esa-sceva/satcom-chunk-collection huggingface dataset:
# Ordered-Bipartite Consensus of Multi-Agent Systems under Finite Time Control
## 2 Preliminaries
### Basic Graph Theory  
\(\mathcal{G}=(\mathcal{V},\mathcal{E})\) is a weighted directed graph in the communication network, with the node set \(\mathcal{V}=\{v_{1},v_{2},\ldots,v_{N}\}\) and the edge set \(\mathcal{E}=\{e_{ij}|v_{i},v_{j}\in V\}\), and \(A=[a_{ij}]\in\mathbb{R}^{N\times N}\) is the adjacency matrix. In this case, the \(i\)-th agent is presented by node \(v_{i}\) in the topology network. This means that the \(i\)-th agent and node \(v_{i}\) are different representations of the same object in its appropriate context. It is always assumed that \(a_{ii}=0\), which implies that the given graph \(\mathcal{G}\) does not include any self-loops. For the directed graph \(\mathcal{G}\), \(a_{ij}\
eq 0\) if and only if there exists a direct edge from \(v_{j}\) to \(v_{i}\), and call \(\mathcal{G}\) a signed graph if and only if the weights of the edge are either negative or positive. A signed digraph \(\mathcal{G}\) is defined as structurally balanced if there exists two subsets \(\mathcal{V}_{1}\) and \(\mathcal{V}_{2}\) such that the node set \(\mathcal{V}\) can be decomposed by \(\mathcal{V}_{1}\cup\mathcal{V}_{2}=\mathcal{V}\) and \(\mathcal{V}_{1}\cap\mathcal{V}_{2}=\varnothing\), and the weights of the edges have the relation \(\Big{\{}a_{ij}\geq 0,\) if \(v_{i},v_{j}\in\mathcal{V}_{\ell},\,\ell=1,2\), Under this sense, it can be noted that \(\vec{i}\) is the set of nodes that have an edge with nodes \(v_{i}\). Then, it is not difficult to explain that \(v_{j}\in\vec{i}\) if and only if \(\vec{i}=\vec{j}\). Next, it is necessary to note that \(\mathcal{P}_{ij}\) are the (directed) paths from \(v_{j}\) to \(v_{i}\) if and only if there exists some indexes \(v_{j_{1}},v_{j_{2}},\ldots,v_{j_{s}}\) such that \(e_{j_{1}j},e_{j_{2}j_{1}},\ldots,e_{j_{s}}\) in \(\mathcal{E}\). Furthermore, the directed graph \(\mathcal{G}\) is said to contain a directed spanning tree if and only if there exists at least one node (called root) that has a path to all the agents in \(\mathcal{V}\). The Laplacian matrix \(L(A)=(l_{ij})_{N\times N}\) related to a matrix \(A\) (or to graph \(\mathcal{G}\)) is defined by \(l_{ij}=\begin{cases}-a_{ij},&i\
eq j,\\ \sum\limits_{\ell\in\mathcal{G}}a_{i\ell},&i=j.\end{cases}\)  
Finally, it is needed to give the algebraic feature of the structurally balanced graph.  
**Lemma 1** ([12]).: \(\mathcal{G}\) _is structurally balanced if and only if there exists a matrix \(\Phi=\text{diag}(\phi_{1},\dots,\phi_{N})\) with \(\phi_{\ell}\in\{1,-1\}\) such that the entries of \(\Phi A\Phi\) are non-negative._
## 4 Results and Discussion
Table 9 compares the achieved results of the CNN-LSTM Roman Urdu sentiment analysis with the respective corpuses to the performance of the other existing techniques, in terms of accuracy, precision, \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Word Embedding** & **Dataset** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **F1-Measure** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Word2 Vec (CBOW)} & UCL & 0.740 & 0.750 & 0.714 & 0.731 \\  & RUSA-19 & 0.748 & 0.762 & 0.729 & 0.745 \\  & \begin{tabular}{c} RUSA \\ dataset \\ \end{tabular} & 0.841 & 0.850 & 0.840 & 0.844 \\ \hline \multirow{4}{*}{Word2 Vec (skip-gram)} & UCL & 0.735 & 0.741 & 0.708 & 0.724 \\  & RUSA-19 & 0.739 & 0.752 & 0.720 & 0.735 \\  & \begin{tabular}{c} RUSA \\ dataset \\ \end{tabular} & 0.837 & 0.835 & 0.824 & 0.829 \\ \hline \multirow{4}{*}{TF-IDF} & UCL & 0.714 & 0.713 & 0.691 & 0.701 \\  & RUSA-19 & 0.726 & 0.722 & 0.700 & 0.710 \\  & \
\begin{tabular}{c} RUSA \\ dataset \\ \end{tabular} & 0.815 & 0.801 & 0.791 & 0.795 \\ \hline \multirow{4}{*}{GloVe} & UCL & 0.728 & 0.728 & 0.711 & 0.719 \\  & RUSA-19 & 0.736 & 0.738 & 0.718 & 0.727 \\  &
\begin{tabular}{c} RUSA \\ dataset \\ \end{tabular} & 0.826 & 0.824 & 0.810 & 0.816 \\ \hline \multirow{4}{*}{FastText} & UCL & 0.738 & 0.737 & 0.730 & 0.733 \\  & RUSA-19 & 0.736 & 0.745 & 0.721 & 0.732 \\ \cline{1-1}  & RUSA & 0.828 & 0.823 & 0.816 & 0.819 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CNN-LSTM Roman Urdu model accuracy using various word embeddings with two-layer LSTM. recall, and F1-score. The proposed CNN-LSTM Roman Urdu sentiment analysis attained the highest performance in all used corpuses with 0.740, 0.748, 0.841, and 0.750 for the accuracy, precision, recall, and F1-score, respectively, for the UCL dataset. Similarly, the proposed model achieved 0.762, 0.850, 0.714, and 0.729 for the accuracy, precision, recall, and F1-score, respectively, for the RUSA-19 dataset and 0.840, 0.731, 0.745, and 0.844 for accuracy, precision, recall, and F1 score, respectively, for the RUSA dataset. This assessment shows the effectiveness of the suggested CNN-LSTM deep learning technique for Roman Urdu SA. The confusion matrix is a measure for assessing the validity of a classification. Figures 5-7 show the proposed model's confusion matrix for the UCL, RUSA-19, and RUSA datasets, respectively. Only 10.60% of positive reviews were mislabeled as negative and 13.90% as neutral, while 75.50% of positive reviews were classified accurately as positive. Only 13.00% and 11.00% of negative reviews were misclassified as positive and neutral, respectively, while 76.00% of negative reviews were classified accurately
# Flow Analysis of a Novel, Three-Way Cartridge Flow Control Valve
## 6 Discussion
First, CFD analyses were carried out, which made it possible to estimate the dynamic flow forces appearing in the \(B\) gap caused by a change in the direction of the working fluid stream. The results were used to build a model in the Matlab/Simulink system and conduct further simulations. Based on the simulation outcomes, the flow characteristics \(Q=f(p)\) and the valve's ability to compensate for load changes were determined. The results of the simulation studies were confirmed on the test bench using the prototype of the proposed valve, achieving a high level of compliance with the correlation coefficient above 0.95. Detailed conclusions are as follows:  
* The proposed valve can be used in practice in all power hydraulic systems where a specific fixed value of the minimum flow rate must be ensured regardless of the load. * There are operational limitations: under conditions of variable load, the possibility of increasing the flow by 10% compared to the required value should be taken into account, especially for low regulated flow (setting 6 dm\({}^{3}\) min\({}^{-1}\) results in accuracy up to 10%, while for 22 dm\({}^{3}\) min\({}^{-1}\) the maximum change does not exceed 4%. * Compared to a typical two-way flow control valve, the proposed solution is more advantageous in terms of energy demand, especially in systems of many valves connected in parallel, since the presence of a relief line prevents the need for higher pressure in the supply line. * The valve can be used in systems and environmental conditions that exclude direct access to the electrical power supply. * To obtain a wide range of adjustable flow rates, springs of different stiffness can be used, with a stiffer spring allowing for higher values. At the same time, the exact setting is achieved by manually adjusting the initial tension of the spring. Conceptualisation, E.L.; methodology, P.P. ; software, E.L. and G.F.; validation, J.R.; formal analysis, E.L. and J.R.; investigation, P.P. ; resources, J.R.; writing--original draft preparation, G.F.; writing--review and editing, E.L. and J.R.; visualisation, G.F.; supervision, E.L.; project administration, J.R.; funding acquisition, E.L. and G.F. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors
# Compact MIMO System Performances in Metallic Enclosures
## 4 Experimental Results and Discussions
### Near-Field MIMO BER and EVM in Empty Enclosure  
In this scenario, a \(2\times 2\) MIMO setup was investigated for BER and EVM measurements in stationary channel conditions. This scenario has direct coupling and rich scattering.  
Table 1 shows the BER and EVM measurements of two USRP channels at a frequency of 5.6 GHz and AGC gain of 60. It can be seen that the BER and EVM of two USRP channels improved as the TX gain of the USRP increased.  
Table 2 shows the BER and EVM measurements of two USRP channels at a frequency of 5.6 GHz and AGC gain of 80. It can be seen that the BER and EVM of the two USRP channels improved as the Tx gain of the USRP increased. A noticeable difference in the measured BER and EVM values can be observed for the AGC gains of 60 and 80, respectively. For both cases, BER and EVM values improved; however, the BER values decreased faster for the AGC gain value of 80. This is because of the extra increase in the AGC gain.  
The measured QPSK constellations shown in this work were recorded after the carrier frequency synchronization. The QPSK constellation diagram was recorded after the carrier synchronizations for different Tx gain values of the USRPs. It can be seen that USRP channel 2 is better than USRP channel 1. Additionally, the two USRP channel QPSK constellation diagrams improved as the Tx gain of the USRP increased, which corresponds to improved BER and EVM measurements.  
Similar to the theoretical predictions and previous measurements, the two USRP channel QPSK constellation diagrams improved as the Tx gain of the USRP increased,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Tx Gain (dB)**} & \multicolumn{3}{c}{**Channel 1**} & \multicolumn{3}{c}{**Channel 2**} \\ \cline{2-7}  & **Prx (dB)** & **EVM (\%)** & **BER** & **Prx (dB)** & **EVM (\%)** & **BER** \\ \hline
2 & \(-71\) & \(87\) & \(3\times 10^{-2}\) & \(-68\) & \(81\) & \(7\times 10^{-3}\) \\
4 & \(-69\) & \(84\) & \(7\times 10^{-3}\) & \(-66\) & \(77\) & \(3\times 10^{-3}\) \\
6 & \(-67\) & \(79\) & \(2\times 10^{-3}\) & \(-64\) & \(7 which corresponds to improved BER and EVM measurements. The measurements were performed at stationary channel conditions when the mode stirrer was off. The mode-stirrer rotation has direct implications on the measured BER and EVM values because when the mode stirrer rotates, the channel becomes non-stationary. The channel power gain of the MIMO communication link varied drastically; as a result, AGC the receiver tried to compensate for low and high gain values to stabilize the received signal for carrier frequency offset correction. Hence, the EVM measurements of two USRP channels were not stationary because of the time-varying channel power gain and time-varying frequency offset. The same measurements were repeated when the mode stirrer was turned on. In the presence of the mode-stirrer rotation, the MIMO channel was time-varying; therefore, the constellation diagram and MIMO digital receiver KPIs were also time-varying.
# Spatial Dynamics of Olive Fruit Fly Adults in the Framework of a Monitoring Trap Network
###### Abstract  
_Bactrocena olea_ ([PERSON]) (Diptera: Tephritidae) is a key pest of olive groves. Adult monitoring is carried out by means of attractant traps of different shapes, which give relevant information for pest control such as the presence of adult flies in the field and their trend, female maturity and sex ratio. However, it is still not entirely clear whether a given density is sufficient for providing a reliable representation of flies in an olive grove. To investigate this question, an experiment was planned, consisting of arranging a high-density network of unbaited sticky panels (UTs) between panels baited with ammonium carbonate (BTs) deployed at a density of 2 traps/ha. The experiment was carried out in Greece, Italy, Lebanon, Spain and Tunisia. The percentage of BT over UT catches varied significantly among the different countries, with BTs ranging from 82% of catches in Italy to 27% in Greece. The Pearson correlation between BTs and UTs was significant under high captures but not significant at low densities. The index of aggregation showed an inverse relationship with baited catches. The distributions of males and females were nearly always positively spatially associated. According to the field data, BTs at the density of 2/ha provide a realistic estimate of the population in the field in the cases of established populations. However, in the periods without population establishment, a denser monitoring trap network is likely required to obtain a reliable estimation of the field population.  
_Bactrocena olea_; population distribution; attractant device; integrated pest management; SADIE; spatial association
# Magnetic Abrasive Finishing of Beta-Titanium Wire Using Multiple Transfer Movement Method
## 3 Experimental Results and Analysis  
### Influence of Particle Size  
Figure 4 shows the changes in surface roughness (Ra) as a function of finishing time for various magnetic abrasive particle sizes. To determine the effect of changes in magnetic abrasive particle sizes (0.5, 1, 3 \(\upmu\)m) we performed experiments with an 8 Hz vibration rotational magnetic field at a rotational speed of 1500 rpm. The smallest (0.5 \(\upmu\)m) grain size abrasive material was found to have the best effect followed by 1 and 3 \(\upmu\)m magnetic abrasive particles. After the finishing process, the surface roughness of the workpieces were 0.12, 0.12, and 0.15 \(\upmu\)m for particle sizes of 0.5, 1, and 3 \(\upmu\)m, respectively. Thus, smaller magnetic abrasive grain sizes produced better results in terms of the surface roughness.  
\begin{table}
\begin{tabular}{c c} \hline Workpiece material & \(\beta\)-titanium wire (L = 50 mm, D = 0.5 mm) \\ \hline Electrolytic iron powder & 1.2 g (\#200) \\ \hline Diamond paste (PCD) & (0.5, 1, and 3 \(\upmu\)m) 0.6 g \\ \hline CNT particle & 0.02 g \\ \hline Lubricant & 0.3 mL (light oil) \\ \hline Magnet & Nd-Fe-B permanent magnet (Size: 20 mm \(\times\) 10 mm \(\times\) 10 mm) \\ \hline Magnetic pole vibration & Amplitude: 2 mm \\ \hline Finishing gap & 3 mm, 5 mm \\ \hline Rotational speed & 700, 1500, 2000 rpm \\ \hline Finishing time & 0, 60, 120, 180, 240, 300 s \\ \hline Vibration frequency & 8 Hz \\ \hline Feed rate & 80 mm/min \\ \hline \end{tabular}
\end{table}
Table 3: Experimental conditions.
# Anomaly Detection Method for Unknown Protocols in a Power Plant ICS Network with Decision Tree
## 3 Results  
To apply the proposed ML supervised learning (decision tree) technique and configure the generated data similar to the demonstration environment, control traffic data from the power plant were collected, and the technology was verified.  
Based on the proposed anomaly pattern extraction model, in the first step, the abnormal symptom detection model was trained by extracting whether anomalies were detected from the 6.5 TB of traffic generated in January 2021.  
For the two-step verification, the model was verified through a traffic simulator, and 100 random anomaly datasets were generated as 26 TB of data from January 2021 to April 2021, and the detection rate was analyzed and verified over the time series.  
To verify the proposed model, the results of the verification were compared with commercial solutions such as Darktrace and improved Darktrace.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 2 UAV Technology in Wireless Communication
### Antenna Tilting and Cell Association  
To provide the best service to ground users, cellular BS antennas are tilted downwards. Aerial coverage has recently received significant attention, mostly for connecting airline passengers on domestic flights. Only a small number of BSs with upgraded antennas are necessary to ensure extensive coverage and continuous connectivity during the flight. However, due to construction and regulatory constraints, these methods cannot be used for commercial UAVs which frequently fly at lower altitudes, such as 50-300 m, as illustrated in Figure 2. UAVs are fundamentally different from terrestrial users since the assumptions that apply to terrestrial users are not applicable to aerial users. Consider the following example, two BSs (A and B) have antennas tilted downwards with the primary lobes facing down towards the earth. The ground user connects to the BS. If the signal strength from both BSs is equal, the user will stay connected to the previous one. In the case of UAVs, side-lobe antennas are useful. Figure 3 shows that despite being closer to BS A than BS B, the UAV at Y1 is served by BS B. This will cause excessive HOs and ping-pong effects. This issue also applies to horizontal locations.  
For locations Y2 and Y4 in Figure 3, the picture can be expanded to include a large number of BS s, signifying that the rate of HO for UAVs will be more excessive than conventional or traditional networks. Increasing the UAV's height will decrease the competitiveness of its service via the main lobes as long as the terrestrial BS antennas are slanted downwards. As a result, the service given to UAVs at high altitudes will be via the side lobes, which is not at the same level offered by the main lobes. Due to the increased potential of line-of-sight (LoS) at such high altitudes, UAV communication will suffer from uplink (UL) and downlink interference. This will create severe interference and navigation  
Figure 2: UAV base station (UBS) and normal BSs in a future ultra–dens heterogenous network.  
management issues. Increasing the altitude will allow the side lobes of the BS antennas to have more than one connection possibility depending on the location of the UAV. This raises the possibility of LoS communication links, which increases interference in neighboring cells when compared to UE ground equipment [39; 40; 41; 42; 43].
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
###### Abstract  
The housing and bracket structure are critical components of multispectral cameras; the mechanical properties significantly affect the stability of the optical system and the imaging quality. At the same time, their weight directly impacts the overall load capacity and functional expansion of the device. In this study, the housing and bracket structure of a three-band camera were optimized based on the initial design. Using a combination of density-based topology optimization and multi-objective genetic algorithms in parametric optimization, redundant structures were removed to achieve a lightweight design. As a result, the total weight of the housing and bracket was reduced from 9.56 kg to 5.51 kg, achieving a 42.4% weight reduction. In the optimized structure, under gravity conditions, the maximum deformation along the \(z\)-axis did not exceed 7 nm, and the maximum amplification factor in the dynamic analysis was 1.42. The analysis demonstrates that the optimized housing and bracket exhibit excellent dynamic and static performance, meeting all testing requirements, and, under gravitational conditions, the spot diagram and modulation transfer function effect are negligible. Furthermore, in a static environment, the detection range across all spectral bands reaches 18.5 km, satisfying the mission requirements. This optimization design provides a strong reference for the lightweight design of future optical equipment.  
Keywords:structural optimization; multispectral camera; topology optimization; finite element analysis; structure +
Footnote †: journal: Applied Sciences
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Ensemble Methods
#### 3.1.1 Bagging Methods  
The basis of bagging methods is the use of the bootstrap sampling method to obtain \(T\) data subsets each containing \(m\) training samples to separately train the base learner. For the dataset \(D\), one sample is randomly selected and added to the data subset, and then returned to the dataset \(D\). This is conducted a total of \(m\) times. After repeating the operation \(T\) times, we obtain \(T\) data subsets, each containing \(m\) samples. Some samples in the dataset \(D\) may appear several times in the data subsets, and some may never appear.  
The RF algorithm is a special kind of bagging method. The base learners of the RF algorithm are provided by the classification and regression trees (CART) model, and random attribute selection is introduced in the CART training process.
## 3 Results  
The simulated results for the five scenarios are presented in accordance with the model outputs presented in Section 2.1. The following results are presented for each use case scenario: (1) operational behavior, (2) maintenance event: timeline and workload, and (3) component failure during a time period of 20 years (175,000 h). In addition, one subsection is dedicated to comparing the corrective maintenance (Scenario 4) with the  
Figure 6: The deterioration curves modeled for each component, individually.  
Figure 10: Operational behavior of Scenario 4 during 20 years of operation.  
Figure 9: Operational behavior of Scenario 3 during 20 years of operation.  
The results highlight that Scenario 1, which is based on case study historical failure data, offers the lowest operational availability of approximately 37.756% availability during 20 years of operation, which is not valid. Scenario 2 provides more valid numbers of availability with 73.384%, as the unreasonably extreme data values (closed dates for maintenance events) caused by human factors in the notification process are manipulated and corrected. Scenario 3, which is based on OREDA failure rates and MTTR values, highlights the availability of 96.220%, which is a significant improvement compared to scenarios 1 and 2. Scenario 4, which is a mixed scenario based on case study failure rates and OREDA MTTR values, shows the availability of 95.750%. Scenarios 3 and 4 demonstrate the effect of failure rates on availability, where the availability decreased 0.470% (96.220-95.750%) when case study failure rates are used. Scenarios 2 and 4 highlight the effect of MTTR on availability, where the availability increased 22.366% (95.750-73.384%) when OREDA MTTR values are used. Scenarios 3 and 4 are valid scenarios when compared with real availability numbers. However, it shows that OREDA is more reliable and valid in terms of MTTR data, as the used unit is hours, not days like in case study data. In final, Scenario 5 addresses a total of operational availability of 96.018%, which corresponds to an improvement of 0.268% (96.018-95.750%) in comparison to Scenario 4, which includes the same input data but without an intelligent maintenance system.  
Figure 11: Operational behavior of Scenario 5 during 20 years of operation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**Operational Behavior [\%]**} \\ \cline{2-4}  & **Availability** & **Unavailability** & **Total** \\ \hline
**1** & 37.756 & 62.244 & 100 \\ \hline
**2** & 73.384 & 26.616 & 100 \\ \hline
**3** & 96.220 & 3.780 & 100 \\ \hline
**4** & 95.750 & 4.250 & 100 \\ \hline
**5** & 96.018 & 3.982 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of operational behavior of the different simulated use case scenarios.