esa-sceva/satcom-chunk-collection huggingface dataset:
# From Brown-Field to Future Industrial Networks, a Case Study
## 4 Brown-Field Traffic Analysis
### Flow Characterization  
The matrix measurement is commonly used to characterize network flows for configuration, fault and security management purposes. The connectivity structure of the network provides the basis to identify all of the communication pairs for matrix measure and flow characterization. This meticulous approach provides a detailed view of all network components, but it scales poorly for larger size networks with more complex flow characteristics. An example of a commonly considered network connectivity structure is shown in Figure 2b. The brown-field network graph, Figure 2a, consists of 6 communi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Devices\#** & **VLAN\#** & **Bandwidth\%** & **Transmissions\%** & **Protocols\#** \\ \hline
**OT** & 124 & 7 & 0.35 & 0.42 & 15 (MMS \(>\) TCP \(>\) UDP) \\
**IT** & 213 & 2 & 0.65 & 0.58 & 40 (TCP \(>\) UDP \(>\) SMB2) \\
**Total** & 337 & 9 & 1 & 1 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the collected traffic.  
Figure 3: Data transmission dynamics in the network for Internet technology (IT) and operational technology (OT) traffics.  
cation clusters with 2 main communication hubs. The degree of the graph connectivity of a single source varies from 1 to 108, and more than 70% of the sources communicate at least with 2 separate destinations. As it is evident from the figure, the connectivity patterns in brown-field are by far more complex than of those considered in the literature. Characterizing brown-field flows using matrix and dynamic mixture then becomes an even more tedious task, which cannot deliver the level of accuracy required for network configuration management due to the imposed complexity.  
The matrix and dynamic measurement is also a method used for anomaly detection in the network. The dynamics of each pair of communicating components are modeled using various probabilistic and mathematical methods. The initial efforts are to model the dominant communication pattern and classify the future traffic to identify the anomalies or security bridges. Figure 4 illustrates the outcome of applying Discrete Time Markov Chain (DTMC) to model communication between a pair of components, associated with the connectivity structure in Figure 2a. The model is applied on 40,000 packet results in 37 states and 277 transitions [7].  
It is evident that the complexity and computational costs associated with new rounds of characterization and re-learning the dynamic model after any communication or structural changes are not going to be negligible. It also affirms scalability and complexity problems of matrix and dynamic characterization for the brown-field, where communication patterns are more complex, and more changes are envisioned from the current state of industrial networks towards the IT-OT converged networks.
# Experimental Approaches in Delineating mTOR Signaling
## 4 Identification and Characterization of mTOR Signaling Pathway Components
### Protein Motif/Sequence Homology Search-Mediated Identification of mTOR Signaling Components
#### 4.5.2 Mammalian Sin1 Was Identified by a BLAST Search of the Yeast AVO1 Gene  
AVO1 (Sin1) was firstly identified as a TORC2 complex component in yeast through 3\(\times\) HA-TOR2 IPs. A BLAST search identified putative AVO1 homologs in Drosophila (dSin1) and human (Sin1) [91]. RNAi-mediated dSin1 depletion in Drosophila S2 cells reduced dAkt-S505 phosphorylation with no impact on dS6K phosphorylation. Similarly, siRNA-mediated Sin1 depletion in human HeLa cells confirmed a critical role of Sin1 in governing Akt-S473 phosphorylation [91]. These data support an essential function of Sin1 in governing mTORC2 kinase activity towards phosphorylation of Akt hydrophobic motif.
# Acoustic Shell Optimization in Opera Houses for Concert Halls
## 3 The Case Study
### The Opera House Used as a Concert Space  
Each possible theatre arrangement (stage extension, acoustic shell and traditional curtain) requires not only a specific range of reference for reverberation time but also some approximate geometrical limits for the seat capacities and for the audience distance from stage.  
When using the opera house as a concert hall, care must be taken to define the main geometrical parameters, the architectural style, the volume, the shape and proportions of the room. It is also challenging to naturally achieve a change in Reverberation Time to adapt the opera house for concerts. The hall volume and total acoustic absorption become the two main variables for this disposal.  
For the concert configuration and its longer reverberation time requirements, compared to the opera configuration, an orchestra shell (made of acoustically reflective materials) was installed on stage. This solution brings about a significant alteration in the acoustic environment, effectively isolating the fly tower volume behind the acoustic shell panels and defining a considerably larger active acoustic space that results in a longer reverberation time compared to reverberation time in the main hall, if an opera set is on stage.  
In the case study, three different acoustic shell configurations are analyzed in detail, through acoustic simulations with Odeon software, starting from the model's calibration, for which the RT measurements on site were taken as reference.  
The acoustic shell implemented in the \"Opera di Roma\" model has a depth of 8 m, a lateral taper of 12 and a height of 12 m at its highest level.  
The first virtual model (layout 1, big acoustic shell) represents the acoustic shell with the orchestra placed on stage, in order to achieve the concert hall use for the theatre. In using the theatre as a concert hall the orchestra pit is removed in favour of a stage extension (OAC2).  
In the second model (layout 1, big acoustic shell) the stage extension is present, and some steps are placed near the back wall of the acoustic shell, to be occupied by the choir (OAC3).  
The third model is characterized by a shorter acoustic shell and the orchestra is placed on the stage extension (OAC5) (layout 2: small acoustic shell).
## 4 Simulation Results
### GNSS Signal Tracking Performance Analysis
When the interference is turned on, the PI algorithm will cause signal energy loss while suppressing the interference as shown in Figure 12. When the C/N0 drops to 30 dBHz, the navigation signal energy after the PI algorithm is too low to cause the signal tracking loop to lose lock. However, both the proposed method and the MVDR method can still guarantee the stability tracking of the signal through the array gain, and the proposed method can achieve gain in the signal direction. Figure 14: DOA tracking results. Figure 12: The punctual branch correlation cumulative value. Figure 9:0 Pseudo-distance measurement value. which significantly improves the tracking performance of the navigation receiver under weak signal conditions. Figure 13 shows the pseudo-range error calculated by the software receiver. It can be clearly seen from the figure that the accuracy of the pseudo-range measurement processed by the proposed method and the MVDR method is better than that of the PI algorithm. Under the condition of a weak signal, the PI algorithm is unable to obtain effective pseudo-range measurement due to the lock-out of the signal tracking loop, which means that the observation output of the receiver is no longer available. Although the accuracy of the pseudo-range measurement obtained by the proposed method has decreased, it can still ensure the availability of the pseudo-range measurement. The above results show that the proposed method can achieve the same performance as MVDR. Figure 14 shows the DOA tracking results. The DOA tracking accuracy is closely related to the C/N0 value. When C/N0 is 45 dBHz, the MSE results are 0.03 degree[2] under no interference and 0.08 degree[2] under interference; when the C/N0 is 30 dBHz, the MSE value is reduced to 0.48 degree[2]. The simulation results show that even under the condition of weak signal, high-precision DOA tracking performance can still be achieved.
## 3 Results and Discussion
### Enzyme Characterization
Despite the difficulties in determining kinetic parameters with a polymeric and rather undefined substrate (in which each molecule has a different number of attacking points), the apparent Km and Vmax values of Man 58 on mannan were measured. When Man 58 was restricted to mannan as the substrate, the rate dependence of the \(\beta\)-mannanase reaction followed Michaelis-Menten kinetics. Non-linear regression data analysis showed an apparent \(K_{\mathrm{m}}\) value of 3.29 mg/mL and \(V_{\mathrm{max}}\) value of 1.76 IU/mL. Man 58 exhibited a lower apparent \(K_{\mathrm{m}}\) value than the \(\beta\)-mannanases from _Pantoea agglomerans_ A021 (_P. agglomerans_ A021) and _Mytitus edulis_ (_M. edulis_) [26,27]. The effects of metal ions and EDTA on Man 58 activity were also investigated (Table 1). Man 58 was inhibited by MgSO\({}_{4}\), FeCl\({}_{3}\), CuSO\({}_{4}\), MgCl\({}_{2}\), ZnCl\({}_{2}\), ZnSO\({}_{4}\), CuCl\({}_{2}\), KCl and EDTA and activated by FeSO\({}_{4}\) and CoCl\({}_{2}\) at 10 mM concentration. Unlike the activity of Man26P from _P. agglomerans_ A021, Man 58 activity was not enhanced in the presence of NaCl [26]. [PERSON] _et al._[28] also reported an inhibitory effect of EDTA on \(\beta\)-mannanase produced by Vibrio sp. strain MA-138. The inhibitory effect of phenols on Man 58 was evaluated by incubating the enzyme with phenolic compounds and then measuring its residual mannanase activity (Table 2). Cinnamic acid was the only phenolic compound with a low inhibitory effect (7%) on Man 58. On the contrary, ferulic acid caused a 51% increase in enzymatic hydrolysis of mannan. [PERSON] _et al._[29] investigated the inhibitory or deactivating effects of phenolic compounds on two purified xylanases (Xyl T1 and Xyl T2) from _A. terreus_. Xyl T1 was inhibited to a greater or lesser degree by phenolic compounds, while Xyl T2 was highly resistant to the inhibitory effects of all phenolic compounds tested. Although the inhibitory effects of phenolic compounds on cellulases and hemicellulases have been documented in the literature, little is known about the nature of these interactions and their inhibitory mechanisms [30,31]. Further studies are necessary for a better understanding of the mechanisms involved in this enzymes resistance to phenolic compounds. On the other hand, the incubation of Man 58 with autohydrolysis liquor [22],  
Figure 5: Thermostability profile of Man 58 at 40, 50 and 60 \({}^{\circ}\)C.  
produced at 200 \({}^{\circ}\)C after 50 min of pretreatment, resulted in a significant loss of activity. Although the liquor obtained after autohydrolysis is rich in xylo-oligosaccharides that act as inducers and can be used as substrate for xylan-degrading enzymes production, under these conditions, the activity of Man 58 was inhibited by 64%. However, incubation of Man 58 with the same liquor obtained after 30 min of pretreatment showed a lower inhibitory effect (25%).
# Mobility, Citizens, Innovation and Technology in Digital and Smart Cities
## 3 Cities Transportation Trends  
This section has the goal of highlighting trends based on academic and industrial perspectives. For this purpose, in order to analyze the trends about cities' transportation, a bibliometrics search on Scopus Database was carried out.  
As can be noticed in Figure 0(a), the number of publications mentioning transportation systems and SC have been increasing over the last few years. Figure 0(b) also shows an increasing number of published papers related to the terms SC and Vehicle Routing.  
However, in terms of the absolute values reported in graphs depicted in Figure 1, one can make conclusions about the potential that these guidelines still have for the next decade. Some of the novel transportation systems are already becoming reality, and others are still being designed. Furthermore, since 2005, more than 12,000 patents (considering a search done with the terms \"Smart Cities\" and \"Transportation\" on Scopus database) have been found, which highlight the efforts made by the industry and the academia in order to find alternative technologies for more profitable and efficient transportation solutions.  
Figure 1: Raw number of published documents (information extracted from Scopus on 20 June 2019).  
In addition, it was possible to analyze that these two keywords are addressed mainly in the fields of Computer Science, Engineering and Social Sciences, following from areas such as: Mathematics, Decision Sciences, Energy, among others. The interest in these terms among different research areas is depicted in Figure 2. Taking a brief look at the history, it can be noticed that smart cities studies started with a technological perspective, and now it is being spread among other research areas, with a significant percentage of publication in the field of social science [60].
## 7 Conclusions and Future Work  
This paper shows the increasing demands and important applications that require the use of UAV swarms such as smart cities, disaster management, and environmental monitoring. Further research on UAV swarm technology has great practical significance. UAV swarms are highly mobile, and their structures frequently change. They require low latency and highly reliable communication networks. Therefore, it is necessary to design a communication architecture and routing protocol with good applicability, high efficiency, and stability for unified and efficient UAV swarm communication. We introduced the general applications of UAV groups or UAV swarms and discussed the communication routing protocols that enable smooth coordination among UAVs to support complex tasks. We also covered control algorithms that support UAVs to operate in the right position for specific purposes. Additionally, AI-based algorithms were considered to support UAVs in performing tasks optimally. We surveyed and reviewed the latest work, and then evaluated the existing results. From there, we suggested suitable solutions tailored to practical applications.  
For future work, there are still many challenges and issues that need further research and development: (i) The use of AI with UAVs has legal and ethical implications. This is especially true in certain search and rescue situations. Consideration of priority areas and targets in rescue missions is a typical example. Legally, the need to share data, monitor, and access information collected by UAVs may affect personal privacy or violate specific laws of a particular area. (ii) The limitation of UAVs is mostly batteries with limited energy. The use of AI methods can increase the complexity of the computation, thereby increasing the CPU power usage in UAVs. Therefore, the calculation and selection of routing protocols and data communication models must be carefully considered. It is necessary to study whether the implementation of ML techniques in the model is commensurate with the efficiency.  
Conceptualization, M.L.T., D.T.N. and M.T.N.; methodology, M.D.N., L.Q.D. and D.R.I.M.S.; software, M.D.N. and D.T.N.; validation, M.L.T., D.T.N. and M.T.N.; formal analysis, L.Q.D. and M.D.N.; investigation, M.T.N. and D.T.N.; resources, M.D.N.; data curation, D.R.I.M.S. and L.Q.D.; writing--original draft preparation, D.T.N. and M.T.N.; writing--review and editing, M.T.N., D.R.I.M.S. and M.L.T.; visualization,; supervision, M.T.N.; project administration, M.L.T.; funding acquisition, M.L.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by the Ministry of Education and Training (MOET) under grant number B2023-GHA-01.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network
#### 2.3.4 Management Plane  
In traditional networking, networking devices are managed and configured by the network administrator using a distributed approach. In SDN, management is centralized; however, it is embedded in the control plane and not decoupled as a separate plane. In KDN, management functions are decoupled from control logic, and separate protocols are used for data collection, network monitoring, and network configuration, which makes troubleshooting in case of failure much easier than in SDN. Furthermore, network management can be automated using knowledge learned from the network to update management policies in the application plane to automatically adapt to network changes in real-time with minimum human intervention.
## 2 System Model
### Transmitter  
The proposed system model is shown in Figure 1. From the figure, the input of the transmitter is a speech sample sequence:  
\[s=[s_{1},s_{2},\dots,s_{W}],\quad s_{w}\in\mathbb{R},\quad w=1,2,\dots,W \tag{1}\]  
where \(W\) denotes the total number of samples, and \(s_{w}\) represents the \(w\)-th speech sample. Each sample \(s_{w}\) is a real-valued scalar, taking positive or negative values within the speech waveform.  
The semantic encoder processes the speech sequence and extracts a high-dimensional semantic feature tensor:  
\[\mathbf{F}=T_{S}(s;\theta_{S}),\quad\mathbf{F}\in\mathbb{R}^{A\times B\times C} \tag{2}\]  
where \(T_{S}(\cdot)\) represents a deep feature extractor parameterized by \(\theta_{S}\), responsible for capturing linguistic, spectral, and phonetic structures. Here, \(A\times B\times C\) denotes the dimensionality of the extracted feature tensor, preserving fine-grained speech information.  
To enable efficient transmission, the semantic compressor transforms the extracted representation into a compressed latent space, reducing dimensionality while retaining essential semantic features:  
\[\mathbf{X}_{s}=T_{\text{C}}(\mathbf{F};\theta_{C}),\quad\mathbf{X}_{s}\in \mathbb{R}^{A^{\prime}\times B^{\prime}\times C^{\prime}} \tag{3}\]  
where \(T_{\text{C}}(\cdot)\) performs structured compression, mapping the original feature tensor \(\mathbf{F}\) into a lower-dimensional latent representation of size \(A^{\prime}\times B^{\prime}\times C^{\prime}\).  
Since deep learning-based semantic features exist in a continuous-valued space, quantization and encoding are required for digital transmission. First, the semantic feature tensor is vectorized into a sequential representation:  
\[\mathbf{Z}=\text{flatten}(\mathbf{X}_{s}) \tag{4}\]  
A quantization function then maps the floating-point values to discrete integer levels while introducing controlled quantization noise:  
\[\mathbf{B}=f_{\text{quant}}(\mathbf{Z}) \tag{5}\]  
where \(f_{\text{quant}}(\cdot)\) denotes a quantization function that determines the trade-off between fidelity and bit efficiency. The quantized values are subsequently encoded into a binary stream:  
\[\mathbf{b}=\text{bin}(\mathbf{B}),\quad\mathbf{b}\in\{0,1\}^{N\times 16} \tag{6}\]  
Before transmission, the digital bitstream undergoes channel encoding to improve robustness against impairments and is then modulated using M-QAM. The modulated signal, denoted as \(\mathbf{X}\), is transmitted over the wireless channel.  
Figure 1: Semantic-aware digital speech communication system model.
## 5 Copper Nanoparticles and Their Antibacterial Role
### Antifungal  
For many years copper has been used as a material in the manufacturing process of pesticides, fungicides and fertilizers. Given the latest technology developments, it has been claimed that copper nanoparticles can be used as a good fungicide.  
Studies reported that CuNPs can be used as fungicide against a broad range of plant fungi, such as _Fusarium_ sp., _Phama destructia_, _Curvalaria lunata_, _Alternaria alternate_, _Fusarium oxysporum_, _Penicillium italicum_, _Penicillium digitatum_ and _Rhizoctonia solani_[73].  
For example, CuNPs synthesized by a green method consisting of a chemical reduction of copper ions with ascorbic acid exhibit antifungal activity against _F. solani_, _Neofisicocum_ sp. and _F. Oxysporum_. The antifungal activity involved damages to the fungus' cell membranes and the intracellular production of ROS. Hence, this was a way of conducting an easy and cheap production method of CuNPs with high antifungal activity. Thus, copper can be used to control and damage threatening fungi to crop and forest species.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Culture**} & \multirow{2}{*}{**Strain No.**} & \multicolumn{2}{c}{**MIC (\(\mu\)g/mL)**} & \multicolumn{2}{c}{**MBC (\(\mu\)g/mL)**} \\ \cline{3-6}  & & **Ag** & **Cu** & **Ag** & **Cu** \\ \hline _Escherichia coli_ & MTCC 443 & 40 & 140 & 60 & 160 \\ _Escherichia coli_ & MTCC 739 & 180 & 220 & 220 & 260 \\ _Escherichia coli_ & MTCC 1302 & 120 & 200 & 160 & 220 \\ _Escherichia coli_ & MTCC 1687 & 140 & 280 & 180 & 300 \\ _Bacillus subtilis_ & MTCC 441 & 40 & 20 & 60 & 40 \\ _Staphylococcus aureus_ & NCIM 2079 & 120 & 140 & 160 & 160 \\ _Staphylococcus aureus_ & NCIM 5021 & 120 & 140 & 160 & 160 \\ _Staphylococcus aureus_ & NCIM 5022 & 120 & 140 & 160 & 160 \\ \hline \end{tabular}
\end{table}
Table 6: Antimicrobial effect of Ag and Co nanoparticles on various microorganisms [68].
## 4 Data Reducing Methods  
### Sideslip Angle Correction for Normalization of Pressure Coefficients and Force Coefficients  
Usually, pressure coefficients are normalized by Equation (2), where \(p_{i}\) is the surface pressure measured by pressure scanner, \(p_{s}\) and \(p_{0}\) are the static and total pressure measured by leading-edge probe, respectively:  
\[Cp_{i}=\frac{p_{i}-p_{s}}{p_{0}-p_{s}} \tag{2}\]  
However, the relative sideslip angle, \(\beta\), which is defined in Figure 8, is usually non-zero in the field environment. In non-zero sideslip condition, flow inside of the airfoil plane plays a dominant role in generation of aerodynamic force. So dynamic pressure in Equation (2), \(p_{0}-p_{s}\), should be corrected as \((p_{0}-p_{s})\cdot\cos^{2}\beta\) in non-zero sideslip condition. The new normalization method is shown in Equation (3):  
\[Cp_{i}=\frac{p_{i}-p_{s}}{(p_{0}-p_{s})\cdot\cos^{2}\beta} \tag{3}\]  
Figure 7: The measuring and controlling system.  
Body-axis forces and their coefficients are calculated by Equations (4) and (5), where \(N\) is the normal force perpendicular to the chord, \(A\) is the tangential force parallel to the chord, \(dx_{i}\), \(dy_{i}\) are the interval of pressure taps parallel and perpendicular to the chord respectively, \(CN\) is the normal force coefficient, \(CA\) is the tangential force coefficient, \(C\) is the chord of airfoil:  
\[\left\{\begin{array}{l}A=\sum\limits_{i=1}^{N}p_{i}\cdot dy_{i}\\ N=\sum\limits_{i=1}^{N}p_{i}\cdot dx_{i}\end{array}\right. \tag{4}\]  
\[\left\{\begin{array}{l}CA=\frac{\sum\limits_{i=1}^{N}p_{i}\cdot dy_{i}}{(p_ {0}-p_{s})\cdot\cos^{2}\beta\cdot C}=\sum\limits_{i=1}^{N}\frac{Cp_{i}\cdot dy _{i}}{C}\\ CN=\frac{\sum\limits_{i=1}^{N}p_{i}\cdot dx_{i}}{(p_{0}-p_{s})\cdot\cos^{2} \beta\cdot C}=\sum\limits_{i=1}^{N}\frac{Cp_{i}\cdot dx_{i}}{C}\end{array}\right. \tag{5}\]  
Air-axis force coefficients are calculated by body-axis force coefficients according to Equation (6), where \(Cd\) is the drag coefficient along the inflow direction, \(Cl\) is the lift coefficient perpendicular to the inflow direction, \(\alpha\) is the effective angle of attack, as shown in Figure 8:  
\[\left\{\begin{array}{l}Cd=CA\cdot\cos\alpha+CN\cdot\sin\alpha\\ Cl=CN\cdot\cos\alpha-CA\cdot\sin\alpha\end{array}\right. \tag{6}\]