esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 2 Related Research Work
### UAV Attack and Defense Security Analysis  
The research on UAV attack and protection mainly focuses on UAV communication and sensors. This paper mainly discusses the attack and protection of UAV communication.  
* WiFi cracking attack. Before turning off the UAV controller, Skylet uses aircraft-ng to detect nearby wireless cyber and clients. This task is carried out when connecting the attacker with the UAV, allowing for full control of the victim's UAV. This type of attack is divided into three stages [36]. In the first step, a wireless sniffing tool, such as airodump, is used to discover WEP-enabled and WPA-2-enabled cyber ranges, as well as an open cyber network. The second step is to use an injection tool, such as \"airplay\", to increase and modify the communication transmission traffic. The third step, \"aircraft-ng,\" allows an attacker to launch a counter-authentication attack that can interrupt a Wi-Fi connection protected by WPA2 encryption.
* Denial-of-service DoS attack. After authentication, the airdump-ng command is used to evaluate the cyber security of the UAV. The aircraft-ng command is then used to disconnect any connected devices, or the aircraft-ng command to disconnect any secure UAV. The use of Kali-Linux as a platform for WiFi interference causes the UAV to crash [37].
* ARP cache poisoning. A malicious script called Scapy is continuously executed through the computer using the Python library until the UAV disconnects from the connected device [38].
* To solve the intrusion problem of UAV, [PERSON] et al. [39] proposed to use ground stations as access points and WPA/WPA2 to encrypt WiFi links. [PERSON] [40] et al. built a security system model for the application scenarios of unmanned equipment such as UAV and unmanned vehicles, and designed a lightweight authentication protocol by using elliptic curve encryption algorithm. [PERSON] et al. [41] designed a certifiateless signature label key encapsulation protocol to realize key negotiation between UAV and other intelligent terminal mobile terminals, taking into account the identity anonymity and data privacy of UAV. The UAV monitoring system designed by [PERSON] [42] et al. uses the regression least squares method to achieve real-time prediction of fuselage and control parameters, and determines the security state of the UAV by analyzing the abnormal state of parameters. [PERSON] [43] et al. proposed the design of a UAV cyber security transmission mechanism based on cognitive radio, providing a new solution for UAV cyber security protection and fault recovery. [PERSON] [44] used Bayesian game theory to realize UAV cyber intrusion detection, effectively balancing the calculation cost of UAV cyber nodes in the intrusion detection process and the correlation between the detection accuracy rates.
###### Abstract  
Expected lives of mechanical parts and structures depend upon the environmental conditions, their dynamic behaviours and the task-oriented spectra of different loadings. This paper exploits contactless full-field _mobilities_, estimated by Scanner Laser Doppler Vibrometry (SLDV), in the real manufacturing, assembling and loading conditions of the thin plate tested, whose structural dynamics can be described in broad frequency bands, with no distorting inertia of sensors and no numerical models. The paper derives the _mobilities_ into full-field strain Frequency Response Functions (FRFs), which map, by selecting the proper _complex-valued_ broad frequency band excitation spectrum, the surface strains. From the latter, by means of the constitutive model, dynamic stress distributions are computed, to be exploited in fatigue spectral methods to map the expected life of the component, according to the selected tasks' spectra and the excitation locations. The results of this experiment-based approach are thoroughly commented in sight of non-destructive-testing, damage and failure prognosis, Structural Health Monitoring, manufacturing and maintenance actions.  
Keywords:structural health monitoring; failure prognosis; SLDV-based full-field FRFs; experiment-based receptances; structural dynamics; fatigue spectral methods; non-destructive testing +
Footnote †: journal: Applied sciences
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 3 Channel Estimation Schemes
### TRFI Estimation Scheme  
The TRFI scheme improves performance of the channel estimation by using the fact that adjacent subcarriers and symbols have high frequency and time correlation properties, respectively. First, the TRFI computes (1), (2) and (3) above. Channel estimate \(\hat{H}(m,k)\) is then employed to equalize the previous received symbol of frequency domain, \(Y(m-1,k)\), as  
\[\hat{T}^{\prime}(m-1,k)=\frac{Y(m-1,k)}{\hat{H}(m,k)}. \tag{7}\]  
Then, \(Y(m-1,k)\) is equalized by \(H(m-1,k)\), which is previous symbol's channel estimates.  
\[\hat{T}^{\prime\prime}(m-1,k)=\frac{Y(m-1,k)}{H(m-1,k)}. \tag{8}\]  
After that, \(\hat{T}^{\prime}(m-1,k)\) and \(\hat{T}^{\prime\prime}(m-1,k)\) are respectively demapped to \(\hat{X}^{\prime}(m-1,k)\) and \(\hat{X}^{\prime\prime}(m-1,k)\) according to the modulation scheme, for use in the time reliability test.  
In the time reliability test, if \(\hat{T}^{\prime}(m-1,k)\) and \(\hat{T}^{\prime\prime}(m-1,k)\) are demapped to the same symbol, the initial channel estimate \(\hat{H}(m,k)\) is determined to be the lastest channel estimate. Otherwise, frequency interpolation is performed using the channel estimates that have passed the time reliability test to obtain the most recent channel estimates.  
The TRFI scheme relies on the precision of the channel estimates that pass the time reliability test. This scheme performs well in high-SNR regions; however, it cannot exactly track the channel variations in low-SNR regions, which reduces the accuracy of the channel estimates; thus, the TRFI scheme perform poorly in low-SNR regions.
## 4 Discussion
### The Limitations of the Study  
This study, while comprehensive in its approach to analyzing the chemical constituents of RoEO over a year, has several limitations. Firstly, the study relied on GC/MS analysis, which, although highly accurate, might not detect all compounds present in RoEO, particularly those in very low concentrations. Additionally, the seasonal variations in environmental factors such as temperature, humidity, and soil conditions were not controlled or recorded, potentially influencing the chemical profiles of RoEO. The sampling process,despite being conducted monthly, might not capture rapid or short-term fluctuations in chemical composition, potentially missing transient changes.  
Another limitation is the geographical specificity of the study. RoEO samples were collected from a single region in Romania, and while this provides a detailed local profile, it might not be representative of RoEO from other regions with different climatic and soil conditions. The comparison with the Tunisian reference is valuable, but it also underscores the regional variability that could affect the generalizability of the findings.  
Furthermore, the study's focus on specific compounds means that other potentially bioactive but less studied compounds might have been overlooked. The correlations identified, while informative, do not establish causation. Additional biological assays and mechanistic studies would be needed to confirm the functional roles of these compounds in antimicrobial and antioxidant activities.
## 3 Materials and Methods  
### Materials  
Acetic acid (CH\({}_{3}\)COOH, \(\geq\)99.0%, Sigma-Aldrich, Poznan, Poland), \(\varepsilon\)-Caprolactone (2-Oxepanone, CL, 99.0%, Aldrich, Poznan, Poland), chitosan (CHT, low molecular weight, 75.0% deacetylated, Sigma-Aldrich, Poznan, Poland), diethylene glycol (DEG, \(\geq\)99.8%, Sigma-Aldrich, Poznan, Poland), dibutyltin dilaurate (DBDLSn, \(>\)96.0%, Sigma-Aldrich, Poznan, Poland), dichloromethane (DCM, CH\({}_{2}\)Cl\({}_{2}\), \(\geq\)99.8%, POCh, Gliwice, Poland), 1,6-diisocyanatohexane (hexamethylene diisocyanate, HDI, 98.0%, Aldrich, Poznan, Poland), poly(ethylene glycol) (PEG, \(M_{n}\) = 1000 g/mol, pure, Sigma-Aldrich, Poznan, Poland), poly(tetrahydrofuran) (PTHF) (Sigma-Aldrich, Poznan, Poland, \(M_{n}\) = 2000 g/mol), methanol (MeOH, Chempur, Warsaw, Poland), immobilized lipase B from _Candida Antarctica_ (CA) (Sigma-Aldrich, Poznan, Poland), N,N-dimethylformamide (DMF, anhydrous, 99.8%, Sigma-Aldrich, Poznan, Poland), acetone (ACS reagent, \(\geq\)99.5%, Sigma-Aldrich, Poznan, Poland), phosphate buffer solution (pH 7.40 \(\pm\) 0.05, 0.1 M, PBS, potassium dihydrogen phosphate/di-sodium hydrogen phosphate, 20.0 \({}^{\circ}\)C, Avantor Performance Materials, Gliwice, Poland), and potassium acetate buffer solution (100 mM, pH 5.5, 0.2 mM filtered, Avantor Performance Materials, Gliwice, Poland) were used as received.
## 3 Results
### Seasonal Dynamics of Environmental and Biological Controls
Given to the complicated interactions among these environmental factors, this study explored the potential path analysis to directly characterize ecosystem WUE variability in the dry regions of Northwest China. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site** & **Function** & **R\({}_{\rm g}\) (W/m\({}^{2}\))** & **T\({}_{\rm a}\) (\({}^{\circ}\)C)** & **T\({}_{\rm s}\) (\({}^{\circ}\)C)** & **VPD(h Pa)** & **P(mm)** & **EVI** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Arou \\ (grassland) \\ \end{tabular} } & GPP & 0.464 & 0.767 & 0.827 & 0.499 & 0.731 & 0.946 \\  & ET & 0.720 & 0.901 & 0.919 & 0.722 & 0.696 & 0.872 \\  & WUE & 0.423 & 0.794 & 0.853 & 0.475 & 0.802 & 0.960 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Daman \\ (cropland) \\ \end{tabular} } & GPP & 0.605 & 0.681 & 0.701 & 0.373 & 0.650 & 0.925 \\  & ET & 0.813 & 0.863 & 0.861 & 0.675 & 0.482 & 0.759 \\ \cline{1-1}  & WUE & 0.583 & 0.714 & 0.734 & 0.365 & 0.715 & 0.941 \\ \hline \hline \end{tabular} Note: Correlation is significant at the 0.01 level in all cases. R\({}_{\rm g}\), T\({}_{\rm a}\) and T\({}_{\rm s}\), VPD and P are observed at the flux tower sites. Time-series EVI data are derived from the MOD09A1 product.  
\end{table} 
Table 2: Pearson correlation analysis between ecosystem water use efficiency and the controlling environmental factors at the two flux tower sites used in this study.
###### Abstract  
This laboratory investigation was designed to test the influence of a novel bleaching formulation based on phthalamilidoperoxycapric acid (PAP) with additives on the surface and mechanical properties of nano- and micro-hybrid restorative composites. Twenty-four bar-shaped and twelve disk-shaped samples from each restorative composite were prepared. The samples from each restorative composite were randomly divided into two groups according to the treatment, i.e., experimental and control. The treated groups went through the bleaching process: by Opalessence Regular or novel PAP+containing gel (HiSmile(tm)). The treated group underwent a bleaching process for seven consecutive days (a 10-min session of bleaching application every day) before the properties were tested. A paired sample _t_-test was performed to compare the results between the experimental and the control groups. The level of significance was set at 0.05. The surface roughness of the nanohybrid composite using Opalessence Regular (_p_ < 0.05) was significant. Additionally, a significant difference in nanohardness and elastic modulus between the experimental and the control groups of the microhybrid composite using Opalessence Regular was observed: \(p\) = 0.041 and \(p\) = 0.023, respectively. While a marked difference in flexural strength was observed in the experimental and control groups using Opalessence Regular, Hismile(tm) bleaching gel caused a diminutive and insignificant reduction in tested properties (_p_ > 0.05). It was found that Opalessence Regular bleaching gel affects the surface and bulk properties of restorative composite materials, whereas the novel PAP+ formulation has no deleterious effect on either nano- or micro-hybrid restorative composites.  
bleaching gel; microhybrid composite; nanohybrid composite; mechanical properties; surface properties; phthalamilidoperoxycapric acid (PAP+); free radical +
Footnote †: journal: Applied Physics Letters  
1 Dental Health Department, College of Applied Medical Sciences, King Saud University,  
Riyadh 11451, Saudi Arabia 1
## 2 Materials and Methods
### Validation Datasets  
A two-part validation of the proposed wavelength selection and SO\({}_{2}\) estimation algorithm set was performed. The first part evaluated the algorithms' performance against a Monte Carlo validation dataset that was generated with CUDAMCML using the skin model described in Section 2.3. This simulation dataset, described in Section 2.2, enabled a direct evaluation of SO\({}_{2}\) prediction accuracy, since the true SO\({}_{2}\) value was known. Validation was performed both on the full 1 nm resolution simulation data to represent the ideal case described in Section 2.2, and on a version of the same data convolved with a 15 nm FWHM Gaussian filter to represent the convolved case.  
The second part of the validation process was performed on the in vivo dataset described in [25], which consists of reflectance data collected from 13 Asian volunteers (aged 24.3 \(\pm\) 2 years). A 9 W white light-emitting diode (LED) ([PERSON], Philips, Schiphol, The Netherlands) illuminated the right index finger of each volunteer from an 80 mm distance at an angle of 20\({}^{\circ}\) from normal. An optical fiber connected to a spectrometer (Ocean Optics USB4000) was placed 8 mm from the fingertip at 15\({}^{\circ}\) from normal and collected reflectance data at a spectral resolution of 0.2 nm in the wavelength range of 200-850 nm. These data were then converted from reflectance to absorbance, the 520-600 nm wavelength range was extracted, and the resolution was adjusted to 1 nm via cubic interpolation.
## 4 Experiment Setup and Performance Analysis  
In this section, we construct a maritime intelligent observation platform and conduct experiments to validate the performance of macrodiversity reception scheme in different scenarios. First, the experiment platform is introduced. Second, we analyze the performance of macrodiversity reception scheme using different number of antennas and the performance of diversity scheme using different antenna placement strategies separately in the park scenarios. Finally, experiments are carried out in the ocean scenarios to verify the transmission gain of the proposed macrodiversity reception scheme.
# Experimenting with Agent-Based Model Simulation Tools
## 4 ABM Tools Comparison
### Available Features
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \(\downarrow\) **Tool/Feature \(\rightarrow\)** & **Programming Language** & **GUI** & **VSL** & **Simulation Environment** & **Visualization** & **Snapshot and Checkpoint** & **Code** \\ \hline \hline
**ActressMAS** & **C\#** & ✓ & & Generic & & & [73] \\
**AgentPy** & Python & ✓ & & Grid, continuous, network & 2D & & [74] \\
**Agents.jl** & Julia & ✓ & & & Grid, continuous, network, & 2D, 3D & & [75] \\
**Care HPS** & C++ & & & & & & & **GIS** & & [43] \\
**Crmas** & VisualWorks & ✓ & ✓ & & & & & **Gerice** & 2D & ✓ & [76] \\
**CppyABM** & C++ and Python & & & & & Generic & 2D, 3D & & [77] \\
**EoLab** & C++ and TCL & ✓ & & & & Grid, continuous, network & 2D & ✓ & [78] \\
**EvoFlex** & C++ & ✓ & & & & Grid, continuous, network & 2D & & [79] \\
**FLAME** & C++ and XXML & & & & & Generic & & & [80] \\
**FLAME GPU** & C++ and Python & & & & & Generic & 3D & & [81] \\
**GAMA** & GAML & ✓ & ✓ & & Grid, continuous, network, & 2D, 3D & ✓ & & [82] \\
**Insight Maker** & JavaScript & ✓ & ✓ & & Grid, network & & & & [83] \\
**Jade** & Java & ✓ & & & Generic & & & ✓ & [84] \\
**JAS-mine** & Java & ✓ & & & & Grid & & & [85] \\
**kxABMaga** & Rust & ✓ & & & & Grid, continuous, network & 2D & & & [86] \\
**MADKIT** & Java & ✓ & & & & & & ✓ & [87] \\
**MASON** & Java & ✓ & & & & Grid, continuous, network, & & & **GIS with GeoMASON [57]** & 2D, 3D & ✓ & & [88] \\
**MASS** & Three versions available: Java, C++, CUDA & ✓ & ✓ & & Grid, network & 2D & & & [89] \\
**Mesa** & Python & ✓ & & & & Grid, continuous, network, & 2D, 3D with & & & [92] \\  & NetLogo, Python [93; 94] and & & & & & Grid, continuous, network, & 2D, 3D with & & & [92] \\  & R [95] with extension & ✓ & ✓ & ✓ & & Grid, continuous, network, & 2D, 3D with & & & [98] \\
**Pandora** & C++ and Python & ✓ & & & & Generic, GIS & & & ✓ & [99] \\
**Repast** & Three version available: C++, & & & & Grid, continuous, network, & & 2D, 3D & ✓ & & [100] \\
**RepastHPC** & XXML & & & & & Generic & 3D & ✓ & [101] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ABM tools based on their features.
## 1 Introduction
[PERSON] divided P300 EEG signals into several equal parts, and then used SVM to train corresponding classifiers for each part, which improved the accuracy of P300 EEG signals recognition [21]. [PERSON] proposed a Bayesian P300 recognition method to complete classification by determining the maximum regression target probability value. Although the accuracy of classification was improved, it took too much calculation time [22]. With the rapid development of deep learning, many scholars began to use convolutional neural network (CNN) to classify P300 EEG signals [23]. [PERSON] realized the use of convolutional neural networks in deep learning to recognize and classify P300 EEG signals. He used convolutional layers to separate the time and space domains of P300 EEG signals. The convolutional neural network is fast but very easy to overfit, which affects the accuracy of recognition [24]. [PERSON] used compact convolutional neural network (CNN) to classify four types of EEG data including P300 EEG signals, and the results shown that the compact CNN has the best classification effect on P300 EEG signals [25]. [PERSON] proposed to use deep belief network (DBN) to classify P300 EEG signals which were extracted from each channel, but only a few subjects have good recognition accuracy [26]. [PERSON] proposed a 3D recurrent convolution neural network (3 DRCNN) based on a recurrent convolution neural network (RCNN) to classify P300 EEG signals. It was processed into 3D-EEG signals, and then input them into the RCNN to achieve nice classification [27]. LIU improved the convolutional neural network on the basis of [PERSON]'s algorithm which was named BN3 algorithm, taking the batch normalization (BN) layer and the dropout (DP)layer to deepen the network layers and overcome the problem of overfitting. The BN3 algorithm achieved good results in classification, but still need to improve the recognition accuracy when the number of experiments are reduced [28]. In this paper, we combined PCA algorithms with new convolution neural network framework to complete the P300 signals classification and recognition (named PCA-CNN). Specifically, the PCA algorithm was used to reduce the dimension of the EEG signal, which not only reduced the calculation time, but also improved the signal-to-noise ratio of the data. The new convolution neural network framework improved single convolution kernel model of traditional convolutional neural network. It contacted multiple convolution kernels to classify P300 EEG signals in the convolution layer, which improved the recognition ability of the convolutional neural network. Two datasets (competition data and self-collected data) were used to verify the effectiveness of the algorithm. The results indicated that the proposed algorithm had a significant effect on the recognition accuracy of P300 EEG signals.
## 3 Thermography Data Collection and Preparation
### Synthetic Thermographic Image Dataset Generation  
The synthetic thermographic data were generated using temperature data obtained from the composite material heat transfer analysis, performed with the FEM in the transient thermal analysis module in Ansys 2021 R2.  
The purpose of generating synthetic thermographic data, substituting for real thermographic data, was to replicate the temperature variations due to differing heat transfer rates between the defect and non-defect regions in the actual specimens.  
The creation of the 3D models for analysis required the fabrication of 26 synthetic specimens, each matching the CFRP thickness, GFRP thickness, defect location, thickness, and size of the 26 real defect specimens. These models were referred to as synthetic specimens. Figure 4 presents the synthetic specimen (SD78-35) corresponding to the real specimen (RD78-35). The synthetic specimens were labeled by modifying the corresponding real specimen IDs, in which 'RD' was systematically replaced with 'SD' to maintain consistency  
Figure 3: Processing Procedure of Raw Thermographic Data into Real Thermographic Image Dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number of Images** & **Number of Thermographic Image Sequence** \\ \hline Non-Defect & 6638 & 22 \\ Defect & 7755 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Class-Wise Distribution of Real Thermographic Image Dataset.  
in identification. The material properties of the designed synthetic specimens were set as shown in Table 4. Furthermore, to simulate delamination, defects represented by Teflon tape in the real specimens were modeled as air gaps in the synthetic specimens.  
The mesh for analyzing the modeled synthetic specimens was constructed using a hex-dominant method, incorporating a combination of Quad and Tri elements, with an element size of 1 mm. The boundary conditions for the analysis, illustrated in Figure 4c, included setting the temperature of the lower heating element to 40 \({}^{\circ}\)C and applying a film convection coefficient of 0.004 W/m\({}^{2}\)\({}^{\circ}\)C to the side and top surfaces, as shown in Figure 4d. The coefficient was initially set to simulate natural convection, gradually increasing to 1 W/m\({}^{2}\)\({}^{\circ}\)C over time. For the interior of the defect, heat transfer was assumed to occur through a stagnant air environment and a very thin film, with a film convection coefficient of 0.004 W/m\({}^{2}\)\({}^{\circ}\)C applied under these conditions. The ambient temperature was set to 22 \({}^{\circ}\)C. The analysis was conducted using a time step of 0.1 s over a total duration of 360 s, with the temperature data from the top surface elements of the synthetic specimen recorded and output in matrix form.