esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Specimens and the Experimental Setups  
An experimental campaign was carried out with a CFRP sample with defects, which corresponded to the case of the numerical simulation with both ageing and a central defect (CADS). The central defect was represented by the absence of an epoxy adhesive layer with a thickness of 1 mm over an area of 100 mm \(\times\) 100 mm, as already used in the previous work [59]. The sample was exposed to laboratory ambiance conditions for 10 years. The experimental setup of the test is shown in Figure 6.  
Figure 7 presents a comparison of the condition of the tested sample before the aging process with the condition of the same sample after 10 years of the natural ageing process. Some differences are noticeable, but the defects themselves are not visible.  
The same microwave system of 2.45 GHz as in the numerical model was used to heat the sample. The pyramidal horn antenna was placed at 550 mm from the sample, with a 35\({}^{\circ}\) direction. A heating power of 1000 W was used for heating (continuous heating mode) the sample for 2 min. An infrared camera was used to record thermograms with a frame rate of one image per second. In order to observe the whole surface of the specimen, the following position of the infrared camera was chosen for our experimental test: normal direction with a distance of 1 m from the sample. It worth noting that the test was carried out inside a protection enclosure (as used in the previous works [36; 38]). The operator must stay outside the enclosure during the test.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Air** & **Aluminum** & **Epoxy** & **CFRP** & **Concrete** \\ \hline Cp [J/(kg-K)] & & N/A & 1700 & 1700 & 750 \\ Density [kg/m\({}^{3}\)] & & N/A & 1150 & 1150 & 2400 \\ Epsilon (relative permittivity) & 1 & 1 & 3.9–0.1 j & 3.6 & 8.4–0.86 j \\ Thermal conductivity [W/(m-K)] & & N/A & 0.1 & 5 & 0.8 \\ Electrical conductivity [S/m] & 0 & \(3.77\times 10^{7}\) & 0 & 0.1 & 0 \\ Relative permeability & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The material properties utilized in the modeling [55; 56; 57; 58].  
Figure 5: Specification of the boundary conditions used in the modeling process.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 4 Results and Discussion
### Control Assessment with Real Measured Wave Data  
To further validate the effectiveness of the proposed control, real wave surface elevation data has been considered. Figure 25 illustrates the wave data that were gathered by an ADCP on 12 May 2014, at 00:00:00 AM at Mutriku. This input wave data will allow us to investigate the developed ANN-based rotational speed control strategy with different wave amplitudes and periods in realistic conditions.  
Figure 24: Generated power for the OWC. (**a**) Uncontrolled case. (**b**) ANN1-rotational speed control. (**c**) ANN2-rotational speed control.  
Figure 23: Turbine torque for the OWC. (**a**) Uncontrolled case. (**b**) ANN1-rotational speed control. (**c**) ANN2-rotational speed control.  
The airflow speed is shown in Figure 26 with high airflow at the crest of the waves namely at 5 s, 8 s, 38 s and 48 s.  
Figure 27 shows the rotational speed in the uncontrolled case and with the ANN rotational speed control. To keep up with the airflow speed and evade the stalling phenomenon, the ANN rotational speed control increases the speed.  
As a result, the flow coefficients presented in Figure 28 have surpassed the threshold value at 5 s, 8 s, 38 s and 48 s in the uncontrolled case. However, in the controlled OWC the coefficient has been regulated.  
Figure 27: Rotational speed for uncontrolled case and ANN-based rotational speed control.  
Figure 26: Airflow speed for real measured wave input in the uncontrolled OWC.  
The produced torques presented in Figure 29 show that the torque of the uncontrolled OWC have indeed suffered of the stalling behavior at the instants 5 s, 8 s, 38 s and 48 s nevertheless thanks to the ANN-based rotational speed control strategy they have been effectively evaded.  
Similar to the torques, the obtained powers have effectively evaded the stalling phenomenon as depicted in Figure 30. From these results the total power increase obtained from using the ANN rotational speed control for only 50 s is 7.16 kW corresponding to 2.06% for one OWC.  
As a final validation test, we considered the produced power of one oscillating water column in NEREIDA of Mutriku at the same time and day. The power data has been obtained from the Basque Energy Agency (EVE). The red curve represents the measured power on 12 May 2014, at 00:00:00 a.m.  
Considering the quality of the power data due to the big sampling period, some similarities can be seen between the power curves. Yet, at the wave crests 5 s, 8 s, 18 s, 38 s and 48 s the numerical power from the ANN-based rotational speed control is higher than the measured power.
## 2 Brief Review of Nanobubble-Generation Approaches
### External Electric Field  
An externally applied static electric field can be used on gas-water systems to induce the formation of nanobubbles and/or nanodroplets [10; 11]. The presence of this external electric field in proximity to a volume of liquid containing a medium facilitates the generation of nanobubbles or nanodroplets via electrostrictive capture of a sub-population of nanobubbles (or -droplets) from the outer periphery of upstream/incipient population of macro-, meso-, or microbubbles. This external-field-induced modus operandi is effective at transforming a large portion of either upward-rising macro- and/or mesobubbles, or vertically or horizontally flowing bubbles, in ultra-dense and long-lived NBs. This energy-efficient, \"sheathed-electrode\" method, bereft of moving parts, lends itself well to low-maintenance and solar-powered operations for off-grid work [14; 18; 19]. The approach has been used for gasification of water for both flow- and submersible-based types of NB generators [30; 31; 32; 33], as well as for petroleum aeration and thermodynamic-cycle efficiency (ICEs) [34].
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Registration and Verification Phase
#### 5.2.2 Generating \(pf\)  
ZKP is utilized for the decentralized identity verification of all system users. The HMC generates proofs for all registered users. The specific process involves signing with the user's real identity \(ID_{R}\), timestamp \(T\), and a random number \(r\), using this signed information as privacy input, computing the proof \(pf_{i}\) utilizing zk-SNARKs technology, and sending the verification key \(VK_{i}\) and \(pf_{i}\) to the user for private storage. When identity verification is required, users need to provide \(VK_{i}\) and \(pf_{i}\) to the system, which then executes the chaincode for decentralized identity verification on the blockchain.
## 3 Results and Discussion
### Quality Control: Global Solar Irradiance and Temperature
#### 3.2.2 Performance
U95 yielded, on average, the following results: \(2.076,49\)Wh/m2 day \(2.065,11\)Wh/m2 day, \(2.034,13\)Wh/m2 day, and \(2.016,86\)Wh/m2 day for ON, BC, HS, and proposed models, respectively, thereby confirming that the proposed model performed better than the other models. MPE presented the BC model as the best-performing model, with 14.45% on average, whereas the HS model was the worst-performing, with 15.31% on average. MAPE revealed the proposed model as the best option, with 35.12% followed by the HS and ON models. The MAPE results were consistent with the other statistical results. The proposed model's empirical coefficients showed a relationship with altitude. Figure 11 presents the linear adjustment of the empirical coefficients for two altitude ranges covering the location of AWS. On the left side, the figures represent the relationship between empirical coefficients and the altitudes below 2500 MASL; on the right side, the figures show the remaining AWS. Statistical analysis indicated that \(R^{2}\) for the \(a\) coefficient was 0.5262 and 0.5995 in the first and second cases, respectively. \(R^{2}\) for the \(b\) coefficient was 0.6069 and 0.8152 in the first and second cases, respectively. This result is remarkable since solar irradiance and temperature change with respect to altitude. These changes occur because of higher altitude and fewer molecules and aerosols scattering and absorbing solar irradiance [47; 48]. Figure 11: Coefficients a and b empirical model–altitude relation in the proposed model.
## 2 Dynamic Modeling of Free-Floating Dual-Arm Space Robot
### Kinematic Modeling  
The free-floating dual-arm space robot used in this study is a typical unrooted tree multibody system [16]. The schematic diagram of the free-floating dual-arm space robot studied in this paper is shown in Figure 1. The topological structure analysis and the establishment of the reference frames of the space robot are introduced below. The topology of a robot is usually composed of nodes representing the links (except the root node) and arcs representing the joints [48]. The schematic diagram of the free-floating dual-arm space robot is shown in Figure 1, where \(\bar{I}k_{I}\) represents the kinematic pair composed of parent link \(\bar{l}\) and child link \(l\). Each arm of the free-floating dual-arm space robot contains three single-DOF revolute pairs. The attitude of the base of the free-floating space robot has three rotational DOF. According to the concept of DEM, after the free-floating base of the space robot is equivalent to three single-DOF revolute pairs, the unrooted free-floating tree-chain space robot can be equivalent to a fixed-base tree-chain ground robot [31]. According to the above analysis, the topology graph of the free-floating dual-arm space robot is shown in Figure 2, where \(i\) represents the inertial space, \(b\) represents the base, \(l\) represents the left arm, \(r\) represents the right arm and \(R\) represents the revolute  
Figure 1: Schematic diagram of the free-floating dual-arm space robot. pair. Except for the root node, the remaining nine nodes in the topology correspond to the nine DOF of the free-floating dual-arm space robot, and these nodes are numbered in turn. In addition, unless otherwise specified, the rest of the symbols used in this article are shown in Table 11 of Appendix A. To improve the calibration accuracy of the robot's structural parameters, we propose a reference system establishment method based on the Axis-Invariant [49]. The reference frames and the Axis-Invariant of the left arm of the free-floating dual-arm space robot are described in Figure 3. The origin of the inertial reference frame \(O_{i}x_{i}y_{i}z_{i}\) is located at the center of the Earth. The origin of the base reference frame \(O_{b}x_{b}y_{b}z_{b}\) is located at the center of mass of the base. The origins of the joint reference frames \(O_{l}x_{l}y_{l}z_{l}\) are located on the rotation axes of the joints. At the initial moment, the directions of these reference frames are consistent. The Axis-Invariant \(\overline{\imath}\eta_{l}\) is defined as the motion axis vector direction of joint \(l\). For more background and applications of the Axis-Invariant, refer to [22; 23; 49; 50; 51].
## 5 Results
### Evaluation of EmoSC
#### 5.2.3 Emotional Adaptability in Response Generation  
In natural emotional conversations, responses do not always need to match the speaker's emotion exactly. For example, when a speaker expresses sadness, a neutral response offering comfort may be more appropriate. Therefore, an effective emotional spoken dialogue system (SDS) should be able to generate emotionally diverse responses that are contextually relevant to the input emotion. Figure 8 illustrates the distribution of response emotions based on input emotions.  
We observe that in DailyTalk, the model tended to overfit to a small subset of response emotions, reducing emotional diversity. This skewed distribution was likely caused by an imbalanced training set, where certain emotions were underrepresented, and a lack of alignment between input and response emotions. As a result, the model struggled to exhibit emotionally adaptive behavior, indicating that the dialogue quality in DailyTalk may not have been sufficient for training emotionally aware SDS models.  
In StyleTalk, we observe that the model exhibited limitations in producing emotionally adaptive responses. For instance, it frequently generated \"friendly\" responses to \"sad\" input emotions or \"cheerful\" responses to \"unfriendly\" inputs, lacking emotional variability.  
In contrast, our dataset enabled the model to generate a wider range of response emotions, demonstrating emotionally adaptive behavior. This was likely because the dataset contained a sufficient number of samples across all emotion categories that also had better emotionally adaptive responses, allowing the LLM to learn diverse emotional conversations and generate responses that better aligned with common sense interactions. Examples of generated responses from EmoSDS are provided in Figure 9.  
Figure 8: Output emotion distributions for input emotions. (**a**) DailyTalk, (**b**) StyleTalk, and (**c**) EmoSC.
###### Abstract  
The stress distribution and fracture parameter calibration of ice-rock models are important aspects of studying rock properties at high altitudes and latitudes. However, progress in ice-rock modeling has been slow and singular, and it is limited due to the discrete nature of rocks and the applicability of fracture mechanics. In this study, a circular inhomogeneous ice-rock model is proposed for the first time, and a method is provided for calculating the stress field of the model under biaxial loading. A method for calculating the single-crack stress intensity factor of the model subjected to biaxial compressive loading is also provided. The novelty of this work is that the inhomogeneous ice-rock model is treated as a superposition of two models, namely, a circular pore plate and circular ice, according to the superposition principle. The key is that the stress field distribution law of the ice-rock model is obtained based on the basis of the displacement continuity of the ice-rock interface. The analytical and approximate solutions of the stress intensity factor of a single crack were also obtained by considering the normal phase effect of the crack surface and combining the stress distribution law of the ice-rock model. Comparison with the CAE method was made to verify the correctness of the stress field and stress intensity factor calculation methods. The evolution laws of lateral pressure coefficients, the elastic modulus ratio of ice and rock on the stress field, and the stress intensity factor were analyzed. The effects of lateral pressure coefficients, elastic modulus ratios, and crack distributions on the failure modes were investigated using the extended finite element method (XFEM). This study can provide a theoretical basis for the evaluation of mechanical properties and prediction of the failure modes of frozen rock bodies.  
ice-rock model; superposition principle; closed-type cracks; stress intensity factor; crack extension +
Footnote †: journal: Applied Sciences
## 3 Proposed Model and its Phases
### Cluster-Head Selection Phase (CHSP) in the Second Level Nodes
To manage this uncertainty, the probability of a single node in a multi-level heterogeneous network, \(P_{i}\) can be calculated as:  
\[P_{i}=\frac{P_{opt}\times n\times(1+\alpha_{i})}{n+\sum_{i=1}^{n}\alpha_{i}} \tag{16}\]  
If \(p\) is the desired percentage of CHs, then the total number of cluster heads in the first-level and second-level regions can be calculated as:  
\[Total_{CHs}=\sum_{i=1}^{m\times p}CHs_{level_{1}}+\sum_{i=1}^{n\times p}CHs_{level _{2}} \tag{17}\]
## 2 Materials and Methods
### Convolutional Layer  
The convolution layer has spatial dimensions (in which convolution is performed in the feature space) and a channel dimension (in which the number of spaces can be converted between input and output). When the data are \(n\)-spatial dimensional, the input/output is \(n+1\) dimensional, which encompasses the spatial and channel dimensions. The kernel used for convolution is \(n+2\) dimensional, including the spatial dimension number \(n\), an input channel dimension, and an output channel dimension [16]. For images, there are two spatial dimensions and the red-green-blue channel dimension (three dimensions in total). The kernel for images has four dimensions: the two-spatial dimensions, red-green-blue(input) channel dimension, and output channel dimension. EEG data contain time-series (time domain) and electrodes, and the dimensional treatment of the data is described in each method in the \"Network structure\" section. An illustration of 2D convolution is shown in Figure 1 (note that the channels are ignored for the illustration purpose).  
The convolution layer is utilized to process inputs that have one or more features with spatial dimensions. A kernel is employed to perform convolution along the spatial dimensions of the features. The utilization of the convolution layer regularizes the weight parameters due to its smaller number of weight parameters as compared to the number of input-output features. As EEG signals are time series in nature, the consideration of regularization parameters is less crucial, given that the convolution layer operates on the time domain.  
For the purpose of this study, the terms \"spatial\" and \"channel\" dimensions are used in their general sense to refer to dimensions, rather than implying any specific spatial information of EEG electrodes (EEG channels).
## 2 Materials and Methods
### Machine Analysis
#### 2.1.1 Mesh Independent Study  
To create finite element models, the generated CAD models were exported to ANSYS Workbench. The meshing operation was the first stage's step. The finite element analysis depends heavily on the mesh-generating process. The precision of the calculation is related to the size of the mesh used. The results can be believed more if the mesh is smaller, but this comes at the expense of significantly more processing time. First, a preliminary mesh generation is performed to find the right balance between mesh quality and processing time. During the analysis, the mesh of the model is divided into multiple elements. During mesh division, the element used was Solid187 tetrahedral nonlinear mesh, and the mesh size of the base, column, saddle, worktable, and spindle head was set at 0.028 m. The remainder of the line rail (0.01 m) and the screw and motor were set at 0.028 m. The total number of grid nodes was 1,830,379, with 1,049,621 elements. Consequently, a suitable mesh size must be selected. [PERSON] et al. [29] established a procedure for the element size meshed in finite element analysis (FEA), resulting in the natural frequency falling inside the convergent range of 1% in the convergent analysis of each subsystem's three-dimensional geometrical model.  
For each case in this study, mesh convergence tests were performed to increase the accuracy of the FE analysis. The findings of the variation of elastic deformations, total deformation, and maximum shear stresses were examined for various element numbers as the mesh convergence tests were carried out. As depicted in Figure 2, with the rise in the number of mesh elements, there was a change in the value of the elastic deformation, total deformation, maximum shear stress, and maximum elastic strain. However, the  
Figure 1: Machine-tool structure design framework based on an integrated structural framework.  
variation in deformation of all variables stabilized after the eighth point. Therefore, for this investigation, the converged mesh number was defined as elements.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Structural Steel**} \\ \hline Density (kg/m\({}^{3}\)) & 7850 \\ \hline Young’s modulus (Pa) & \(2\times 10^{11}\) \\ \hline Poisson’s ratio & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural steel properties [30].  
Figure 2: The mesh convergence test results of the finite element model.
## 2 Methodology  
### Field Instrumentation  
The behavior of the field concrete base plate was evaluated through temperature and strain measurements. A total of eight full-scale CRTS III concrete base plates were instrumented, with two plates for each curing regime. Each plate was instrumented with twelve vibrating wire strain gauges (VWSGs) for the measurements of strain and temperature variations at different locations. The length of the VWSG was 143 mm. Strain measurement accuracy was \(\pm\)2 \(\mu\)e, and temperature measurement accuracy was \(\pm\)0.1 \({}^{\circ}\)C. This type of strain gauge was selected mainly because temperature data were simultaneously recorded by it, and temperature compensation relative to the measured strain was automatically calibrated by a software designed for it.  
Figure 2a,b shows the arrangement of the VWSGs on the plane and in the depth direction, respectively. Considering that early transverse cracks were generally localized in the middle portion of a base plate, seven VWSGs were placed in the high-cracking-tendency mid-plate cross-section (positions A, B, and E), These strain gauges were used to monitor strain developments within the concrete up to the occurrence of first cracking. At positions A and B, the VWSGs were placed in the top, middle, and bottom layer to measure strain and temperature distributions along the plate depth. The other five VWSGs were placed at the edge (positions C and F) and the corner (position D), and the strain variations were expected to be significantly affected by the surrounding environment due to lower degrees of restraint at these locations. All field instrumentation was connected to a data acquisition system (DAS) remotely controlled by a laptop computer. The interval of data recording was 20 min.
## 4 The Reachable Zone Predictive Method  
The predictive method of reachable zone determination based on the database is proposed in this section. It can help us to quickly predict the reachable zone based on the current state information of the vehicle and information about the perception of the environment and to determine the current vehicle safety and the likelihood of completing the mission, as well as provide a theoretical basis for further autonomous online replanning decisions.