esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Predicting Reservoir Characteristic Parameters Using the Rock Physics Model  
Well A in the research area was selected to further verify the validity of the established rock physics model. Its reservoir characteristic parameters, such as S-wave velocity (Vs), pore aspect ratio and fracture density, were calculated from conventional logging data, including acoustic velocity, density, porosity, water saturation and mineral composition content curves, using the established rock physics model [50].  
As shown in Figure 9, although all the rock physics models provide reasonable estimates of Vs, a better result of Vs prediction was produced by the established rock physics model, and the estimated results were in good agreement with the true Vs Data, with the relative error being less than 10%. Compared with the traditional Xu-White model for the prediction of Vs, the improved rock physics model gives a satisfactory result for velocity prediction.  
Figure 8: The shear modulus of dry rock samples estimated from the rock physics model. **(a)** The comparison between measured values (circle) and predicted values (star) drawn from the Xu–White model. **(b)** Comparison between measured values (circle) and predicted values (star) drawn from the improved rock physics model.  
Next, we analyzed the results of estimated pore aspect ratio and fracture density utilizing the imaging log and the reservoir lithofacies data. As shown in Figure 10, the red rectangular regions with high pore aspect ratio and fracture density correspond withthe dissolution-dominated and fracture developing zone, which is the position of favorable reservoirs. The change trend of the pore aspect ratio and fracture density conformed to the logging response characteristics of favorable lithofacies, which can effectively indicate the location of high quality oil-gas reservoirs.  
Figure 10: The comparison between the estimated pore aspect ratio and fracture density by rock physics model and logging interpretation. The high values of fracture density and aspect ratio curve indicate fracture developing zones and dissolution-dominated areas, which correspond to the position of favorable lithofacies.  
Figure 9: S-wave velocity (Vs) estimated from the rock physics model. (**a**) Comparison of real velocities (blue) and estimated velocities (red) based on the Xu–White model. (**b**) The relative error of Vs in (**a**). (**c**) Comparison of real velocities (blue) and estimated velocities (red) based on the improved rock physics model. (**d**) The relative error of Vs in (**c**).
# Telepresence Social Robotics towards Co-Presence: A Review
## 2 Co-Presence Taxonomy
### Immersive Qualities
#### 2.1.2 Visual Representation
This suggeststhat avatars can be used as a tool to assess social presence, with the advantage that they enable behavior cue segmentation. Another interesting fact is that users tend to direct their heads to their partner's image but their gazes towards the workspace. A justification for this behavior, even knowing that it is a computer representation of the partners, might be related to the human's unconscious social etiquette, that being to keep the face directed to the interaction partner. Studies show that behavioral realism tends to contribute consistently and positively to social presence [28; 30], while photographic and anthropomorphic realism presents varying effects (positive [56], neutral [50; 57], or even negative contributions [17]). The justification for these discrepancies might be related to several facts reported in the literature: (1) photographic realism is not the main contributor to social presence (i.e., the appearance of the visual representation has a secondary role in comparison with cues from social behavior) [58], (2) manipulations of small features of the visual representation may not be reflected in social presence questionnaires, and (3) the degrees of behavioral realism differ from study to study, making a quantitative comparison with photographic realism vary [59]. In [60; 61], the researchers evaluated the effect of visual and behavioral realism on the perceived quality of communication using avatars. They found a positive effect on social presence when there was consistency between both realism components [60]; that is, although visual representation does not represent a major contribution, the participants felt a greater social presence when the avatar, demonstrating more realistic behaviors (e.g., inferred vs. random eye gaze) which were complemented by a higher level of photographic realism (avatar with a human-like face instead of a dummy face). [PERSON] [61] pointed out the consistency between photographic and behavioral realism as a positive social presence predictor. In [62], the effect of the 3D avatar type (character-like vs. realistically reconstructed) on users' trust and co-presence in a mixed reality-based collaborative teleconference was explored. Visual representation based on realistically reconstructed avatars has been shown to elicit the user's sense of co-presence. In [30], virtual humans that demonstrated higher responsiveness to events (behavioral realism) contributed positively to the co-presence in mixed reality environments. Experimental conditions involved remote collaboration, in-person collaboration, and communication interactions via mixed reality, augmented reality, virtual reality, video chat, text apps, and virtual assistants.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.5 Binary Payload Support  
The support for data transmission of binary payloads is particularly relevant for Industrial IoT scenarios, given the distances to be covered in remote regions, or when addressing large-scale grids (e.g., water pipes). Payloads need to be as compact as possible to minimise battery consumption, and most available protocols support a binary format. All approaches (with the exception of HTTP) already support binary payloads.
## 2 Materials and Methods
### Methods
#### 2.3.2 Random Grid Search Algorithm (RGSA)  
Hyperparameter tuning is a critical step in developing ML models. Manual tuning can be time-consuming and labor-intensive due to the complex interrelationships be-tween hyperparameters. An automated hyperparameter tuning algorithm, namely RGSA, was developed to address this optimization challenge. The RGSA methodology effectively optimizes the hyperparameters of RF and XGB models, thereby enhancing their predictive performance. Unlike Bayesian optimization [45] which relies on probabilistic surrogate models, RGSA employs a two-stage approach combining random and grid search [23; 46] strategies with lower computational overhead, while incorporating a unique stability evaluation mechanism through the PSI (population stability index) index [47]. The detailed workflow and implementation framework of RGSA are illustrated in Figure 2.  
Figure 2: Schematic workflow of the Random Grid Search Algorithm (RGSA), illustrating the iterative process from hyperparameter space initialization through random sampling with cross-validation to fine-grained grid search, culminating in optimal parameter selection after stability verification.  
1. Initialize the upper limit (\(param_{max}\)), lower limit (\(param_{min}\)) and coarse step size (\(step_{rough}\)) for the parameters to be adjusted.
2. Use random search within the upper and lower limits for \(step_{rough}\), and calculate the average accuracy of each point using ten-fold cross-validation.
3. Determine the coarse optimal point (\(param_{rough}\)) and obtain the range \(g_{range}\in[param_{max}-param_{min},param_{max}+param_{min}]\).
4. Use grid search within the \(g_{range}\) to determine \(param_{best}\).
5. Evaluate the model's stability using the \(PSI\) index (Equation (1)). If \(PSI<0.1\), the model is considered stable; otherwise, return to step 1 and reinitialize the parameters. \[PSI=\sum\biggl{[}\left(y^{i}-x^{i}\right)\times\ln\left(\frac{y^{i}}{x^{i}} \right)\biggr{]}\] (1) where \(x^{i}\) is the \(i\)-th model's estimated GPP, and \(y^{i}\) is the \(i\)-th flux tower's measured GPP.
## 4 Simulation
### Comparison of Motor Power Distribution before and after  
#### 4.4.1 Efficiency Comparison of a Single Permanent Magnet Motor  
The curve of the power efficiency of the permanent magnet motor with the load torque in the simulation model of this paper is shown in Figure 21.  
It can be seen from the figure that the number of motors is switched from 6 to 3, the rated power of the permanent magnet motor is 20.9 kW, and the output power of the single motor before power distribution is as follows:  
\[P_{2}=\frac{T_{e}n}{9549}=2100\times 0.5\times 80/9549=8.797\,\mathrm{kW} \tag{24}\]  
The ratio of a single motor to the total power load is \(P_{2}/P_{\mathrm{N}}\)\(=0.421\), the output efficiency is 94.41%, the average input power of a single motor is 9.318 kW, and the input power of the whole belt conveyor is 55.907 kW.  
Figure 20: Three-dimensional fluctuation diagram of conveyor belt speed and tension. (**a**) Speed fluctuation. (**b**) Tension fluctuation.  
After power distribution, the output power of a single motor is as follows:  
\[P_{2}=\frac{T_{e}n}{9549}=4210\times 0.5\times 80/9549=17.635\,\mathrm{kW} \tag{25}\]  
The ratio of the total power load is \(P_{2}/\mathrm{P_{N}}\)\(=0.872\), the output efficiency is about 95.89%, the average single motor input power is 9.391 kW, and the input power of the whole belt conveyor is 54.010 kW. Before and after the power redistribution, the specific parameters are shown in Table 2.  
Although the absolute value of power reduction may appear modest, considering the reduction in the number of motors and the significant increase in load rate, these improvements will result in considerable energy savings over long-term operation. The enhancement in system efficiency indicates that our proposed method optimizes energy utilization.  
After the switch, the motor output efficiency improved by 1.48%, with each motor saving approximately 0.184 kW in energy consumption. The total power savings for the conveyor belt amounts to 1.1 kW. For a 3 km long conveyor system with 100 motors, operating 4 h a day under lower load conditions, and working 200 days a year, the annual energy savings would be approximately 14,720 kW-h.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 2 Optimization of Trusses
### MINLP Approach to Truss Synthesis  
Considering the optimization of trusses, the Mixed-Integer Non-linear Programming (MINLP) approach was, in the past, applied to discrete dimension optimization only [29; 30; 31] and was seldom used for the topology optimization. The simultaneous topology and discrete sizing optimization represents an extensive non-convex and non-linear discrete/continuous optimization problem that cannot be solved without suitable MINLP algorithms and strategies. The practical applicability of the MINLP approach to structural optimization was, for example, presented in [59; 60] and employed for the simultaneous topology and standard dimension optimization of hydraulic steel gates [61; 62], single-story steel building structures [63] and composite floor structures [64].  
The present paper introduces the structural synthesis of trusses performed by the MINLP optimization approach. Truss synthesis is performed through three steps; see also [59]. The first is the generation of a truss superstructure for different structure/topology and other design alternatives that are all candidates for a feasible and optimal solution. The second is the development of a special MINLP model formulation for the defined truss superstructure in an equation-oriented environment. The last step is the solution of the defined MINLP model, performed by a suitable MINLP algorithm and strategies, which, in the simultaneous MINLP optimization approach, yields an optimal solution.  
As a result of the simultaneous optimization procedure, the MINLP truss synthesis yields:  
* the optimal structure mass or costs as the defined objective;
* the optimal truss topology with the optimal number and configuration of bars;
* the optimal joint coordinates, which define the shape of the truss; and
* the optimal discrete/standard cross-section dimensions of bars.  
While the structure mass and joint coordinates represent continuous parameters, which are optimized inside the continuous space, the optimization of topology and discrete/standard dimensions require discrete decisions. The discussed truss synthesis thus corresponds to a mixed discrete/continuous non-linear optimization problem, which can be solved by the MINLP.
## 4 Results  
This section presents the temporal analysis of SSIM/PSNR performance between RS-CONV and BCH-LDPC concatenated pairs in AWGN and Rayleigh channels and QPSK, 16-QAM, and 64-QAM modulated signals. The methodology proposed in the study was used to calculate the SSIM/PSNR values of both the original and resulting videos obtained from the simulations. Each video was simulated in two channels and three modulation schemes, totaling 990 simulations. Each simulation was repeated 33 times to ensure statistical variability. Table 4 provides comprehensive details of all performed simulations.  
Figures 6 and 7 serve as examples of how the data generated by the methodology behave. Due to space constraints, not all figures are shown. In Figures 6 and 7, the colored curves are the SSIM values extracted from the videos resulting from the simulations, and the reference curves are the SSIM values from the original videos. The colored points represent the values where there are losses in quality; therefore, it is possible to observe that, as the sequence of frames advances, the loss of quality increases, being more critical for RS-CONV/64-QAM.
## 3 Stability Preserving Criterion
### Smart CPL Management
In particular, the green curve in Figure 8 is related to the power reduction function capable of guaranteeing the rated stability index, whereas greater/smaller values of \(\Delta\) are ensured when the power reduction follows the red/blue curves. Clearly, if the requested stability index exceeds the rated value (0.6 vs. 0.516, red curve), then the rated power is not reachable even with a fully charged battery. Conversely, the DC microgrid can feed the rated CPL power with a partially charged battery (e.g., \(e_{\%}\) = 0.95) if the stability performance is downgraded (0.4 vs. 0.516, blue curve). As stated in References [11; 12; 13; 14; 15; 16; 17; 18; 19; 20], the stability of a DC power system supplying a CPL is closely related to the filter parameters. For this reason, Figure 9 depicts the influence of the parameter \(K\) on the power function. In particular, by halving \(K\) (i.e., the capacitance \(c
## 4 Part II: The Impact of Head Tracking on the Localization Performance
### Experiment Design  
#### 4.1.1 Response Method: GUI  
The GUI used in Part I, shown in Figure 4, was used with a few modifications. Since the experiments were designed to have only virtual presentations, no reference signal was provided for the perceived source distance. Therefore, the button \"Reference\" was omitted from the GUI. Subjects had to judge the perceived distance solely with respect to their own bodies, using an ordinal scale ranging from 0 to 3, corresponding to (0) in the head, (1) outside but near the head, (2) outside the head and within reach and (3) outside the head and further away. The frontal direction of azimuth = 0\({}^{\circ}\) and elevation = 0\({}^{\circ}\), corresponding to the forward head orientation, was marked in front of the subjects in the room. In TestDynamic, subjects were asked to orient their heads towards this point in the room and click the \"Reset\" button on the GUI to reset the head tracker before listening to the virtual sources. In TestStatic, the \"Reset\" button was omitted from the GUI.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 2 Materials and Methods  
The framework of the proposed method is described in Figure 1, which consists of a traffic perception system based on V2X, an edge computing module, and a traffic state prediction output module. Traffic states from various perception sources, such as roadside sensors, OBU, and traffic signal controller, can be collected by traffic perception system based on V2X. These states will then be delivered to a MEC server for further processing. Through graph constriction of intersection network, spatiotemporal feature analysis and soft-attention mechanism, the short-term traffic state at the intersection based on fusion results of V2X multi-source sensors can be accurately depicted and predicted. The involved modules are described in Sections 2.1-2.3.
## 5 Test Results and Discussion
### G Measurements  
As already mentioned, limitations of the RC device exist, as the measurements cannot reach very small strains (\(\gamma=10^{-6}\)). Figure 8a shows that there is no pronounced constant \(G\) for soils with low plasticity, but that they increase slightly with decreasing \(\gamma\). In contrast, soils with higher plasticity in Figure 8b tend to have constant \(G\) at \(\gamma<10^{-5}\).  
In RC experiments, the highest measured \(G\) can be regarded as \(G_{max}\). However, to minimize the effects of measurement errors, it may be more appropriate to fit the measurements to the modified Hardin-Drnevich (Equation (2)) model, as shown in Figure 8. This leads to a slightly higher estimate of \(G_{0}\). The comparison between \(G_{max}\) and \(G_{0}\) resulting from fitting the modified Hardin-Drnevich model shows that \(G_{max}\) is consistently about 10% lower than fitted \(G_{0}\) regardless of the isotropic or anisotropic stress conditions (Figure 9).  
The measurements with the BE test could be compared with the modified Hardin-Drnevich model, which was adapted to the measurements with the RC test (Figure 10). The results show that there can be a difference of about \(\pm 10\%\) between the \(G_{0}\) determined with the BE test and the \(G_{0}\) resulting from the fit of the modified Hardin-Drnevich model. Only at two data points is there a discrepancy between the BE and RC measurements of more than 10%, the cause of which is not immediately apparent. In addition, these two outliers  
Figure 8: Example of fitting the modified Hardin–Drnevich model: \(G-\gamma\) curves for a specimen (**a**) with low and (**b**) with high _1P_.  
Figure 9: Comparison between \(G_{0}\) (model) and \(G_{max}\) (RC), where solid black lines lay \(\pm\) 10% from identity line.  
were subsequently excluded from the regression analysis. There were also no significant differences under anisotropic stress conditions.
## 3 Results
### Antibacterial Activity of Mango By-Products
#### 3.2.1 Acetic Acid Bacteria
Under the tested concentrations, most of the AAB species displayed differences between the MIC and MBC values, which indicated that the mango extracts had a bacteriostatic effect. The MBCs ranged between 17 and 400 mg GAE/mL. In general terms, the differences between bacterial species or genotypes and between cultivars were similar to those described in the case of MICs; Group I included species with MBCs \(\leq\) 100 mg GAE/mL, and Group II species with MBCs \(\geq\) 100 mg GAE/mL (Table 1). Likewise, peel extracts obtained by process A showed higher MBC values than extracts obtained by process B in each bacterial genotype tested in at least in one of the cultivars. Finally, the MIC values were correlated with the polyphenic content of the extracts to examine if any of the phenolic compounds were associated with the antibacterial activity. As shown in Table 2, the statistical correlation analysis showed that the phenolic compounds quantified in our study had no significant correlation with the MICs of the AAB tested, except _A. cerevisiae_ (Ac5). In this case, a significant (\(p<0.05\)) negative correlation was observed between MIC values and the concentration (g/100 g dw) of flavonoids (r = \(-0.8385\)), gallates and gallotannins (r = \(-0.7267\)) and total phenolic compounds (r = \(-0.8385\)) (Table 2). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Species** & **Isolate Code** & **Flavonoids** & **Xanthones** & **Gallates** & **Ellagic Acid** & **Total** \\  & & & & **Gallotannins** & **Derivatives** & **Phenol** \\ \hline _A. corevisiae_ & Ac3-6A2 & \(-0.4518\) & \(0.3708\) & \(-0.5809\) & \(0.5809\) & \(-0.5164\) \\ \hline _A. cerevisiae_ & Ac5-T5 & \(-0.8385\)\({}^{\star}\) & \(0.2725\) & \(-0.7267\)\({}^{\star}\) & \(0.5404\) & \(-0.8385\)\({}^{\star}\) \\ \hline _A. cerevisiae_ & Ac2-6A1 & \(-0.4433\) & \(0.4445\) & \(-0.7714\)\({}^{\star}\) & \(0.4699\) & \(-0.5497\) \\ \hline _A. pasteurianus_ & Ap16-Lz75 & \(0.5285\) & \(-0.5339\) & \(0.4766\) & \(0.0693\) & \(0.5632\) \\ \hline _A. malorum_ & Am17-T33 & \(0.0559\) & \(0\) & \(0.3354\) & \(-0.4099\) & \(-0.0745\) \\ \hline _A. malorum_ & Am25-P21 & \(0.0559\) & \(0\) & \(0.3354\) & \(-0.4099\) & \(-0.0745\) \\ \hline _A. malorum_ & Am26-Lz67 & \(0.0559\) & \(0\) & \(0.3354\) & \(-0.4099\) & \(-0.0745\) \\ \hline _A. tropicalis_ & At1-T191 & \(0.2673\) & \(-0.2745\) & \(0.5612\) & \(-0.3742\) & \(0.1604\) \\ \hline _A. tropicalis_ & At2-T59 & \(0.2673\) & \(-0.2745\) & \(0.5612\) & \(-0.3742\) & \(0.1604\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spearman correlations between the phenolic compounds and the minimum inhibitory concentration (MIC) of each bacterial species tested.
# Ankle Joint Dynamic Stiffness in Long-Distance Runners: Effect of Foot Strike and Shoes Features
## 2 Materials and Methods  
### Participants  
Forty male long-distance runners gave their personal consent to take part in this study. Participants were excluded if they had not been running for at least 5 years, with an average of at least 40 km/week, and had not been free of neurological, cardiovascular or musculoskeletal problems within the previous six months. A number of 21 runners were found eligible. One subject was unable to complete the study protocol, which resulted in a tested sample of 20 subjects (age: \(31.2\pm 6.9\) yrs, height: \(1.77\pm 0.07\) cm, weight: \(73.4\pm 7.9\) kg). Participants were classified as rearfoot strikers (RFS, n = 10) or forefoot strikers (FFS, n = 10) based on their habitual mode of foot/ground initial contact. To classify their foot strike loading type, the participants were asked to run on an instrumented treadmill (AMTI Pty, Watertown, MA, USA) at their preferred speed, wearing their habitual running shoes. After a standardized 7 min of progressive warm up (starting from 3 min at 6 km/h, followed by 2 min at 8 km/h, then 2 min at 10 km/h) and accommodation period (i.e., quantitatively assessed stable foot strike angle--around 2 min), participants ran for 3 min at their preferred running speed, which was identified from the protocol suggested by [PERSON] and [PERSON] (2018). Habitual foot strike mode was assessed on the basis of data collected on the last minute of running (~60 steps). The ankle internal moment of the dominant  
Figure 1: Example of moment-angle loop for the ankle joint. IC = initial foot contact; TO = toe off. Dashed line represents commonly computed slope for ankle stiffness during the loading phase. Red line represents foot/ground initial loading; blue line represents loading phase; light-blue line represents unloading phase.  
leg was analysed to this purpose within a short period, from initial ground contact to the time at which the vertical ground reaction force exceeded a threshold corresponding to body weight. Runners displaying an internal plantar flexor moment for at least 90% of this period were classified as forefoot strikers (FFS); conversely, those who displayed an internal dorsiflexor moment for at least 90% of the analysed period were classified as rearfoot strikers (RFS). This foot strike classification method was shown to perform best among other conventional methods [25].