esa-sceva/satcom-chunk-collection huggingface dataset:
# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
###### Abstract  
The central intelligence offered by Software Defined Networking (SDN) promise the smart and reliable reconfiguration which enables the scalability of dynamic enterprise networks. The decoupled forwarding plane and control plane of SDN infrastructure is a key feature that supports the SDN controller to extract the physical network topology information at runtime to formulate network reconfigurations. This SDN-based network reconfiguration enables application-aware routing capability for Internet of Thing (IoT). However, these IoT enabled SDN-based routing protocols face some performance limitations in iterative reconfiguration process due to complete centralized path selection mechanism To this end, in this paper, we propose SDN-Based Application-aware Distributed adaptive Flow Iterative Reconfiguring (SADFIR) routing protocol. The proposed routing protocol enables the distributed SDN iterative solver controller to maintain the load-balancing between flow reconfiguration and flow allocation cost. In particular, the proposed routing protocol of SADFIR implements multiple SDN controllers that collaborate with network devices at forwarding plane to develop appropriate clustering strategy for routing the sensed information. This distributed SDN controllers are assisted to clustering topology that successfully map the residual network resources and also enable unique multi-hop application-aware data transmission. In addition, the proposed SADFIR monitor the iterative reconfiguration settings according to the network traffic of heterogeneity-aware network devices. The simulation experiments are conducted in comparison with the state-of-the-art routing protocols which demonstrates that SADFIR is heterogeneity-aware which is able to adopt the different scales of network with maximum network lifetime.  
software-defined networking; Internet of Things; network reconfiguration; application-aware; heterogeneity-aware +
Footnote †: journal: Sensors
## 3 Results  
### Evaluation of SR Anatomy  
In total, 104 students at the School of Dentistry experienced and evaluated SR Anatomy. The participants were all second-year students of the School of Dentistry, Kanagawa Dental University, who participated in the head and neck dissection training curriculum. The head  
Figure 6: Example of DSR View (CT).  
Figure 7: Example of DSR View (CBCT).  
and neck dissection training curriculum consisted of 20 periods--the method proposed in this paper was used for one of these periods (1.5 h). When the students practiced head and neck dissection, they experienced SR Anatomy individually in a separate room in the same building as the dissection training room. The SR Anatomy experience per student lasted about 3 min. Students completed a questionnaire before and after the experience. The response time was 5 min each. We conducted a questionnaire survey of the 104 students before and after the experience and received valid responses from 99 students. We excluded seven inconsistent responses from the 99 respondents, leaving 92 for analysis. The validity of the survey questions and quantities was verified and determined by several anatomy faculty members and a faculty member with extensive experience in statistical analysis. The content of the questionnaire survey (Supplementary Materials) was reviewed and approved by the Ethics Review Committee of the Kanagawa Dental University.  
In their impressions of the spatial reality display (SRD) experience, 53% of the students answered that their experience went extremely well and 44% answered that it went very well. Regarding their comprehension of anatomy using the SRD, 18% of the students answered that it went extremely well and 64% answered that it went very well. Before the SRD experience, in the application of SRD to anatomy education, 21% of the students answered that the application of SRD is absolutely necessary and 62% answered that it is necessary. After the SRD experience  
Figure 11: Comprehension of anatomy.  
Figure 10: Impressions of the SRD experience.  
Figure 9: Another example of a dental student experiencing the SRD.  
Figure 12: Necessity of the application.  
Figure 13: Necessity of the application.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Impressions of SRD Experience**} \\ \cline{3-6}  & & **Extremely** & **Satisfied** & **Satisfied** & **Neither** & **Moderately** & **Dissatisfied** & \(p\)***** \\ \hline \multirow{4}{*}{Comprehensions of anatomy using SRD} & Extremely well & 14 & 3 & 0 & 0 & \\ \cline{3-6}  & Extremely well & 82.4\% & 17.6\% & 0.0\% & 0.0\% & \\ \cline{2-6}  & & 31 & 29 & 1 & 0 & \\ \cline{2-6}  & Very well & 50.8\% & 47.5\% & 1.6\% & 0.0\% & \\ \cline{2-6}  & & 3 & 9 & 1 & 1 & \\ \cline{2-6}  & Moderately well & 21.4\% & 64.3\% & 7.1\% & 7.1\% & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation between the comprehension of anatomy using the SRD and impressions of the SRD experience.
## 6 Numerical Simulation Analysis of the Dual-Electrode Excitation Method
### Analysis of Influencing Factors on the Distribution of the Magnetic Induction Intensity Formation
#### 6.4.6 Analysis of Influencing Factors on Mud Resistivity  
The mud resistivity \(R_{m}\) is modified, and the numerical simulation is carried out with 0.01 \(\Omega\cdot\)m, 0.1 \(\Omega\cdot\)m, 1 \(\Omega\cdot\)m, 10 \(\Omega\cdot\)m, and 100 \(\Omega\cdot\)m, respectively.  
Figure 9(a) illustrates the impact of varying mud resistivities on the casing current. As the mud resistivity increases, the amplitude of the casing current also increases, although the rate of increase gradually diminishes. When \(R_{m}\) exceeds 1\(\Omega\cdot\)m, there is only a marginal increase in the casing current, with maximum values as follows: 0.3 A, 1.59 A, 2.65 A, 2.86 A, and 2.87 A, respectively. These results indicate that a higher \(R_{m}\) can amplify the casing current amplitude due to weakened leakage effects in the mud channel and increased injection into the formation when \(R_{m}\) surpasses a certain resistance threshold.  
Figure 9(b) demonstrates how different mud resistivities influence the induced magnetic field intensity along the axis of the drilling well, with \(B_{c}\) exhibiting similar attenuation characteristics to \(I_{c}\). The maximum magnetic field intensity corresponding to various mud resistivities is recorded as follows: 2.57 nT, 12.82 nT, 21.32 nT, 22.9 nT, and 23.1 nT. At a measurement position z\({}^{\prime}\) = \(\pm\)2.5 m from the magnetic field source point (z = 0), the respective magnetic field intensities are as follows: 2.5 nT, 12.57 nT, 21.1 nT, 22.4 nT, and 22.9 nT, respectively. These findings provide valuable insights into how different levels of mud resistivity affect the induced magnetic fields within drilling wells and their surrounding environments.  
Figure 10: The amplitude of the casing current and the intensities of the induced magnetic field on the axis of the drilling well with different mud resistivities.
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 4 Numerical Simulation
### Two Radars under Attack  
Consider the case that radar 1 and radar 3 are under an FDI attack. The attack begins at time \(t=5\) and ends at time \(t=20\). We first verify the effectiveness of the injected data detection method with DSE-FDI. Figure 2 plots the \(\partial^{2}\) distribution variable \(J_{i}(k)\) over time for radars 1-8. It can be seen that for the attacked radars (radar 1 and radar 3), the alarm triggering condition in Equation (9) was satisfied when an attack existed. For the radars not under attack, \(J(k)\) satisfied \(0\leq J_{i}(k)\leq J_{th},i=2,4,5,6,7,8\). In most of the time, with only some false alarms occurring. Note that false alarms will affect the detection flag, resulting in a false trigger of the estimation method. However, as long as the alarm triggering threshold is set properly, the false alarm will not last. Thus, false alarm had little effect on the estimation accuracy. In order to reduce the false alarms, the alarm triggering threshold can be increased appropriately.  
Figure 1: Simulation scene. (**a**) 3D. (**b**) 2D.  
After verifying the effectiveness of the proposed injected data detection and attack parameter estimation method, we compared DSE-FDI with the DSE method. Figure 5 plots the RMSE of the estimated position and velocity over time for DSE-FDI and the DSE method. It can be seen that DSE-FDI performed the same as the DSE method when not attack existed, but it performed much better than the DSE method when suffering from any FDI attack.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 2 Underwater Acoustic Communications  
Acoustic communications enable the transmission of information over long ranges up to tens of kilometers. Performance in terms of distance and bit rate depends on the carrier frequency and bandwidth of the transducers. Available bandwidth is usually very limited and the latency is large [5]. Underwater acoustic channel (UAC) is characterized with time-variable multipath propagation [6] resulting in time and frequency selectivity. The selectivity generates a loss of performance which can be reduced if diversity is used at the receiver side. Combining several realizations of the same information transmits at different times or frequencies allows a gain depending on the number of realizations considered. So, long ranges communications need to repeat the information at the transmitter using for example spreading codes or forward error coding inducing a loss on the useful bit rate.  
Regarding the literature in underwater communications, performance of several academic and commercial modems have been plotted on the Figure 1 in terms of bit rate and ranges. Refs. [7; 8; 9] give references and description of different modems.  
Modems are often dedicated to a specific use for a given distance or bit rate. In our application, the distance between ship or buoy and AUV may vary. It is required to define a modem able to adapt its parameters to respond to this challenge. For that, we propose to use a spread spectrum waveform which allows communications over short and large ranges depending on the size of the spreading factor.
# Noble Metal-Based Heterogeneous Catalysts for Electrochemical Hydrogen Evolution Reaction
## 4 Noble Metal-Based Heterogeneous Catalysts
### Interface Engineering
#### 4.2.4 Single Atom Noble Metal Catalyst  
Single-atom doping plays an essential role in catalytic reactions by maximizing atomic efficiency and activating catalytic sites. Atomically dispersed metals are usually anchored to a variety of substrates, including graphene, metal-organic skeleton-derived porous carbon, metal oxides and zeolites, which in most cases can provide pores, vacancy defects or strong interactions [14]. At present, single-atom HER catalysts of the noble metals Pt, Pd, Ru and Ir have been reported, and these single atoms are fixed by coordination with C, N, P, and S atoms on the substrate [124,125]. The reason for improving the activity of a noble metal single-atom catalyst is not only to maximize the utilization of atoms but also to optimize the electronic structure of the noble metal single atom and its heteroatoms by bonding and interacting with the coordination atoms on the substrate, resulting in \(\Delta\)G\({}_{\text{H}^{*}}\) being closer to 0 [124]. The coordination structure has a great effect on the catalytic performance. However, it remains a big challenge for researchers to elaborate the design of the coordination number without changing the atomic dispersion [126,127]. [PERSON] et al. [91] prepared the Ru SAs@PN catalyst using amorphous phosphoinide nitrides nanotubes (HPNs) as the substrate of the stabilized Ru single atoms (SAs) (Figure 11a). There is a strong coordination interaction between the lone pair of electrons of N in the HPN substrate and the d-orbitals of Ru, thus anchoring the Ru single atoms. The Ru SAs@PN catalyst shows high HER catalytic activity  
Figure 10: (**a**) SEM image and elemental mapping analysis of Pt@mh–3D MXene. (**b**) HRTEM image of Pt nanocrystallites on mh–3D MXene. (**c**) TEM image of Pt@2D MXene. (**d**) The HER polarization curves of Pt@mh–3D MXene, Pt@2D MXene, Pt@mh-rGO, and Pt/C at a scan rate of 10 mV s\({}^{-1}\) in 1 M KOH. (**e,f**) Comparison between Pt@mh–3D MXene and Pt/C in \(\Pi_{10}\) and Tafel slope in the full pH range, respectively [90]. Copyright 2020 John Wiley and Sons.  
(in 0.5 M H\({}_{2}\)SO\({}_{4}\), \(\eta_{10}\) = 24 mV) (Figure 11b). The DFT calculation shows that the \(\Delta\)G\({}_{\text{H}^{*}}\) of Ru SAs@PN is closer to 0 than that of Ru/C, C and C\({}_{3}\)N\({}_{4}\)-loaded Ru SAs, which is conducive to hydrogen precipitation (Figure 11c). However, atom-anchored materials still face a great challenge, namely the difficulty of achieving large-scale stable SAs doping at high loads, which tend to aggregate and agglomerate to form particles. [PERSON] et al. [12] reported a new concept of selective single-atom doping with high loads through lattice mismatch of multicomponent heterogeneous nanostructures (e.g., NiS@Al\({}_{2}\)O\({}_{3}\)[12], MoS\({}_{2}\)/NiS\({}_{2}\)[92], etc.). Due to the special properties of heterogeneous nanostructures, a large number of vacancy defects or voids are generated on the heterogeneous interface so that more atoms can be trapped.
## 3 Energy-Sensitive X-ray and Gamma-ray Imaging in Wide FoV
### Radiation Field Component Discrimination, Segmented Region Response  
The detection response and resolving power of radiation field composition was investigated. Tests were performed with a mixed field of X ray, gamma ray and alpha particle components from a laboratory radionuclide 241-Am source. The integrated detection (i.e., over many individual Timepix frames) and track visualization of the irradiated field by the instrument are presented in Figure 11.  
Figure 10: Same as Figure 9 showing in detail several detector frames at selected positions in the scanning range from \(-\)25 to +50 degrees in both directions with step 25 degrees. The entire detector pixel matrix is shown in all figures i.e., 256 \(\times\) 256 pixels = 14 mm \(\times\) 14 mm.  
The results shown correspond to two measuring modes of the Timepix detector which are suitable for performance testing purposes of the developed instrument. The _countingmode_ allows for detailed event counting at the pixel level and serves for determination of the position of the X-ray source. The _spectralmode_ allows for energy measurement also at the pixel level and is suitable for the production of deposited energy spectra of registered particles. Figure 12 shows 5 histograms corresponding to the whole detector (ROI = 0) and the 4 segmented quadrants of the segmented optics-collimator (ROI = 1: 1D horizontal, ROI = 2: open window with cross frame, ROI = 3: 2D, ROI = 4: 1D vertical quadrant) - see description list in Section 2.1. A broad binning was chosen given the spectral resolution and resolving power of the pixel detector [29]. The ratios and derived pattern distributions serve to characterize the response and operational range between the segment regions.  
Figure 11: Detection and radiation field visualization of a mixed-field consisting of X rays, gamma rays and alpha particles from a 241-Am source by the developed payload. The images show the integrated (long time exposure over many Timepix frames) detection and track visualization of the irradiated field registered by the pixel detector with a 2000 CdTe sensor. Data are shown in counting mode (**left**) and spectral mode (**right**)—see text. The per-pixel counting and energy response, respectively, are displayed in color.
# An Estimator for the Kinematic Behaviour of a Mobile Robot Subject to Large Lateral Slip
## 3 Experimental Identification of the Estimator
### Results
The theoretical kinematic model tends toward \(R_{th}=\frac{\tau_{1}}{\dot{\phi}_{1}}\), while the kinematic model with the corrected track length \(\hat{i}\) tends to the value of \(\hat{R}\), the radius of the best-fitting circle. As described before, the virtual track length that better fits the experimental trajectories \(\hat{i}\) is always larger than the robot actual track length \(i=0.286\) m to mathematically describe the under-steering behaviour of the robot that, otherwise, the theoretical kinematic model could not represent. Figure 7, in particular, illustrates that there is a strong correlation between \(\hat{i}\) and the front module yaw rate \(\dot{\varphi}_{1}\): the quicker the robot turns, the higher the fitted virtual length is, or, from another point of view, the quicker the robot turns, the more evident the dynamic effects on the handling, such as under-steer and wheel slip, become more relevant. Figure 5: Test 1: trajectories comparison. (**a**) Comparison between kinematic models and measured data. The theoretical kinematic curve (“Th. Kin.”) uses the actual robot parameters, while the other trajectory (“Fit Kin.”) use the best fitting track length \(\hat{i}\). (**b**) Comparison between measured data, steady-state kinematic trajectory with radius \(R_{th}\) and best fitting circle with radius \(\hat{R}\). Figure 6: Test 1: Trajectory radius comparison. Figure 7 also shows that the data trend could be approximated by the linear function:  
\[\hat{i}=p_{1}\dot{\varphi}_{1}+p_{2} \tag{16}\]  
where \(p_{1}=0.049\) m s and \(p_{0}=0.286\) m are the coefficients of the linear function. This very simple relation could be used to predict and, therefore, partially compensate the unmodelled dynamic effects of the kinematic model. It is important to state that this particular relation holds only for working scenarios identical, or at least very similar, to the experimental setup. Unfortunately, this simple approach has as a downside the limitation of being valid only in tested and specific scenarios. Nevertheless, a robot working in a well-known and uniform environment could benefit from this approach. The latter equation can be rewritten in order to explicitly show the relation between \(\hat{i}\) and the radius \(R\) by recalling that \(R=v_{1}/\dot{\varphi}_{1}\):  
\[\hat{i}=p_{1}\frac{v_{1}}{R}+p_{0} \tag{17}\]  
Due to its simplicity, the relation can also be easily rewritten as:  
\[R=\frac{p_{1}v_{1}}{\hat{i}-p_{0}} \tag{18}\]  
Hence, depending on the case and the known and unknown parameters, it is possible to compute, using a simple linear equation, the correction parameters that could compensate for undesired behaviour in a robot.
## 2 Methodology
### ML Algorithms Considered
#### 2.1.4 Support Vector Regression (SVR)  
The SVR approach, a machine learning algorithm widely used in regression problems [39], has its origin in Support Vector Machines (SVM), which are considered a binary classification method with high generalization capability in high-dimensional problems [40]. While historically SVMs have primarily been utilized for binary classification problems, their applicability has expanded in recent decades to cover multiclass problems and regression tasks, leading to the development of Support Vector Regressors (SVRs) [41]. Both SVMs and SVRs revolve around constructing a regressor hyperplane that best fits the training dataset. In the case of regression, a tolerance margin is introduced between the samples and the hyperplane, ensuring that the predicted data lie within a specified margin distance. Consequently, the objective becomes selecting a hyperplane with the maximum margin between the support vectors within the dataset. Notably, certain samples may fall outside the tolerance margin and are considered errors, prompting the calculation of their distance from the nearest boundary, denoted as \(\varepsilon\)[41]. To train the model, an optimization problem needs to be solved based on the following equation:  
\[min\frac{1}{2}||w||^{2}+C\sum_{i=1}^{N}(\xi_{i}+\xi_{i}^{*}) \tag{4}\]  
In the equation, \(\omega\) represents the magnitude of the hyperplane, while \(C\) is a positive constant that determines the tolerance for deviations beyond the support vectors. It is important to note that \(C\) should be greater than 0. A high value of \(C\) indicates a stricter representation of the dataset by the defined hyperplane, while a small value of \(C\) allows for a larger number of errors to be permitted by the model. The effectiveness of the algorithm relies on properly selecting and implementing the \(C\) parameter as well as the chosen kernel [42].
## 4 Experiments
### Experimental Environment  
We verified the performance through various methods to confirm the performance of the Gomoku system proposed herein. We performed experiments with \"the number of wins without an action filter,\" \"the number of batch sizes of replay memory without an action filter,\" \"the number of wins with an action filter,\" and \"the number of batch sizes of replay memory with an action filter\" on the Gomoku game. In this study, the experiment was developed with C/C++, Python 3.0, and TensorFlow. A CPU with an I-7 processor, 16 GB RAM, and a GTX-1050 GPU were used in the experimental environment.  
In this study, a 7 \(\times\) 7 board is used in the Gomoku games. The neural network of the proposed DQN consists of three hidden layers. Each hidden layer consists of 49,128,516 neurons. The output and input layers were designed to have 49 neurons, which corresponds to the total number of intersections on the board.  
In the experiments, 1000 Gomoku games were iterated for each experimental case. We calculated the winning rate by grouping 1000 experimental data into ten categories. A Gomoku board is utilized as a DQN's environment. States are described by the status of the Gomoku board. Each game was forced to start at the (4, 4) position. The proposed enhanced DQN framework was applied to the white stones (DQN's Agent). The genetic algorithm was applied to the black stones. If black stones form the first five-in-a-row, it will be considered as a win for the black stones. Therefore, the goal of white stones is to prevent black stones from forming a five-in-a-row. If white stones form the first five-in-a-row or fill up all 49 cells such that the game can no longer proceed, it will be a win for the white stones.
## 4 Results of Experiment and Simulation
### Disturbance Rejecting Ability Simulations and Experimental Results
#### 4.3.1 Simulation Results  
The disturbance rejecting ability simulation results are shown in Figure 13. From the simulation results in Figure 13, the \(p\)-\(p\) values of \(\Delta\theta_{a}\) are \(0.5^{\circ}\) and \(1.0^{\circ}\) when the disturbances are added to the conventional PID controller at time 70 s and 130 s, respectively, while the tracking errors of the conventional TSM method are \(-2.0\times 10^{-3}(^{\circ})\) and \(-0.48^{\circ}\) at time 70 s and 130 s, respectively; the tracking errors \(\Delta\theta_{a}\) of the NTSM method in reference are \(9.9\times 10^{-4}(^{\circ})\) and \(-0.44^{\circ}\); the \(p\)-\(p\) values of \(\Delta\theta_{a}\) are \(8.6\times 10^{-5}(^{\circ})\) and \(0.42^{\circ}\) at time 70 s and 130 s when NFTSM is applied, respectively.  
Figure 12: Pulse disturbances added to the system.  
Figure 13: Comparative azimuth gimbal absolute angle simulation results with four different methods: (**a**) PID method; (**b**) TSM method in reference [19]; (**c**) NTSM method in reference [15]; (**d**) the proposed NFTSM method; (**e**) NFTSM-RBFNN method; (**f**) the proposed NFTSM-BSNN method.  
However, when the RBFNN-based NFTSM is applied, the \(p\)-\(p\) values of \(\Delta\theta_{a}\) are \(7.7\times 10^{-5}(^{\circ})\) and \(17\times 10^{-3}(^{\circ})\) at time 70 s and 130 s, respectively; the \(p\)-\(p\) values of \(\Delta\theta_{a}\) are \(4.0\times 10^{-5}(^{\circ})\) and \(6.2\times 10^{-5}(^{\circ})\) at time 70 s and 130 when the BSNN-based NFTSM is applied, respectively. Therefore, it is apparent that the neural network is good at nonlinear approximation, and the proposed BSNN performs better. The forms of B-spline curves are shown in Figure 14.