esa-sceva/satcom-chunk-collection huggingface dataset:
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 11 Conclusions  
The LPS synthesis is subjected to a tight regulation at variety of steps. Regulation of LpxC amounts by FtsH and LapB constitutes an essential step to maintain a balanced synthesis of LPS and phospholipids. Further, MsbA by preferential selectivity of hexaacylated LPS provides an early checkpoint, preventing transport of precursor underacylated LPS species. LapB recruits LpxC, FtsH, glycosyltransferases, myristoyltransferase LpxM and other proteins that are required for LPS synthesis at the IM, presumably acting as a scaffold to deliver completely synthesized LPS to its other interactingpartners in the Lpt transport system. LPS is heterogeneous in composition and its composition is dynamically altered by a host of factors. These LPS alterations are regulated by the RpoE sigma factor, TCS like BasS/R, PhoP/Q, PhoB/R and Rcs system with an overwhelming role played by noncoding sRNAs. The Rcs system senses LPS defects resulting into phosphorylation of RcsB, which leads to activation of transcription from _rpoE_P3 promoter leading to RpoE induction. Finally, the transenvelope Lpt complex of seven essential proteins acts as a single unit and in an ATP-dependent manner ferries LPS across the periplasmic bridge comprised of LptA for delivery to the OM component of LptD/E in a continuous manner. Although tremendous progress has been achieved in the recent past, it is still unknown how the LptB cytoplasmic component of Lpt system is localized to the IM and how the proteolytic activity of FtsH is regulated by LapB protein and other factors. Further studies are also needed to demonstrate that LapB acts as the scaffold for LPS assembly and its coordination with the Lpt transport system, although LapA/LapB proteins have been demonstrated to co-purify with Lpt components. As LPS is essential, its essential steps in biosynthetic pathway and Lpt transport have been validated targets of discovery of new antibiotics. Already inhibitors of LpxC and a peptidomimetic POL7080 based on the cationic antimicrobial peptide that inhibits the LptD activity are in advanced clinical trails and many additional antimicrobial compounds using LPS synthesis/assembly are bound to be discovered in future.  
[PERSON] and [PERSON] participated in draft preparation, review and editing. All authors read and approved the final manuscript. This research was funded by National Science Center (NCN) 2017/25/B/NZ6/02021 to SR.  
The authors declare no conflict of interest.
# How Human Mobility Models Can Help to Deal with COVID-19
## 3 Mobility Models
### Synthetic Models  
This category comprises all models that generate user's mobility synthetically. We include in this subsection pure basic random models, such as Random Waypoint Model (RWM) and more complex random models, which combines randomly generated mobility with some real human mobility patterns (the latter are also known as hybrid models).  
Random movement models use stochastic movement patterns to move a node within a given area. Random Waypoint Model (RWP) [43] is the most used synthetic mobility model due to its simplicity, allowing for analytical evaluation. Basically, the mobile node selects a destination randomly and moves towards it with a random speed. There are other mobility models based on RWM, such as time-dependent mobility models, that is, the next movement depends on the previous movement; and spatial-dependent models, that is, they tend to change the movement in a correlated way with the previous position; and finally, geographically restricted models, where movement is restricted by streets, buildings or obstacles.  
However, the utility of these pure random models is quite limited, since these models should imitate human behaviour, where the movement of an individual is also influenced by their daily activities (at home, school, or work), by their means of transport (on foot, bicycle, bus, etc.) or by the behaviour of other users in their neighbourhood or environment. Many of these activities are related to user behaviour and social relationships.  
A way to improve these basic synthetic models is to include some parametrisrisation related to human behaviour. These extended models are usually known as hybrid models, since, besides using basic mobility models as the RWM, they can include more complex mobility pattern, such as the frequency of user movements or the daily routes, which can be obtained from real mobility traces or user experience. As an example, the Small World In Motion (SWIM) model [44] is based on the assumption that a user selects as the next Point of Interest (PoI), a place near their home or a very popular place (for example, a popular restaurant). Additionally, other models are based on the social relationships between nodes and location preferences, for example: periodic trips over short distances, movements coordinated by social relationships, etc: Smooth [45], Self-similar Least Action (SLAW) [46], Small World In Motion (SWIM) [44], HCMM [47], Work Day Movement (WDM) [48], Time-Variant Community model (TVC) [49], Heterogeneous Human Walk (HHW) [50][83], and Sociological Orbit models (SOLAR) [51].  
Summing up, these hybrid models try to model the real properties of human movements taking into account social behaviour but also information from real measurements of human mobility. In general, these hybrid models achieve better performance and scalability.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 2 Multicarrier Modulations and FIR Filter
### Modulation Waveforms
#### 2.1.5 F-Ofdm  
The basic principle of F-OFDM technique is to divide the carrier bandwidth of OFDM into sub-bands with different parameters, filter the sub-bands, and leave less isolation bands in the sub-bands [46]. Firstly, to support diverse businesses, F-OFDM modulation supports flexible sub-band configurations for different subcarrier spacing. Secondly, it supports different sub-band configurations allowing different CP lengths to better adapt transmission channels, and different sub-bands on asynchronous signal transmission are also supported, thus saving the signaling overhead. Finally, with better out of band suppression characteristics compared to OFDM, it saves the cost of protection zone, that is, the spectral efficiency is improved. However, filters need to be dynamically designed for each fragment, which makes the use of F-OFDM systems challenging [88].
## 3 Discussion
### Effect of Poisson's Ratio  
The elastic modulus corresponding to various diameters is selected. When the other parameters remain unchanged, the Poisson's ratio \(\mu\) of the SCA model is varied so as to simulate the effect of [PERSON]'s ratio. The simulation results show that the axial expansive pressure increases linearly with the Poisson's ratio. In order to keep the simulated radial expansive pressure consistent with the measured data, the amplitude coefficient \(k_{i}\) for temperature increase needs to be adjusted. The amplitude coefficient \(k_{i}\) after adjustment decreases linearly with increasing Poisson's ratio. Taking the simulation results for models with cylinder diameter of 50 and 90 mm for example, the relationships between [PERSON]'s ratio and the amplitude coefficient are shown in Figure 9.  
Based on the above simulation results, the axial expansive pressures corresponding to various Poisson's ratios are compared. Given the simulated radial expansive pressures being consistent with the measured values, the relationships between [PERSON]'s ratio and the axial expansive pressure for models with cylinder diameter of 50 mm and 90 mm are plotted in Figure 10. It can be seen that the Poisson's ratio has little impact on the axial pressure, which is almost constant. In another word, the ratio between the axial and radial pressure is independent of Poisson's ratio.
# MALDI-TOF/MS Analysis of Extracellular Vesicles Released by Cancer Cells
## 2 Applications
### Breast Cancer
Using a database of the differently expressed proteins in human cancer, it was found that 64% of the proteins of blood cell-associated exosomes from cancer patients were of tumor origin. These and other findings highlighted the role of plasma and blood cell-associated exosomes in the dissemination of the tumor process. Very recently, a sequential size-exclusion chromatography approach [28] was successfully used by [PERSON] et al. for the fast (less than 2 h) and efficient isolation of exosome fractions from human plasma samples collected from breast cancer (79), pancreatic cancer (57), and healthy (84) individuals. The vesicle membrane was then destructed to release their content and to proceed to the obtainment of MALDI fingerprints. A multi-classifier artificial neural network model was eventually adopted using extracted MS feature peaks as inputs, successfully allowing for the discrimination of the different types of samples.
## 3 Simulations and Experiments on Brake Pressure Response  
### Solving the Pipe Control Equations  
#### 3.1.1 CIP Method  
First proposed by [PERSON] [18; 19] in the 1980s, the CIP method is an effective way to solve hyperbolic PDEs. It uses the function value and its derivative on a spatial grid, and then a cubic interpolation is constructed so that the values between nodes can be solved inversely. The CIP method is an explicit scheme of numerical computation with third-order accuracy [20]. Equation (16) is a typical hyperbolic PDE:  
\[\frac{\partial f}{\partial t}+u\frac{\partial f}{\partial x}=0 \tag{16}\]  
If the speed \(u\) is positive and constant, the analytical solution is \(f(t,x)=f(t-\Delta t,x-u\Delta t)\). This means that the value of a node at the next time step can be obtained from the current one; however, the location is not on the node but is shifted by an amount \(u\Delta t\) from it. As we know, \((x-u\Delta t)\) is located between two nodes, so we need to solve the value at \((x-u\Delta t)\) by interpolation. The highly accurate interpolation expression proposed by [PERSON] is given as  
\[f(x)=a_{i}(x-x_{i-1})^{3}+b_{i}(x-x_{i-1})^{2}+f^{\prime}_{i-1}(x-x_{i-1})+f_ {i-1} \tag{17}\]  
The coefficients in Equation (17) can be obtained using Equations (18)-(21):  
\[a_{i}=\frac{\left(f^{\prime*}_{i}+f^{\prime*}_{iup}\right)}{\Delta^{2}}+\frac {2\left(f^{*}_{i}-f^{*}_{iup}\right)}{\Delta^{3}} \tag{18}\]  
\[b_{i}=\frac{3\left(f^{*}_{iup}-f^{*}_{i}\right)}{\Delta^{2}}-\frac{\left(2f^{ \prime*}_{i}+f^{\prime*}_{iup}\right)}{\Delta} \tag{19}\]when \(u_{i}<0\), \(\Delta=-\Delta x\), \(iup=i-1\); and if \(u_{i}>0\), \(\Delta=\Delta x\), \(iup=i+1\).  
\[f_{i}^{n+1}=a_{i}\xi^{3}+b_{i}\xi^{2}+f_{i}^{\prime*}\xi+f_{i}^{*} \tag{20}\]  
\[f_{i}^{\prime n+1}=3a_{i}\xi^{2}+2b_{i}\xi+f_{i}^{\prime*} \tag{21}\]  
where \(\xi=-u_{i}\Delta t\).  
To verify the accuracy and suitablity of the CIP method, a rectangular wave, as shown in Figure 5a, was used to assess the advantages of the CIP method. The control equation is expressed as Equation (16). Moreover, the velocity of propagation is \(u=1\), the spacial step is \(\Delta x=1\), the time step is \(\Delta t=0.1\), and the results after 400 steps are shown in Figure 5b-d that includes the CIP method, the up-wind difference method, and the Lax-Wendroff method. Clearly, the CIP method is best suitable for a rectangular wave with large pressure difference. The up-wind method with first-order accuracy is the worst, and the Lax-Wendroff method with second-order accuracy can relativly express the wave-type well, but it has oscillation and divergence in the process. The pressure wave in the pneumatic brake system is large when the brake control valve opens or closes. Thus, it is better to introduce the CIP method for its stability and high accuracy for solving the pressure response with large pressure fluctuation.
## 6 Conclusions
PL and RSS of the Hata propagation model of the distance between the ground station and the drone at the cloud layer show reasonable results, in which the obtained values satisfy a high level of reception with the smallest attenuated signal, besides achieving line of sight wireless connectivity in an efficient and timely manner. More elaboration can be taken into consideration for future work, e.g., to develop a machine learning algorithm to predict environmental conditions and then suggest solutions and/or precautions. A decision support system framework could be introduced as part of a future project to link the farm's smart devices together to achieve an autonomous system. Moreover, the power consumption of the drone can be optimized to enhance flight time, which leads to covering wider farm zones. Conceptualization, methodology, software, validation, writing, visualization, review, and editing, F.A.A. and B.O.S. ; Project administration, and funding acquisition, F.A.A. ; Supervision, S.H.A. and H.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding for practical work. Not applicable. The data that support the findings of this study are available on request from the corresponding author. The authors are grateful to the Deanship of Scientific Research at Taif University, Kingdom of Saudi Arabia for funding this project through Taif University Researchers Supporting Project Number (TURSP-2020/265). The authors declare no conflict of interest.
## 1 Introduction  
Most industries depend heavily on the functionality of large and costly systems with tightly integrated hardware, software, and human components. Safety risks, financial concerns, and industrial regulations require modeling and predicting the health state of such complex engineering systems (CESs). One modeling framework, dynamic Bayesian networks (DBNs), has shown the ability to infer complex time-dependent causal relationships between nodes connected within these models [1]. As such, there has been growing interest in using DBNs to assess current CES health and model future system health. Applying causal-based reasoning to CES operational data has the potential to generate diagnostic system state models, as well as prognostic outlooks on the future health of the system or potential causes of system failure. However, there are still questions regarding how to make this prognostics and health management (PHM) process effective and efficient for the fast-paced cycle of industry operations and accident sequences.  
This paper shows how a DBN model helps provide system health prognostic and diagnostic capabilities for monitoring the health of a CES. Due to the wide range of failure points, components,and data streams that a CES may have, a structured approach is needed to systematically describe and define regions within the model that capture both the CES and the accident scenario space. Using simulated accident sequence data from a transient overpower (TOP) event within a sodium fast nuclear reactor (SFR) as a case study, a joint diagnostic- and prognostic-focused DBN model is constructed from the reactor's operational data and accident scenario conditions. The output of the model is a posterior estimate for the overall health of the system, the nature of the accident, and potential reactor outcomes.  
This paper first discusses the need CESs have for diagnostic and prognostic functions and the challenges that current PHM approaches face when applied to complicated systems (Section 2). An introduction into DBNs and their inference capabilities is then followed by an explanation of how DBNs can be structured around the requirements of CES health monitoring. This framework is then demonstrated through the SFR TOP case study. After the example reactor and accident scenario setups are described, the paper traces the process of constructing a prognostic and diagnostic model for the SFR (Section 3). Model verification results and observations on the model construction process are included in the discussion section, as well as thoughts on future work to improve the model framework's effectiveness and relevancy to real world systems (Section 4).
## 3 Preliminaries  
### Network Architecture  
All nodes are deployed randomly in three dimensional network field also shown through Figure 2. The volume of the network is divided logically into small cubes to perform distributive routing in each cube. The number of cubes are represented as \(C_{n}=\{c_{1},c_{2},c_{3},...,c_{n}\}\) where each cube nodes are connected to its adjacent neighbor cube nodes through acoustic link. The sensor nodes operate in two modes: the first one is, sensing mode where node predicts the environmental effects and the second one is transmit mode in which sensed data is delivered to the destination through acoustic data link. The network is homogeneous and consists of \(N_{n}=\{N_{1},N_{2},N_{3},...,N_{n}\}\) nodes deployed inside the water along with set of sonobuoys \(S_{s}=\{s_{1},s_{2},s_{3},...,s_{n}\}\) which are positioned at the water surface. Also, an assumption is made that each node is capable to transmit data successfully within its communication range. All the sensing devices are provided with limited memory, modem for acoustic communication, transceiver and battery. While sonobuoys have both acoustic and radio modems. The former is used to retrieve information of interest from the underwater sensors, and latter to deliver data for further processing to the offshore data center. Further, an assumption is made that every node knows its location in advance. Whereas, the depth of void node also adjusted through the same mechanism as discussed in [30; 31]. The cost associated with depth adjustment is similar as provided in [32]. For simulations, we consider the unit disk graph model in which data packet always received successfully within the transmission range [33].
## 5 Conclusions
**Data Availability Statement:** The ionosonde data are provided by the National Institute of Information and Communications Technology (NICT) at the ionosonde database website ([[https://wdc.nict.go.jp/IONO/HP2009/ISDJ/index-E.html](https://wdc.nict.go.jp/IONO/HP2009/ISDJ/index-E.html)]([https://wdc.nict.go.jp/IONO/HP2009/ISDJ/index-E.html](https://wdc.nict.go.jp/IONO/HP2009/ISDJ/index-E.html)) (accessed on 20 January 2023)). The two-dimensional global maps of ROTI indices are available on the website of the Institute for Space-Earth Environmental Research (ISEE) at Nagoya University ([[https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/](https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/)]([https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/](https://stdb2.isee.nagoya-u.ac.jp/GPS/GPS-TEC/)) (accessed on 20 January 2023)). The detrended TEC maps are provided on the NICT Dense Regional and Worldwide International GNSS-TEC observation (DRAWING-TEC) website ([[https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/](https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/)]([https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/](https://aer-nc-web.nict.go.jp/GPS/DRAWING-TEC/)) (accessed on 20 January 2023)). The ROCSAT-1 satellite data can be downloaded from NCU ([[http://sdbweb.ss.ncu.edu.tw/v2/public/](http://sdbweb.ss.ncu.edu.tw/v2/public/)]([http://sdbweb.ss.ncu.edu.tw/v2/public/](http://sdbweb.ss.ncu.edu.tw/v2/public/)) (accessed on 20 January 2023)). The CHAMP satellite data are obtained from the Information System and Data Center (ISDC) of GFZ German Research Centre for Geosciences ([[http://isdc.gsfz-potsdam.de](http://isdc.gsfz-potsdam.de)]([http://isdc.gsfz-potsdam.de](http://isdc.gsfz-potsdam.de)) (accessed on 20 January 2023)). The IMF data are available from NASA/GSFC's Space Physics Data Facility's OMNIWeb service ([[https://omniweb.gsfc.nasa.gov/ow_min.html](https://omniweb.gsfc.nasa.gov/ow_min.html)]([https://omniweb.gsfc.nasa.gov/ow_min.html](https://omniweb.gsfc.nasa.gov/ow_min.html)) (accessed on 20 January 2023)). The Kp index is obtained from the Center for Space Standards and Innovation ([[http://www.celestrak.com/SpaceData/](http://www.celestrak.com/SpaceData/)]([http://www.celestrak.com/SpaceData/](http://www.celestrak.com/SpaceData/)) (accessed on 20 January 2023)). **Acknowledgments:** The authors gratefully acknowledge the data providers for providing these data. The authors sincerely thank [PERSON] for his continued support and constructive discussion. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Computation Time  
The final aspect of the analyses being investigated is the computation time, which is one of the most important constraints in a numerical analysis, especially in large-scale simulations. For the relatively small and geometrically simple models considered in this study, the COHESIVE model consumed 1445 s solution time on a workstation using eight cores of an E5520 Xeon processor. In contrast, the MIXED model analysis took 2320 s (i.e., 60% more time compared with the COHESIVE model). Therefore, the use of the MIXED approach has its merits for understanding the crack propagation mechanisms but may not be feasible for large-scale simulations. However, its use may be justifiable if the crack path is either not known a priori or has a high influence on the outcome of the simulation. In cases where the crack path is predictable or confined, such as modeling the interfacial delamination in fiber-metal laminates, the use of cohesive elements could be considered as a more suitable choice.
###### Abstract  
Marine regions are undergoing rapid evolution, primarily driven by natural and anthropogenic activities. Safeguarding these ecosystems necessitates the ability to observe their physical features and control processes with precision in both space and time. This demands the acquisition of precise and up-to-date information regarding several marine parameters. Thus, to gain a comprehensive understanding of these ecosystems, this study employs remote sensing techniques, Machine Learning algorithms and traditional in situ approaches. Together, these serve as valuable tools to help comprehend the distinctive parametric characteristics and mechanisms occurring within these regions of the Maltese archipelago. An empirical workflow was implemented to predict the spatial and temporal variations in sea surface salinity and sea surface temperature from 2022 to 2024. This was achieved by leveraging Sentinel-2 satellite platforms, the random forest Machine Learning algorithm, and in situ data collected from sea gliders and floats. Subsequently, the numerical data generated by the random forest algorithm were validated with different error metrics and converted into visual representations to illustrate the sea surface salinity and sea surface temperature variations across the Maltese Islands. The random forest algorithm demonstrated strong performance in predicting sea surface salinity and sea surface temperature, indicating its capability to handle dynamic parameters effectively. Additionally, the parametric maps generated for all three years provided a clear understanding of both the spatial and temporal changes for these two parameters.  
remote sensing; in situ measurements; machine learning algorithm; random forest; sea surface salinity; sea surface temperature Article