esa-sceva/satcom-chunk-collection huggingface dataset:
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 4 Experimental Validation and Evaluation  
### Emulation Platform  
DTN-related experimentation has been a long-standing challenging task, largely owing to the fact that conditions like short-lived contacts and multi-hop transmissions are too costly to provide in laboratory environments. As a hybrid approach between virtual simulation and physical testbed, emulations are viable for indoor spaces and its support for real codes can yield more convincing results. An emerging trend in emulation is that virtual nodes gain ever-growing attention and widespread adoption for their better scalability and deployability. In this paper, we propose a decentralized and lightweight emulation platform based on virtual machines (VM), as VM with a standalone kernel and network protocol stack offers the capability to thoroughly examine the implementation and a higher degree of fidelity [110] compared to the kernel-based containers.  
Unlike the majority of works on emulation which assign a dedicated central controller to govern the evolution of network topology [111; 112; 113], inspired by several works in ad-hoc networking [114], we leverage the \"Link Emulator\" module to make each node automatically manage its own links and hence it becomes an autonomous entity in the experiment progress. Only a simple document that records positions is necessary for a node to virtually move accordingly.  
Each node is instantiated as a VM running an Ubuntu distribution. All nodes are interconnected via LAN. To settle the time synchronization problem, a \"starting pistol\" node is set up to launch the initialization script and time adjustment with an NTP server on every DTN node almost simultaneously with \"Parallel SSH\" [115] tool. Afterwards, this node will not intervene in the experimental process and other DTN nodes will behave independently.
## 2 Light Field Transmission Model
However, due to the pupil function's band-limiting effect, only low-frequency angular spectrum components can pass through the optical system and reach the receiving fiber end-face, forming a speckle pattern. This phenomenon is subsequently validated in our numerical simulations. Based on the fiber coupling efficiency calculation formula, the coupling process is characterized by the angle between the spatial vectors of the light field to be coupled and the light stably transmitted within the fiber. \[ \begin{split}\eta_{c}&=\frac{\left|(U_{F,RX}(x,y),U_ {F,TX}(x,y))\right|^{2}}{\left|U_{F,RX}(x,y)\right|^{2}\cdot\left|U_{F,TX}(x,y) \right|^{2}}\\ &=\frac{\left|\int\limits_{A}U_{F,RX}(x,y)\cdot U_{F,TX}(x,y)dxdy \right|^{2}}{\iint\limits_{A}U_{F,RX}(x,y)\cdot U_{F,RX}(x,y)dxdy\cdot\iint \limits_{A}U_{F,TX}(x,y)\cdot U_{F,TX}(x,y)dxdy}.\end{split}  \tag{16}\]  
In the above equation, it can be concluded that the closer the distribution of the light field to be coupled is to the distribution of the light field stably transmitted within the fiber, the greater the amount of energy that can be coupled into the fiber and contribute to the interference.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment  
The mathematical model for this novel fault diagnosis method includes two parts: fault feature extraction and fault recognition. In the part of fault feature extraction, the original rotor vibration signal is decomposed by VMD and the signal is reconstructed by the CEF method, which can eliminate the disturbing IMF component. Then, a method is adopted to extract the fault feature of the snowflake image of the reconstructed signal and compose state feature vectors. In the part of fault recognition, the fuzzy neural network is used to perform fault recognition so that the accurate fault classification result is obtained.
## 3 Results
### Pre-Tests  
The setup's symmetry must be confirmed to utilise the model generalisation described in Section 2. The spectra shown in Figure 7 represent the measurement points above (red) and underneath (green) the cell. The behaviour of both positions exhibits a similar result. The differences in the specific heat capacity and the cross-plane thermal conductivity stay in a range of less than 2% for all performed tests. For further evaluation, both corresponding values from above and below the cell are averaged.  
The frequency band is mainly defined by comparing two tests using different lower boundaries. Adding two additional frequencies at 100 \(\upmu\)Hz and 50 \(\upmu\)Hz, as described in Table 1, results in the red-coloured spectrum in Figure 8. Comparing the extended spectrum to a standard test using the Nyquist plot in Figure 8a and the Bode plot in Figure 8b also shows good test repeatability.  
Both tests show practically similar values; especially the specific heat capacity is roughly the same. When averaging both central measurement points above and underneath the cell, the difference between both tests stays lower than 1%. The difference between the cross-plane thermal conductivities shows a higher deviation of approximately 7%. The thermal influence of the surroundings causes these variations as the heating phases become longer. Therefore, a higher overall temperature increase results in a smaller general thermal  
Figure 8: Comparison of two tests using a different lower boundary frequency. (**a**) Nyquist plot; (**b**) corresponding Bode plot; top: amplitude; bottom: phase.  
Figure 7: Symmetry of the test setup on the top and bottom types at the same conditions (\(T_{amb}=25\)\({}^{\circ}\)C, SOC = 50% and \(l\simeq 2\)C).  
resistance \(R_{th}\) and a higher corresponding thermal conductivity \(\lambda_{\perp}\) while testing lower frequencies.
## 2 Related Works
### RSUs Deployment in Rural Areas  
Roadside Units (RSUs) are pivotal in vehicular networks, enabling short-range wireless communications via IEEE 802.11p and a DSRC spectrum, essential for both data processing and internet connectivity [26]. These units are integral in managing traffic data and facilitating connections with larger networks. RSUs further improve network performance through inter-unit communications. The deployment of RSUs is sophisticated, the NP-hard combinatorial optimization challenge [27] demands strategies like Voronoi diagrams [28] and Constrained Delaunay Triangulation for optimal placement [29]. Voronoi diagrams involves partitioning the map into regions based on distances to a specified set of points (potential RSU locations). Each point (RSU) would have a corresponding Voronoi cell such that any location within this cell is closer to that RSU than to any other RSU. It is useful for understanding and optimizing coverage. Constrained Delaunay Triangulation (CDT) is an extension of Delaunay Triangulation where some edges are constrained in the triangulation process. This is especially useful when there are natural barriers or roads that must be considered in the network layout. CDT can help to ensure that the network connectivity is maintained while considering these constraints.  
In contrast to earlier studies, this paper delves into the effectiveness of utilizing a novel Pre-Signature scheme in disconnected areas, elucidating its role in enhancing the reliability of vehicular communication in rural settings without the constant need for RSUs. We offer a comprehensive comparative analysis with existing systems, focusing on both efficiency and the strategic deployment of RSUs. Through detailed simulations, our study clearly demonstrates the practical advantages and enhanced feasibility of this approach in real-world scenarios, distinguishing it from prior methodologies.
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
## 4 Discussion  
This study systematically examined the instability characteristics of the hypersonic boundary layer over a flat plate at a zero angle of attack, employing a combined approach of experimental analysis and linear stability analysis. The compression modification formula was fitted using the modified \(\gamma\)-Re\(\mathfrak{e}\mathfrak{t}\) model and the experimental results of the flat plate in a hypersonic wind tunnel. The validation results show that the predictions of the modified \(\gamma\)-Re\(\mathfrak{e}\mathfrak{t}\) model are more in line with the experimental results, which verifies the correctness of the modified \(\gamma\)-Re\(\mathfrak{e}\mathfrak{t}\) model. The application of PCB technology and infrared thermography facilitated the characterization of instability waves and enabled the calculation of the Stanton number along the streamwise direction. Additionally, the \(e^{N}\) method was implemented to determine the \(N\)-factor at the transition location within the wind tunnel. The conclusions derived from this study are outlined as follows:  
1. The transition model based on compressibility correction can better predict the transition position of the hypersonic boundary layer, and the simulation results are in good agreement with the experimental results. Moreover, the freestream unit Reynolds number has a great effect of the transition Reynolds number of the flat-plate boundary layer. As the unit Reynolds number increases, the transition position moves forward, and the transition Reynolds number also increases;
2. The LST results show that the first mode and the second mode are both present in the hypersonic boundary layer at the Mach number 5. Combined with the PCB results of the experiments, the second-mode frequency range predicted by the LST matches the frequencies measured in the experiments, with a second-mode frequency range from 100 to 250 kHz;
3. The \(N\)-factor of wind tunnel transition location predicted by LST is about 0.98 and 1.25 for \(Re_{\text{unit}}\) = 6.38 \(\times\) 10\({}^{6}\) and 8.20 \(\times\) 10\({}^{6}\), respectively. With the increase in the unit Reynolds number, although the transition position moves forward, the \(N\)- factor of the transition position increases due to the increase in the magnification of the disturbance.  
Conceptualization, H.D.; validation, S.L.; formal analysis, Y.J.; investigation, S.L.; data curation, Y.J.; writing--original draft, Y.Y.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Numerical Wind-Tunnel (No. 2021-ZTNNW-QT00-50042, No. 2020-DY01-001), the National Natural Science Foundation of China (No. 11872208).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 4 Conclusions  
This study has investigated a two-degrees-of-freedom electromechanical vibration energy harvester. The numerical simulations and experimental prototype validation have confirmed the theoretical predictions. Magnetic suspension forces were estimated experimentally, and mathematical models were proposed.  
The two resonances were detected and experimentally verified. In the resonance curves, the effect of dynamic vibration absorption was noticeable. A small shift between the numerical and experimental resonance peaks was observed. The frequency analysis showed the hardening effect for higher amplitudes of excitation. Inside the first resonance, an unstable region caused by the Neimark-Sacker (torus) bifurcation was observed. The bifurcation analysis showed the presence of multi-solution regions. Apart from the standard region with two stable and one unstable solution (hardening effect), two unstable and one stable solution were observed. The energy harvester benefits from the multistability because both the output response amplitude and the recovered power can be higher.  
The analysis of the electromechanical couplings showed that these parameters acted as damping, generally reducing the main resonance. However, in the second resonance, an increase in the electromechanical coupling improved the induced current. Therefore, it can be concluded that a proper configuration of both couplings could increase the effectiveness of energy harvesting. This means that electromechanical couplings can be used to control harvester dynamics as well as energy harvesting.  
In the future, the multistability and electromechanical coupling modification will be controlled by design of the magnet-coil system and also by using additional magnets close to the coil.
## 5 Conclusions  
In this study, experiments were conducted to find out the change in mechanical properties when WIP is introduced into an ME process that uses a filament containing metal powder. The results could be summarized as follows:  
1. In the specimens used in this study, the tensile strength increased by a factor of 2.5 with WIP treatment. This shows that the specimen-manufacturing process conditions contribute to increasing the tensile strength, even considering the lack of optimization.
2. The strain at ultimate tensile strength also increased fourfold with WIP. This was because, in the absence of WIP treatment, the large number of pores played an important role in crack initiation even at small strain and produced brittle behavior. If the process conditions were optimized, the effect of WIP treatment would be reduced, but not eliminated.
3. Two types of hardness tests were performed. The Rockwell (macroscopic) hardness was affected by WIP (probably through pore generation); the Vickers (microscopic) hardness was not, because the WIP process was not performed at a high enough temperature to change of the microstructure of the metal.
4. At the grain boundary, there were round secondary-phase particles composed of chrome oxide. Although chrome oxide exists on the surface of a typical SUS material, it seems that in this case, the chrome oxide phase only on the surface of the printed strand was impregnated inside.  
The main purpose of this study was to confirm the possibility of improving mechanical properties by the introduction of the WIP process. Although such an improvement was confirmed, the conditions used in this study were not optimal ones but just effective conditions for individual processes. In the future, the authors plan to study optimization conditions, as well as the relationship between design factors and process parameters in the metal-filament extrusion process with WIP.  
Writing--original draft preparation, B.-Y.C.; data curation, B.-Y.C., S.-J.P., S.-J.H. and H.-G.K.; funding acquisition, Y.S.; writing--review and editing, I.-H.A. and W.-C.C. All authors have read and agreed to the published version of the manuscript. This study has been conducted with the support of the Korea Institute of Industrial Technology as \"Development of root technology for multi-product flexible production (KITECH EO-20-0015)\". The authors declare no conflict of interest.
# Experimental Study and Numerical Simulation of W/O Emulsion in Developing Heavy Oil Reservoirs
## 3 Results and Discussion
### Analysis of Emulsion Flooding  
#### 3.2.1 The Influence of Permeability Contrast  
Figure 19 displays the flow distribution of high-permeability and low-permeability pipes under different permeability contrasts.  
As shown in Figure 19, the ability of the emulsion with 40% water cut to blank off the high permeability tube was demonstrated after the injection of 0.65 PV when the permeability contrast was 2.03, and the low-permeability tube was activated. At 1.54 PV total injection (including emulsion injection of 1 PV), the blocking ability disappeared. The low-permeability pipe was initiated at a permeability contrast of 3.24 after the injection of 0.82 PV. The emulsion shut-off capacity disappeared at a total injection volume of 1.51 PV. The low-permeability pipe was activated when the emulsion was injected with 0.93 PV at a permeability contrast of 5.92, and the emulsion containment capacity disappeared when the total injection was 1.48 PV. At a permeability contrast of 8.34, the emulsion with 40% water cut could not start a low-permeability pipe. With the increase in the permeability contrast, the shut-off capacity of the emulsion with 40% water cut was gradually weakened.  
Figure 20 shows the relationship between water cut in the production fluid and the overall oil displacement efficiency under different permeability contrasts.  
Figure 19: Influence of permeability contrast on fractional flow: (**a**) 2.03; (**b**) 3.24; (**c**) 5.92; (**d**) 8.34.  
Figure 20: The relationship between water cut and oil displacement efficiency under different permeability contrasts.  
As shown in Figure 20, the oil displacement efficiency with a permeability contrast of 2.03 was the highest, followed by 3.24, 5.94, and 8.34. The water cut in the production was about 40%, and the emulsion could be used to block off at the permeability of 5.92, but not by much. At a permeability contrast of 3.24, the effect of emulsion blocking off was obvious, whereas, at a low water cut, the oil displacement efficiency was obviously lower than the permeability contrast of 2.03.
## 1 Introduction
### MEMS Vibrational Power Generator  
As described above, the vibration power generation device (MEMS vibrational power generator) has a fixed and a movable comb electrode. Presuming that the same impedance as the internal  
Figure 1: A schematic model of the vibration energy harvester [22].  
Figure 3: A photograph of the applied vibration-based micro energy harvester (MEH) device (Device size is \(23\times 23\) mm) [22].  
Figure 2: Cross-sectional view of microelectromechanical systems (MEMS) vibration energy harvester.  
impedance of the vibration power generator is connected to the device, the expected RMS output power can be calculated from Equations (1) and (2), where the conversion efficiency from the linear equivalent circuit of the electrostatic actuator is considered [22].  
\[P=\ \frac{ma^{2}Q}{4\omega_{0}}\cdot\eta \tag{1}\]  
\[\eta=\ \frac{1}{1+\left(\frac{kC_{0}}{QA^{2}}\right)^{2}} \tag{2}\]  
where \(P\) is power generation amount [W], \(m\) is mass [kg], \(a\) is acceleration [m/s\({}^{2}\)], \(Q\) is quality factor [-], \(\omega_{0}\) is angular frequency [rad/s], \(\eta\) is efficiency [-], \(k\) is spring constant [N/m], \(C_{0}\) [F] is electrostatic capacity, and \(A\) is force coefficient [C/m].  
The effective value is the root mean square (RMS) of all instantaneous values within a certain interval, which means the power contained in the vibration waveform. Since all values other than the acceleration are determined by the structure of the MEMS vibrational power generator, the power generation depends only on the magnitude of the effective acceleration value. Knowing that acceleration is proportional to vibration amplitude and frequency squared, it is expected that the generated energy would increase as amplitude and frequency increase.  
The force factor \(A\) is expressed by Equation (3) and the relationship between the force factor and the current \(I\) (short circuit current) is expressed by Equation (4).  
\[A\ =\frac{2n\varepsilon_{0}bE}{d} \tag{3}\]  
\[I\ =\ Av \tag{4}\]  
where \(n\) is number of comb electrodes, \(\varepsilon_{0}\) is the permittivity of the vacuum [F/m], \(b\) is the thickness of the comb electrode [m], \(E\) is the charged voltage [V], \(d\) is the gap between the fixed and the movable comb electrodes [m], and \(v\) is the vibration speed [m/s RMS].  
Table 1 shows the design parameters of the MEMS vibrational power generator which is investigated in this study.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 9 Preliminary Analysis C: Evaluating the Hotspot Scenario  
The last set of investigations before presenting the proposed solution evaluates the impact of hotspots on system performance. The goal is to compare a scenario with hotspots to a scenario with users distributed uniformly.  
Figure 14: Full factorial design using the strict frequency reuse: results for the SINR CDF. Each curve is a different bandwidth distribution.  
Figure 13: Full factorial design using the strict frequency reuse: results for the 10 th percentile throughput. Each curve is a different bandwidth distribution.
## 2 Sensitivity-Based VPP Control Algorithm
### Sensitivity Based VPP Control  
The Sensitivity-based VPP control differentiates control actions based on the impact of resources on the power grid, as determined based on their sensitivity values. In this method, resources with higher sensitivity values perform greater control actions, while resources with lower sensitivity values are subjected to relatively less control.  
The sensitivity ratio method determines the extent of control for each resource according to its sensitivity value. Specifically, the sensitivity values of all resources are normalized by dividing each value by the highest sensitivity value among them. This calculation establishes the proportional contribution of each resource to the control actions.  
For example, as shown in Table 3, if there are three resources with different sensitivity values, the sensitivity ratio for each resource is calculated by dividing its sensitivity value by the highest sensitivity value among the three. Based on this ratio, the output control allocation is determined, as presented in Table 4. In this case, DER-3, which has the highest sensitivity, performs the largest share of output control, whereas DER-1, with the lowest sensitivity, performs the least amount of output control.
## 4 Hybrid Harmony Search-Simulated Annealing Algorithm
### Hybrid Harmony Search--Simulated Annealing  
To prevent the possibility of the HM population been trapped into local optima due to premature convergency, the proposed HS algorithm is further enhanced by hybridizing a SA with the implementation of probabilistic acceptance rule based on the Boltzmann factor, \(e^{-\frac{\Delta}{T_{it}}}\) during the improvisation procedure of the HS. In SA, the worst solution will be accepted within a probability and allows the search to proceed to its neighbourhood (Algorithm 1). By preserving the worst solution in a population, it provides practical randomness into the search to avoid the local extreme points. The probability of acceptance in SA is depending on the value of annealing temperature and the difference in the value of an objective function. At each iteration, the annealing temperature will be reduced according to the value of the cooling rate, which is a function of temperature. The higher value in the cooling rate, the slower the temperature reduction [37]. The procedures of probabilistic acceptance rule in SA described in Algorithm 1 are of a standard SA that has been adapted into the proposed hybrid HS-SA as shown in Algorithm 2.  
```
1: initialize parameter, \(T_{0}\) = initial temperature, \(\alpha\) = cooling rate;
2: generate initial solution, \(x\);
3: calculate objective function, \(f(x)\);
4:if\(=\ 0\);
5:while\((it<MaxIt)\)
6: generate new solution \(x\prime\) in the neighbourhood of \(x\)
7:if\(f(x\prime)<f(x)\)
8:\(x\gets x\prime\)
9:else
10:\(\Delta\ =\ f(x\prime)-f(x)\)
11:if\(rand<exp\Big{(}\frac{\pi\Delta}{T_{it}}\Big{)}\)
12:\(x\gets x\prime\)
13:\(T_{it+1}\ =\ \alpha\times T_{it}\)
14:\(it\ =\ it+1\)
15:endwhile
```  
**Algorithm 1****SA Algorithm.**
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 6 Evaluation
### User Privacy against Other Users
#### 6.2.2. Agent Receives ARMs from Requester  
As has already been stated, ARMs are encrypted with the \(EK_{System}\) (System Encryption Key) and, therefore, cannot be read by the Agent, who has no knowledge of the corresponding decryption key \(DK_{System}\). Recall that ARMs contain an Interest-ID, which is unique for the entire system and known to all users, but also contain a key \(K_{i}^{user}\), which is generated by the user. This essentially means that any two ARMs that contain the same Interest-ID are highly unlikely to produce the same cryptograms as the \(K_{i}^{user}\) is also included as part of the plaintext. An encrypted ARM is therefore secure as it would be infeasible for an attacker to guess its content by comparing it to other encrypted ARMs that he/she creates himself/herself (chosen plaintext attack).