esa-sceva/satcom-chunk-collection huggingface dataset:
# Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation
###### Abstract  
This work developed a method that uses a single monochrome high-speed camera without sacrificing the spatial resolution to measure both in-plane and out-of-plane full-field vibrations. By using the high-speed camera and a two-dimensional digital image correlation (2D-DIC) algorithm, the method first extracts the out-of-plane displacement field from the measured virtual in-plane strains. Then it retrieves the in-plane displacement field after eliminating the out-of-plane motion-induced virtual component. For validation, in-plane and out-of-plane translation tests and single-frequency vibration experiments were carried out. The measurement results show good agreement with the reference values, indicating the effectiveness of the proposed high-speed 2D-DIC (HS-2D-DIC). Further, the natural frequencies and mode shapes of a rectangular cantilever panel were also measured successfully, exhibiting the method's effectiveness in practical applications. Since the HS-2D-DIC requires only a single monochrome camera, no complex optical setup, and no complicated calibration process, the method can be developed as a competitive tool for full-field vibration characterizations.  
vibration measurement; full-field measurement; two-dimensional digital image correlation; photogrammetry +
Footnote †: journal: Applied  
0000-0002-313-4257
# Rank-Based Ant System with Originality Reinforcement and Pheromone Smoothing
## 3 Extension of Rank-Based Ant System
### Algorithm
```
0: ACO parameters
1:\(\tau\leftarrow\) initialise pheromone trails
2:\(\
u\leftarrow\) initialise matrix \(\
u\)
3:while termination condition not met do
4:for\(k=1\) to \(m\)do\(\triangleright\) for every ant
5:for every step until the \(k\)-th ant has completed the tour do
6: select next node \(j\) according to the transition rule \(\triangleright\) Equation (1)
7:\(v_{i,j}=v_{i,j}+1\)
8: (if symmetric) \(v_{j,i}=v_{i,j}\)
9:endfor
10:endfor
11:\(s_{1:(w-1)}\gets EvaluateAndSelectBest(s_{1:m})\)\(\triangleright\) select \((w-1)\) best ant tours
12:\(s_{gb}\gets SelectBest(s_{1},s_{gb})\)\(\triangleright\) update global-best solution
13:\(r_{o}\leftarrow EvaluateOrigality(s_{1:(w-1)},v)\)\(\triangleright\) evaluate originality, Equation (9)
14:\(\tau_{i,j}=(1-\rho)\tau_{i,j}+\sum_{\tau=1}^{w-1}(w-r_{o})\bigtriangleup\tau_{i,j}^ {r}+w\bigtriangleup\tau_{i,j}^{gb}\)\(\triangleright\) Update pheromones
15: (if symmetric) \(\tau_{i,j}=\tau_{i,j}\)
16:if approach stagnation then
17: pheromone smoothing \(\triangleright\) Equation (10)
18:endif
19:endwhile
```  
**Algorithm 2** AS\({}_{Rank}\) with originality reinforcement and pheromone smoothing  
Furthermore, at the end of each algorithm iteration, it is checked whether the algorithm is entering a stagnation situation (line 16). The criterion considered for triggering the pheromone smoothing strategy described by Equation (10) is when the standard deviation of the fitness of the 80% of ants tours of the iteration is zero. Hence, the pheromone smoothing is applied before the algorithm enters stagnation. Finally, once the termination condition is met (maximum number of iterations) the algorithm returns the global best tour. Furthermore, as in symmetric TSP instances, it is assumed that \(\tau_{i,j}=\tau_{j,i}\)\(\
u(i,j)\); ACO algorithms ensure this by updating the symmetric pheromone element (line 15). Similarly, in the case of symmetric TSP, \(\
u_{i,j}\) counts the number of ants that have traversed either \(arc(i,j)\) or \(arc(j,i)\) (line 8).
# Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide
## 3 Results and Discussion
### Surface Analysis
The presence of sulfide at a concentration of only 10 ppm leads to the formation of pits with a depth of about 90 \(\upmu\)m and a diameter of up to 200 \(\upmu\)m, which is almost twice as large as the dimensions without S\({}^{2-}\) ions. When the sulfide ion concentration is further increased, the size of the pits does not change, but their number increases slightly. The deterioration of corrosion resistance of stainless steel exposed to polluted seawater can be explained by the mechanism described in the literature [13, 21, 71, 72]. The proposed mechanism involves the adsorption of S\({}^{2-}\) ions on the surface, which prevent the growth of the passivation film and increase its defectiveness. In this way, the adsorbed sulfide interacts with cations in the passive film on the surface and leads to the formation of sulfur-containing second phases, which results in an increase in the defect concentration of this layer (VM\({}^{\prime\prime}\)) and, consequently, its ionic conductivity and ionic current density through the oxide [14, 21, 71, 72]:  
\[\mathrm{M}_{\mathrm{M}}^{\prime\prime}+\mathrm{HS}_{\mathrm{ads}}^{-}\to \mathrm{MHS}_{\mathrm{ads}}^{+}+\mathrm{V}_{\mathrm{M}}^{\prime\prime} \tag{5}\]  
\[\mathrm{MHS}_{\mathrm{ads}}^{+}\to\mathrm{MS}_{\mathrm{1-x}}+\mathrm{xHS}^{-} +(1-\mathrm{x})\mathrm{H}^{+} \tag{6}\]  
Figure 13: Topographic view of the segment of the corroded AISI 304L surface after potentiodynamic polarization measurement in seawater at (**a**) 288 K, (**b**) 298 K, and (**c**) 308 K. The depth and width of pits are determined along the red lines. Moreover, the reaction described above leads to local acidification and, thus, to an increase in the dissolution rate of the passivation layer. The local acidification is so efficient that the primary passive film is dissolved and active sulfide-mediated dissolution of the metal occurs, as has been proposed for acidic solutions containing H\({}_{2}\)S [14,21,73,74]:  
\[\mathrm{M+HS^{-}\to MHS_{ads}+e^{-}} \tag{7}\]  
\[\mathrm{MHS_{ads}\to MHS_{ads}^{+}+e^{-}} \tag{8}\]  
\[\mathrm{MHS_{ads}^{+}\to M_{aq}^{2+}+HS^{-}} \tag{9}\]
## 4 Results
### Detection of Eruptive Deposits (SWIR, DInSAR, SAR)
#### 4.2.2 Using SAR Intensity  
The interferometric coherence is hardly exploitable in regions with very dense vegetation, or where the surface is likely to change rapidly due to various environmental factors (e.g., snow or sand). In such context, changes in the intensity of SAR images can help identify eruptive deposits. Figure 7 illustrates this, taking as example the 2018 eruption of Fuego (Guatemala) which killed over 200 persons due to pyroclastic flows [5]. The interferometric coherence image (Figure 7c) computed between SAR image 1 and 2, respectively acquired before and after the eruption, is entirely incoherent and therefore unusable. However, computing the log ratio between the two intensity images (Figure 7a) reveals substantial changes: areas in blue are those where the intensity has decreased, whereas areas in red are those where the intensity has increased. The latter are mainly confined in the valleys, and likely  
Figure 6: Example of lava flow emplacement detection at Erta Ale (Ethiopia) using both S1 interferometric coherence and S2 SWIR analysis. **(a)** Decorrelation area related to volcanic activity, calculated as the number of pixels where coherence \(<\)0.5, multiplied by the pixel area (\(14\times 14\) m). Black curve: decorrelation on \(2\times 2\) km mask centered at the summit to image the lava lake activity. Blue curve: decorrelation on \(50\times 50\) km mask to image the lava flow emplacement. Because on such a large scale the coherence map also includes sandy areas where coherence is \(<\)0.5, these regions where manually excluded for this specific figure so that the curve only depicts the area of the active lava flow. **(b)** Number of hot pixels (\(\times 10^{6}\)) detected in the S2 SWIR image (\(50\times 50\) km mask). **(c)** Deformation score DEF computed from the deformation map (unwrapped interferogram). Coherence maps (top image row) and SWIR images (bottom image row) are displayed at selected dates, with spatial scales varying according to the eruptive phase: (i) pre-eruptive phase with intense activity at the summit lava lakes, \(2\times 2\) km mask; (ii) eruptive onset, manifested by surface deformation and aperture of a new eruptive vent, \(\sim\)\(12\times 12\) km mask; (iii) early stages of the effusive activity (highlighted by a gray box), showing the emplacement of lava flows on both NE and SW flanks of the volcano, \(\sim\)\(12\times 12\) km mask; (iv) advanced stages of the effusive activity, when the lava flow front reaches its maximum distance from the vent, \(11.5\times 19.5\) km mask. The red box displayed in the coherence images refer to the \(2\times 2\) km mask centered on the active lava lakes.  
correspond to the rougher block-and-ash deposit [24]. Conversely, areas where intensity decreases are concentrated around the summit vent, which could be associated to the deposition of ash.
## 2 Methods
### Density-Based Spatial Clustering of Applications with Noise  
In practical engineering, sensor monitoring is usually subject to harmful factors such as harsh environments or network transmission errors, resulting in abnormal values of equipment in data information. The density-based spatial clustering of applications with the noise (DBSCAN) method is introduced to reduce these random errors and eliminate device outliers, which has been proven to be capable of handling large database [28; 29; 30]. The DBSCAN is a density-based clustering algorithm. It assigns any point to a specified radius area and calculates the total number of points in the area to obtain the density of the specified point. When the density is higher than the preset maximum point set threshold, these points will be constructed into clusters. Based on this concept, a huge feature space is grouped into multiple regions with high density, so as to achieve the purpose of screening out random outliers of the monitoring equipment. We choose the DBSCAN method because it has the ability to divide clusters of arbitrary shapes in the noise spatial database, such as linear, elliptical shapes, etc. The detailed process of DBSCAN is described in Algorithm 1, where \(n\) and \(m\) are the number of samples and clusters, respectively. \(k\) represents any observed data in the original data set. \(k_{i}\) represents the \(i\)-th observed data in the original dataset. \(x\) represents the number of observed data existing in the radius of \(k_{i}\). \(N\) represents the set of all observed points in each cluster. \(y\) represents the number of observed data existing in the radius of \(k_{i}\).  
```
Inputs: Dataset \(D^{(n\times 1)}\), radius \(\varepsilon\), domain density threshold MinPts Outputs: Density-based cluster set \(D_{C}{}^{(m\times 1)}\)  Marking \(k\) in \(D^{(n\times 1)}\) as UNVISITED, \(k\in[1,n]\) For\(k_{i}\)in \(D^{(n\times 1)}\): If\(k_{i}\) is UNVISITED  Marking \(k_{i}\) as VISITED If\(\infty\)= MinPts  Create a new cluster \(C\) and add \(k_{i}\) to C to form a list set \(N\) For each point \(k_{i}\)' in \(N\) If\(k_{i}\)' is UNVISITED  Marking \(k_{i}\)' is VISITED If\(y\)= MinPts and each one in y \(\
otin\) any cluster  Adding them into C End if End if End for Else  Marking \(k_{i}\) as outlier End if End if End for End for
```  
**Algorithm 1** Equipment outlier filtering
## 5 Real-Time Evaluation of HRI with BCI  
As mentioned above, the optimal level of these psychological effects in interaction often varies depending on specific HRI scenarios. Take the motor priming effect as an example. Motor priming can be used to facilitate the execution of desired movements for human actors. As a preliminary application of robotic motor priming, in a clinical trial [53], adding a robotic device to prime the required movements has been shown to facilitate rehabilitation compared to a task-oriented approach alone. However, sometime this priming effect can also be detrimental. For example, for the robotic \"nurses\" designed to hand surgical tools to a surgeon, if the velocity of their movement is too fast, the surgeon might be primed by this action, hence endangering the safety of the patient [42]. Therefore, it is of vital importance to measure these critical psychological effects in HRI in a real-time and quantitative way and find their optimal level.  
A promising technique that bears such potential is the brain-computer interface (BCI; [54]). Compared to traditional HRI evaluation methods such as questionnaires applied during or after the interaction, BCI can provide a non-disruptive and real-time evaluation of implicit cognitive processes throughout the interaction [55]. Specifically, due to its non-invasive and portable characteristics, electroencephalography (EEG)-based BCI has become the most popular form among other neural recording modalities [56]. Endeavors have been made using EEG-based BCI to evaluate interactive systems (e.g., [57]). In these applications, the measurement of human mental states such as workload, attention, and emotions has been shown to benefit a lot from the EEG technique [55]. We propose that the psychological effect of robotic agents on humans in HRI could also be quantified with BCI.
## 2 Materials and Methods
### MM/G(P)BSA Calculations  
The enthalpy contributions to the free energies of binding of the different ligands (estradiol, progesterone, cholesterol or SHH-N-cholesterol) with AR, PR or ER\(\alpha\) were calculated by using the MM/PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) [46] approach implemented in the AMBER 20 program. Calculations were performed for the protein with dielectric constant 2.0 immersed in the solvent with dielectric constant 80. The ion concentration was 0.1 M. The polar component of the enthalpy of solvation was calculated by applying the Poisson-Boltzmann method and the nonpolar component was determined by \(\Delta H_{\text{nonpol}}=\gamma SASA+\beta\), where the surface area accessible by the solvent (SASA) was calculated using the MolSurf program [47]. The surface tension \(\gamma\) and the offset \(\beta\) were set to the standard values of 0.0378 kcal (mol A\({}^{2}\))\({}^{-1}\) and 0.5692 kcal/mol, respectively.  
To elucidate the differences in binding energies and identify the residues critical for protein-protein and protein-ligand binding, the free energy of binding was decomposed into the contribution of each residue by using the MM/GBSA (Molecular Mechanical Generalized Born Surface Area) approach implemented in the AMBER 20 software. Residue-based MM/GBSA free energy analysis was performed to highlight the residues relevant to the stabilization of the protein-ligand and protein-protein complexes. Calculations were performed for the enzyme with a relative permittivity of 1.0 immersed in a solvent with a relative permittivity of 80.0.  
The MM/PBSA binding energy calculations were performed on the different time intervals obtained throughout the MD trajectory, considering structures sampled every 40 ps. The per-residue based MM/GBSA calculations were performed on the (last) 0.5 \(\upmu\)s-long time intervals obtained from MD simulations, except for ER\(\alpha\)-SHH-N-cholesterol complex, where the last 600 ns (from 1.4 th till 2 nd \(\upmu\)s) were considered. Herein, structures sampled every 200 ps were analyzed. We calculated the contribution of enthalpy to binding free energy, since conformational entropy is usually neglected due to its high computational cost when only the relative binding free energies of similar ligands are needed.
# Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study
## 3 Methods
### Emognition Dataset  
The Emognition dataset [63] encompasses data from 43 subjects aged between 19 and 29, including 21 females, who were exposed to emotionally stimulating film clips specifically designed to evoke nine distinct emotions (Table 1). The short film clips were chosen from databases with established reliability and validity for eliciting targeted emotions. The duration of each clip is typically short, often ranging from a few seconds to a few minutes, to maintain participant engagement and ensure a focused emotional response. These selections were made based on prior research indicating their effectiveness in evoking specific emotional responses. Facial features were automatically extracted using the OpenFace toolkit [64] (version 2.2.0, default parameters) and Quantum Sense software (Research Edition 2017, Quantum CX, Poland). The OpenFace library provides essential facial landmark points and Action Units' values while the Quantum Sense software identifies fundamental emotions including neutral, anger, disgust, happiness, sadness, surprise (Table 1), and head pose. The ground truth for emotions was determined through participant self-selection from a pool of nine emotions: amusement, anger, awe, disgust, enthusiasm, fear, liking, sadness, and surprise, alongside assessments of valence, arousal, and motivation.  
Figure 1: Personalized vs. generic model workflow. In addition to training a traditional one-size-fits-all model, we propose the development of a single model per individual. While we evaluate this procedure for affective computing, this paradigm can be applied to precision health more broadly.  
We initially considered all 43 subjects from the Emognition dataset. However, a critical challenge we faced was the uneven distribution and scarcity of emotion labels across different subjects. A significant number of subjects predominantly displayed neutral expressions, leading to an inadequate representation of diverse emotions necessary even for binary classification. To address this, we established specific selection criteria, prioritizing subjects with a balanced and sufficient number of emotion labels. This process led us to focus on 10 subjects whose data not only met our criteria for label balance but also offered a fair representation of at least two distinct emotions. This decision was pivotal in ensuring the effectiveness and accuracy of our emotion recognition analysis, albeit at the cost of reducing our sample size. The selection of these 10 subjects was imperative for maintaining the integrity of our classification task, as it provided a more reliable and representative dataset for recognizing and differentiating emotional states. Because the \(\geq\)2 emotions available in the dataset varied across these 10 subjects, we performed different classification tasks based on the label population for each emotion, ranging from binary classification to the classification of all six emotion labels.
## 4 Simulation Setup and Parameters  
### Channel and Interference Modelling  
The simulation environment replicates a dense urban environment, where the radio channel is characterised by both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation. Each user's received signal is modelled as a combination of a dominant LoS path, multiple reflected multipath components, and Rayleigh fading to capture small-scale variations. Multipath arrivals are simulated with random angular spreads and delays, emulating reflections from buildings and obstacles typical of city environments. Additive white Gaussian noise (AWGN) is present at each antenna element, and a configurablesignal-to-noise ratio (SNR) range between 0 and 30 dB allows for stress-testing under both favourable and adverse channel conditions. Interference is explicitly modelled by injecting additional signal sources at fixed or time-varying angles, permitting the evaluation of spatial suppression and inter-user isolation by the beamforming subsystem. Doppler effects, dynamically calculated according to user velocity and trajectory, further enhance realism by introducing frequency shifts and rapid channel variation.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## ZigBee (IEEE 802.15.4)
### Wi-Fi (IEEE 802.11)  
Wi-Fi technology, based on the IEEE 802.11 family of standards, is a wireless networking technique that is being widely used for Internet access. It can also be a good choice in the context of smart grid, which enables consumers to monitor the improvement their energy use [93]. Wi-Fi solutions are already being utilized in a number of devices that contribute to the so-called smart home. For instance Wi-Fi is used in thermostats, appliances, and new smart energy home devices that will connect them all together to help consumers manage their own energy consumption [93; 94; 95].
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 5 Performance Analysis
### Total Transmission Time  
The simulation results of the total transmission time vs. the UE moving speed, the cache size, and CDF (cumulative distribution function) are shown in Figure 12a-c, respectively. As UE moving speed increases, the total transmission time reduces because that the handover frequency increases. The more number of handover requests is, the higher handover latency time will be. As shown in Figure 12a, the total transmission time can be reduced if the more packets can be discovered in the cache. For instance, Figure 12a shows that the total transmission time is 23 s using _PMIPv6 with FMC_. However, the total transmission time of our proposed scheme with FMCL are 22.6 s, 22 s, 20 s and 18 s if the cache hit rate is 0, 10%, 40%, and 70%, with the UE moving speed fixed at 5 m/s, respectively. Figure 12a shows that the total transmission time of PMIPv6 with FMC was \(>\) that of our proposed scheme with FMCL (the cache hit rate = 0%) was \(>\) that of our proposed scheme with FMCL (the cache hit rate = 10%) was \(>\) our proposed scheme with FMCL (the cache hit rate = 40%) was \(>\) our proposed scheme with FMCL (the cache hit rate = 70%) was, under the different UE moving speed. Figure 12b shows the total transmission time vs. the different size of the cache (is ranging from 100 M to 200 M). The higher the size of the cache is, the lower the total transmission time will be. This proposed scheme aims at developing a pre-transmission method to increase the number of packets in cache. For instance, Figure 12b shows that the total transmission time is 22.5 s using PMIPv6 with FMC if the cache size is 150 (M). However, the total transmission time of our proposed scheme with FMCL are 22 s, 21 s,  
Figure 11: Example of the time and space costs of **(a)** proxy MIPv6-based FMC, and our proposal scheme with FMCL if **(b)**\(\alpha=\beta=100\%\), and **(c)**\(\alpha=10\%\) and \(\beta=20\%\).  
18.5 s and 15.6 s if the cache hit rate is 0, 10%, 40%, and 70%, if the cache size is 150 (M). Similarly, Figure 12b shows that the total transmission time of PMIPv6 with FMC was \(>\) that of our proposed scheme with FMCL (the cache hit rate = 0%) was \(>\) that of our proposed scheme with FMCL (the cache hit rate = 10%) was \(>\) our proposed scheme with FMCL (the cache hit rate = 40%) was \(>\) our proposed scheme with FMCL (the cache hit rate = 70%) was, under the different cache size. In addition, Figure 12c offers the result of CDFvs. the total transmission time.  
Figure 12: _Cont._