities for intuitive design conceptualization and efficient scheme adjustment in architectural and industrial design. Overall, this research introduces a transformative approach to 3D shape reconstruction from FDS, leveraging deep learning techniques to overcome traditional challenges in fidelity, symmetry, and geometric precision, setting a new benchmark in the integration of AI and creative design workflows.
## 4 Methodology
### Data Analysis  
The hypotheses were tested using structural equations modelling (SEM) with IBM AMOS 26. SEM is a second-generation multivariate technique that has significant advantages over first-generation techniques such as multiple regression; specifically, SEM allows researchers to test relationships among multiple predictor and criterion variables, to model errors in measurements for observed variables, and to conduct confirmatory analyses [59]. In short, SEM allowed us to test the entire conceptual model in one analysis, and to examine the relationship between ability, motivation and opportunities and the different types of SPP at the same time. With SEM, the overall fit of the model was determined using a combination of absolute and relative fit indices [60]: specifically, two absolute fit indices: the chi-square \"goodness of fit test\" (CMIN/DF), and the root mean squared error of approximation (RMSEA) [61; 60]. An SMRM and RMSEA value equal to or below 0.08 and a non-significant chi-square value with a CMIN/DF value below 2.0 indicate that the model has an acceptable model fit [61; 60; 62; 63; 64]. Additionally, three relative fit indices were checked: the Tucker-Lewis Index (TLI), comparative fit index (CFI) and a goodness of fit index (GFI). The threshold value for all three relative fit indices for a good model fit is greater than 0.9 [63]. As our final measurement model includes the mediation effects, a bootstrapping method was also employed via AMOS [65]. In the bootstrapping method, 5000 samples (with replacement) were created for the available observed sample. Via these samples, the significance of the indirect relations was calculated. The descriptive statistics and basic inferential statistics were calculated with IBM SPSS Statistics version 25.  
As the data is self-reported and collected amongst a single group, it could be subject to a common method bias [66]; to rule this out, a Harman one-factor was carried out on all the items used to measure the hypotheses to see if the majority of the variance could be explained by a single factor [67; 68]. Together, the components accounted for 71.9% of the variance, and no single component was accountable for more than 28.4% of the variance. As no single factor emerged from the factor analysis and common method, variance could therefore be ruled out.  
In Table 1, the bivariate correlation, means, and standard deviations are presented. The results show that GPP has the most frequent application, with a mean of 3.676, directly followed by SROI (3.578), scoring well above the theoretical mean. However, even the least frequently reported type of SPP, BBPP, scores barely below the theoretical mean (2.242).
###### Abstract  
The realization of high-speed and high-power gallium nitride (GaN)-based devices using high-quality GaN/Aluminum nitride (AlN) materials has become a hot topic. Raman spectroscopy has proven to be very useful in analyzing the characteristics of wide band gap materials, which reveals the information interaction of sample and phonon dynamics. Four GaN samples grown on different types of buffer layers were fabricated and the influence of graphene and sputtered AlN on GaN epitaxial layers were analyzed through the E\({}_{2}\) (high) and A\({}_{1}\) (LO) phonon. The relationship between the frequency shift of E\({}_{2}\) (high) phonons and the biaxial stress indicated that the GaN grown on the graphene/sputtered AlN buffer layer was stress-free. Furthermore, the phonon lifetimes of A\({}_{1}\) (LO) mode in GaN grown on graphene/sputtered AlN buffer layer suggested that carrier migration of GaN received minimal interference. Finally, the Raman spectra of graphene with the sputtered AlN interlayer has more disorder and the monolayer graphene was also more conducive to nucleation of GaN films. These results will have significant impact on the heteroepitaxy of high-quality thin GaN films embedded with a graphene/sputtered AlN buffer, and will facilitate the preparation of high-speed GaN-based optoelectronic devices.  
E\({}_{2}\) (high) phonon; A\({}_{1}\) (LO) phonon; stress-free GaN; graphene; sputtered AlN +
Footnote †: journal: Applied Physics Letters
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 4 CloudMonitor as Our Conceptual Framework
### The CloudMonitor Use-Case  
This section describes a use-case for CloudMonitor to present its effectiveness. \"OwnCloud\" is a collection of client-server software components designed for establishing and using the services of file storage. OwnCloud has a similar functionality compared with the widely used Dropbox or Google drive. We used OwnCloud as the cloud server where consumers can upload and/or download files as desired. Normally, the OwnCloud server can be accessed via a SOAP and RESTful API [68]. In the CloudMonitor implementation, we have only demonstrated this access via SOAP API. Three users have been created on the OwnCloud server to validate the implementation of the CloudMonitor framework. Each user is expected to upload a file via a modified web browser. The browser is modified with a plugin that attaches outgoing HTTP requests with a set of identification information along with the user and file metadata. The file metadata includes where the file is locally stored. Subsequently, by intercepting the user's request, the API stub inspects the request content and retrieves the user information. It then matches the request against the consumer's rules residing in the policy store.  
If a service request fails to match the rules, a message is sent to the user for acknowledgment. In the other case, where the user request matches with the rules in the policy store, then the API stub executes the request. Based on the segment of the network in the local infrastructure of the consumer from which the user is sending the request, (i) the system informs the user about the confidentiality of the file being uploaded. As a result, (ii) the file is either labeled confidential or not confidential. The system API stub then (iii) continues with the file upload request to the OwnCloud server. Here, (iv) the request along with its labels are matched against the service provider policy and a decision is taken on whether to store the data or reject the request. Once the data storage is accepted (v) the data flow tracking (DFT) is performed within the boundaries of a well-defined DFT domain. Whenever the data crosses a specified domain, (vi) the CloudMonitor logs the action in its audit database. CloudMonitor has a user interface that leverages Cloudopsy (i.e., a web-based data auditing dashboard) so (vii) the user can have a comprehensive view of all audit information.
## 3 Joint Resource Allocation and Trajectory Planning under Known Users' Location  
In this section, we propose a joint resource allocation and trajectory planning algorithm to solve problem \(\mathbf{P1}\) under known users' location. For the known users' location, we can ignore the estimation error \(\varepsilon_{n}\) of users, i.e., let \(\hat{\mathbf{q}}_{n}:=\mathbf{q}_{n},\forall n\in\mathcal{N}\). Then, the original optimization problem \(\mathbf{P1}\) can be transformed to  
\[\mathbf{P2}: \max_{\Theta}\min_{n\in\mathcal{N}}\hat{E}_{gain,n}, \tag{11a}\] \[s.t. C1-C6. \tag{11b}\]  
Due to the coupling among the optimization variables, \(\mathbf{P2}\) is still difficult to solve. Thus, we divide the optimization problem into two parts, i.e., computation offloading optimization and trajectory planning, and optimize them alternately. Firstly, we optimize the computation offloading resources by Lagrangian duality method under given UAV trajectory. Then, the SCA method is adopted to optimize the UAV trajectory for given computation offloading resources.
## 5 Market Survey on Small Satellites Monopropellant Propulsion  
This section will discuss state-of-the-art small-sized spacecraft green monopropellant propulsion systems, refer to Figure 8. These propulsion modules are proposed for Earth-orbiting missions as well as Lunar missions. It was observed that propulsion systems manufacturers recently started to orientate toward unconventional feed and pressurization systems to overcome small size restrictions onboard small satellites while obtaining maximum total impulse performance possible. It was also seen that electric pump feed is being considered in very recently published and released work by Georgia Tech. and NASA for near future Lunar missions [52].  
Figure 8: State of the art in green monopropellant propulsion: (**a**) EPSS C1 CubeSat Propulsion System. (Image courtesy of NanoAvionics [53]); (**b**) Busek 1U CubeSat Green Propulsion Module. (Image Courtesy of [PERSON] et al. [45]—Busek Co., Inc.); (**c**) MPS-135 Modular Propulsion System. (Image Courtesy of Aerojet Rockedyne [54]); (**d**) Pinot-G propulsion module. (Image courtesy of IHI Aerospace Co. [55]); (**e**) HPGP 100 mN thruster. (Image Courtesy of Bradford-ECAPS); (**f**) BGT-X1 and BGT-X5 green monopropellant thrusters. (Image Courtesy of Busek Co.).
# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 5 Result and Discussion
### Limitations and Future Directions  
First and foremost, we are unable to provide a clear explanation for the observed improvement in detection performance resulting from the choice of time window. Intuitively, we can comprehend that cumulative anomalies exhibit a gradual change in their data distribution over time until an anomaly occurs. However, the selection of multiple time windows in this study occasionally leads to the selection of a detector that may not necessarily be the best performing model. As a consequence, the final integration of anomalous results is affected, as evidenced by its poor performance in certain datasets. The interpretability of the method presented in this paper lacks a clear intuitive explanation.  
Moreover, the integration models based on precision and sensitivity present conflicting perspectives. An excessive focus on accuracy can sometimes lead to lower recall and F1 values for the method. Conversely, being overly sensitive to anomalous data can result in higher false alarm rates. The paper does not adequately address how to strike a balance between these two aspects. The consideration of this balance is not well addressed in this study.
# Electrochemical Detection of Penicillin G Using Molecularly Imprinted Conductive Co-Polymer Sensor
## 3 Results and Discussion
### Sensor's Performances  
As the final objective of these sensors is to perform measurements in milk, in order to get as close as possible to this type of media, a phosphate-buffered saline (PBS) solution (twice diluted) is used to perform the measurements. This means that instead of using a 1\(\times\) PBS solution, a 0.5\(\times\) PBS solution is used. This solution has a similar conductivity and pH as milk. The measurements are performed by electrochemical impedance spectroscopy (EIS) in this solution.
## Nomenclature  
\begin{tabular}{l l l l} \(b\) & ironic mobility,\(\left[m^{2}\left/\left(sV\right)\right.\right]\) & \(T\) & Temperature, \(\left[K\right]\) \\ \(C_{{}_{P}}\) & specific heat capacity, \(\left[\mathrm{kJ}/\left(kg\ K\right)\right]\) & \(u,v\) & Cartesian components of velocity \\  & & & \(\overset{\rightarrow}{V}\), [m/s] \\ \(D_{{}_{E}},D\) & diffusion number \(\left(=\mu_{{}_{0}}/\left(\rho_{{}_{0}}D_{{}_{0}}\right)\right)\), & \\  & charge diffusion coefficient, \(\left[m^{2}\left/\ s\right.\right]\) & _Greek symbols_ \\  & Eckert number & & \\ \(Ec\) & \(\left(=\rho_{{}_{F}}U_{{}_{Lid}}^{{}^{2}}/\left(\left(\rho C_{{}_{P}}\right)_{{} _{f}}\left(T_{{}_{1}}-T_{{}_{0}}\right)\right)\right)\) & \(\varepsilon\) & dielectric permittivity \\ \(\overset{\rightarrow}{E},E_{{}_{x}},E_{{}_{y}}\) & electric field, \(\left[V\left/\ m\right]\) & \(\phi\) & Volume fraction \\ \(\overset{\rightarrow}{F}_{{}_{E}}\) & Coulomb force, \(\left[N\right]\) & \(\mu\) & Dynamic viscosity \\ \(\overset{\rightarrow}{\mathbf{J}}\) & electric current density, \(\left[A\left/\ m^{2}\right.\right]\) & \(\rho\) & Density \\ \(L\) & characteristic length, \(\left[m\right]\) & \(\sigma\) & electric conductivity \\ \(N_{{}_{E}}\) & electric field number & & \\ \(p\) & Pressure, \(\left[Pa\right]\) & _Subscripts_ \\ \(Pr\) & Prandtl number \(\left(=v_{{}_{f}}/\alpha_{{}_{f}}\right)\) & _ave_ & Average \\ \(Pr_{{}_{E}}\) & electric Prandtl number & & \\ \(q\) & electric charge density, \(\left[C/\ m^{3}\right]\) & \(s\) & Solid particles \\ \(Re\) & Reynolds number \(\left(=\rho_{{}_{F}}U_{{}_{Lid}}L/\mu_{{}_{f}}\right)\) & \(f\) & Base fluid \\  & Lorentz force number & & \\ \(S_{{}_{E}}\) & \(\left(=q_{{}_{0}}\Delta\varphi/\left(\rho U_{{}_{Lid}}^{{}^{2}}\right)\right)\) & \(h\) & Hot \\ \(t\) & Time, \(\left[s\right]\) & _nf_ & Nanofluid \\ \end{tabular}
# Effects of Transport Network Slicing on 5G Applications
## 6 Impact of Network Slicing on 5G Applications
In the downlink traffic experiments, a total of 10 PigTalk video streams travel along the path (2)-(4)-(6)-(9) in Figure 2. This MBB application results in a total sending rate of 2.632 Gb/s. The background UDP traffic is generated from the iPerf with a data rate of up to 10 Gb/s. Figure 7 illustrates how network slicing improves the three measures of throughput, loss rate, and latency for application traffic flows. The graphs (a), (c), and (e) evaluate how application traffic flows, both baseline flows (namely non-slicing flows) and slicing flows, are affected by various background traffic volumes. On the other hand, (b), (d), and (f) illustrate how the output measures of the application traffic flows are affected by various CIR ratios when network links are saturated with 10 Gb/s background traffic. In Figure 7, the dashed curves in graphs (a), (c), and (e) demonstrate how baseline flows are affected by various background traffic volumes, and the solid curves show how network slicing with a 100% CIR ratio improves traffic performance. Figure 7: \(Cont.\)
## 3 Numerical Simulation of Shockwave Soil Loosening
### Assumptions  
The numerical simulation employed the CFD self-code [30] to model the propagation of shockwaves through a soil medium. The following methodologies and assumptions were applied:* Shockwave characteristics: The initial shockwave was generated with a pressure of 10 atm (about 1000 kPa) at the shock tube inlet, simulating the shockwave start-pressure of the system.
* Soil model: The soil was modeled as a porous medium with specific properties, including a density of 1500 kg/m\({}^{3}\) and a porosity of 30%.
* Boundary conditions: The shock tube inlet was set as a pressure inlet with an initial high-pressure patch, while the far-field boundaries were defined as pressure outlets to minimize reflections.
# A Review of Experimental and Numerical Studies of Lithium Ion Battery Fires
## 3 LIB Chemistry
### Separator  
The separator provides the physical separation between the positive and negative electrodes, which prevents an electrical short circuit between the electrodes. Additionally, the separator acts as the electrolyte reservoir for the transport of Li-ions during the discharge and charging process. Upon failure of the separator, a short circuit between the anode and cathode occurs, which likely leads to thermal failure of the LIB. The following factors are considered critical requirements of the separator: (1) chemical/mechanical stability, (2) thickness, (3) thermal stability, (4) wettability, (5) barrier property, and (6) porous property. Polyolefin-based membranes such as polyethylene (PE), polypropylene (PP), and PP/PE/PP composite membranes have been the most commonly used as separators in LIBs due to their robust mechanical properties, low cost, and chemical stability [49]. Various composite materials including bromine have been investigated as being able to improve the flame retardancy (FR) of the separator while maintaining the critical parameters listed above.  
Recently most research efforts have been dedicated to improving the performance of the polyolefin-based separators through the surface coating, coated PO membranes, ceramic blended PE membranes [50; 51; 52; 53], ceramic filled nonwovens, and nanofiber separators [54; 55], and additives to improve the wettability of the separator towards nonaqueous electrolytes [56].  
[PERSON] [57] published results from experimental work on the development of plasma-treated polypropylene nonwoven-based composites for high-performance LIB separators. When fluorinated segments are introduced onto the surface of PP nonwoven through plasma treatment, the composite separators display increased electrolyte uptake and ionic conductivity in addition to the electrochemical stability and discharge capacity. [PERSON] [58] presented a study in which they present an in situ formed silica@silica-embedded polyimide (in situ SiO2@(PI/SiO2)) nanofabric as a new high-performance inorganic--organic hybrid separator.