esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Crack Detection in Plates Using Dynamic Response (DR)
### Forward Problem for the DR of the Cracked Plate
#### 3.1.2 Plate of Thickness (VT)
\begin{table}
\begin{tabular}{c c c c} \hline
**Author** & **Method** & **Type of Damage** & **Plate Shape** \\ \hline [PERSON] et al. 2020 [148] & Symplectic superposition method & Side crack & \\ [PERSON] et al. 2020 [149] & RRM & Side crack & \\ [PERSON] et al. 2023 [150] & Back-propagated artificial neural networks (ANN) & Central crack & \\ [PERSON] et al. 2009 [151] & Extended FEM & Central crack & RP \\ [PERSON] et al. 2015 [152] & Extended iso geometric approach (XIGA) and HSD & Central and edge crack & \\ [PERSON] et al. 2023 [153] & Symplicit superposition method (SSM) & Edge crack & \\ [PERSON] 2022 [154] & RRM & Central and edge crack & \\ \hline [PERSON] et al. 2004 [155] & Strain energy method & Surface crack & CP \\ \hline [PERSON] et al. 2006 [156] & Wavelet-based & Central defect & \\ [PERSON] et al. 2023 [157] & Higher-order XFEM & Edge and internal crack & SP \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of forward studies that discuss the detection of damage in UT plates. parameter. A plate with PVT was more nonlinear compared to LVT cases. Additionally, the presence of a crack further increased the nonlinearity of the plate. [PERSON] et al. (2023) [167] combined the FSDT and FE methods to study the vibrational properties of the RP with PVT and multi cracks. Their results showed that when a plate's crack length increases or the number of cracks grows, its stiffness decreases, leading to a lower free vibration frequency. Similarly, an increase in the sheet thickness ratio or the plate length ratio, which makes the plate thinner, also resulted in a reduction in the vibration frequency, as shown in Figure 4.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 7 Human Related Challenges and Trends
### Trust Calibration in Collaborative Robotics  
Effective human-robot collaboration hinges on maintaining appropriately calibrated trust, avoiding both over-reliance and under-utilization. Recent literature underscores the efficacy of systems where robots monitor and adapt to human trust in real time [39; 84; 139]. For instance, internal performance self-assessment mechanisms and confidence signaling strategies have demonstrated measurable improvements in both perceived trust and team coordination [140]. These enhancements occur without altering the robot's functional capabilities but by adjusting its behavioral transparency and responsiveness [141; 142].  
Trust calibration in human-robot collaboration relies on both subjective and objective metrics. Common subjective methods include Likert-scale surveys (e.g., perceived safety, reliability, and predictability) and structured instruments like the Trust in Automation Scale. Objective measures often involve physiological monitoring (heart rate variability, galvanic skin response, and gaze tracking) to detect stress or vigilance changes.  
Vision and AI systems can support trust calibration by modeling human states in real time--detecting signs of hesitation or disengagement via facial expressions, posture, or gaze aversion. Adaptive systems can then modulate robot behavior by slowing movements, increasing transparency (e.g., visual cues or explanations), or requesting confirmation before acting.  
Computer vision and AI integration are central to these systems: vision modules capture behavioral cues such as gaze aversion or hesitation, while AI algorithms determine when and how to intervene to preserve trust levels [143]. Trust repair is also emerging as a critical component, with evidence showing that timely explanations or apologies--especially when synchronized with human expectations--can restore trust after errors [144; 145]. Such mechanisms are particularly vital in high-stakes domains like healthcare and manufacturing, where a misalignment of trust can compromise safety and performance.
## 6 SAR Payload Performance Analysis
### Azimuth Ambiguity Ratio  
The difference between the Doppler frequency of the target echoes at certain angles and the main beam is an integral multiple of the PRF azimuth ambiguity resulting from Doppler frequency aliasing. In particular, the azimuth ambiguity is primarily caused by the finite sampling of the Doppler spectrum at an interval of PRF. Moreover, the ratio of the ambiguity to the expected signal is defined as the azimuth ambiguity-to-signal ratio (AASR or AAR) [2; 3; 4], expressed as [45]  
\[\text{AASR}=\frac{\sum_{\begin{subarray}{c}m\to M\\ m\
eq 0\end{subarray}}^{M}\int_{\frac{B_{d}}{2}}^{\frac{B_{d}}{2}}G_{t}\big{(}f_{d}+ m\!\cdot\!f_{p}\big{)}\,G_{r}\big{(}f_{d}+m\!\cdot\!f_{p}\big{)}\,df_{d}}{ \int_{-\frac{B_{d}}{2}}^{\frac{B_{d}}{2}}G_{t}(f_{d})\!\cdot\!G_{r}(f_{d})\,df_{ d}}, \tag{13}\]  
where \(G_{t}(f_{d})\) and \(G_{r}(f_{d})\) denote the azimuth antenna gain patterns (one-way) for Tx and Rx in the Doppler spectral domain, respectively; \(B_{d}\) represents the Doppler bandwidth; and \(M\) denotes the maximum azimuth ambiguity number (typically \(M=30\) for performance analysis).  
The AAR improves with the increasing PRF. Therefore, the RAR and AAR exhibited a trade-off relationship depending on the PRF selection. Thus, the PRF should be selected to satisfy the appropriate RAR and AAR. The antenna gains from the azimuth ambiguities in the PRFs of 3500 and 8400 Hz are presented in Figure 30a,b, respectively. Figure 31a,b imply that AAR is satisfied in all operating incidence angle intervals for the selected PRFs in the high-resolution and wide-swath modes, respectively.  
Figure 30: Azimuth ambiguities at PRFs of **(a)** 3500 and **(b)** 8400 Hz. Each color bar corresponds to the azimuth ambiguity at a specific azimuth angle. No overlap between azimuth ambiguities is observed for a PRF of 8400 Hz, which is larger than the Doppler bandwidth.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 6 Implementation Architecture  
Many architectures were proposed to manage VNFs in edge environments [39]. In particular, the European Telecommunications Standards Institute (ETSI) [40] proposed the open source NFV management and orchestration (OSM) that is a reference architecture for multi-access edge computing (MEC). The document describes an MEC system that enables MEC applications to run efficiently and seamlessly in a multi-access network. We consider SFCs as MEC applications.  
The architecture proposed in this study complies with [40]. Figure 3 shows a complete view of the physical devices and software components of the proposed architecture, with the MEC orchestrator in charge of defining in which edge node each VNF of an SFC must be placed.  
Leveraging the standard ETSI architecture, we change the component _VNF placement_ and propose a new component called _VNF mapping_. These components work within the MEC orchestrator module that already exists in the ETSI architecture. The _VNF mapping_ component is used to map the VNFs demanded by the requested SFC to already created VNF instances during the VNF placement. The VNF placement component will implement our proposed VNF placement heuristic described in Section 5.  
Figure 2: VNF sharing during phase 1 of the smart VNF placement algorithm.
# Modified Dual-Site Langmuir Adsorption Equilibrium Models from A GCMC Molecular Simulation
###### Abstract  
In the modern industrial separation process, the pressure swing adsorption technology is widely used to separate and purify gases due to its low energy consumption, low cost, convenience, reliability, and environmental benignity. The basic elements of the design and application of the pressure swing adsorption process are adsorption isotherms at different temperatures for adsorbents. The dual-site Langmuir (DSL) adsorption equilibrium model is the mostly used model; however, this model is based on the assumption that the adsorption energy on the surface of an adsorbent is uniform and remains unchanged. Here, a grand canonical Monte Carlo (GCMC) molecular simulation was used to calculate the CO\({}_{2}\) adsorption equilibrium on MIL-101 (Cr) at 298 K. MIL-101 (Cr) was chosen, as it has more a general pore structure with three different pores. The calculation results showed that the adsorption energies with different adsorption pressures fitted a normal distribution and the relationship of the average adsorption energies, E with pressures had a linear form described as: E = aP + c. With this relationship, the parameter b = k-exp(E/RT) in the DSL model was modified to b = k-exp((aP + c)/RT), and the modified DSL model (M-DSL) was used to correlate the adsorption equilibrium data on CO\({}_{2}\)-MIL-101 (Cr), C\({}_{2}\)H\({}_{4}\)-HHPAC, CH\({}_{4}\)-BPL, and CO\({}_{2}\)-H-Mordenite, showing better correlations than those of the DSL model. We also extended the parameter q\({}_{\rm m}\) in the M-DSL model with the equation q\({}_{\rm m}\) = k\({}_{1}\) + k\({}_{2}\)T to adsorption equilibrium data for different temperatures. The obtained model (M-TDSL) was checked with the abovementioned adsorption equilibrium systems. The fitting results also indicated that the M-TDSL model could be used to improve the correlation of adsorption equilibrium data for different temperatures. The linear relationship between the average adsorption energy and adsorption pressure could be further tested in other adsorption equilibrium models to determine its universality.  
GCMC molecular simulation; adsorption energy characteristics; adsorption equilibrium model; parameter fitting; model modification +
Footnote †: journal: Applied Soft. 2020, 10, 1311; doi:10.3390/app10041311
## 4 Evaluation Metrics
### Feature Similarity Index Measure  
Feature similarity index measure (_FSIM_) [24] is mainly used for quality assessment of feature similarity. _FSIM_ employs phase congruency (_PC_) and gradient magnitude (_GM_) features to complement each other. Based on the relative invariance of _PC_ to the image change, the stability features in the image are extracted. Conversely, _GM_ is mainly used for feature extraction when the image changes.  
It is assumed that the phase congruency of the raw image \(f_{1}\) and the denoised image \(f_{2}\) can be represented by \(PC_{1}\) and \(PC_{2}\), while the gradient features are represented by \(G_{1}\) and \(G_{2}\). The similarity between these two images is computed as:  
\[S_{PC}=\frac{2 PC_{1}PC_{2}+T_{1}}{PC_{1}^{2}+PC_{2}^{2}+T_{1}} \tag{21}\]  
Figure 3: Unsupervised Learning Model based on Improved Neighbor2 Neighbor.  
where \(T_{1}\) is a positive constant that can improve the stability of \(S_{PC}\).  
Similarly, the similarity of \(G_{1}\) and \(G_{2}\) can be computed:  
\[S_{G}=\frac{2G_{1}G_{2}+T_{2}}{G_{1}^{2}+G_{2}^{2}+T_{2}} \tag{22}\]  
where \(T_{2}\) is a positive invariable hinging on the actual scope of gradient magnitude values.  
The similarity \(S_{L}\) of \(f_{1}\) and \(f_{2}\) is calculated by \(S_{PC}\) and \(S_{G}\).  
\[S_{L}(x)=[S_{PC}(x)]^{\alpha}+[S_{G}(x)]^{\beta} \tag{23}\]  
The relative importance of PC and GM characteristics is modified by parameters \(\alpha\) and \(\beta\). In our research, for convenience, let us set \(\alpha=\beta=1\). The value of FSIM ranges from \([0,1]\).
# Randomized Feature and Bootstrapped Naive Bayes Classification
## 3 Proposed Methodology
### Comparison of RFB-NB with TAN, WNB, and RF  
It is important to acknowledge that the individual components of our proposed approaches--randomized feature selection and bootstrap aggregation--are established techniques widely used in ensemble learning frameworks. However, the novelty and contribution of the RF-NB and RFB-NB classifiers lie specifically in their integration of these ensemble strategies within the naive Bayes framework, which traditionally does not exploit such techniques. Unlike TAN [25], which explicitly models feature dependencies through a Bayesian network [26,27], or WNB, which assigns fixed feature weights, our methods implicitly handle feature interactions through randomization without additional structural complexity. Moreover, while inspired by random forests, our proposed methods maintain the probabilistic interpretable nature of NB rather than relying on decision trees, thus providing a distinct trade-off--an ensemble-enhanced naive Bayes classifier that achieves improved robustness and stability without sacrificing interpretability and computational simplicity.
## 1 Introduction
Additionally, it was found that an efficient equalization method could decrease the channel distortions caused by inter-symbol and inter-carrier interferences. [PERSON] et al. [13] analyzed the performance of the MIMO-FBMC system theoretically, and integrated the filter output truncation method into the analysis model. The proposed method increases the signal to interference ratio of each symbol, resulting in increased BERs; overhead and filter truncations were not required in the proposed method. [PERSON] et al. [14] demonstrated a two-tap MIMO maximum likelihood detection scheme using a two-dimensional ordering algorithm and used it to reduce multi-dimensional residual interference in the MIMO-FBMC system. The method enabled efficient cancellation of interferences in the system. [PERSON] et al. [15] proposed a Filtered Multitone (FMT) modulation underwater acoustic communications system, integrated with low-complexity channel-estimation-based minimum MSEs adaptive turbo equalization, (7,5) convolutional channel coding, adaptive binary phase shift keying (BPSK), and eight phase shift keying modulation. The BERs and data symbol error (DSE) rates of the proposed system are explored. [PERSON] et al. [16] proposed a \(2\times 2\) MIMO OFDM-based high speed underwater acoustic transmission system, with low density parity check (LDPC) channel coding,orthogonal matching pursuit (OMP) channel estimation, and minimum mean-squared error (MMSE) equalization methods integrated into the underwater acoustic transceiver. The BERs of the transceiver were studied. [PERSON] et al. [17] adopted the FBMC technology with a cost function to optimize the filterbank prototype filter in doubly dispersive underwater acoustic communication channels. Direct-mapping (DM) orthogonal variable spreading factor (OVSF)-based [18], MIMO-OFDM-based [19], Gold sequence (GS) MIMO OSVF/OFDM-based [20], and OVSF/OFDM-based [21] underwater acoustic transmission methods have also been studied. The DM-based MIMO-FBMC underwater acoustic transmission method was used to transmit audio [22] and data [23] signals. In this paper, a DM-based MIMO-FBMC underwater acoustic multimedia communication architecture (UAMCA) is presented, as are its applications in the transmission of multimedia signals. The DM-based MIMO-FBMC UAMCA method is proposed in Section 2 of this paper, while the simulation results are presented in Section 3, and the concluding remarks are presented in Section 4.
## 3 Trajectory Optimization Design
### Objective Function and Motion Constraints  
#### 3.2.1 Objective Function  
For the trajectory planning of the pickup manipulator, the objective function is constructed with the shortest picking time. Then the time of each trajectory curve is optimized in turn. The established objective function of the pickup manipulator is:  
\[T_{all}=\min\sum_{j=1}^{m-1}h_{j},j=1,2,3,\cdots,m-1, \tag{13}\]  
In the formula, \(T_{all}\) is the total time that the pickup manipulator runs from the initial position to the final position, and \(h_{j}\) is the time value obtained after decoding, \(m\) is the number of discrete path points that the pickup manipulator runs from the initial point to the picking target point.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
###### Abstract  
Frequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (\(<\) 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7-17.5%, for Peru 80% as compared to 48.6-65.7%.  
f 2020; 12, 727; doi:10.3390/rs12040727
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## The Proposed Protocol
### SRPL-RP Implementation
#### Rank and Version Number Attacks Mitigation  
For mitigation purposes, in the version number attack, if a node has malicious behavior, then the malicious version number will behave as a legitimate node by updating its version number to the same one as in the neighbor list table. With this technique, nodes are prevented from being the attacker. At every DIO reception, the table will be updated as in Algorithm A4, found in the appendix section. In the rank attack, we set the attack status in the monitoring table to restrict the malicious node from being a parent node in Algorithm A5 found in the appendix section, referred to in Figures 1 and 2 in Section 3. Hence, the mitigation mechanism occurs.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 5 Evaluation  
Table 1 presents a categorisation of the key generation algorithms employed by this study, mapped to their equivalent NIST Security Levels interpreted by the author from NIST SP 800-57 Part 1 Revision 5 [43]. The selection of algorithms provided coverage across all five NIST security level equivalents, encompassing established classic cryptography with RSA and Elliptic Curve Cryptography (ECC), and the emerging post-quantum cryptography standards. Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) as specified in FIPS 203 (Kyber) was the primary algorithm on test, with the key generation part of ML-DSA used for check-and-balance purposes.  
For clarity, common alternative names and relevant FIPS standards are included in brackets alongside the protocol identifier where applicable. Additionally, Table 1 provides the estimated equivalent symmetric key size in bits for each algorithm, offering a comparative measure of their current perceived security strength.
## 2 Methodology
### Bending Moment Measurement Using Strain Gauges  
The strain gauge method is a high-accuracy technique recommended by ISO International Standard for bending moment measurement [35]. The most common strain gauge wiring configuration is in the form of a Wheatstone bridge [36; 37; 38]. Depending on the  
Figure 1: The flowchart of research approach.  
measurement task, the bridge layout can be configurated as full-bridge, half-bridge or quarter-bridge. In this measurement, the half-bridge configuration was applied with two active strain gauges positioned on the same shaft cross-section at 180 degrees to each other, as described in Figure 2. This can compensate for the temperature effect and exclude the axial vibrations (only the bending moment is measured). The position of the strain gauge bridge is defined as the position of strain gauge No. 1.  
The bending moment of the shaft at the position of the strain gauge bridge (local bending moment) can be obtained from the bending strain as follows [27]:  
\[M_{\mathrm{s}}=\frac{E\varepsilon}{I}\frac{d}{2}\times 10^{-6} \tag{1}\]  
where:  
\(M_{\mathrm{s}}=\) local bending moment (kN\(\cdot\)m);  
\(\varepsilon=\) measured strain by strain gauge bridge (m/m);  
\(E=\) Young's Modulus (MPa);  
\(I=\) second moment of shaft cross-section (mm\({}^{4}\));  
\(d=\) diameter of the shaft cross-section (mm).  
In the case of the solid shaft:  
\[I=\frac{\pi d^{4}}{64} \tag{2}\]  
Therefore:  
\[M_{\mathrm{s}}=E\varepsilon\frac{\pi d^{3}}{32}\times 10^{-6} \tag{3}\]  
The local bending moment for a full revolution is depicted in Figure 3. Accordingly, the local bending moment at any angle can be determined by:  
\[M_{\mathrm{s}}=M_{b}cos(\theta-\lambda)+\overline{M_{\mathrm{s}}} \tag{4}\]  
where:  
\(M_{b}=\) nominal bending moment (kN\(\cdot\)m);  
\(\theta=\) angle of rotation related to vertical direction (degree);  
\(\lambda=\) angle between normal to neutral axis (NA) and vertical direction or bending angle (degree);  
\(\overline{M_{\mathrm{s}}}=\) mean value of local bending for one revolution (kN\(\cdot\)m).  
Figure 2: The Wheatstone half-bridge strain gauge configuration for bending moment measurement.  
Under the assumption that the propeller forces are the same for all propeller blades, the mean value of local bending, \(\overline{M_{s}}\), is constant with the variation in the shaft speed. It can be zeroed if the zero calibration is performed when the bridge is located at the horizontal position. To calculate the nominal bending moment, only the zero-to-peak value is needed:  
\[M_{b}=\frac{M_{s\ max}-M_{s\ min}}{2} \tag{5}\]  
The bending angle, \(\lambda\), is determined by the travel from the upper vertical to the point of maximum local bending moment. This technique is very simple and precise for the pure sinusoid or approximate sinusoid of the static bending moment.
## Appendix A Application of the FFT  
To construct the discrete Fourier transform item in the simulation, Equation (36) can be rewritten as  
\[\begin{array}{rl}x_{j}(p\Delta t)&=\text{Re}\Bigg{\{}2\sqrt{\Delta\omega}e^{ -i\Delta\omega p\Delta t}\sum\limits_{q=1}^{N_{\Phi}}q_{q}^{jj}(p\Delta t) \sum\limits_{l=0}^{M-1}B_{q}^{jl}e^{-i(l\Delta\omega)(p\Delta t)}\Bigg{\}}\\ p&=0,1,\cdots,M-1\end{array} \tag{10}\]  
where \(B_{q}^{jl}\) is given by  
\[B_{q}^{jl}=\Bigg{\{}\Phi_{q}^{jj}(l\Delta\omega+\Delta\omega)\sum\limits_{k= 1}^{j}\beta_{jk}(l\Delta\omega+\Delta\omega)e^{i[\theta_{jk}(l\Delta\omega+ \Delta\omega)-\phi\omega]}\hskip 14.226378 ptl=0,1,\cdots,N-1\\ 0&l=N,N+1,\cdots,M-1\end{array} \tag{11}\]  
The time and frequency increments have the following relationship, i.e.,  
\[\Delta\omega\Delta t=2\pi/M \tag{12}\]  
Substituting Equation (12) to Equation (10), one had  
\[\begin{array}{rl}x_{j}(p\Delta t)&=\text{Re}\Bigg{\{}2\sqrt{\Delta\omega}e^ {-i\Delta\omega p\Delta t}\sum\limits_{q=1}^{N_{\Phi}}q_{q}^{jj}(p\Delta t) \sum\limits_{l=0}^{M-1}B_{q}^{jl}e^{-ilp\frac{2\pi}{M}}\Bigg{\}}\\ p&=0,1,\cdots,M-1\end{array} \tag{13}\]  
Obviously, the FFT technique could be utilized for the summation with respect to \(l\) in Equation (13).  
To avoid aliasing, the following requirement should be satisfied, i.e.,  
\[\Delta t\leq 2\pi/(2\omega_{u}) \tag{14}\]  
Substituting Equation (12) to Equation (14), one had  
\[M\geq 2N \tag{15}\]