esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design
### Two-Element MIMO Antenna Design  
Figure 5 shows the modified two-port MIMO antenna with 44 \(\times\) 28.22 mm\({}^{2}\) substrate dimensions. In order to create a dual-port MIMO antenna, two separate investigations are undertaken with the objective of attaining optimal outcomes. Initially, the focus of the study is on optimizing the spacing between two antenna elements. Subsequently, the antenna is examined in three distinct orientations, labeled as I, II, and III.
# Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \(T\) (\({}^{\circ}\)C) & \(\phi_{p}\) (\%) & Experiment (P) & RBF Prediction (Q) & \(\frac{1P-Q1}{P}\times\)**100\%** \\ \hline
9.970 & 1.000 & 1.204 & 1.181 & 1.94  
Figure 7 compares the experimental viscosity ratio of [PERSON] et al. [15] with the predicted values of the RBF neural network for the SiO\({}_{2}\)-ethylene glycol nanofluids at \(T=25\)\({}^{\circ}\)C as a function of the nanoparticle volume fraction and diameter. It can be found from Figure 7a that the RBF predicted viscosity ratio of nanofluids are obviously enhanced with the increase of the SiO\({}_{2}\) nanoparticle volume fraction and the decrease of the nanoparticle size, which are consistent with the experimental results. All the prediction relative errors are within \(\pm 8\%\), as shown in Figure 7b. On this basis, Figure 8 illustrates the comparisons between the RBF predicted values and the corresponding experimental data of [PERSON] et al. [19]. The results indicate that the RBF neural network developed in this study can be applied successfully for predicting the effects of the nanoparticle volume fraction and temperature on the viscosity ratio of SiC-ethylene glycol/water (40:60 by weight ratio) nanofluids with a satisfactory accuracy. In addition, a similar analysis is performed for the CuO-ethylene glycol/water (60:40 by weight ratio) nanofluids as a function of temperature, which is presented in Figure 9. It is demonstrated that the viscosity ratio characteristics of the above nanofluids are effectively predicted by the RBF neural network in a wide range of nanoparticle volume fractions (from 1% to 6%) and temperatures (from \(-35\) to 50 \({}^{\circ}\)C). The maximum prediction relative errors are only 4.2%. All the above analyses further demonstrate that the RBF neural network is one of the potential tools to quantitatively establish nonlinear relationships between inputs and outputs. Figure 7: (**a**) Predicted comparisons and (**b**) relative errors of the RBF predicted \(\mu_{\#}/\mu_{f}\) and the experimental data [15] for SiO\({}_{2}\)-ethylene glycol nanofluids at \(T=25\)\({}^{\circ}\)C.  
Figure 8: (**a**) Predicted comparisons and (**b**) relative errors of the RBF predicted \(\mu_{qf}/\mu_{f}\) and the experimental data [19] for SiC-ethylene glycol/water (40:60 by weight ratio) nanofluids at \(d_{p}\) = 30 nm. Figure 9: _Cont._  
Table 8 shows the prediction performance of the RBF neural network using different viscosity data sets. It is worth noting that the data sets are selected randomly. From Table 8, we found that the size of the data set can affect the modeling and prediction of the RBF neural network significantly. With the decrease of the data set size, the prediction accuracy will decrease. This may mean that to accurately predict the viscosity of ethylene glycol/water based nanofluids using the RBF neural network, a large enough data set is necessary.
###### Abstract
The influences of surrounding rock strength, reinforcement ratio, and secondary lining thickness on the bearing capacity of the secondary lining were systematically evaluated. The following findings were obtained: (1) The tunnel group effect was found to be negligible when the spacing (\(D\)) was \(\geq\)65 m and the burial depth was 1600 m. (2) Both \(P_{0.3}\) and \(P_{\text{max}}\) of the secondary lining increased linearly with reinforcement ratio and thickness. (3) For surrounding rock of grade III (IV), 95% \(u_{\text{lim}}\) and 90% \(u_{\text{lim}}\) were found to be optimal support timings, with secondary lining forces remaining well below the cracking stress during construction. (4) For surrounding rock of grade V in tunnels with a burial depth of 200 m, 90% \(u_{\text{lim}}\) is recommended as the initial support timing. Support timings for tunnels with burial depths between 400 m and 800 m are 40 cm, 50 cm, and 60 cm, respectively. Design parameters should be adjusted based on grouting effects and monitoring data. Additional reinforcement is recommended for tunnels with burial depths between 1000 m and 2000 m to improve bearing capacity, with measures to enhance impermeability and reduce external water pressure. These findings contribute to the safe and reliable design of support structures for deep-buried diversion tunnels, providing technical support for design optimization and long-term operation. keywords:tunnel groups; ground reaction curve; support characteristic curve; support timing; reserved deformation amount for excavation
# Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications
## 4 Discussion
### Sewer Flow Simulations  
For low flow situations, the model can accurately estimate flows using radar and gauge data. However, for flows over 0.5 m\({}^{3}\)/s, the gauge estimations underestimate the flow peaks in most cases. Results using radar data produce better estimates for higher flows and are able to predict peak intensities more accurately for flows around 0.5 m\({}^{3}\)/s. For higher intensity flows the model also underestimates the flow in most cases. For low-intensity flows, both the gauge and radar estimations produce accurate results and mimic the flow pattern. Flow simulation with rain gauges have a lower RMSE (0.084 m\({}^{3}\)/s) than using radar rainfall (0.096 m\({}^{3}\)/s). These results are expected given the fact that the rain gauges used to simulate the flow are located within the urban area.
## 2 Definition of System Requirements and Assessment of Fitting Communication Technologies
### Interoperability  
Interoperability is achieved when components of the system can communicate properly [33]. The choice of the VANET technology, which is supposed to be available in cars, implies that all vehicles should comply with IEEE 802.11p-based physical and medium access communication layers [34] and with Internet Protocol-based network layer. Moreover, vehicles should be capable of fully processing messages generated by the proposed system.
# Research and Design of the RF Cavity for an 11 MeV Superconducting Cyclotron
## 4 Design and Simulation of the RF Cavity
### Numerical Calculation of Electromagnetic Fields of the RF Cavity  
The SIR structure is introduced to realize the miniaturized design of the RF cavity, and the 3D electromagnetic field numerical simulation software is used to compute the electromagnetic field distribution and resonant frequency of the RF cavity. After determining the boundary conditions of the cavity, the calculation results can be obtained by using the eigenmode solution method.  
Since the RF cavity can be viewed as consisting of several segments of transmission lines with different characteristic impedances and a normalized resonator length of 0.42, the impedance ratio can be calculated to be 0.117. However, the characteristic impedance of the Dee and Stem is actually a variable, especially in the Dee region, where the characteristic impedance can be viewed as a function. So, shortening the quarter-wavelength resonator to the desired size is not purely dependent on the contribution of the discontinuity alone, and its impedance ratio \(R_{\rm Z}\) will fluctuate compared to the theoretically calculated value 0.117.  
From Figure 9, it can be seen that the electric field is mainly distributed at the Dee, which is called the capacitive region, while the magnetic field is mainly distributed at the Stem, which is called the inductive region. Where the electric field is the strongest at the edge of the Dee near the center area, here the magnetic field is the weakest, and where the magnetic field is the strongest at the end of the Stem, here the electric field is the weakest. The distribution of the electromagnetic field in the cavity can also be used as a reference for the design of subsequent water-cooled structures.  
Figure 8: Effects of different round coaxial resonator lengths on the surface area of the round coaxial resonator and unloaded quality factor at the same resonant frequency.
# Discrimination Accuracy of Sequential Versus Simultaneous Vibrotactile Stimulation on the Forearm
## 3 Methods
### Mapping Patterns to Vibrotactile Cues  
Our primary aim was to assess the accuracy with which sighted participants could identify vibrotactile patterns on their skin with simultaneous versus sequential presentations. Additionally, we sought to understand how the length of these patterns (i.e., the number of activations participants needed to discern) influenced their recognition capabilities. The aim of this research is to lay a foundation for the future development of a wearable device that can present Braille-like patterns through vibrotactile stimulation.  
We used a set of 14 unique patterns derived from the Icelandic Braille alphabet. These patterns were divided into 2 categories: short-length patterns, which involved the use of 2-3 actuators, and long-length patterns, requiring 4-5 actuators. The specifics of these patterns can be observed in Figure 2 and are detailed further in Table 2.  
Figure 2: Assignment of pattern dots to the six actuators in this study.  
It is important to emphasize that our study did not require participants to have any prior knowledge of Braille reading or recognition of specific Braille characters. Instead, we aimed to assess their capability to accurately reproduce the vibrotactile patterns using a keyboard interface. This design allowed us to evaluate the efficacy of our vibrotactile system purely based on participants' ability to discern and reproduce the patterns they felt on a keypad.  
There were a total of 168 trials in the study, divided into 24 miniblocks (each containing 7 trials): 12 miniblocks for short patterns and 12 for long patterns. Within each block, there were 28 trials. Half of these miniblocks involved simultaneous presentation, while the other half involved sequential presentation. Every trial began with the presentation of a pattern, and subsequently, participants were tasked with reproducing the pattern using a keypad. The order of presentation was randomized (details in Table 2).  
Table 2 elucidates the characters, corresponding patterns, and the actuators used for each pattern.  
For simultaneous presentation, all actuators were activated simultaneously for 1000 ms. A 4000 ms interval followed, during which participants responded. During sequential presentation, each actuator was triggered successively for 500 ms, separated by a 450 ms pause. As for simultaneous presentation, there was a 4000 ms response interval.  
The stimulation was set at a constant frequency (100 Hz) and amplitude (9.6 g acceleration). Notably, this frequency aligns with the optimal detection range for human vibrotactile perception (based on our previous studies [36]). The entire experiment took approximately 35 min. for each participant.
## 1 Introduction
Earlier research concerned membranes based on various polymer matrices such as ethylcellulose (EC), poly(2,6-dimethyl-1,4-phenylene oxide) (PPO), sulfonated poly(2,6-dimethyl-1,4-phenylene oxide) (SPPO), linear and hyperbranched polylimides (LPI and HBPI) as well as magnetic powders and iron-encapsulated multi-wall carbon nanotubes (Fe@MWCNT) as inorganic fillers [20; 21; 22; 23; 24; 25; 26; 27]. During this research, it was found necessary to have a suitable tool that would allow for the optimal selection of hybrid membrane components. At the same time, this tool should ensure appropriate separation properties of the proposed membranes, which is the subject of the following article. The following article proposes a modern computer application MOT (membrane optimization tool) for modeling of gas transport processes through mixed-matrix membranes (MMMs). The current  
Figure 1: Gas permeation through mixed-matrix membranes. version of the application was based on the simplest Maxwell model, used to model the gas transport through the simplest types of hybrid membranes without any defects. The application has been verified on the example of four types of hybrid membranes, consisting of various types of polymer matrix, such as: poly(vinyl acetate), 2, 2'-BAPB + BPADA, Ultem, hyperbranched polyimide (ODPA-MTA) and zeolite 4A. The AARE (average absolute relative error) and RMSE (average absolute relative error) errors were calculated in order to compare the theoretical results obtained from the application MOT with experimental results obtained from the literature and the authors' own tests. The article presents also the comparison of MOT-predicted data obtained with [PERSON] and [PERSON] models.
## 3 System Structure
### Robust Denoising Algorithm and IDA Detection Algorithm
#### 3.2.2 IDA Detection Algorithm
If the L2-norm is greater than a certain preset threshold value, the data are classified as abnormal and the algorithm terminates. Otherwise, the detection algorithm proceeds to check for the occurrence of a target attack. **Detection of Target Attacks:** The goal of target attacks is to make the outputs of the penultimate layer of the learning model be the same for the training data and testing data, respectively. In other words, the training dataset and target data appear to be the same to the ML model even though their resulting labels are actually different. That is, the ML model misclassifies the true label of the testing data point. Consequently, poisoned data points can be identified by comparing the outputs of the penultimate layer of the model for the testing data points with the outputs obtained for the training data points. As shown in Figure 5, the ML model is thus trained, as usual, using the training dataset, and the labels produced at the penultimate layer of the model are stored in a collision table. For each subsequent testing point, the output of the penultimate layer is compared with the entries in the collision table. If the output label is found to be very close to one of the values in the collision table, the data point is judged to be abnormal.
## 4 Proposed Lightweight Authentication Based on Multi-Factor Hash Chain with T-OTP in VMF
### Lightweight Authentication Processes Based on Hash Chain
#### 4.2.3 Re-Authentication and Revocation Phases  
This phase included security considerations for the initial operation of various VMF-based closed CNRs and the rapid additional authentication of new squad nodes based on an extension of secure communication and authentication sessions. In addition, it was necessary to maintain an authenticated connection between squad members in the command system who were conducting squad operations and refresh the new authentication status with legitimated squad members after a certain amount of operation time had elapsed. The related phases are shown in Figure 4.  
1. Identify the state: The current authentication state of each squad member node in VMF-based CNR networks and a tactical system is identified.
2. Check if a failure session occurs within this arbitrary time: The re-authentication variable-based military scenario in dynamic battlefields is recognized.
3. Whether a user has already achieved authentication within this arbitrary time is checked: The squad node authentication and re-authentication are executed in CNR networks for refreshing.
4. Setting of a time scheduler for clock synchronization: The re-authentication time scheduler for a configuration of the dynamic time stamp is set.
5. Re-configuration of time slot length: The slot length for clock synchronization is configured.
6. Phase execution: The re-authentication and execution of this overall phase is awaited.
7. Phase repetition: The authentication and verification phases with the re-authentication scheduler (if necessary) are repeated.  
During the re-authentication and revocation phases, a scenario-based theoretical scheme was proposed to improve the robustness of each operation phase, the responsiveness and speed of each squad, the robustness and agility of the authentication for rapidly changing battlefield environments, the maximization of allied nodes and minimization of potential enemy nodes in CNRs related to digital-covert activities, and the independence of seed hash re-initialization based on securing transmission integrity, lower network transaction, and the reception of tactical information.
# Recurrence Quantification Analysis (RQA) of Toroidal End Tool Milling Process
## 1 Introduction
The RQA indicators that demonstrated a relationship with surface roughness included ENTR, DET, LAM, and VMAX. An artificial neural network was utilized to predict the roughness, achieving an accuracy of over 90%. Similarly, recursive methods were applied to estimate surface quality after burnishing [36]. The surface quality parameter in this case was cross-sectional hardness. The study found that the highest cross-sectional hardness corresponded to the lowest values of DET, LAM, and RPDE, indicating that surface quality can be monitored through selected RQA indicators of the vibration signal. An application of recursive methods is the analysis of cutting tool wear. [PERSON] et al. explored the possibility of identifying cutting tool defects using RP and RQA indicators [37]. Their study demonstrated that both methods are suitable for detecting tool defects. Most of the RQA indicators analyzed showed a correlation with tool damage. Similar findings were reported by [PERSON] et al. [38], who investigated the wear of the flank during the milling process. They concluded that both the RP and RQA methods are effective for tool wear detection. Of the RQA indicators, RR, TT, LAM, and ENTR were found to be suitable to detect flank wear. In the case of RP, wear could be identified by observing specific color patterns corresponding to tool flank wear. Recursive methods are extensively used in the analysis of cutting processes, with key applications including tool wear detection, the evaluation of cutting process stability, and chatter detection. Depending on the specific application, various RQA indicators have been found to be useful. However, the relationship between RQA measures and cutting parameters has not been systematically investigated, despite its potential for analyzing the dynamics of the cutting process. To address this gap, we applied recursive analysis to study the toroidal milling process. The objective was to determine the influence of the cutting parameters on the RP and RQA measures. Functional relationships between cutting parameters and the RQA indicators were established, and a statistical analysis of these relationships was performed. The study identified which RQA measures are most relevant to analyzing the dynamics of the toroidal milling process. To the best of the authors' knowledge, there are no papers studying the dynamics of the toroidal end tool milling process by means of advanced recurrence techniques. To fill this gap, the current paper investigates the relevance of Response Surface Methodology (RSM) and RQA in evaluating the forces in flat surface toroidal tool milling. The most important indicators of the RQA adopted for the discussed process are identified. The results of classical statistical analysis are also given for comparison purposes.
## 2 Image Processing in Remote Sensing
### Change Detection
Some of the evaluation metrics for assessing the performance of image change detection include Overall Accuracy (OA) which is the ratio of the changed and unchanged pixels which are correctly detected, False Alarm (FA) which is the ratio of the unchanged pixels which are found as changed ones and Missed Alarms (MA) is the ratio of changed pixels which are identified as unchanged ones.
## 4 Simulation Environment
### Calculation of Real SINR and BLER  
During the radio resource scheduling stage (Section 4.3), the expected \(SINR_{i,r}\) is used, which is based on the past inter-cluster interference reported by the UEs. However, the interference highly depends on the group of UEs scheduled within a particular time slot. Thus, the reported value usually differs from the real interference observed at a given UE, as well as the nonlinear distortion pattern. Thus, the next step of the main simulation loop is to calculate the real SINR using (4) with full knowledge of the radio channel coefficients, simulated nonlinear distortion, and scaling coefficients. With the calculated real SINR\({}_{i,r}\) for every UE and RB, along with the \(\beta_{mcs}\) factor related to the selected MCS, one can calculate the real values of EESM\({}_{i,mcs}\). During the scheduling phase, it might happen that a particular UE wrongly estimated the inter-cluster interference, e.g., observing much higher real interference. This will influence the achieved rate. The real BLER can be calculated as follows:  
\[\text{BLER}_{i,mcs}=0.5\cdot\text{erfc}\left(\frac{\text{EESM}_{i,mcs}-b_{mcs}} {\sqrt{2}c_{mcs}}\right), \tag{12}\]  
where \(b_{mcs}\) and \(c_{mcs}\) are taken from Table 1 based on the MCS selected for the \(i\)-th UE.