esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization
## 4 Experimental Results and Discussions
### Analysis of CFA Strategies
#### 4.2.4 Responsive Strategies Based on Landscape Analysis  
As noted in Section 3.5, the responsive strategies employed by the CFA are made adaptive based on the analysis of function landscape. The landscape analysis makes distinctions of two classic function forms: single-modal and multi-modal. The responsive strategies then vary the movement step size and the frequency of the local search and the multi-start activations to make the CFA search more effective. Table 6 lists the comparative performance of the CFA with or without performing the landscape analysis. It is noted that the version of the CFA without adaptive landscape analysis still embeds the local  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Functions**} & \multicolumn{2}{c}{**No Adaptive Local Search**} & \multicolumn{2}{c}{**With Adaptive Local Search**} \\ \cline{2-5}  & **Mean** & **Std** & **Mean** & **Std** \\ \hline [PERSON] (2) & \(-\)0.99997 & 0.000020 & \(-\)1.0 & 0.0 \\ [PERSON] (2) & \(-\)185.779 & 0.963969 & \(-\)186.723 & 0.016331 \\ [PERSON] (2) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (2) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (3) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (4, 5) & \(-\)6.50369 & 2.613879 & \(-\)8.09613 & 3.003044 \\ [PERSON] (4, 7) & \(-\)7.40191 & 1.802712 & \(-\)10.2611 & 0.440954 \\ [PERSON] (4, 10) & \(-\)7.80353 & 1.033856 & \(-\)10.3554 & 0.966638 \\ [PERSON] (10) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (10) & 0.797320 & 1.594630 & 0.002511 & 0.009392 \\ [PERSON] (10) & 0.734548 & 0.827677 & 0.646338 & 0.800856 \\ [PERSON] (10) & 0.015448 & 0.011793 & 0.016450 & 0.009914 \\ [PERSON] (10) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (20) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (20) & 0.797828 & 1.594400 & 0.267050 & 0.994099 \\ [PERSON] (20) & 2.590780 & 1.391706 & 2.466102 & 1.765681 \\ [PERSON] (20) & 0.012054 & 0.013121 & 0.007213 & 0.012479 \\ [PERSON] (20) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (30) & 0.0 & 0.0 & 0.0 & 0.0 \\ [PERSON] (30) & 0.801375 & 1.599876 & 0.801094 & 1.598004 \\ [PERSON] (30) & 4.704743 & 1.842029 & 4.680690 & 2.632701 \\ [PERSON] (30) & 0.005663 & 0.007891 & 0.003364 & 0.007581 \\ [PERSON] (30) & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The mean best function value and the standard deviation obtained by CFA with or without adaptive local search.  
search and the multi-start procedures as its rudimentary components, which, however, are executed with fixed parameter values. From the tabulated result, we conclude that the landscape analysis can proliferate the performance gains possibly obtained by the local search and the multi-start strategies.
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 4 Experimental Configuration
### Driving Test Environment  
As the radio range is limited due to high pathloss of the mmWave channel, different driving environments on a campus and on city roads were considered to estimate the effect of vehicle speed and deceleration/acceleration.  
Figure 6a shows the campus map of Gachon University, South Korea and the arrows in the map indicate the driving route for the measurement campaign, which was approximately 3 km. The measurement duration was approximately 400 s and the average speed was 27.7 km/h (campus speed limit is 30 km/h). A major part of the route consisted of sloping roads that could cause disconnection due to misaligned beam elevation. Further, the GiV2V connectivity was demonstrated on city roads, as shown in Figure 6b. The entire route was approximately 8 km and the speed limit was 80 km/h. There was no traffic during the measurement; however, 10 traffic signals for pedestrians and intersections existed along  
Figure 4: Testbed scenes.  
Figure 5: IEEE 802.11 ad module installation on vehicles [58].  
the route. For each stop, the acceleration and deceleration of the vehicles were repeated, which caused the vehicles to be apart by more than the reachable range specified by the 802.11 ad mmWave link.  
During the measurement time in both scenarios, two vehicles attempted to maintain a convoy model; however, the two vehicles were positioned in the diagonal direction, as shown in Figure 1, during a short period owing to lane change. Here, the beam form was selected between the two patterns shown in Figure 7, which follow the IEEE 802.11 ad beam training procedure.
# DEM Simulation of the Load Transfer Mechanism of a GRPS Embankment with a Fixed Geogrid Technique
## 3 Results and Discussions  
### Settlements and Particle Displacements  
Under the surcharge load on the embankment surface, the difference in stiffness between the pile and the subsoil causes the differential settlement. In this study, the piles were considered to be rigid walls, and the differential settlement between the pile and the subsoil was entirely contributed by the compression settlement of the subsoil. Figure 6 shows the displacement amplitude of the particles under the surcharge load of 100 kPa. For the three numerical simulations, the settlement on the top of the embankment was the largest. The results showed that the geogrid layer could reduce the total settlement on the embankment surface settlement. Figure 7 shows the settlement of the subsoil under different surcharge loads to identify the influence of reinforcement on the settlement characteristics of the subsoil. Compared with the unreinforced embankment, the effect of the reinforcement on reducing the settlement of the subsoil was obvious. The maximum settlement value of the subsoil was reduced by 14%, compared to that without reinforcement. For the fixed geogrid and unfixed geogrid conditions, the settlement at the centerline of the subsoil was almost the same, while the settlement of the subsoil that was closer to the pile in the fixed geogrid case was smaller. This effect was more pronounced with increasing surcharge load. In the model with the unfixed geogrid, the settlements of the subsoil particles near the pile (distance from 50 to 150 mm) ranged from 16.79 to 20.14 mm under the surcharge load of 500 kPa, while the corresponding range was 6.71-16.79 mm in the model with the fixed geogrid. This reflects the advantages of the fixed geogrid technique in controlling the differential settlement between the piles and subsoil.
# Privacy-Preserving Solution for European Union Digital Vaccine Certificates
## 8 Experimental Results
### Cryptographic Core Performance  
To measure the time consumption of our algorithms, we developed a test application in the Java programming language that does not perform any communication overhead between system entities (i.e., issuer, user, and verifier). Each algorithm of the cryptographic core runs in this test application. Therefore, saving and loading of individual parameters was not performed. The application implements the cryptographic core listed in Section 7.1. The application uses the \(\mathsf{measureTimeMillis()}\) function to measure the elapsed time of each algorithm. This feature allows us to measure Central Processing Unit (CPU) time with millisecond accuracy. Our experimental results are presented in Table 1 and Figure 9. The measurements confirmed the expectation that the time requirement of the algorithm increases with the number of hidden attributes. To make the experimental results more indicative, we performed all tests with all hidden attributes except the first attribute that was revealed. The most computationally demanding algorithm is the \(\mathsf{Verify}\) algorithm. However, even when revealing a theoretical number of 500 attributes (which is a highly inflated value) from a cryptographic credential, the required time reaches less than 300 ms. Therefore, the measurements show that the cryptographic core does not represent any performance issue on current smartphone platforms.  
Figure 9: Performance of the cryptographic core depending on the number of hidden attributes.
## 5 Results and Analysis  
As mentioned before, the primary objective of this work was to compare the performance of GPS and GPS/GLONASS integrated with low cost INS with a particular focus on assessing the benefits of including GLONASS. Both loose and tight integration strategies are tested to determine if the type of integration plays a significant role. The pseudo-measurements based on assumptions about the typical vehicular behavior are included in both integration architectures to assess the effectiveness in this context, as are GNSS-derived yaw observations.  
To this purpose, several processing configurations are considered. The baseline configuration in the loose and tight integration cases are based on GPS alone and are respectively denoted as \"LC GPS/INS\" and \"TC GPS/INS\". Similarly, the inclusion of GLONASS is denoted as \"LC GG/INS\" and \"TC GG/INS\". The use of constraints are denoted by appending a Y (GNSS-derived yaw), V (velocity pseudo-measurement) and H (height pseudo-measurement). For example, LC GG/INS YVH represents the loosely couple GPS/GLONASS solution with all three constraints applied.  
Figure 9: GNSS visibility on the Segment 3 trajectory.
# Improving Bearing Fault Diagnosis Using Maximum Information Coefficient Based Feature Selection
## 4 Experimental Results and Analysis
### Experiments on CUT-2 Datasets  
In order to validate the adaptability of the proposed feature selection method, vibration signals were collected and experiments were conducted on the CUT-2 platform. The whole experimental test rig is shown in Figure 4. The test rig was composed of an oscilloscope, a data acquisition system, a speed governor, a CUT-2 platform and a computer. All the measurements in the experimental system were carried out without load. In terms of vibration signal collection, the type of sensor was CT1010L, which is a kind of piezoelectric sensor, and the sensor was located in the horizontal direction of the bearing box. As shown in Figure 4b, the bearing tested in the experiment was placed at the far end of the motor. In addition, in order to validate the proposed method on the CUT-2 platform, an electric discharge machine was utilized to set three different kinds of faults. Figure 5 shows the bearing, of which the type was 6900 ZZ in the experiments. The fault diameters were 0.2 mm and 0.3 mm. The vibration signals were collected by accelerometers at 3 different motor speeds of 2000 rpm, 2500 rpm and 3000 rpm, where the sampling frequency was 2 kHz.  
Figure 3: Average diagnosis accuracy of single fault and number of features selected at two motor speeds. (**a**) Average diagnosis accuracy and number of features selected at 1750 rpm; (**b**) average diagnosis accuracy and number of features selected at 1772 rpm.
# Matching the Ideal Pruning Method with Knowledge Distillation for Optimal Compression
## 2 Related Works
### Pruning
This demonstrates the success of our approach in effectively balancing compression and performance. Previous works, such as [PERSON] and [PERSON] (2018), [PERSON] et al. (2019), [PERSON] et al. (2019), [PERSON] and [PERSON] (2020), [PERSON] et al. (2021), and [PERSON] et al. (2022), have explored the combination of pruning and knowledge distillation for model compression. These studies have shown that knowledge distillation can be used after pruning to improve accuracy and restore performance in pruned models. Our current research represents a natural progression from our previous paper, where we introduced a pioneering method that prioritized knowledge distillation before pruning [1]. This approach highlighted the importance of using distilled knowledge to increase model compression while maintaining or even improving performance. In our previous work, we demonstrated the efficacy of this approach and its advantages over conventional methods. By starting with knowledge distillation, we provided the student model with advanced insights from the teacher model, resulting in more efficient pruning and improved compression rates [1]. Building upon these foundational findings, the present study extends the investigation by focusing on a comparative analysis of weight and channel pruning techniques within the framework of knowledge distillation. By systematically evaluating their performance across various metrics and datasets, we aim to elucidate their respective strengths and limitations in the context of model compression. This comparative analysis allows us to deepen our understanding of optimal compression strategies and inform future developments in the field of deep learning model optimization for edge device deployment.
## 4 Analysis of the Magnetic Nanoparticles
### Force Analysis of MNPs in a Magnetic Field  
The magnetic field generated by a stimulation coil around MNPs can be considered a uniform field in calculations. Herein, a model of Helmholtz coil was established to generate a uniform magnetic field. The magnetic force can be calculated using the Maxwell stress tensor method. The Maxwell stress tensor is a second-rank tensor used in classical electromagnetism research to represent the interaction between electromagnetic forces and mechanical momentum [14].  
Generally, the Maxwell stress tensor is an integral along with a closed area S around the target domain. As shown in Figure 6a, if the closed area S is located in a linear material, and the magnetic force can be obtained by Equation (6).  
\[F=\frac{1}{\mu_{0}}\oint\limits_{S}\left(B(Bn)-\frac{1}{2}B^{2}n\right)dS, \tag{6}\]  
where \(S\) is the surface element, and \(n\) is a normal vector on closed area.  
According to the previous research, taking into account the medium viscosity (0.95 cP), the magnetic force calculated based on the Maxwell stress tensor method is shown in Figure 6b. During the stimulation of the magnetic field, the maximum magnetic force of MNPs induced by the applied magnetic field is around 1.5 nN. According to a previous study [15], the magnetic force can influence cellular activity. The MNPs cooperated with a magnetic field can be considered as a \"nano-motor,\" and can be used to interfere with the growth of the cells.
## 3 Materials and Methods
### Features
#### 3.3.2 IP Address--Port Pairs  
**Feature feasibility:** In ICS networks that use static IP allocation, IP address--port pairs are expected to remain constant: appearance of new IP-port pair indicates a new service being started on the system containing the IP.  
**Our data:** The initial and most simple features that would be usable are the address pairs of connections or packets consisting of source and destination internet protocol addresses and ports. This is simple to achieve and in a static environment would quickly bring to fore any sudden new addresses appearing.  
Strong multicast environment poses real challenges for using IP - port pairs, as the multicast protocol obscures the destination data. However even in this environment, tracking IP - port pairs highlights the atypical unicast flows.
## 3 5G and Beyond
### Management and Deployment of 5G and Beyond Technology
The complexity of 5G network functions obliges to split them into different planes (control plane functions and data plane functions) by the introduction of the software-defined paradigms where abstraction is needed among the different layers. SDN is considered to be an abstraction of network control, where the OpenFlow implementation acts only on the routing capabilities. However, orchestration covers a larger area of network operations. It supports not only network control, but also implies task coordination across multiple entities. The concept of orchestration also comes around when discussing service-oriented architectures, and management of resources in network infrastructures [33]. Slicing for 6G: Despite flexible allocation of communication resources by slicing in 5G, it can hardly respond to the high expectations of 6G services in terms of delay, reliability and efficiency. Therefore, 6G network slicing technology should be upgraded to support fine-grained, more flexible and intelligent resource allocation. Supporting the cooperation of multiple distributed edge core networks, guaranteeing service migration and the quick deployment and releasing of network slices should be also addressed in the next generation of network slicing [36].
## 3 Experiment
### Experiment Setup
In the circular TE\({}_{11}\) LMPS, the microwave transmission line from the power generator to the waveguide mode converter was same as that of the quasi-coaxial waveguiding structure, as shown in Figure 3. In the circular waveguiding structure, WR340 twisted waveguide was used to set the electric field in the required direction because the plasma interacting surface typically faced the deposition region, and the waveguiding structure was changed from a rectangular WR340 waveguide to a circular waveguiding structure through a homemade rectangular to a circular waveguide converter. For the experiment with the linear MWP source, a cylindrical chamber with a diameter of 500 mm and a height of 150 mm was fabricated, as shown in Figure 4a,b. The microwave power was designed to propagate along the straight line in the quartz tube. To increase the spatial uniformity of the gas flow, a showerhead type gas inlet structure was used, which was located opposite to the deposition region, 40 mm away from the quartz window surface. The images of the plasma generated near the window surface were taken, as shown in Figure 4b. It represents the images of the MWP plasma optical emission patterns along the linear direction from the front side and right side views. The mounting position of the camera in the plasma chamber is shown in Figure 4b as the front and side views. The equipment used to record the shape of plasma was a cylindrical camera (model: Microsoft Life Cam Studio) suitable for the rounded type chamber flange. The brightness of the emitted light was slightly distorted because the plasma shape was captured by a camera equipped with a neutral density(ND) filter (product of HORUSBENNU, 49 mm), which was used to reduce the brightness to 1/400. Figure 3: Schematic of the experimental system showing the microwave coupling arrangement. (**a**) Quasi-coaxial TEM linear MWP and (**b**) circular TE\({}_{11}\) waveguiding structure. Figure 5 shows the plasma diagnostics using a homemade single Langmuir probe(LP) in the deposition region. The deposition process is usually carried out 20-25 mm away from the window surface. Hence, the diagnosis was performed in the deposition area at five points (20-25 mm away from the window surface) at 20 mm intervals from the center, as shown in Figure 5. The tip of the LP was made of a 100 \(\upmu\)m diameter tungsten wire (10 mm in length), which was designed to satisfy the collisionless thin sheath condition [22]. The electron energy distribution function (EEDF) was obtained by calculating the second derivative of the measured I-V curve [23; 24]. The second derivative of the measured current with respect to the probe bias voltage, \(I_{e}^{\prime\prime}\), was proportional to the electron energy probability function (EEPF) \(f_{e}(\varepsilon)\), which was related to the EEDF as \(g_{e}(\varepsilon)=\varepsilon^{1/2}f_{e}(\varepsilon)\). The electron density is calculated by integrating the EEDF, and the electron temperature was measured as an effective electron temperature \(T_{eff}\), which corresponds to a mean electron energy from the integrals of the EEDF [23; 24].
## 2 Preliminaries  
### Network Model  
A WSN consists of one _server_ or one _sink node_ and a number of _sensor nodes_. A sink is wall-powered while every sensor node is battery-powered. A node has a limited transmission range for energy saving as well as spectrum efficiency. Thus, the nodes can form a multi-hop tree originating from a sink. Two nodes that can communicate directly with each other are said to have a _link_. A link can be broken due to node mobility, node failure, battery depletion, or the intervention of some obstructions, thereby causing network topology to be changed dynamically. Especially, a link between a parent and its child is called a _tree-link_.  
In tree topology, a server sends a command message to a specific node or a group of nodes whenever it needs. Basically, this can be achieved if every internal node rebroadcasts a received message after a server broadcasts the message. In this paper, we do not deal with the problem of minimizing the number of rebroadcasted nodes (see the optimized broadcast problem in Reference [18]).
## 2 Methodology
### Computational Domain and Boundary Conditions  
As shown in Figure 4, the computational domain consists of three zones: the background domain, denoted as Zone 1, and the overset domains, denoted as Zone 2 and Zone 3. The length, width, and height of the background domain are 93.75 H, 26.04 H, and 13.02 H, respectively, yielding a small blockage ratio, which is defined as the area of the two trains projected onto a plane in the longitudinal direction divided by the cross-sectional area of the background domain, 0.37%, which meets the requirements of CEN (2013) [24]. A pressure outlet boundary condition was employed in the four sides and upper faces of the background domain, and the static pressure \(P_{\infty}\) was set to zero. The ground and two rails were set as the stationary walls. The dimensions of the overset domain were 19.53 H (length) \(\times\) 1.28 H (width) \(\times\) 1.43 H (height), and all faces were overset boundary conditions. To ensure full development of the wake flow fields, the computational domain was extended by 30.7 H backward from the tail nose of the train. Zone 2 and Zone 3 were moved in opposite directions at 200 km/h. In Zone 2, Train A travelled in the positive \(x\)-direction, while in Zone 3, Train B travelled in the negative \(x\)-direction.  
The relative speed between the trains was 400 km/h, resulting in a Mach number (Ma = \(U/c\), \(U\) is relative speed of two trains, \(c\) is the local sound velocity, which is 340 m/s usually) larger than 0.3. According to [PERSON] et al. [25], the compressibility of the flow had to be considered. To obtain a relatively steady flow field before the two MTs met, the initial distance between the head nose of the two trains was set to 15.63 H. Based on the train velocity and train height, the Reynolds number (\(Re\)) was 7.47 \(\times\) 10\({}^{5}\), which is larger than the minimum \(Re\) (0.25 \(\times\) 10\({}^{5}\)) recommended by CEN (2013) [24] for the scale-model test.  
Figure 3: Monitoring points: (**a**) train surface; (**b**) stationary points on the trackside.