esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 7 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study Name** &
\begin{tabular}{} \end{tabular} & **Research Questions** \\ \hline Microservices: A Systematic Mapping Study [20] & 21 & What are the main practical motivations behind using microservices? \\  & & What are the different types of microservice architectures? \\  & & What are the existing methods, techniques, and tool support to enable microservice architecture development and operation? \\  & & What are the existing research issues and what should be the future research agenda? \\ \hline The pains and gains of microservices: A systematic grey literature review [21] & 51 & How much evidence of microservices experimentation from the industry is available online? \\  & & What are the technical and operational “pains” of microservices? \\ \hline A Systematic Mapping Study in Microservice Architecture [22] & 33 & What are the architectural challenges that microservices systems face? \\  & & What architectural diagrams/views are used to represent microservices architectures? \\  & & What quality attributes related to microservices are presented in the literature? \\ \hline Architecting with microservices: A systematic mapping study [6] & 103 & What are the publication trends of research studies about architecting with microservices? \\  & & What is the focus of research on architecting with microservices? \\  & & What is the potential for industrial adoption of existing research on architecting with microservices? \\ \hline A Systematic Literature Review on Microservices [23] & 37 & What type of research is conducted on microservices? \\  & & What are the main practical motivations behind microservices-related research? \\  & & What are the emerging standards and de facto tools for microservices solutions? \\ \hline On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study [24] & 55 & What are the problems or opportunities that are addressed using microservice analysis techniques? \\  & & Does microservice analysis overlap with other areas of software analysis, or are new methods or paradigms needed? \\  & & What potential future research directions are open in the area of microservice analysis? \\ \hline Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review & 85 (This study) & What are the identified challenges of microservice architectures? \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of related works with this SLR study. Complementary to this study, various other secondary studies have been published on the key elements of microservice architectures [29; 30].
## 3 Design of The Experiment  
### The Experimental Steps  
The experiment was conducted in a typical indoor environment laboratory, as shown in Figure 5. To simulate the NLOS condition, UWB stations A, C, and D were specially installed in obstructed positions. A was surrounded by several metal baffles, and C was mounted at the end of a narrow corridor. D was installed at the edge of the window near the ceiling, about 20 cm higher than the lower edge of the ceiling. Therefore, in the vast majority of the experiment locations, the UWB tag can only receive a clear LOS propagation condition from the UWB station B.  
The detailed experimental steps are listed below:  
* The RTS was set up on a tripod with visibility to all four UWB base stations.
* The RTS's WLAN was connect to the PC's mobile hotspot so that the control software could communicate with the RTS.  
Figure 4: Principle of the observation matching.  
Figure 5: The panorama view of the experimental scene. A, B, C, and D each denote a distinct UWB base station.  
* The UWB stations A, B, C, and D were mounted as shown in Figure 5, connecting the UWB station A interface to the PC through a USB cable to establish the data communication between the whole UWB system and the PC.
* The RTS measured the \(x\), \(y\), and \(z\) coordinates of each UWB station antenna, as shown in Table 3, recorded them using the control software, and then wrote them into the UWB system. In this way, the coordinates measured by the RTS and the UWB were unified into the same coordinate system.
* The moving target was mounted on the top of a surveying pole, including a prism and a UWB tag.
* The RTS was allowed to aim at the moving target roughly and before starting to lock the prism.
* The UWB system was allowed to start calculating the position of the tag.
* The control software was allowed to record the RTS and UWB observations as two separate files, and then, the surveying pole moved around the room.
* The experiment was terminated when enough observations were collected.
* Matching the RTS and UWB observation files, the UWB's positioning errors in the \(x\), \(y\), and \(z\) directions were calculated.
* A neural network was constructed to train the matched observation files. The structure diagram of the neural network is shown in Figure 6. The divisions of the dataset are shown in Figure 7.
* The accuracy of the network was evaluated by a verification dataset, which the trained network has not used.
## 3 Results
### Effect on Biofilm Formation  
To assess the impact of copper salts on biofilm development, strains of _E. coli_ and _E. faecalis_ were subjected to copper sulfate and copper nitrate within a concentration range of 0.00005-100 mg/mL. The extent of biofilm formation was measured and categorized into four classifications based on absorbance readings: non-adherent, weakly adherent, moderately adherent, and strongly adherent (see Figures 3 and 4). At elevated concentrations, both types of copper salts markedly inhibited biofilm formation across all bacterial strains. Conversely, exposure to sublethal concentrations resulted in varied outcomes. Specifically, _E. faecalis_ ATCC 29212 and _E. faecalis_ ATCC 19433 showed an enhancement in biofilm strength, transitioning from moderately to strongly adherent classifications. These results indicate that while high levels of copper salts hinder biofilm formation, sublethal levels may provoke stress responses that promote biofilm production, especially in _E. faecalis_.  
Figure 1: Antibacterial potential of copper salts on different bacterial strains: _E. coli_ ATCC 14169 (blue), _E. coli_ ATCC 25923 (red), _E. faecalis_ ATCC 19433 (yellow), and _E. faecalis_ ATCC 29212 (green). The antibacterial effects were tested using (**a**) copper sulfate and (**b**) copper nitrate.  
Figure 2: Growth comparison of _E. coli_ ATCC 14169 (blue), _E. coli_ ATCC 25923 (red), _E. faecalis_ ATCC 19433 (yellow), and _E. faecalis_ ATCC 29212 (green) in the presence of (**a**) copper sulfate and (**b**) copper nitrate.
## Appendix B Proof of Theorem 1 in (18) and (19)  
From Equation (12), the outage probability of IoT relay node is:  
\[\hat{P}_{Out_{IoT_{R}}}=Pr(min(\hat{\gamma}_{s_{rec}}^{x_{ IoT_{R}}\to x_{g}},\hat{\gamma}_{IoT_{rec}})<\hat{\psi})\] \[\hat{P}_{Out_{IoT_{R}}}=1-Pr\big{(}\frac{\phi_{2}|XY+1}{\phi_{1}| XY+1}\geq\hat{\psi},\frac{\phi_{2}|XZ}{\phi_{1}|XX+1}\geq\hat{\psi}\big{)}\]  
where  
\[\hat{P}_{IoT_{R}}=\frac{2\alpha\eta P_{S}|h_{IoT_{R}}|^{2}}{(1-\alpha)}\triangleq \frac{2\alpha\eta\delta X}{(1-\alpha)},l=\frac{2\alpha\eta\delta}{(1-\alpha)}\]  
\[X=|h_{IoT_{R}}|^{2},Y=|h_{s_{rec}}|^{2},Z=|h_{IoT_{rec}}|^{2},\sigma_{IoT_{rec}} ^{2}=1,\sigma_{s_{rec}}^{2}=1\]  
\[=1-Pr\big{(}Y\geq\frac{\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{\psi})|X},Z\geq\frac {\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{\psi})|X}\big{)}\]  
Conditioning on \(X\), we have,  
\[=1-\int_{0}^{\infty}Pr\big{(}Y\geq\frac{\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{ \psi})|x}\big{)}\times Pr\big{(}Z\geq\frac{\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{ \psi})|x}\big{)}f_{X}(x)dx\]  
put  
\[\begin{array}{l}\frac{\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{\psi})^{2}x}=T\\ =1-\int_{0}^{\infty}Pr(Y\geq T)Pr(Z\geq T)f_{X}(x)dx\\ =1-\int_{0}^{\infty}\big{(}\int_{T}^{\infty}\lambda_{g}e^{-\lambda_{g}y}dy \big{)}\big{(}\int_{T}^{\infty}\lambda_{g}e^{-\lambda_{g}z}dz\big{)}\lambda_{h }e^{-\lambda_{h}x}dx\\ =1-\int_{0}^{\infty}e^{-\lambda_{g}x}e^{-\lambda_{g}x}\lambda_{h}e^{-\lambda_{ h}x}dx\\ =1-\int_{0}^{\infty}e^{-\lambda_{g}x}e^{-\lambda_{g}x}\lambda_{h}e^{-\lambda_{ h}x}dx\end{array}\]  
substituting the value of \(T\) above  
\[=1-\int_{0}^{\infty}e^{-\lambda_{g}\frac{\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{ \psi})|x}}e^{-\lambda_{g}\frac{\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{\psi})|x}} \lambda_{h}e^{-\lambda_{h}x}dx\]  
let  
\[\begin{array}{l}d=\frac{\hat{\psi}}{(\phi_{2}-\phi_{1}\hat{\psi})|}\\ =1-\int_{0}^{\infty}e^{-\lambda_{g}\frac{\hat{\psi}}{\hat{\psi}}}e^{-\lambda_{ g}\frac{\hat{\psi}}{\hat{\psi}}}\lambda_{h}e^{-\lambda_{h}x}dx\\ =1-\lambda_{h}\int_{0}^{\infty}e^{-\frac{\hat{\psi}}{\hat{\psi}}}e^{-\lambda_{ g}\frac{\hat{\psi}}{\hat{\psi}}}\lambda_{h}e^{-\lambda_{h}x}dx\end{array}\]  
Now, using the formula  
\[\begin{array}{l}\int_{0}^{\infty}e^{-\frac{\hat{\psi}}{\hat{\psi}}}dx=\sqrt{ \frac{\hat{\psi}}{\hat{\psi}}}K_{1}\sqrt{\beta\gamma}\\ =1-\lambda_{h}\sqrt{\frac{4(\lambda_{g}+\lambda_{z})d}{\lambda_{h}}}K_{1}\Big{(} \sqrt{4(\lambda_{g}+\lambda_{z})d\lambda_{h}}\Big{)}\\ \hat{P}_{Out_{IoT_{R}}}=1-2\sqrt{d\lambda_{h}(\lambda_{g}+\lambda_{z})}K_{1}\Big{(} 2\sqrt{d\lambda_{h}(\lambda_{g}+\lambda_{z})}\Big{)}\end{array}\]  
Putting the value of \(\hat{P}_{Out_{IoT_{R}}}\) in Equation (14), we get,  
\[\hat{Thr}_{IoT_{R}}=\frac{R(1-\alpha)}{2}\left(2\sqrt{d\lambda_{h}(\lambda_{g}+ \lambda_{z})}K_{1}\Big{(}2\sqrt{d\lambda_{h}(\lambda_{g}+\lambda_{z})}\Big{)}\right)\]This ends the proof of Theorem 2.
# Enhancement of Wheat Flour and Dough Properties by Non-Thermal Plasma Treatment of Wheat Flour
## 2 Materials and Methods
### Analysis of Flour and Dough  
#### 2.3.1 Flour Hydration Properties  
Water plays an important role in wheat dough strength due to the ability of protein matrixes to absorb and retain bound, capillary and physically entrapped water against gravity [49]. At lower hydration levels, water bonds flour proteins and starch granules via hydrogen bonds. Free water phase starts to appear at higher hydration levels (water fraction from 0.23 to 0.35), where chemical reactions (i.e., development of gluten matrix) occur during the dough development process [50]. Hence, small changes in both bonded and free water levels result in considerable variation in dough visco-elastic properties.  
For this reason, the water-holding capacity (WHC) and the water-binding capacity (WBC) of untreated (control) and treated flour samples were measured and compared. The  
Figure 2: Test reactor for dielectric barrier discharge (DBD) plasma flour interaction.  
WHC is the amount of water retained by flour particles without any stress conditions. For this purpose, 1 \(\pm\) 0.05 g wheat flour was mixed with 10 mL of deionized water and left for 24 h. Afterwards, the supernatant was filtered and the wet wheat flour sample was weighed again. The WBC is the amount of water retained by flour after centrifugation. The samples were prepared according to the WHC procedure, and after 24 h centrifuged at 2000 revolutions for 10 min. Both WHC and WBC were measured according to the procedure described by [PERSON] et al. [41].
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Transport Plane
#### 3.2.2 Transmission Guarantee  
Because of the fact that the inter-satellite optical links in the space-based optical backbone network have no external protection layers that exist in the terrestrial network, they are more likely to be disturbed by factors such as space radiance, particle flow and so on. This affects the stability of transmission in the inter-satellite optical links. Therefore, we designed an ACK(acknowledgement) mechanism to guarantee the transmission of management and control messages in DCN channels. The ACK mechanism is designed as follows:  
1. When a node sends any management or control message, it saves the message after it has been sent.
2. When the corresponding node has received the message, it immediately extracts special parameters to create an ACK message and replies to the corresponding node.
3. Having received the ACK message, the node locates the corresponding message saved previously, and deletes it. If the node has not received the ACK message within a specified timeframe, the node automatically sends the message again.  
Figure 7: Data communication network (DCN) Channel.  
From the design above, it can be seen that the ACK mechanism effectively avoids the loss of management and control messages caused by the interference in inter-satellite optical links. A similar mechanism could also be designed in an AOS framework to avoid the loss of AOS frames due to the same reason.  
As can be seen, the ACK mechanism guarantees the transmission of messages and improves the stability of transmission in inter-satellite optical links.
## 3 Results and Discussion
### Changes in Acetaldehyde Content and Sensory Evaluation Results  
The acetaldehyde content and sensory scores are presented in Table 4. As can be seen, the highest acetaldehyde content at the beginning and at the end of the storage was determined in the WFG1 variant (0.44 \(\pm\) 0.10 mg/dm\({}^{3}\) and 1.65 \(\pm\) 0.04 mg/dm\({}^{3}\), respectively), whereas the lowest content was noted for the fermented whey (W) (0.07 \(\pm\) 0.00 mg/dm\({}^{3}\) and 1.34 \(\pm\) 0.04 mg/dm\({}^{3}\), respectively). A significant increase (\(p\) < 0.05) in acetaldehyde content was observed during storage in all the FWB variants. The highest increase was found for the WMP (1.470 mg/dm\({}^{3}\)). The addition of FG generally resulted in an increase in acetaldehyde content. Acetaldehyde is the main compound shaping the aroma of fermented milk, synthesized from lactose or amino acid threonine [2].
## Materials and Methods
### Volatile Compounds
#### 2.3.2 GC-MS Analysis of SPME Extracts  
After extraction, the SPME device was inserted into the injection port of an Agilent 7890A gas chromatography system coupled to an Agilent 5975C detection system equipped with an automated injection system (CTC-CombiPAL). A capillary column HP-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness) (Agilent, Santa Clara, CA, USA) coated with (5% Phenyl Methyl Silox) was used for the chromatographic separation of volatile compounds. The oven temperature program used was 2 min at 40 \({}^{\circ}\)C isothermal and an increase of 4 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C. Helium was used at 3 mL/min as carrier gas. The sample injection mode was splitless. Mass spectra were measured in electron ionisation mode with an ionisation energy of 70 eV, the scan range from 20 to 280 _m/z_ and the scan rate of 5.3 scans/s. The data were controlled and stored by the HP G1034 Chemstation system. Identities were confirmed by running the samples on a Stabilwax-DA (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m film thickness) polar column from Restek (Bellefonte, PA, USA). Volatile compounds were identified or tentatively identified by comparison of each mass spectrum with spectra from authentic compounds analysed in our laboratory, or from the NIST mass spectral database (NIST/EPA/NIH Mass Spectral database, 2014), or spectra published elsewhere (see Supplementary Data S1 for GC-MS chromatograms and compound fragmentation spectra). A spectral quality value >80 was used alongside linear retention index to support the identification of compounds where no authentic standards were available. LRI was calculated for each volatile compound using the retention times of a homologous series of C\({}_{6}\)-C\({}_{25}\)_n_-alkanes and by comparing the LRI with those of authentic compounds analysed under similar conditions. The compound peak areas were normalised and converted to the relevant abundance of each component as a percentage of the total peak area.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 3 Design and Implementation
### Transceiver Design  
#### 3.5.1 GMSK Transmitter  
The transmitter design is implemented as shown in Figure 8. The input message is sent to an AX.25 encoder that generates bitstreams. Then, it is packed into data bytes (8 bits) to make it appropriate input for the \"GMSK Mod\" block, which will perform the modulation to GMSK. Then, the modulated baseband signal is passed through a rational resampler to generate the desired data rate. Then, the signal is ready, and it will be saved in a virtual sink.  
Figure 8: GMSK Transmitter Design in GNU Radio.  
Figure 6: Data input block.  
Figure 7: Data input block.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Condition Monitoring Algorithm
#### 2.2.5 Multicollinearity Check  
The squared Mahalanobis distance calculation uses the covariance information among the input variables. The inverse matrix of the covariance coefficients may become inaccurate if the variables are highly correlated [18]. The residual calculation removes the strong correlation between the shaft speed and input variables (see Table 4), but the residuals may be strongly correlated with each other. This may happen when several redundant features, such as \(l_{p}\) norms with different order \(p\), are used together. To elude the use of highly correlated variables, the multicollinearity of the residual sets is evaluated by using the Variance Inflation Factor (VIF)  
\[VIF=\frac{1}{1-R_{j}^{2}}, \tag{5}\]  
where \(R_{j}^{2}\) is the coefficient of determination from the regression of explanatory variable (\(r_{j}\)) onto all other explanatory variables. If the coefficient of determination is close to one, collinearity is present and VIF is large. VIF = 1 indicates the complete absence of collinearity. As a rule of thumb, a VIF value that exceeds 5 or 10 could indicate a problematic amount of collinearity [37]. If VIF goes above the threshold, the selection of different features is recommended as shown in the flowchart in Figure 2.
## 4 Methods
### Lava Flow Mapping
All objects that are \(<\)15 pixels and completely enclosed by the lava area were included in this object. Using a majority filter, the edges and borders of the lava flow object were refined. The final lava area was modelled upstream starting from the volcanically overprinted area (_dNDVI_\(<-0.3\) or NHI hotspot), through the distance to potential runoff streams and changes in the VHR satellite data (texture and brightness), to the potential origin of the lava in the non-vegetated area. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Classification Feature** & **Karangetang I** & **Karangetang II** & **Krakatau I** \\ \hline GLCM difference homogeneity & \(>\)0.02125 & \(>\)0.02125 & \(>\)0.005 \\ GLCM difference contrast & \(<\)\(-\)1000 & \(<\)\(-\)600 & \(<\)\(-\)250 \\ GLCM difference entropy & \(<\)\(-\)0.075 & \(<\)\(-\)0.05 & \(<\)\(-\)0.5 \\ GLCM contrast (post-scene) & \(<\)710 & \(<\)900 & \(<\)1000 \\ GLCM spatial standard & & \(<\)44 & \(<\)42.5 \\ deviation (post scene) & \(<\)0.8 & \(<\)1.8 & \(<\)0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal threshold values of GLCM statistics and overall brightness ratio for the classification of lava flows of the events Karangetang I and II and Krakatau I. If a segment meets one of the defined thresholds, it is assigned to the class “potential lava flow area with changes” (see Figure 2).
# Performance Comparisons of Broadband Power Line Communication Technologies
## 4 BER Comparisons and Discussion
### Channel Model  
The broadband channel model in [18] is described briefly. The frequency response of the channel is as follows.  
\[H(f)=\sum_{k=1}^{K}g_{k}\cdot A(f,d_{k})\cdot e^{-\beta\pi f(d_{k}/v_{p})}, \tag{4}\]  
where \(g_{k}\) is a weighting factor and \(A(f,d_{k})\) is an attenuation. The last portion \(e^{-\beta\pi f(d_{k}/v_{p})}\) represents delay, where \(d_{k}\) and \(v_{p}\) are the length of the \(k\)th path and the propagation speed on the transmission line, respectively. \(K\) represents the number of signal paths between the transmitter and receiver. The attenuation \(A(f,d_{k})\) and the parameters \(g_{k}\) and \(d_{k}\) are obtained from field measurements.  
When a power line has one tap and the number of signal paths is 4, the attenuation and the parameters are obtained in [18]. The magnitude response of this model with the attenuation and parameters is shown in Figure 11(a). This model covers all substantial effects of the transfer characteristics in the frequency range from 500 kHz to 20 MHz. However, observing that the attenuation decreases as the frequency increase in the range above 20 MHz, it is believed that this model can be used up to 28 MHz without loss of generality.  
For the simulation, the frequency response is cut to the range of half the sampling frequency of IoT PLC and sampled with the frequency resolution of subcarrier spacing. Then, the discrete impulse response obtained from the sampled channel frequency response is convolved with the sampled IoT PLC signal to produce the channel output. This method also applies to HPGP and HS PLC. Since their sampling frequency and subcarrier spacing are different, each discrete channel impulse response is obtained accordingly. The discrete impulse response for IoT PLC is shown in Figure 11(b) as an example.