esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Routing Protocols for UWSN
### Regional Sink Mobility (RSM) and Vertical sink Mobility (VSM)
#### 3.7.1 Regional Sink Mobility (RSM)  
In RSM the whole region is divided into three equal parts which is depicted in Figure 12. All the nodes in the network are placed arbitrarily in these three regions. Three Mobile sink are deployed in three different regions. They move randomly in each round within their own region. When sink 1 moves and takes a new position, the position of the sink two must be such that it maintains maximum distance from the sink 1. Similarly sink 3 must maintain the maximum distance based on the new position of the sink 2. This condition is applied to get a larger coverage area.  
The sensor nodes lying in regions one and two transmit the data to sink one or two. Similarly sink two or three collect the data from the area two and three. if the sensor nodes fall within the range of the designated sink then it directly delivers the data to the sink. But if any node falls out of the range of the sink then it chooses another suitable node through which it can send its data to the sink. When a node falls out of the threshold range but remains within the transmission range then it is considered as a neighbor of the sender. Now if a neighboring node of the sender is in the range of the sink one or sink two, can be declared as eligible neighbor for receiving. Then the sender node transmit the data packet to the neighboring node and from that node mobile sink gets the data. If a node neither finds any sink within its threshold nor finds an eligible neighboring node then data of this node is discarded.  
Figure 12: Data transmission in Regional Sink Mobility (RSM) [64].
# Physical, Thermal, and Mechanical Characterization of PMMA Foils Fabricated by Solution Casting
## 2 Materials and Methods
### Methods of Fabrication of Cast Foils and Characterization
#### 2.2.2 Characterization Techniques  
PMMA foils prepared by various S/L ratios were characterized by physical, thermal, and mechanical characterization. The quality and uniform thickness of the foils was observed with Keyence Optical Microscope VHV-7000N. Thermal characterization for analyzing glass transition temperature (T\({}_{\mathrm{g}}\)) by differential scanning calorimetry (DSC) analysis was carried out with DSC 25 (TA Instruments Trios v5.1.1.46572, New Castle, DE, USA). The sample was cooled to 0 \({}^{\circ}\)C, then heated up to 150 \({}^{\circ}\)C for cast foils. However, for PMMA powder and commercial foils, the temperature was swept between 0 and 130 \({}^{\circ}\)C and 0 to 270 \({}^{\circ}\)C. The analysis was performed at the rate of 5 \({}^{\circ}\)C/min with a flow of N\({}_{2}\) gas in the chamber with a flow of 60 mL/min.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Command and Data Management System  
The data management system and all avionics consist of a network of redundant SKITH modules. The OBC is the central element, responsible for commanding, telemetry, surveillance, housekeeping, payload interfaces and power control. There are four instances of the OBC on the satellite. The software will be based on Rodos, as it offers the optimal support for distributed and redundant computing.
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 2 Methodology
### Concept  
HRAN is an effort to translate the \"heat trails\" concept from the real world onto a representation of a network's topology. By periodically emitting heat signals to its neighbors, each node generates a hotspot around itself. Nodes inside this zone \"feel\" the heat and have a hint on how to reach it. Those nodes nearer the goal \"feel\" the node closest to the destination, while those farther away \"feel\" the coldest heat channel. If a communicating node is located outside of the heat zone of the destination, it will have to randomly probe the network until it finds a node inside the heat region. At that point, consumer demand shifts from cooler to warmer climates. Once a path is determined, a heat tunnel may be built. This technique is helpful since it aids in subsequent searches for the same destination, fixes any route failures, and optimizes the NoNs that make up the route [12; 13; 14].  
Figure 1 depicts the varying temperature gradients at HRAN's node D, which are essential to the network's functionality (destination). D-nodes are represented by darker nodes and have a warmer tone than lighter nodes. An image of a heat tunnel is shown there. Directed search and random search are conceptual models of the process of information exchange between nodes B and D.  
A node's \"warmth\" is fully represented by the adapted bandpass filter, which includes all relevant data. The BF is a small data structure that checks whether an item is in a set, however it's possible for the check to provide false positives. A limited collection of hash functions (HFs) of sizes m and k are used to instantiate BF instances. These HFs each designate a specific location in the array m for use by a single set member. You need to execute each HF on its ID to obtain k positions and add a new member to the set. The number 1 is used for every pixel in the bitmap. To verify an item's membership in the set,just re-enter its ID into each of the k HFs to determine where it is stored in the array. If any of these bits is set to 0, then the element does not belong to the set [15; 16; 17].  
In the HRAN protocol, versions of these tiny binary structures are organised into gradients, where the location of the gradient relates to the distance of the No. of intermediate nodes whose nodes are positioned relative to each other. In order to disseminate topology information across the network, neighbours regularly swap these bespoke BFs with one another.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 4 RSU Allocation
### Service Capacity with Multiple Sources and Multiple Destinations  
Given the three types of data communications and the vehicle density, we can have a directed graph \(G\) with edge capacities \(u(e)\in\mathbb{R}_{+}\) and \(e\in E(G)\) as the estimated communication connectivity between neighbor regions. Each region includes bidirectional traffic flows for content dissemination. There are especially two specific regions as \(s\) (i.e., the region allocated with RSUs as the source node) and \(\chi\) (i.e., the region as the destination node). Our main motivation is to improve the communication capacity from \(s\) to \(\chi\). Within the quadruple \((G,u,s,\chi)\), we can have a definition as follows:  
**Definition 1**.: _Given a digraph \(G\) with capacities \(u(e)\), a flow is a function \(f(e)\in\mathbb{R}_{+}\) with \(f(e)\leq u(e)\) for all \(e\in E(G)\). The excess of a flow \(f\) at \(v\in V(G)\) is_  
\[ex_{f}(v):=\sum_{e\in\delta^{-}(v)}f(e)-\sum_{e\in\delta^{+}(v)}f(e).\]  
Now, given a network \((G,u,s,\chi)\), an \(s\)-\(\chi\)-flow is a flow \(f\) from source node \(s\) to destination node \(\chi\), and we can define the value of an \(s\)-\(\chi\)-flow \(f\) as \(|f(s,\chi)|=ex_{f}(\chi)\).  
**Definition 2**.: _Given the definition of an \(s\)-\(\chi\)-flow for \(\chi\in X\), we can further define a flow set as \(F(S,X)\) with a value \(|F(S,X)|=\min\{ex_{f}(\chi)\mid\chi\in X\text{ and }s\in S\}\)._  
Accordingly, we can formulate the problem as follows:  
\[\text{Max.} \left|F(S,X)\right|,\] \[s.t. \quad ex_{f}(S)=\sum_{s\in S}ex_{f}(s)\leq 0,\] \[\forall v\in V(G)\backslash\{S,X\}\ ex_{f}(v)=0.\]  
Figure 3: RSU allocation in a constructed graph.  
In this problem, we are motivated to increase the service connectivity of the destination region set (i.e., \(X\)) with a limited number of RSUs allocated as the source nodes (i.e., \(S\)).  
**Theorem 1**: _For any \(Q\in V(G)\) such that \(s\in Q\), \(\chi\
otin Q\) and any \(S\)-\(\chi\)-flow \(f\), we have (a) value(\(f\))= \(\sum_{e\in\delta^{+}(Q)}f(e)-\sum_{e\in\delta^{-}(Q)}f(e)\), (b) value(\(f\))\(\leq\)\(\sum_{e\in\delta^{-}(Q)}u(e)\)._  
(a): Since the flow conservation rule holds for \(v\in Q\backslash\{s\}\),  
\[value(f) = \sum_{e\in\delta^{+}(s)}f(e)-\sum_{e\in\delta^{-}(s)}f(e)\] \[= \sum_{v\in Q}\big{(}\sum_{e\in\delta^{+}(v)}f(e)-\sum_{e\in\delta ^{-}(v)}f(e)\big{)}\] \[= \sum_{e\in\delta^{+}(Q)}f(e)-\sum_{e\in\delta^{-}(Q)}f(e);\]  
(b): this follows from (a) according \(0\leq f(e)\leq u(e)\) for every edge \(e\in E(G)\).  
Here, we can consider \(Q\) as a group of relay regions between the source RSUs and the destinations. The value of a maximum flow can not exceed the link capacity between the neighbor RSUs allocated.
# Study on Flow Characteristics of Venturi Accelerated Vortex Drainage Tool in Horizontal Gas Well
## 2 Structure and Working Principle of Venturi Accelerated Vortex Tool  
To enhance the liquid-carrying capacity of the horizontal wellbore, discharge liquid load, and increase gas well production, a venturi accelerated vortex tool for horizontal gas wells was developed and designed, as shown in Figure 1. The specific structural parameters are detailed in Tables 1 and 2.  
The outer diameter of the venturi accelerated vortex tool is consistent with that of the coiled tubing. The four-helix swirl structure was designed in the tool. It was attached to the bottom of the coiled tubing in the horizontal section. The flow pattern of the gas and liquid mixture flow can be transformed to swirl laminar flow with gas inside and liquid outside in the horizontal section, as shown in Figure 2. This achieved gas-liquid separation flow can induce the flow of the gas along the center of the horizontal tube, while the liquid flows spirally along the wall due to the impact of the centrifugal force. Simultaneously, the inlet velocity of the vortex tool is increased, the distance of the swirling flow is extended, and the drainage efficiency is enhanced by the design of the venturi acceleration structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Length (mm) & Outer Diameter (mm) & Number of Helix Length (mm) & Helix Width (mm) & Helix Height (mm) & (mm) \\ \hline
400 & 50 & 4 & 70 & 6 & 10 \\ \hline \end{tabular}
\end{table}
Table 1: Structure parameters of vortex tool.  
Figure 1: Venturi accelerated vortex tool physical map.  
\begin{table}
\begin{tabular}{c c c c c c} \hline Length (mm) & Outer Diameter (mm) & Inner Diameter (mm) & Throat Diameter (mm) & Contraction Angle (°) & Diffusion Angle (°) \\ \hline
550 & 50 & 40 & 20 & 22 & 8 \\ \hline \end{tabular}
\end{table}
Table 2: Structure parameters of venturi acceleration structure.
## 4 Before and After Travel Characteristic Analysis
### Travel Time  
The average travel time for three groups of the population is studied in this section. These groups include overall samples, relocates, and non-relocatees. The average travel time before and after Wanda for these three groups reveals some interesting results. After Wanda's opening, for all three groups, the shopping-related trips' average travel time decreases (third and fourth rows in Table 1) even though the average travel time for all trips increases (first two rows in Table 1). We cannot draw any conclusion on causal effects on this basis, but we found this travel time change in these two time periods (before and after Wanda's opening) for the two different samples in our study site. Wanda may have contributed to this, but further studies are required to confirm this.  
Table 1 also shows that the average travel time of relocates is always greater than that of non-relocatees, no matter whether for all trips or shopping-related trips, and no matter whether before or after Wanda's opening (comparing the columns of relocates and non-relocatees). These differences are always significant as well (\(p\)-values shown in the blue arrows). The average travel time of relocatees is significantly larger than that of non-relocatees. The possible reason for this could be that, compared to relocatees, non-relocatees had the initiative to choose a place of residence and thus tend to have a lower travel time for either overall trips or shopping-related trips.  
Figure 1: Shopping-Related Trips’ Destinations Before and After Wanda Shopping Plaza was open (Left Heat Map: Before Wanda Shopping Plaza was open, in 2011; Right Heat Map: After Wanda Shopping Plaza was open, in 2012; Wanda and JBD are shown as the red spots within the black circles in the two maps).  
This finding matches the conclusion in a previous study, which showed that from a travel-equity perspective, the relocation process and the development of the megacity periphery would have a greater impact on the travel of relocatees than other populations, and relocatees would have a lower travel quality than non-relocatees in developing countries [3]. The previous finding focused on overall travel purposes. What we find in this study can extend this from overall trips to shopping-related trips as well.
# Performance of a Closed Cycle Power Takeoff for a Shore-Based Wave Energy Device
## 3 Results  
In order to investigate the behaviour of the CCPTO and turbine in the real environment of the breakwater at Mutriku, the CFD study was extended to the larger pressure drops that infrequently occur in the case of larger waves. Sixty additional design points were explored, with pressure drops ranging from 3800 to 21,000 Pa and rotation speeds up to 5000 rpm. Figure 16 presents the resulting efficiencies for two cases: the straight blade from the preliminary design and the free vortex design blade. Increasing the rotational speed at high-pressure drop allows to maintain and even increase the turbine performances. As would be expected, the free vortex design consistently leads to better results than the straight blade, especially at peaks where the rotational speed is exactly optimised for the pressure drop. Although the improvement is modest, the free vortex design turbine will thus be used for the rest of the study.  
The performances were computed in increments of 500 rpm in this study, but finer increments could be used to enhance the reliability of the estimation of overall system power predictions. Figure 17 shows the suitable rotational speed to adopt for the free vortex design turbine in each case at mean tide in Mutriku and the associated efficiency. Note that the actual efficiencies will be lower since various sources of losses have been neglected, particularly the tip leakage. Nevertheless, the speed-adjusting strategy is validated by these results.  
Figure 16: Variation in efficiency with pressure drop for the straight blade (**a**) and the free vortex design blade (**b**).  
Figure 15: Velocity streamline of the flow around the straight (**a**) and the free vortex design (**b**) rotor blade at nominal flow coefficient.  
Using these efficiencies, a further step can be taken from the power available at the turbine in Figure 4 to the power generated by the turbine, which is presented in Figure 18. A slight shift in the contour lines toward the upper left corner between these two figures can be observed. Finally, based on the corrected power output, the energy expected to be generated by this turbine for each sea state in April in Mutriku is calculated. It is shown in Figure 19. This estimation assumes that the tide is constant at mean level during the entire month of April. The sum of the matrix is 1544 kWh; it represents the total energy expected to be produced in that month. The outcome is a rough estimate, but it illustrates the expected benefits of the CCPTO arrangement for an oscillating water column installation.  
Figure 17: Free vortex design turbine rotational speed (rpm) (**a**) and associated efficiency (**b**) for different sea states in Mutriku at mean tide.  
Figure 18: Average power generated by the turbine at mean tide (kW).
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 3 Computational Results
### Comparison of Temperature Elevation in Rat and Human Models  
Figure 5 shows the peak temperature elevation of skin and brain in the human models for different SARs and antenna-head distances. For comparison, the temperature elevation in the rat skin for \(d\) = 5 mm was also plotted. As shown in Figure 5a-c, the temperature elevation in the skin increased as the frequency increased. The peak temperature elevation in the human model was approximately twice as high as that in the rat at \(d\) = 5 mm. The peak temperature elevation decreased as the antenna was moved away for the human. For \(d\) = 15 and 25 mm, the temperature elevation in the human model was lower than that in the rat model at 10 GHz. As shown in Figure 5d-f, the peak temperature in the brain varied significantly with frequency for the rat model, but was almost independent of frequency for the human model. The peak temperature elevation in the human brain was lower than that in the rat brain at \(d\) = 5 mm.  
Figure 5: Peak temperature elevation of (**a**–**c**) skin and (**d**–**f**) brain for each peak SAR averaged over 10-g tissue. The distances between the skin surface and the dipole antenna \(d\) were set to (**a**,**d**) 5 mm, (**b**,**e**) 15 mm, and (**c**,**f**) 25 mm. Each point represents the average value and error bar showing the standard deviation (\(n\) = 4). For comparison, the temperature elevation of rat brain tissue was also plotted.
## 2 Materials and Methods
### Testing Procedure
Each acquisition, encompassing both actuation and measurement of the released wave signals, was repeated 64 times; the average of the 64 final raw signals gathered by each sensor is calculated to prevent potential errors during the test, including those induced by environmental noise. Figure 5 represents the test rig along with the wave propagation measurement instruments. The current research tackled two distinct issues: damage localization (R-Class) and damage severity detection (S-Class). For each of these categories, a unique dataset was generated using UGW test. The initial hypothesis involved 160 sample tests, whereas the second scenario incorporated 540 observations. Upon data collection, three procedures were implemented: noise reduction, feature extraction, and feature selection. The features identified through these stages were then inputted into machine learning classifiers. All the aforementioned steps are detailed for each of the two classification issues in the subsequent sections. Figure 6 illustrates the workflow of the research process. Figure 4: Actuator input voltage applied to the central PZT transducer. Figure 5: Test rig (**a**) and wave measurement (**b**) instruments.
## 4 Experiment and Discussion
### Experimental Results
#### 4.4.1 Performance Validation of the ADCNN Model
Figure 16b is the confusion matrix of LSTM, which shows that there were three errors in predicting that the tool was normal but was actually worn. Figure 16c is the confusion matrix of CNN-LSTM, showing seven errors in predicting that the tool is normal but was actually worn. Figure 16d is the confusion matrix of ConvLSTM, showing 73 errors in predicting that the tool is normal but was actually worn, as well as three other errors. The confusion matrix results show that the most errors were when they were actually worn yet predicted as normal tools. It shows that the differences between the characteristics of the normal processing tools and the worn tools are minuscule, making it difficult to correctly predict the classification. All other classification items have significant features and classifications can be accurately predicted. The results show that the proposed ADCNN method can strengthen the classification of subtle features and also has excellent prediction performance. Figure 14: Experiment results of other models: (**a**) loss of 1 DCNN; (**b**) loss of LSTM; (**c**) loss of CNN-LSTM; (**d**) loss of ConvLSTM. The experiment of predicting classification accuracy uses the test set for model prediction. All methods are averaged after ten tests to draw a bar graph. The results are shown in Figure 17. The figure shows that ADCNN has the highest prediction accuracy at 100%, and ConvLSTM has the worst accuracy at 77.32%. Figure 16: Confusion matrix predictions for other comparison methods: (**a**) 1 DCNN; (**b**) LSTM; (**c**) CNN-LSTM; (**d**) ConvLSTM. Figure 15: ADCNN confusion matrix predictions.
## 4 Energy Replenishment Strategy
### Charging Cell Selection  
The selection of charging community has a great impact on the overall energy replenishment effect. In order to reduce the average upload distance of all nodes, the MCGV should gather data from as many different cells as possible. However, visiting too many cells increases the moving time of the MCGV and decreases the benefit of charging. In light of the above analyses, a rule is introduced to select charging cells.  
At the start of each round, the MCGV checks discharge rate of each cell according to the state information of all nodes. For instance, the cell \(c_{m}\)'s discharge rate is calculated via the formula below.  
\[ce_{c_{m}}=\max_{i\in c_{m}}\left(\frac{e_{i}}{E_{i}}\right), \tag{3}\]  
where \(e_{i}\) denotes the energy consumption in the last round. \(E_{i}\) is node \(v_{i}\)'s residual energy. The discharge rate is related to how quickly the charge is drawn down. The node with high energy consumption or low residual energy should be charged in a timely manner, so as to avoid energy gaps between nodes. The assumption is that there is zero energy consumption, and the residual energy is initially \(E_{o}\).  
Then, the cells are sequenced from high to low based on the discharge rate, and the sequence is recorded by the set \(PAS\). \(PAS(m)\) represents the \(m\)-th element of the \(PAS\), denoting the cell with the \(m\)-th highest discharge rate. Thus, the \(PAS\) is a sorted list of cells based on discharge rate. The MCGV, starting from the first element, visits charging cells sequentially according to the \(PAS\), and the charging cell set can be determined as follows:  
\[CV=\{PAS(m)\big{|}m\in[1,N_{ac}],L(m)/S\leq\alpha\cdot(T-m\cdot t_{o})\}, \tag{4}\]  
where \(CV\) is the charging cell set, and \(m\) represents the serial number of cells in \(PAS\). \(N_{ac}\) denotes the number of cells in which the node is dotted. \(L(m)\) is the shortest moving distance to visit the top \(m\) cells in \(PAS\). \(\alpha\) is the moving distance constraint factor, where \(\alpha\in[0,1]\). It is used to determine the maximum time that can be allocated for moving in a round in order to control the time for charging. A larger \(\alpha\) means more time for MCGV to visit different cells and reduce the average upload distance of all nodes while reducing the total charging time. Correspondingly, a smaller \(\alpha\) means more time for MCGV to charge and focus on replenishing low-energy nodes while reducing the number of charging cells and increasing the average upload distance. Hence, the number of charging cells and the performance of the algorithm are related to \(\alpha\). \(t_{o}\) is the threshold time to ensure stable data gathering, and the sojourn time of the MCGV in each charging cell is at least \(t_{o}\). It is assumed that \(t_{o}\) is far less than round time \(T\). We find the maximum number of charging cells without exceeding the moving distance threshold in order to minimize the average upload distance.