esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison with Other Systems and Price of the Employed Components  
At this point, we are going to perform a comparison between our developed solution and the current systems described in the related work for tanks and ponds, the systems proposed for marine cages are not included. We summarize the characteristics of the current systems and our proposal in Table 1. The main differences are that the other systems monitor between 2 and 4 parameters while our systems are capable to monitor 10 parameters. Moreover, the other proposals employ commercial probes and our system is based on our own sensors. Which makes that the price of the system decreases. In our proposal, we also consider the fish movement among the water quality monitoring. Finally, in our solution we showed the location of the sensors in the tank. The majority of the papers do not pay attention to the location of the sensors.  
Figure 23: Data gathered by the feed falling sensor.  
Figure 22: Data gathered by the fish presence sensor.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Paper** & [12] & [13] & [14] & [15] & [16] & [17] & [18] & [19] & [20] & [21] & **Our Proposal** \\ \hline Communication technology: & ZigBee & ZigBee & ZigBee & N.I. & ZigBee & ZigBee & LR-WPAN & N.I. & ZigBee & N.I. & WiFi \\ Applied on: & Tanks & Tank & Tanks & Ponds & Ponds & Tanks & Ponds & N.I. & Tank & Ponds & Tanks \\ Considers the location of the sensor? & & & & & & & & & & & & x \\ Develop their own sensors? & & & & & & & & & & & x \\ Store information? & x & x & x & x & x & x & x & x & x & x \\ Send alarm? & x & x & & & & & & x & x & x & x \\ pH & & x & x & x & x & x & x & & x & & x \\ Water level & & & & & & & & & & & x \\ Temperature & x & x & x & x & x & x & x & x & x & x & x \\ Pressure & x & & & & & & & & & & & \\ Dissolved oxygen & x & x & & & & x & x & x & x & x & x \\ Conductivity & & & & x & & & & & & & x \\ Ammonia & & & & & & & & & & & x \\ Others & & & & & & & & & & & x \\ Fish monitoring & & & & & & & & & & & x \\ Total of monitored parameters & 3 & 4 & 3 & 3 & 4 & 4 & 3 & 3 & 4 & 2 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of characteristics of the current system and our proposal.  
As a final point, the price of the components to create this WSN for one tank is shown in Table 2. The price of employed sensors is 47.44 \(\upepsilon\) and the price of the nodes and their accessories is 40.22 \(\upepsilon\). The most expensive item is the workers presence, this sensor has a cost of 15.23 \(\upepsilon\). All the sensors for water quality have a price of 3.11 \(\upepsilon\). The system for fish detection cost 16.5 \(\upepsilon\) and the camera for feed detection 5.35 \(\upepsilon\). To this price some minor costs as boxes for the nodes or plexiglass tubes must be add. The cost of this system can be assumed by the fish farmers, at least in the tanks with the most sensible and/or expensive fish are kept at the beginning and expand the system in the future. The system presents high scalability and only the need components must be bought.
## 4 Conclusions  
In this paper, the AOA has been designed to solve the allocation problems of DGs and CBs in the radial systems. Four different scenarios have been realized for examining allocation problems in this study. In the allocation problems, the optimum placement and capacity of the CBs and the optimum placement, capacity, and power factor of the DGs are determined to reduce the total power loss and voltage deviation. The AOA has been implemented in IEEE 33-bus and IEEE 69-bus systems in different scenarios. The results of whole scenarios have revealed that the AOA provided a better performance of power loss minimization and voltage profile enhancement compared to other optimization methods. In both radial systems, the best results were obtained when only DGs were placed, and the optimum placement, capacity, and power factor were determined. The total power loss reduction in IEEE 33-bus and IEEE 69-bus systems have been achieved at 94.42% and 98.03%, respectively; the power factor of DGs was determined in addition to the placement and capacity.  
In future works, the allocation problem of DGs and CBs could be examined in different load demands, or the allocation problem could be solved together with the reconfiguration problem. All these processes could be affirmed in real-time operations.  
N.P. and U.E.U. contributed to the design and modelling of the system, analysis of the results, and writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData will be made available upon reasonable request. The data are not publicly available due to privacy.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.24 Distance and Signal Quality Aware Routing (DSQR)  
DSQR protocol [48] bases its forwarding decisions on mid-area node selection; it evaluates the direction and distance of neighbor nodes, and also considers link quality to elect the best next forwarder node toward the destination node. DSQR is based on the following metrics:  
* Forwarding region. DSQR uses the distance of nodes and defines the mid (midrea) and border area of the transmission range. To give higher priority to mid-area nodes, it reduces the breaking probability of the next forwarder, packet error, and delay. If there is no vehicle node located within the mid-area, then the source node adopts the carry-and-forward approach; an example is shown in Figure 14.
* Distance and direction. In geographic protocols, distance is a very important parameter for next-hop selection. Node positions are determined through GPS, where the Pythagoras theorem is utilized for distance evaluation between sources with its neighbor node. In this proposal, using a 3D space for distance calculation was considered, focusing on multilevel flyovers, bridges, tunnels, and over- or underpasses. * Link-quality estimation. DSQR considers channel-quality measurement at the MAC. To evaluate channel quality, DSQR uses received signal strength (RSS) and the average link quality (ALQ), using past and current channel quality. Figure 14: Mid-area and border-area example [48]. The DSQR initiates forwarding selection by looking for that vehicle node that is located in the mid-area; if there are no nodes in that area, then the source adopts the carry-and-forward approach to hold the packet for a specific time interval. A summary of these proposals is presented in Table 5, where we can see the protocol, the year of the presentation, the metrics used to select the optimal path, the area of evaluation (urban or highway), if they use carry-and-forwarding as an alternative to dropping information in order to minimize losses, and, finally, which simulators were used in order to be evaluated and compared (including network simulator and mobility generator).
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 7 Open Issues and Future Research Directions
### Future Research Directions  
#### 7.4.1 Integrated Blockchain, 5G-Based Mec and Big Data  
In the big data era, a large volume of multimedia data generated from integrated 5G-based MEC and V2X nodes could be used for data-related applications such as data mining, machine learning-based data extraction [112]. Nonetheless, big data analytics on 5G-based MECs will pose a range of threats, ranging from data privacy leakage, cyber-attacks, and access control to security breaches leading to extremely advanced data theft [113]. The blockchain can solve major related security vulnerabilities by eliminating the fear of security bottlenecks. The blockchain can facilitate decentralized data sharing that allows large-scale big data deployments in 5G V2X [114]. Many of the research results suggest that blockchain can offer multiple benefits in terms of security, privacy preservation and performance enhancement in big data analytics in 5G-based system [115; 116].
## 3 Proposed System Model
### NSR Deployment Strategy
Constraint (8) ensures that the physical node can only host one node from the same request. The CPU capacity is represented in constraint (9). Constraint (10) ensures the security constraints of each node. Constraint (11) indicates the flow passing through physical nodes. Constraint (12) guarantees that the bandwidth requested by the NSR link should not exceed the available bandwidth in the physical link. The objective of this work was to improve the use of resources and the adoption rate of network slicing. Physical infrastructure restrictions were kept in mind as part of this solution method. Every NSR requirement is guaranteed to be within the stated limits given below:  
\[ \begin{split} N_{min}\leq N_{NSR}\leq N_{max}\end{split}  \tag{13}\]  
\[ \begin{split} BW_{min}\leq BW_{NSR}\leq BW_{max}\end{split}  \tag{14}\]  
\[ \begin{split} CPU_{min}\leq CPU_{NSR}\leq CPU_{max}\end{split}  \tag{15}\]  
\[ \begin{split} LT_{min}\leq LT_{NSR}\leq LT_{max}\end{split}  \tag{16}\]  
\[ \begin{split} SPL_{min}\leq SPL_{NSR}\leq SPL_{max}\end{split}  \tag{17}\]  
\[ \begin{split} T_{max}>LT_{max}\end{split}  \tag{18}\]  
where \(N_{min}\) and \(N_{max}\) are the minimum and maximum number of nodes per NSR, respectively, \(BW_{min}\) and \(BW_{max}\) are the minimum and maximum number of BW per NSR, respectively, \(CPU_{min}\) and \(CPU_{max}\) are the minimum and maximum number of CPUs per node per NSR, respectively, \(LT_{min}\) and \(LT_{max}\) are the minimum and maximum life time per NSR, respectively, and \(SPL_{min}\) and \(SPL_{max}\) are the minimum and maximum security level per node per NSR, respectively. The limitations specifiedin Equations (12)-(16) correspond to the NSR limits on the node count, bandwidth, CPU capacity, security level, and life duration. Constraint (17) ensures that the NSR's lifetime \(LT_{NSR}\) does not exceed the overall transmission time interval \(T_{max}\).
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results and Discussions
### Isothermal Calorimetry  
Isothermal calorimetry was employed on various mixes to investigate the rate of reaction at the very early ages (up to 40 h) as shown in Figure 5. Point A represents dissolution stage of cement in water, point B indicates the dormant time, point C represents hydration reaction at peak, point D represents sulfate consumption, and point E indicates increased aluminate activity [36]. The results demonstrate that the peaks amplified and shifted to former times when ash had introduced. Figure 5 makes it obvious that the mixes with W1 and W3 ash particles showed prominently amplified heat release peaks compared to the mixes with W2 ash particles. For instance, at point C (hydration reaction at peak), a normalized heat flow of 3.62 mW/g, 3.91 mW/g, 3.62 mW/g, and 3. 93 mW/g was measured for REF, W1/20, W2/20, and W3/30 specimens, respectively. It is mainly due to the amplified number of reaction sites for the finer ash particles which enhanced the hydration reaction [24; 37]. The results of the study carried out by [PERSON] et al. [24] reveal that the ultrafine particles of SCBA caused an abbreviation of the induction period and amplified the peak heat release rate due to heterogeneous nucleation.
## 2 The Proposed Hybrid Time Series Forecasting Methodology
### Modeling the Decomposed Sub-Series
#### 2.2.1 Autoregressive Model  
The autoregressive model (AR) model uses a linear combination of \(x\) lagged observations of \(\mathfrak{C}_{\text{n}}\) to explain the short-term dynamics of \(\mathfrak{C}_{\text{n}}\)[29] and can be expressed as  
\[\mathfrak{C}_{\text{n}}=I+\xi_{1}\mathfrak{C}_{\text{n}-1}+\xi_{2}\mathfrak{C }_{\text{n}-2}+....+\xi_{x}\mathfrak{C}_{\text{n}-x}+\epsilon_{n}, \tag{2}\]  
where \(\xi_{i}\)\((i=1,2,\ldots,r)\) are the parameters of AR model and \(\epsilon_{m}\) denotes the white noise process. In the present study, the maximum likelihood method is used for parameter estimation. The lags 1, 2, 3, 4, and 5 were included in the model due to their significant results after the plotting of autocorrelation function (ACF) and partial autocorrelation function (PACF) for the series.
## 4 Results and Discussion
### Assessment of the POCS Pressure Drop
#### 4.2.2 Exhaust Line Equipped with EHC-POCS Assessment
In particular, the percentage abatement profiles of the three considered pollutants are reported in Figure 13:1  
Figure 13:2 Cold start assessment: CAT1 temperature fields comparison—simulation end-time. Figure 13:3 Cold start assessment: pollutants’ percentage abatement comparison. The heating strategy and the pre-catalyst behavior achieved through the EHC-POCS are effective in inducing significant pollutant conversions. Even in the first phases of the cold start, the abatement of the three pollutants resulted in 25% due to the combination of the POCS pre-catalyst behavior and a modest spur of the CAT1 light-off. When the maximum exhaust gas mass flow rate was discharged, the abatement efficiency was slightly affected, while it was promoted as the inlet exhaust gases temperature increases. In addition, the tail-pipe pollutants' cumulative emissions are evaluated for both the configurations under investigation, which are the ATS equipped with the EHC-POCS and the base exhaust line. The reductions induced by the POCS structure are reported in Figure 13:4 The presented values show how the EHC-POCS behavior as a pre-catalyst is effective in reducing the pollutants' emissions in the first phases of a cold start. However, the heat losses associated with the late exhaust gases and EHC-POCS interaction result in a reduction in the fluid temperatures prior the first catalytic device. As a consequence, the chemical reactions characterized by a high activation energy could be penalized. For most of the simulated time-range, the reference exhaust line provides a lower conversion efficiency than what can be obtained by exploiting the EHC-POCS. Towards the end of the simulation, the previously addressed thermal inertia effects are such that the maximum temperatures of the first catalyst are lower than those of the reference case and a lower HC abatement is obtained (Figure 20). This aspect is detrimental for the abatement of the C3H8 chemical species, whose reaction rate strongly depends on the temperature. The percentage abatement of the two considered hydrocarbons is reported in Figure 21, highlighting the appointed phenomena: towards the end of the simulation, the C3H8 conversions in the reference case are higher than in the POCS case results.
## 7 Results and Discussion
### Error Analysis  
Errors in intent detection have been thoroughly analyzed to understand the model's performance and categorize the areas in which the model did not perform well. The salient observations are given below:  
1. Skewed dataset: As per the general searching trends, web queries datasets consist of more informational queries than navigational and transactional queries [12]. This characteristic was also present in the Urdu web queries dataset in which 76% of the queries were informational. A maximum inter-class confusion was observed between the NAV-INFO and TRAN-INFO classes; however, the proposed model performed better in discriminating these classes as compared to the baselines.
2. Named entities: NAV class queries that did not have \"www\" or domain identifier, e.g.,.com, or.pk, tended to be misclassified as INFO. A deeper analysis showed that most of those queries had brand names or other named entities that had a very low occurrence in the dataset, potentially causing the mis-classification.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Prec.** & **Recall** & **F1** & **Acc.** \\ \hline U-IntentCapsNet-100 & 0.8533 & 0.8607 & 0.8500 & 0.8607 \\ U-IntentCapsNet-200 & 0.8702 & 0.8754 & 0.8712 & 0.8754 \\ \hline
**U-IntentCapsNet** & **0.9083** & **0.9112** & **0.9084** & **0.9112** \\ \hline \hline \end{tabular}
\end{table}
Table 16: Intent detection results by varying dimensionality of input word vector in the proposed U-IntentCapsNet model.  
Figure 7: Influence of routing iterations on the proposed U-IntentCapsNet model.  
Queries with more than one valid intent: Due to the nature of the data, it is possible that the user intent could have belonged to two intent classes. For this reason, the TRAN suffered the most as transactional queries, being more descriptive, adopted the jargon and characteristics of the INFO queries. For example, \">>\",\" (Doremon), and \">\" (Analysis on Bang-e-Dara, pointing to a book download), was predicted as INFO although it was annotated as TRAN, when both labels can also be true. Similar confusions could be seen in other NAV queries as they were largely misclassified as INFO.
## 4 Image Processing Methods of the Mode Shape
### Mode Classification Based on the K-Means Clustering Algorithm  
In the study of guided waves, researchers usually classify guided wave modes according to the mode shapes. Lamb waves in plates are divided into symmetric and antisymmetric modes. The modes in the pipeline are divided into the longitudinal mode, torsional mode, and bending mode. Due to the complexity of the cross-section, the modes in the rail are divided into the longitudinal mode, torsional mode, and bending mode according to the mode shapes at low frequencies. However, the mode is very complicated at high frequencies, e.g., at 35 kHz, there are 20 modes in the rail as shown in Figure 17.  
Figure 16: The gradient edge extraction and binarization image of mode 7.  
Figure 15: The gradient edge extraction and binarization image of mode 3.  
As clearly shown in the figure, the guided wave modes are complex and cannot be classified intuitively from the mode diagrams. Therefore, we converted the mode shapes in Figure 17 into RGB images as shown in Figure 18.  
According to the color image of Figure 18, the common image classification algorithms in image processing, such as the K-means clustering algorithm, can be used to classify the color image of modes. K-means is one of the clustering algorithms [25], where K represents the number of categories and means represents the mean. The K-means clustering algorithm divides similar data points by a preset K value and the initial centroid of each category. The optimal clustering results are obtained by iterative optimization of means.  
Firstly, the RGB images of the guided wave modes in Figure 18 is compressed to reduce the dimension of image data processing. The original image resolution of the mode shape is \(601\times 705\), and the color image is compressed to \(32\times 32\) pixels as shown in Figure 19.  
Figure 17: Guided wave mode shapes at the frequency of 35 kHz.  
Figure 18: RGB images of guided wave modes at the frequency of 35 kHz.  
It can be seen from Figure 21 that the K-means clustering algorithm divides the mode shapes into five categories at the frequency of 35 kHz in the rail. The first type of mode shape has large vibration over the whole rail cross-section. In the second type, the maximum vibration position is located at the rail base. The vibration is mainly located at the rail head in the third type. The fourth type is a torsional vibration. The vibration of the rail head and the rail base is small, and the vibration of the rail web is large. In the fifth type, the vibration position is in the rail web, and its vibration is S-type and non-torsional. By classifying the guided wave modes in rails at specific frequencies, the vibration relations and regularity of each mode are found. It is of certain theoretical guiding significance for the application of guided wave technology to detect internal defects of rails at different locations.
# Multiple SSO Space Debris Flyby Trajectory Design Based on Cislunar Orbit
###### Abstract
Another author [38] considers the removal mission of two Cosmos-3M second stages. The spacecraft performs maneuvers to approach the object, and then the spacecraft captures the object and attaches the thruster deorbit kit. A further author [39] considers a mission to deorbit five large pieces of debris each year. They suggest that the object in the orbit close to coplanar should be deorbited first because changing the orbital plane is costly. One author [40] assesses the effectiveness of three heuristic algorithms in the optimal design of the active debris removal mission by comparing the solutions and running times. Another author [41] designs an on-orbit serving mission to deorbit a non-cooperative object by using space robotic systems to manipulate and deorbit the object. These studies require spacecraft rendezvous with debris to allow the robotic arm or other removal kit to perform deorbit, which requires large velocity and faces complex technical issues. Inspired by the contactless removal methods of artificial atmosphere and expanding foam, this paper proposes a concept that uses one spacecraft to fly by much Chinese space debris in SSO orbit. When the spacecraft flies by one piece of space debris, it can release a gaseous cloud to deorbit the space debris. This \"one-to-many\" method has high efficiency and low cost, and is easier to realize in space debris active removal missions. This paper focuses on the multiple SSO space debris flyby trajectory design, and the detail of the deorbit by the gaseous cloud is beyond the scope of this paper. This paper is organized as follows. Section 2 presents the problem statement and the equations of motion. Section 3 selects the target debris in SSO. Section 4 designs the orbit by which the spacecraft departs from DRO and performs a near-coplanar flyby of a SSO target piece of debris using two impulses. Section 5 uses the multi-objective evolutionary optimization algorithm and a semi-analytical optimization method to design the multiple highly elliptical orbits. Section 6 presents the design results of the flyby mission. Section 7 concludes this paper.