esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Cloud Based E-Government from the Perspective of Developing Countries
## 4 Case Study
### Cloud Services and Deployment Model  
The driving licensing department was using windows servers 2008 which needed to be replaced to accommodate the government requirement. The government official initially wanted to move file, email and application servers to the cloud and keep the database and directory service server on premises. However, there were dependencies and middleware used among the server and moving part of the system to the cloud would be very complex as it would have to go through the re-configuration and networking to allow them to communicate efficiently. Only moving part of e-government system might not perform well in the new environment, therefore the existing servers and the virtual machines need to be migrated to the cloud environment. The cloud expert from Data Hub & network Ltd. and government system architectet agreed to adopt infrastructure as a service model. The Data Hub & Network Ltd. agreed to train the government IT staff in relation to the day to day operation of the cloud based system i.e., networking in cloud, security, configuration of the servers and new application etc. Until government staff are fully trained, the day-to-day operation of the cloud environment would be supported by the staff of Data Hub & Network Ltd. Data Hub & Network Ltd. has also agreed to lease a dedicated high-speed internet connection to the department to overcome bandwidth issues.  
Data Hub & Network Ltd. had given an option to the government official where they procure all the hardware and software on the government premise and manage it for the government. However, this is not a cost-effective option. The other option was to have dedicated private cloud computing infrastructure allocated off premise and the legacy and some other services remain on premise. The official from the licensing department chose the second option that is cost effective and could be acquired in less time. Therefore, the hybrid model of the cloud was adopted, where some services of e-government will be migrated to the cloud and some of them remain on government premise. In the brainstorming session, the government employees were interested to know the possibility of establishing their own private cloud infrastructure which could be shared among the different government departments. A detailed feasibility study is required in order to decide whether a private cloud deployment model can be adopted.
# Fully Printed Flexible Single-Chip RFID Tag with Light Detection Capabilities
###### Abstract  
A printed passive radiofrequency identification (RFID) tag in the ultra-high frequency band for light and temperature monitoring is presented. The whole tag has been manufactured by printing techniques on a flexible substrate. Antenna and interconnects are realized with silver nanoparticles via inkjet printing. A sprayed photodetector performs the light monitoring, whereas temperature measurement comes from an in-built sensor in the silicon RFID chip. One of the advantages of this system is the digital read-out and transmission of the sensors information on the RFID tag that ensures reliability. Furthermore, the use of printing techniques allows large-scale manufacturing and the direct fabrication of the tag on the desired surface. This work proves for the first time the feasibility of the embedment of large-scale organic photodetectors onto inkjet printed RFID tags. Here, we solve the problem of integration of different manufacturing techniques to develop an optimal final sensor system.  
flexible substrate; inkjet printing; organic photodetector; printed electronics; spray deposition; OPD +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Hs-Spm-Gc-Ms Evaluation of Sputtered Carbon Fibers with Commercially Available Test Mixes  
Four different sputtered carbon SPME fibers were prepared with carbon thicknesses of 0.25, 2.5, 11.5, 20 \(\upmu\)m, and were coated with the PDMS stationary phase [20]. Carbon  
Figure 3: Static, advancing, and receding WCAs of a bare silicon surface, a silicon surface coated with sputtered carbon, and the carbon surface coated with PDMS. The error bars for these results are smaller than the symbols used here.  
Figure 2: XPS survey scans of (**a**) a bare silicon surface, (**b**) a silicon surface coated with sputtered carbon, and (**c**) a sputtered carbon surface after PDMS deposition.  
is expected to provide complicated interactions with analytes, i.e., via sp\({}^{2}\) type graphitic carbon and oxygenated carbon species. The interactions of the analytes with the PDMS stationary phase are also expected to be substantial, where the PDMS is expected to help trap/retain analytes. Initial testing of the fibers was conducted with a mixture of commercially available [PERSON] and Grob's test mixes. These analyses were performed in HS mode (by HS-SPME-GC-MS) because of the volatile nature of many of the analytes in these mixes. As shown in Figure 4, the analytical responses of our sputtered carbon fibers are less than desired for some of the compounds, e.g., 2,3-butanediol and 1-octanol, but this observation is expected given the hydrophobic nature of sp\({}^{2}\) carbon and PDMS. In addition, the hydrophobic compounds such as heptane and octane, which were extracted more efficiently, showed higher variability than desired (note the y-error bars (standard deviations) in Figure 4). These observations are consistent with previous studies, as [PERSON] et al. demonstrated that sputtered silicon coatings perform better in DI mod [19; 20]. The lower-than-desired analytical response of our sputtered carbon fibers in HS mode may be attributed to the inefficiency of extraction of some analytes with high vapor pressures and lower molecular weights because of the relatively thin coatings of our SPME fibers, i.e., when compared with the 95 \(\upmu\)m carbon wide range (CWR) commercially available SPME fiber. In general, the thicker sputtered carbon coatings seem to show higher responses, which is consistent with intuition and the aforementioned theory. Accordingly, we chose to further study the 11.5 \(\upmu\)m sputtered carbon fibers with our custom evaluation mix (see next section) because it provided the best level of performance, i.e., the highest sum of responses of all compounds relative to the other thicknesses.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 3 Technical Overview
### Network Infrastructure
#### 3.3.2 Star Topology  
The star topology is facilitated by a superframe structure (Figure 3) consisting of a beacon, contention free and contention based timeslots. The number of timeslots in the CFP is limited to seven. This topology is established between a single PAN coordinator and several slave devices. Passive scanning is used to discover the nodes in the vicinity.
## 6 An Illustrative Example  
The feasibility and characteristics of the proposed multi-criteria design procedure are discussed in this section. It is combined with a solving algorithm from heuristic techniques to illustrate the implementation and results of an example using a four-bar linkage design problem. The dynamics of a closed-loop linkage is usually highly nonlinear due to the asymmetry of the geometrical structure. Thus, it represents a challenge to consider traditional design requirements and even more with the inclusion of other criteria. The four-bar mechanism has a significant number of applications in many different engineering solutions [32], and researchers keep studying it as, for example, in [33] with a review of some mechanical applications. However, currently user needs require that this mechanism accomplish more challenging tasks and fulfill user expectations in a better way. Design variables, as illustrated in Figure 3 are those parameters obtained as a result of solving the formulated optimization problem. Table 2 lists design parameters (\(\mathbf{P}\)) and its bounds referring to the design problem for the four-bar linkage in Figure 7. Table 3 lists the obtained values of a selected solution and classifies them. There are four parameters for a kinematic synthesis of the four-bar linkage, twelve parameters that tune its dynamic behavior, and three parameters that tune a control algorithm. These three synthesis processes are carried out concurrently in order to fulfill strict and soft design requirements. Soft design requirements and selected criteria, as illustrated in Figure 3 are formulated into three objective functions that are listed in Table 2. The first objective function (\(\Phi_{1}\)) corresponds to a system performance criterion when doing a task. It measures the accumulated error generated by the system, and the target is a constant angular velocity of 30 rad/s. User expectation referring to a reduction of energy consumption is formulated using the second objective function (\(\Phi_{2}\)) in Table 2. This criterion measures the accumulated power required by multiplying the driving torque and the angular velocity of the input link for the four-bar linkage in Figure 7. Another user expectation considered in this example is the reduction of overshoot when doing the selected task of reaching a constant angular velocity. The third objective function (\(\Phi_{3}\)) formulates the reduction of overshoot as a soft design requirement in Table 2. The evaluation of these three criteria and constraints are computed after solving the dynamic model in Equation (10) as referring to Equations (8)
## 3 Results  
### Missel Survey  
Over the course of the study, a total of 174 mussels belonging to nine different species were collected at 19 of the 36 sites, with 17 sites yielding no mussels. Although none of the nine species were federally listed as threatened or endangered, _Anodontoides radiatus_, _Elliptio crassidens_, _Pleurobeema beadleianum_, and _Villosa vibex_ are considered species of greatest conservation need in Louisiana [50]. Due to concerns regarding identification of _P. beadleianum_, identification was confirmed based on tissue sequenced (CO1/16S primers) and compared to reference sequences in Geneious software (unpublished data). Total abundance and species richness averaged 4.83 (+1.96 SE) mussels (range 0-66) and 1.22 (+0.26 SE) species (range 0-5) per site, respectively. _Villosa lienosa_ was the most abundant and widely distributed species, with six of the species represented by less than 10 individuals (Table 1, Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & _Anodontoides_ & _Elliptio_ & _Lampsilis_ & _Plectomers_ & _Pleurobema_ \\
**Site** & _radiatus_ & _crasidens_ & _claibornensis_ & _dombeyanus_ & _beadleianum_ \\  & **([PERSON], 1934)** & **([PERSON], 1819)** & **([PERSON], 1838)** & **([PERSON], 1827)** & **([PERSON], 1961)** \\ \hline \hline Adams Creek & & & & & \\ \({}^{1}\) Adams Creek & & & & & \\ \({}^{1}\) Bogue Lusa Creek & & & & & \\ \({}^{1}\) Bogue Lusa Creek & & & & & 2 \\ \({}^{1}\) Crains Creek & & & & & 1 \\ Deer Lick Creek & 1 & & 1 & & 2 \\ House Creek & & & & & \\ Mill Creek & & & & & \\ \({}^{1}\) Mill Creek & & & & & \\ Miller Creek & 1 & & 1 & & 6 \\ Peter’s Creek & & & & & 6 \\ Pushepatapa Creek & & & & & 5 \\ \({}^{1}\) Pushepatapa Creek & & & & & 1 \\ Silver Creek & 3 & 52 & & & 3 \\ Silver Springs Creek & & & & 1 & \\ Talley’s Creek & & & & 3 & \\ \({}^{1}\) Talley’s Creek & & & 1 & & \\ West Hobol
## 4 Effective Attenuation Factor for Layered Composites  
In the same structure as that of the effective attenuation coefficient simulation of the composite, an independent shielding simulation of each material was performed, and the attenuation coefficient values obtained from the simulation results were compared with the WinXcom[26] standard data provided by NIST to secure the reliability of the simulation results. Since the standard data of WinXcom shows the mass attenuation coefficient value according to the photon energy, the mass attenuation coefficient values of concrete, copper, lead, and tungsten were calculated using MCNP and compared Figure 4-- the MCNP value is calculated in simulation and there is no error bar-, and the coefficient of determination (R2) was calculated. The coefficient of determination is a coefficient indicating to what extent the regression line can adequately explain the data. The coefficient of crystallinity for each material was calculated as 0.63 for concrete, 0.80 for copper, 0.92 for lead, and 0.99 for tungsten. In general, the data is considered reliable when coefficient of determination is >0.6 [27] and this was true for all the materials considered in this work. The disagreement apparent above 10 MeV is likely due to photon-neutron reactions, which were not considered in MCNP simulations [22]. Here, concrete is not a single substance, but a mixture of various substances such as clay, iron ore, and limestone, so it is difficult to find any particular tendency. The magnitude of the linear attenuation coefficient of each material was in the order of tungsten > lead > copper > concrete > PE (Figure 5). In particular, when the energy of gamma rays is 100 MeV, the linear attenuation coefficient of concrete has almost the same value as PE. Figure 4: Mass attenuation coefficient of concrete at WimXcom and MCNP; (**a**) Concrete, (**b**) Copper, (**c**) Lead, (**d**) Tungsten. Figure 5: Linear attenuation coefficient of materials. The thickness of the composite needed to attenuate 99.99% of the radiation was also calculated using Equation (5). (Table 3). \[ln\bigg{(}\frac{I_{t}}{I_{0}}\bigg{)}=-\mu t \tag{6}\]  
\[t=-\frac{ln\Big{(}\frac{I_{t}}{I_{0}}\Big{)}}{\mu} \tag{7}\]  
When the shielding rate is 99.99%, \(\frac{I_{t}}{I_{0}}=0.0001\). \[t_{99.99}=\frac{ln(0.0001)}{\mu} \tag{8}\]  
Shielding material thickness is \(t_{99.99}\) when the shielding rate is 99.99%.
## 4 Discussion
To identify metabolites discriminating between AF cases and controls, we performed targeted metabolomic analysis which allowed us to find a significantly lower concentration of the lysophosphatidylcholine lysoPC a C20:3 in AF cases. Tests performed for commonly prescribed ATC level 2 medications of AF cases suggest this difference being most likely independent of these treatments, even if drug intake and dosage were not considered. This result is consistent with previous data reporting altered lipid metabolites and, in particular, a minor amount of lysoPC a C20:3 in AF patients [11]. Moreover, in a recent analysis [48] conducted in a related population-based study from the same region, we observed an association between higher levels of another phosphatydilcholine, the PC38:3, and longer P wave duration, which is also a risk factor for AF [49]. Even though the association was replicated in an independent population sample from Scotland, proving its robustness, mediation and Mendelian randomization analyses showed that the association between PC38:3 and P wave was caused by the confounding role of BMI: in fact, BMI was proven to cause both PC38:3 increase and P wave prolongation [48]. Thus, any association between lipids and AF or its markers should be considered carefully, until proof of causality is given. Since in blood, significant amounts of lysophosphatidylcholines are formed together with cholesterol esters by a specific enzyme system, lecithin cholesterol acyltransferase (LCAT), we decided to also test the LDL, HDL, and total cholesterol levels. LDL and total cholesterol were significantly lower in AF cases compared to controls, and HDL also showed a tendency of a lower concentration in AF cases. Of note, in a recent paper, a significant reduced activity of the LCAT enzyme in AF patients was also described [50], and it has been reported that LCAT could modify oxidative and inflammatory processes, as supported by an inverse relationship with the anti-oxidative, anti-inflammatory, and anti-thrombotic functions of HDL [50,51]. Several works have indicated that cardiomyocyte NACHT, LRR, and PYD domains containing protein-3 (NLRP3)-inflammatory signaling play a causative role in AF development [52, 53, 54, 55]. Notably, the expression of NLRP3 and its downstream inflammatory cytokines is reduced in the presence of HDL [56], and it appears that the inhibition of NLRP3 inflammasome is due to the blocking of the serum amyloid A activity caused by HDL [57]. Taking into account all the above evidence, we can speculate that the lower concentration levels of lysoPCa C20:3 and HDL found in AF cases could have a role in the progression of AF through the NLRP9 inflammasome activation. The lower amount of lysophosphatidylcholine and cholesterol could be caused by a decreased LCAT activity with a subsequent NLRP9 activation. This hypothesis, of course, deserves further investigations, which are, however, beyond the scope of this work and should be more properly carried out in large cohorts of AF patients.
## 4 Applications
### System Development Technology  
The overall development framework of the intelligent batching system is illustrated in Figure 7. The intelligent batching system is composed of a data server, control server, human machine interface, intelligent batching model, and database. The system employs \(\mathtt{C\#}\) for web development, MATLAB for the core algorithmic logic and models, and SQL Server for backend database management. It also utilizes \(\mathtt{PLC}\) for effective on-site communication, as illustrated in Figure 7.  
Figure 6: The fine adjustment process.  
Figure 7: The intelligent batching system executed in a steel plant in China.
## 3 Results and Discussion
### Dynamic Changes during Processing Analyzed via GC-MS
#### 3.3.2 Dynamic Changes in SFABT at Different Manufacturing Stages  
In order to more thoroughly comprehend the differences in the volatile profile of SFABT at different manufacturing stages, a total of 106 volatile compounds underwent OPLS-DA for assessing their variability. The model's fitness was interpreted as excellent due to the high values of R\({}^{2}\)Y and Q\({}^{2}\) achieved (Figure 5A). The score exhibited a distinct spatial segregation of SFABT during processing. Specifically, fresh leaves were situated within the fourth quadrant, while withered and shaken samples were stationed within the first quadrant. Rolled, fermented, and dried samples were positioned in the third quadrant. The closer the spatial distance was, the less the volatile metabolites changed; thus, noticeable alterations occurred in the rolling stage, which was consistent with the results of the GC-E-Nose and GC-IMS. A permutation test with a random 200-fold arrangement was executed to confirm the dependability of the OPLS-DA model. The results indicated that the OPLS-DA model was dependable and displayed no overfitting phenomena, as evidenced by the intercepts of R\({}^{2}\) = (0.0, 0.38) and Q\({}^{2}\) = (0.0, -0.764) (Figure 5B).  
In addition, a dual criterion was implemented to determine the most prominent variables during processing. A total of 26 volatile biomarkers were pinpointed, strictly adhering to variable importance in the projection (VIP) exceeding 1.0 and the significance analysis (\(p<0.05\)). An informative heat map was implemented to visually present the notable variations throughout the seven stages of black tea processing. Clear differences can be observed for these 26 volatile compounds, where green signifies reduced levels, while red denotes elevated levels. As shown in Figure 5C, indole (84) and (E)-2-hexenyl butanoate (67) showed higher concentrations in fresh leaves than in other manufacturing procedures. Heptanal (21), citral (81), 2-heptanone (17), dimethyl sulfide (1), and geranyl acetone (96) showed higher concentrations in the withering stage. As for the shaking processing, the representative compounds mainly included 2-methylpropanal (2), 2-methylbutanal (5), (Z)-3-hexenyl pentanoate (76), ethyl salicate (82), and (Z)-3-hexenyl benzoate (103). As far as fermentation processing is concerned, higher concentrations of 2-ethyl-2-butanal (12) and (E)-2-hexenal (13) were found. Safranal (72) and (Z)-3-hexenyl (Z)-3-hexenoate (91) were found in higher concentrations in the final firing than in the other manufacturing procedures. The specific reasons for the fluctuations within the aforementioned volatiles will be investigated in depth in the future.
## 2 Details and Performance Analysis of QODELFA
### Main Procedures of QODELFA
#### 2.1.3 Concept of QOBL  
The opposition-based learning (OBL) was essentially developed for the purpose of reducing the computational time, as well as improving the convergence abilities of different EAs [35]. By considering each of the current populations and their opposite populations based on OBL, the candidate solution will be improved. This concept is simple and easy to implement which makes it suitable to enhance the performance of the combined DELF algorithm proposed in this paper. As mentioned in [35], an opposite candidate solution (CS) might be closer to the global optimal solution than an arbitrary CS. Hence, the comparison between a random CS and its opposite will lead to the global optimum with faster convergence rate. The quasi-opposite number was further investigated in [36] and proved that it is usually closer to the optimal solution than the opposite number. This improved concept of QOBL has been used to solve the OPDG problem by enhancing the performance of some algorithms [28; 29], but it has not been utilized to improve such a combined DELF algorithm as proposed in this paper. Accordingly, the initial population of this algorithm is generated based on the QOBL concept, where the greedy selection of DE is employed to determine whether an initial random solution is better than its quasi-opposite solution or not. As a result of this comparison, the best among original and quasi-opposite solutions will be kept in the initial population. This will increase the diversity and exploration of the generated initial population. Consequently, the algorithm will mostly converge to the global optimum with faster rate. The definitions of opposite number, opposite point, quasi-opposite number, and quasi-opposite point are given as follows [28]:  
For any random number \(x\in[a,b]\), its opposite number \(x_{o}\) is given by  
\[x_{o}=a+b-x, \tag{8}\]  
while the opposite point for multi-dimensional search space (\(d\) dimensions) is defined as  
\[x_{o}^{i}=a^{i}+b^{i}-x^{i};\;i=1,2,\ldots,d \tag{9}\]  
and the quasi-opposite number \(x_{qo}\) of any random number \(x\in[a,b]\) is given by  
\[x_{qo}=rand(\frac{a+b}{2},x_{o}), \tag{10}\]  
similarly, the quasi-opposite point for multi-dimensional search space (\(d\) dimensions) is defined as  
\[x_{qo}^{i}=rand(\frac{a^{i}+b^{i}}{2},x_{o}^{i}). \tag{11}\]