esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell Disruption and Hydrolysis of _Microchloropysis salina_ Biomass as a Feedback for Fermentation
## 3 Results and Discussion  
### Composition of M. salina Biomass  
Figure 1 illustrates the elemental and macromolecular composition of the microalgae _M. salina_. The carbon content of dry _M. salina_ biomass (\(x_{\text{C,X}}\)) grown under nutrient-replete conditions was measured to be 53%. This, as well as the overall elemental composition of the microalga determined in this work, were in accordance with the literature values (50-55% C in CDW) [28; 29]. The ash content of 10.0% reported by [PERSON] et al. [12], which was used for calculations in this study, was also in the range of 4.2-11.7% as reported for  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Carbohydrate Moiety** & \
\begin{tabular}{c} **Mass Fraction in** \\ **CDW, mg g-1** \\ \end{tabular} &
\begin{tabular}{c} **Mass Fraction in** \\ **Total Sugars, \%** \\ \end{tabular} \\ \hline Glucose & 161.5 & 62.7 \\ Mannose & 50.1 & 19.5 \\ Galactose & 17.2 & 6.8 \\ Rhamose & 12.8 & 5.0 \\ Other sugars & 15.9 & 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sugar composition of _M. salina_ grown in thin-layer cascade photobioreactors in a nutrient-replete ASW medium [12].  
various algae species [30]. Based on its elemental composition, the molecular formula of \(M\). _salina_ biomass was determined to be CH\({}_{1.69}\)O\({}_{0.39}\)N\({}_{0.17}\).  
Macromolecular analysis showed that the dry \(M\). _salina_ biomass contains 26% carbohydrates and 46% proteins, which indicates that around 68% of dry microalgae biomass could be utilized as a carbon source in a subsequent fermentation using microalgal biomass hydrolysate as feedstock. In the literature, a protein content between 30 and 60% of the dry biomass has been indicated for various microalgae species [26; 29]. [PERSON] et al. [29] measured 35% carbohydrates, 13% lipids, and 39% protein in the dry \(M\). _salina_ biomass when the remaining moisture content was excluded. However, the biomass they used was cultivated as a feedstock for biofuel production, most probably under nutrient-deplete conditions, which influences the macromolecular composition [12]. On the other hand, for \(M\). _salina_ grown under nutrient-replete conditions, [PERSON] et al. (2019) [12] reported 26% carbohydrates, 16% lipids, and 48% proteins in the dry mass of \(M\). _salina_, which was remarkably similar to the macromolecular composition determined in this study.
# Enhancing Reliability Analysis with Multisource Data:
## 1 Introduction
### Research Significance and Objectives  
Figure 1a shows WIM systems consisting of quartz load sensors and magnetic loops installed on the roadway as well as a data acquisition system shown on the right. The load sensor is a piezoelectric device and thus involves electric polarization resulting from the application of vehicle weights. Transportation agencies and departments of public safety are expanding the WIM networks to better understand live load demands on bridges and roads and associated risks. WIM systems are capable of recording vehicle characteristics such as axle spacing, speed, axle weight, length, and number of axles and represents true traffic live loads on highways. Researchers around the world have started to realize the potential of WIM data and its application for bridge monitoring. [PERSON], [PERSON], [PERSON] and [PERSON] [6] have used the NBI condition rating data and truck traffic from WIM data to derive a deterioration model for bridge deck. [PERSON], [PERSON], [PERSON] and [PERSON] [9] have used WIM data and NBI database to predict the effect of heavy weight vehicle on the reduction of bridge fatigue life. [PERSON] et al. [16] have investigated the correlation between several bridge components from the NBI database using advanced machine learning algorithms. Several other studies emphasized the importance of determining vulnerability [17; 18; 19; 20] of bridge elements such as a deck element due to aging and other environmental factors.  
Although there have been several studies related to the reliability of bridges [6; 14; 19], a reliability analysis of bridges using both WIM data and the NBI database for an analysis of a large inventory of bridges has not been conducted and used for monitoring bridges. In recent years, heavyweight traffic has been significantly growing on interstate and major state highways. This study aims to propose a live-load informed risk analytics method that can be used for assessing risk for a large number of bridges in a state inventory as state DOTs face the big data environment. Thus, the novelty of this paper is twofold: (1) implementation of a gradient boosting algorithm to verify a relationship between operating load ratings and NBI attributes including condition ratings and (2) provides bridge monitoring personnel and MRR decision makers with more complete information about live load demands, corresponding to the capacity of bridges, in the larger information and data discovery environment.
## 1 Introduction
Technical constraints obstruct the seamless operation of automated surveillance systems. Poor lighting, low-resolution cameras, and limited storage capacity can all have an impact on the effectiveness of these systems. To improve the robustness and reliability of automated surveillance, a comprehensive approach to addressing these technical limitations is required. This study focuses on the technical limitations caused by low-quality videos: specifically, poor lighting and low spatial resolution. These difficulties have been identified as critical factors influencing the perceptual quality of video streams and thereby influencing the accuracy of anomaly detection systems according to [16]. The contributions of this study can be summed up as follows:  
* This study meticulously identifies and articulates two critical issues inherent in surveillance videos: high intra-class variability and low inter-class variability. These challenges, which are inextricably linked to the temporal properties of video streams, both short- and long-term, are exacerbated by the prevalence of low-quality videos. * This study makes an outstanding contribution by introducing an innovative super-resolution approach designed to mitigate the impact of low-quality videos caused by downscaling. This approach outperforms traditional bicubic interpolation by using an encoder-bank-decoder configuration to upscale videos. The primary goal is to improve the spatial resolution of videos in order to increase the accuracy of anomaly detection. The addition of a pre-trained StyleGAN as a latent feature bank is a critical step forward that enriches the super-resolution process and, as a result, improves anomaly classification accuracy. * The study implies a two-stream architecture for anomaly classification. The spatial stream uses a pre-trained CNN model for feature extraction, whereas the temporal stream employs an innovative approach known as Stacked Grayscale Image (SG3I). SG3I effectively lowers the computational costs associated with optical flow computation while accurately capturing short-term temporal characteristics. The extracted features from both streams are concatenated and fed into a Gated Recurrent Unit (GRU) layer, which allows the model to learn and exploit long-term dependencies. * Experiments show that the super-resolution model improves classification accuracy by 3.7% when compared to traditional bicubic interpolation methods. When combined with the encoder-bank-decoder super-resolution model, the classification model achieves an impressive accuracy of 92.28%, an F1-score of 69.29%, and a low false positive rate of 4.41%. To sum up, this research not only identifies and understands the difficulties that are associated with surveillance footage, but it also introduces novel approaches to deal with those difficulties. The end result of these efforts is observable improvements in performance and accuracy for the classification of anomalies in surveillance videos.
## 5 Conclusions  
In this study, the ability of the decoy state enabled QKD systems to detect PNS attacks is analyzed and demonstrated. In contrast to most decoy state protocol research which focus on decoy state security bounds and estimates, this work focuses on the protocol's occurrence percentages to both maximize signal state quantum throughput and assure PNS attacks are detectable with high confidence. Additionally, practical implementation performance and security guidance is provided for system developers and users. Lastly, this work demonstrates a repeatable methodology for studying QKD system implementation security to support formal certification efforts [68].  
Future suggested work includes optimization of the decoy state protocol in a fielded QKD system along with a detailed study of how the recommended occurrence percentages should be balanced with other constraints such as the need for bounded error rates. Additionally, in terms of validating the proposed decoy state configuration's ability to detect PNS attacks, a decoy state enabled QKD system should be tested against PNS-like attacks (see [20] for an example), since it is currently impossible to build a fully functional PNS attack. Lastly, the author recommends continued emphasis on studying QKD implementation security issues towards formal certification of decoy state enabled systems as they remain the most commercially viable option in the near future (especially when considering practical issues such as distance limitations and delivered key rates).  
In this work [PERSON] is the principle investigator with [PERSON] providing research advisement and expertise in quantum key distribution, [PERSON] providing modeling and software expertise, [PERSON] providing assistance in model development and execution, [PERSON] providing expertise in quantum optics, and [PERSON] providing expertise in quantum communications.  
The authors declare no conflict of interest. The views expressed in this paper are those of the authors and do not reflect the official policy or position of the United States Air Force, the Department of Defense, or the U.S. Government.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 3 Decolorization Methods
### CDF Method  
This method utilizes information from the cumulative distribution function of each color channel to calculate the weights of the image elements in each channel, which are then combined to produce the output grayscale image [9]. As with the basic method, three primary color signals are extracted at the beginning, after which the weights for each of them are computed, and finally, the products of the corresponding channels and weights are summed, resulting in the output grayscale image. The key and most computationally demanding part of this decolorization method is the weight calculation, and the process can be viewed as a series of steps. Initially, the cumulative distribution function values for each image channel--\(CDF_{R}\), \(CDF_{G}\), and \(CDF_{B}\)--are calculated, and the optimal distribution function value, \(CDF_{OPT}\), is determined as the average of the previous three. This idea is drawn from a simple histogram equalization process that enhances the image contrast. In the next step, the weight values are calculated and are finally normalized to be in the range (0,1], where each weight value indicates its importance in the final sum.
## 1 Introduction
One of the problems that arise when using fractional derivatives is that there is no unambiguous definition of them. Numerical methods for solving problems for equations with fractional derivatives depend on the selected derivative, so there is a need to analyze and compare the results obtained using different definitions and numerical methods [31]. In [32], finite-difference approximation was proposed for the space fractional convective-diffusion model, which has the coefficients of space variable on the given bounded domain over time and space. It was shown that the Crank-Nicolson difference scheme based on the right shifted Grunwald-Letnikov difference formula is unconditionally stable and also has second-order consistency both in temporal and spatial terms with extrapolation to the limit approach. An experimental study of the solute transport in a cylindrical zone with a central cylindrical macroporce was the subject of work [33]. The influence of the fluid velocity on the transport characteristics is studied. In the experiments, tritium was used as a portable solute. The fluid velocity varied by two orders of magnitude, the feed had the character of a \"short\" and \"long\" pulse length. A review of laboratory and field experiments, where mass transfer rates are modelled with the first order mass transfer and the Fickian pore-diffusion models, is given in [34]. It was shown that one of the causes non-adequate description of the experimental data with the Fickian pore-diffusion model can be alternative mass transfer mechanisms, i.e., advection-diffusion processes then diffusion only. Therefore, in general, transport mechanism in both macro and micropores is advection-diffusion. In this work, we consider an anomalous solute transport problem in a two-zone coaxial cylindrical porous medium. Intrinsic cylinder is treated as macropore with comparatively well permeability and other solute transport characteristics, and external cylindrical zone is treated as a micropore with comparatively small permeability and solute transport characteristics. Fist we present a mathematical model, i.e., fractional differential equations and corresponding initial and boundary conditions. Then a numerical procedure of the solution of posed problem is presented. After that some results are presented in graphical form and analysed. Influence of anomalous effects on solute transport characteristics is shown. Note, that in the work in distinguish with two-zones mobile-immobile solute transport problem we consider both zones as permeable media. So we have two zones with mobile liquid, but one of them is low permeable for the liquid. It considerably alters all filtration and solute transport characteristics.
# Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
## 1 Introduction  
Since 2016, drone technology has seen an increase in consumer popularity, growing in market size from 2 billion USD in 2016 [1] to 22.5 billion USD in 2020 [2]. As small form factor UAVs similar to the drone pictured in Figure 1 flooded the market, several industries adopted these devices for use in areas including but not limited to cable inspection, product monitoring, civil planning, agriculture and public safety. In research, this technology has been used mostly in areas related to data gathering and analysis to support these applications. However, direct development of navigation systems to provide great automation of drone operation has become a realistic aim, given the increasing capability of Deep Neural Networks (DNN) in computer vision, and its application to the related application area, vehicular autonomy. The work outlined in this paper is twofold: (1) it provides a common vocabulary around levels of drone autonomy, mapped against drone functionality, and (2) it examines research works within these functionality areas, so as to provide an indexed top-down perspective of research activity in the autonomous drone navigation sector. With recent advances in hardware and software capability, Deep Learning has become very versatile and there is no shortage of papers involving its application to drone autonomy. While domain-knowledge engineered solutions exist that utilize precision GPS, lidar, image processing and/or computer vision to form a system for autonomous navigation, these solutions are not robust, have a high cost for implementation, and can require importantsubsystems to be present for optimal operation, such as network access. The focus in this paper is on navigation works that utilise Deep Learning or similar learning-based solutions as a basis for implementation of navigation tasks towards drone autonomy. Just as Deep Learning underpins the realisation of self-driving cars, the ability of trained Deep Learning models to provide robust interpretation of visual and other sensor data in drones is critical to the ability of drones to reach fully autonomous navigation. This paper aims to highlight navigation functionality of research works in the autonomous drone navigation area, across the areas of environmental awareness, basic navigation and expanded navigation capabilities. While the general focus is on DNN-based papers, some non-DNN-based solutions are present in the collected papers for contrast.  
Research projects focused specifically on the development of new navigational techniques with or without the cooperation of industry partners form the definition of what is considered as the state of the art--not as currently implemented solutions in industry but solutions and implementations being actively researched with the potential for future development.
## 4 Seismic Excitation
### The Role of the Stiffness of the External Structure and the Clearance of the VI-NES  
The initial analysis aims to examine the influence of the parameters \(\beta_{c}\) and \(\rho_{e}\). Maps of the gain coefficients \(\alpha_{1}\) and \(\alpha_{2}\) are generated, maintaining constant characteristics for the frame structure (Frame 1, as detailed in Table 2), along with fixed coefficients \(\eta=0.1\) and \(r=0.7\). The excitation is provided by the Kobe earthquake record. Figure 3 displays gain maps for two distinct coupled systems. The maps in the first row pertain to a frame structure that was coupled with an external structure with a total mass equal to \(m_{e}+M\). Those in the second row correspond to a frame structure coupled with an external structure of mass \(m_{e}\), which was equipped with a vibro-impacting mass \(M\). As the total mass of the two systems remained the same, comparing the results of these two cases revealed the advantage of having a moving (and impacting) mass rather than a fixed one.  
When the mass is fixed, a reduction is observed only in the displacements of the story directly connected to the external structure, as indicated by the advantage region (light gray region) in the \(\alpha_{1}\) map. Conversely, in this system, there is no reduction in the drift of the superstructure, as evidenced by the dark gray region that encompasses the entire \(\alpha_{2}\) map. It should be noted that, given the fixed mass, the clearance of the vibro-impacting mass \(\beta_{c}\) does not impact the behavior of the system. Consequently, the two maps could be represented by a single section each. However, presenting the results as separate maps is necessary for the purpose of comparison with the case featuring the vibro-impacting mass.  
The utilization of VI-NES significantly enhances the seismic behavior of the coupled structure. This improvement is evident in the bottom maps of Figure 3, where the advantage regions now encompass nearly the entire parameter plane in both the \(\alpha_{1}\) and \(\alpha_{2}\) maps. The two maps show that the vibro-impacting mass can significantly reduce the displacement of the connection floor with a 40% reduction in \(u_{1}\) compared to the 22% reduction observed in the previous case. Additionally, the drift of the superstructure is reduced by approximately 30% compared to the drift of the uncoupled frame structure. Both maps show points of a relative minimum (i.e., points where the maximum reduction of displacements occurs). The minima in the \(\alpha_{1}\) and \(\alpha_{2}\) maps were not perfectly coincident, although they were quite close to each other. A suitable choice of the parameters \(\beta_{c}\) and \(\rho_{e}\) would allow for optimizing the performance of the coupled system.  
Figure 2: Kobe, Newhall, and Pacoima earthquakes: (**a**) time histories and (**b**) acceleration response spectra.
# A Learnable Viewpoint Evolution Method for Accurate Pose Estimation of Complex Assembled Product
## 3 Experiment and Results
### In-Depth Analysis of the Proposed Method
#### 3.3.1 Verifying Robustness to Initial Pose Deviation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **Noise-0** & **Noise-2** & **Noise-4** & **Noise-6** & **Noise-8** & **Noise-10** & **Noise-12** \\ \hline \(\overline{\tilde{a}}_{d}(^{\circ})\) & 5.32 & 5.94 & 5.74 & 6.12 & 7.75 & 8.97 & 11.74 \\ \(\overline{\tilde{t}}_{d}(^{\circ})\) & 167.42 & 178.22 & 183.28 & 187.53 & 194.13 & 226.56 & 277.29 \\ \hline  & A & B & A & B & A & B & A & B & A & B \\ \hline \(\overline{\tilde{a}}_{e}(^{\circ})\) & 0.26 & 0.23 & 0.28 & 0.24 & 0.31 & 0.27 & 0.34 & 0.26 & 0.46 & 0.34 & 0.59 & 0.43 & 0.78 & 0.71 \\ \(\overline{\tilde{a}}_{e}\_{std}(^{\circ})\) & 0.18 & 0.13 & 0.17 & 0.14 & 0.21 & 0.15 & 0.23 & 0.22 & 0.29 & 0.24 & 0.34 & 0.29 & 0.47 & 0.46 \\ \(\overline{\tilde{a}}_{e}\_{min}\) & 0.11 & 0.10 & 0.10 & 0.09 & 0.14 & 0.11 & 0.13 & 0.10 & 0.15 & 0.13 & 0.20 & 0.15 & 0.17 & 0.18 \\ \(\overline{\tilde{a}}_{e}\_{max}\) & 0.54 & 0.44 & 0.61 & 0.50 & 0.75 & 0.59 & 0.78 & 0.58 & 0.93 & 0.77 & 1.07 & 0.92 & 1.35 & 1.23 \\ \(\overline{\tilde{t}}_{e}\) (mm) & 32.66 & 23.71 & 31.54 & 22.97 & 33.46 & 25.61 & 37.35 & 28.38 & 45.52 & 34.27 & 51.63 & 42.57 & 73.83 & 66.34 \\ \(\overline{\tilde{t}}_{e}\_{std}\) (mm) & 17.14 & 12.87 & 16.82 & 13.21 & 19.52 & 12.79 & 23.63 & 18.75 & 26.48 & 27.82 & 31.57 & 32.11 & 41.63 & 39.69 \\ \(\overline{\tilde{t}}_{e}\_{min}\) & 10.27 & 6.42 & 9.17 & 8.36 & 9.49 & 7.94 & 14.49 & 8.34 & 13.53 & 11.75 & 17.93 & 13.56 & 17.31 & 14.57 \\ \(\overline{\tilde{t}}_{e}\_{max}\) & 54.53 & 43.08 & 59.31 & 42.24 & 61.40 & 47.75 & 64.37 & 51.85 & 71.59 & 73.33 & 83.23 & 77.59 & 92.64 & 88.62 \\ \(\overline{iter}\) & 12.48 & 8.14 & 12.75 & 8.21 & 12.69 & 8.17 & 12.95 & 8.39 & 13.44 & 8.80 & 12.95 & 8.91 & 14.27 & 9.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The robustness to initial pose deviation. Figure 9: Visualized comparison of robustness of Proposed-A and -B to pose deviation.
_Appl. Sci._**2015**, \(5\), 1639-1664; doi:10.3390/app5041639  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Magnetohydrodynamic Flow and Heat Transfer of Nanofluids in Stretchable Convergent/Divergent Channels  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 Department of Mathematics, Faculty of Sciences, HITEC University, 47080 Taxila, Pakistan;  
E-Mails: [EMAIL_ADDRESS] (U.K.); [EMAIL_ADDRESS] (N.A.)  
2 Department of Mathematics, College of Science, King Saud University, P.O. Box 2455,  
Riyadh 11451, Saudi Arabia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +92-0323-5577701.  
Academic Editor: [PERSON]  
_Received: 21 September 2015 / Accepted: 19 November 2015 / Published: 4 December 2015_  
This article is dedicated to analyzing the heat transfer in the flow of water-based nanofluids in a channel with non-parallel stretchable walls. The magnetohydrodynamic (MHD) nature of the flow is considered. Equations governing the flow are transformed into a system of nonlinear ordinary differential equations. The said system is solved by employing two different techniques, the variational iteration method (VIM) and the Runge-Kutta-Fehlberg method (RKF). The influence of the emerging parameters on the velocity and temperature profiles is highlighted with the help of graphs coupled with comprehensive discussions. A comparison with the already existing solutions is also made, which are the special cases of the current problem. It is observed that the temperature profile decreases with an increase in the nanoparticle volume fraction. Furthermore, a magnetic field can be used to control the possible separation caused by the backflows in the case of diverging channels. The effects of parameters on the skin friction coefficient and Nusselt number are also presented using graphical aid. The nanoparticle volume fraction helps to reduce the temperature of the channel and to enhance the rate of heat transfer at the wall.  
nanofluids; MHD flow; analytical solution; Nusselt number; convergent and divergent channels