esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Parametric Studies
#### 4.2.2 Effect of Recovery Stress  
Figure 14a shows the comparison of the load-displacement curves according to the recovery stress level of the RC beam strengthened with an Fe-SMA strip. Figure 14b shows the initial crack load, yield load, and ultimate load of the RC beam according to different recovery stress levels of the Fe-SMA strip. Four recovery stress levels of 50 MPa, 100 MPa, 200 MPa, and 400 MPa were considered. As shown in Figure 14b, as the recovery stress of the Fe-SMA increased from 50 MPa to 400 MPa, the initial crack load and yield load increased by 80.68% and 19.94%, respectively. On the other hand, the ultimate load did not show remarkable improvement, regardless of the level of recovery stress of the  
Figure 14: Effect of prestressing level (MPa): (**a**) Comparison of load- displacement curves; (**b**) Comparison of load stages for the number of Fe-SMA strips.  
Figure 13: Effect of concrete compressive strength (MPa): (**a**) Comparison of load- displacement curves; (**b**) Comparison of load stages for the number of Fe-SMA strips.  
Fe-SMA. The displacement ductility index tended to slightly decrease to an insignificant degree as the recovery stress of the Fe-SMA increased. It was observed that the increase in the recovery stress of the Fe-SMA improved the initial stiffness of the RC member, which resulted in its improved serviceability.
## 4 Neural Network Architecture
By putting the one-dimensional max pooling layer on top of the local vectors, the network is enhanced, and it can capture the most useful local features for a task with a fixed size. **layer-2**: The output of layer-1 is used as the input to this layer. Layer-2 is another layer consisting of a one-dimensional convolutional operation and a max pooling operation that is the same as layer-1. **layer-3**: The output of layer-2 is then fed into a fully connected layer (regularized by dropout). The activation function is _relu_. **layer-4**: The output of layer-3 is fed into a fully connected layer (regularized by dropout) at the end to classify the output. The activation function is _sigmoid_. Figure 1: Architecture of LM-CNN-LB for cross-domain product review sentiment classification. \[f(x)=\frac{1}{1+e^{-x}} \tag{5}\]  
In this scheme, a two-layer convolutional neural network is constructed and trained on the source domain data from \(D_{b_{l}}\). The constructed model is designated as \(S_{cnn}\). To transfer the data, another model (designated \(T_{cnn}\)) is built with the same neural network architecture as that of \(S_{cnn}\). The parameters are transferred from \(S_{cnn}\) to initialize the corresponding layer in \(T_{cnn}\). By freezing the embedding layer, the remaining layers of \(T_{cnn}\) are trained on a small quantity of target domain data from \(D_{l}\) to obtain the model \(T_{cnn}\) for the test target domain from \(D_{b}\). For example, for task B\(\rightarrow\)D, first \(S_{cnn}\) is trained on the auxiliary dataset \(B_{D_{bl}}\). Then, the parameters of \(S_{cnn}\) are transferred to \(T_{cnn}\) to initialize its neural network architecture. After freezing the embedding layer of \(T_{cnn}\), the remaining layers are fine-tuned on small amounts of \(D_{D_{l}}\). Finally, \(T_{cnn}\) is tested on \(D_{D_{b}}\). Introducing the large-scale auxiliary data causes the small-size benchmark to become sufficient for training the neural network. Moreover, these conditions are suitable to fully demonstrate the transferability of deep neural networks. Furthermore, \(S_{cnn}\) can automatically extract feature representations that can be shared across the source and target domains. Finally, fine-tuning the parameters transferred from \(S_{cnn}\) using small amounts of target domain data enhances \(T_{cnn}\), causing it to learn features that are specific to the target domain task and increasing its accuracy.
## 7 IoT-Defined Application Protocols
### Constrained-Application Protocols (CoAP)
#### 7.1.5 Separate Response  
When an IoT node needs information from another network node/centralized database, it sends a GET command using CoAP to the addressee node. The receiver of the message thus posts the required data using the same CoAP to the sender. When the network power constraints are not optimized, the network uses two separate messages for responding tothe query. First, it sends an acknowledgment message to the sender that includes information about the query the receiver (server/node) has encountered. The response is then posted as a separate packet back to the sender with a totally new header. Such messaging is known as separate messaging.
[PERSON] and [PERSON] optimized the analytical model for determination of the friction coefficient in deep drawing with respect to the distribution of the contact pressure at the drawing radius [10]. [PERSON] et al. [11] improved the conventional test based on parallel strip drawing by modifying the tester to allow a controllable speed and normal load during experimenting, a tool size variable over a wide range, and direct force measurement. [PERSON] et al. [12] experimentally studied the friction coefficient in sheet metal forming. They developed a friction coefficient measuring apparatus based on the drawing die of the cylindrical parts. According to the relationship between the drawing force and the stroke, the linear fitting algorithm of the friction coefficient was given based on the least squares method. [PERSON] et al. [13] presented advanced friction model for large-scale forming simulations based on the surface changes on the micro-scale. They developed the friction model which accounts for the change of the surface texture on the micro-scale and its influence on the friction behavior on the macro-scale. After implementing the friction model in a simulation, they reached a realistic distribution of the coefficient of friction depending on the local process conditions. [PERSON] and [PERSON] [14] studied the effect of sheet deformation on the change of the surface roughness parameters and friction coefficient in the strip drawing test. They found that the friction coefficient determined for all pre-strained samples decreased with increasing nominal pressure of rolls under both dry and lubricated conditions. An increase in the plastic deformation of sheets under the uniaxial tensile stress state caused a nearly linear increase in the value of basic amplitude parameters of surface roughness. [PERSON] et al. [15] analyzed the effect of the friction coefficient on the deep drawing of aluminum alloy at elevated temperatures experimentally and numerically. Results showed that the friction coefficient and lubrication position significantly influenced the minimum thickness, the thickness deviation, and the failure mode of the formed parts. Temperature-induced friction effects in sheet metal forming was the subject of study by [PERSON] et al. [16] as well. [PERSON] and [PERSON] summarized the methods used to describe friction conditions in conventional sheet metal forming and incremental sheet forming [17]. Based on searching many databases, they described and discussed the main disadvantages and limitations of the methods of modeling the friction phenomena in specific areas of the material to be formed. The aim of the article is to determine the friction coefficient in different regions of the die during the stamping process--under the blankholder and on the die radius. Experiments were performed on two types of steel with different surface and material properties. Data recorded from the strip drawing test were processed by analytical models to calculate the friction coefficients. These results were set in simulation software and friction coefficients were determined by regression analysis the results of the cup test.
## 2 Cluster-Based Sample Inheritance Statement  
### Overview of Kriging Model  
The Kriging model consists of regression terms and error terms, which can be expressed as follows:  
\[Y(\mathbf{X})=F(\beta,\mathbf{X})+z(\mathbf{X}), \tag{1}\]  
where \(Y(\mathbf{X})\) represents the performance response of the system; \(z(\mathbf{X})\) denotes a random process with the expectation of 0 and variance of \(\sigma^{2}\). \(F(\beta,\mathbf{X})\) is a regression term, which encompasses a linear combination of polynomial functions, shown in the following:  
\[\begin{array}{ll}F(\beta,\mathbf{X})&=\beta_{1}f_{1}(\mathbf{X})+\beta_{2} f_{2}(\mathbf{X})+\ldots+\beta_{p}f_{p}(\mathbf{X})\\ &=\left[f_{1}(\mathbf{X})\ldots f_{p}(\mathbf{X})\right]^{T}\beta=\mathrm{f}^{ T}\beta.\end{array} \tag{2}\]  
\(f_{i}(\mathbf{X})\) represents the polynomial function of \(\mathbf{X}\), \(\beta=(\beta_{1},\beta_{2},\ldots,\beta_{p})\) denotes the coefficient of the regression term, and \(p\) represents the number of polynomial functions.  
The error term satisfies the following properties:  
\[E[z(\mathbf{X})]=0, \tag{3}\]  
\[Var[z(\mathbf{X})]=\sigma^{2}, \tag{4}\]  
\[Cov[z(x_{i}),z(x_{j})]=\sigma^{2}R(x_{i},x_{j}). \tag{5}\]  
Among them, \(R(x_{i},x_{j})\) is a correlation function with parameters, which means that random variables at different positions in the design space tend to possess a certain correlation. The correlation function in the form of Gaussian function can be expressed as follows:  
\[R(x_{i},x_{j})=\exp(-\sum_{k=1}^{n}\theta_{k}\Big{|}x_{ik}-x_{jk}\Big{|}^{2}), \tag{6}\]  
where \(\theta_{k}\) is the unknown parameter. It is apparent that only the weight coefficient, \(\theta=\left[\theta_{1}\,\theta_{2}\ldots\,\theta_{k}\right]^{T}\), is necessary to give an estimation regarding any point in the sample space. The weight coefficient can be obtained via optimization, which is indicated in the following:\[\begin{cases}\text{find}&\theta\\ \text{min}&Var[\hat{y}(\mathbf{X})-y(\mathbf{X})]\\ \text{s.t.}&F^{T}\theta-\text{f}=0\end{cases} \tag{7}\]  
As for the reliability analysis based on the Kriging model, its procedure is elaborated in Figure 1. It mainly encompasses five steps: (1) step 1: generate the initial test points via Latin hypercube sampling and calculate the corresponding responses, where at least \((n+1)(n+2)/2\) test points are selected based on the sampling center for the first fitting [37]; (2) step 2: select the sample points as the training samples and establish the training set; (3) step 3: calculate the surrogate model on the basis of the pre-specified training set; (4) step 4: evaluate the reliability index based on the obtained surrogate model; and (5) step 5: examine whether the condition of convergence has been satisfied. The procedure is suspended and outputs the reliability index if so; otherwise, the cycle continues. Notably, the second iteration is carried out to generate test points again and subsequently fit the model.
## 3 Results and Discussion
### Scanning Electron Microscopy  
Figure 4A-F shows the microstructure of the experimental costeno-type cheees. The cheese microstructure can be viewed as a continuous protein gel network disrupted with interspersed fat globules [37; 40]. The scanning electron micrographs showed that all the experimental costeno-type cheese had a discontinuous structure with numerous irregular cavities. Similar observations have previously been reported by [PERSON] and [PERSON] [28] in fresh Mexican cheese.  
From a rheological viewpoint, the structural discontinuities may result in the lack of tensile strength in many kinds of cheese, which in practical terms may be reflected as crumbliness, shortness, and fracturability, as occurs in cheees such as Feta, Stilton, and Cheshire [41].  
The average pore size of the cheese microstructures evidences a variability in the pore distribution of the microstructures (Figure 4G). The cheees Q\({}_{C}\), Q\({}_{4}\), and Q\({}_{5}\) had more homogeneous pore sizes (Figure 4A,E,F) compared to cheees with less NaCl addition (Q\({}_{1}\), Q\({}_{2}\), and Q\({}_{3}\)) (Figure 4B-D). This pore variability can be attributed to changes in the texture of the cheees.  
Figure 3: Frequency sweep in experimental costeno-type cheese. (**A**) The storage modulus (G’) for the cheees Q\({}_{C}\) (black circles), Q\({}_{1}\) (black squares), Q\({}_{2}\) (black diamonds), Q\({}_{3}\) (crosses), Q\({}_{4}\) (black diamonds) and Q\({}_{5}\) (asterisks), and (**B**) loss modulus (G”) for the cheees Q\({}_{C}\) (white circles), Q\({}_{1}\) (white squares), Q\({}_{2}\) (white diamonds), Q\({}_{3}\) (short dashes), Q\({}_{4}\) (white triangles) and Q\({}_{5}\) (long dashes).  
[PERSON] et al. (2018) [22] indicated that temperature influences the cheese structure through its effect on the components of cheese and their interactions, including changes in the physical state of fat. The molecular interactions between the casein show a relationship between the influence of the increase in cooking temperature on the microstructure of these cheeses. However, Q\({}_{2}\) and Q\({}_{3}\) cheeses' microstructures do not establish a relationship between increasing the cooking temperature on the cheese's microstructure.  
Figure 4: Scanning electron micrograph of costeño-type cheese at 10 \(\upmu\)m scale at 4500\(\times\). Q\({}_{1}\) (**B**), Q\({}_{2}\) (**C**), Q\({}_{3}\) (**D**), Q\({}_{4}\) (**E**), Q\({}_{5}\) (**F**), and average pore size (**G**), where error bars represent one standard deviation. The light areas in the micrograph are the protein phase, and the dark areas are the fat or serum phase. Arrows indicate coalesced fat globules.  
Cheese Q1 showed a microstructure with coalescences of fat globules observed as larger and irregularly shaped openings and discontinuities in the para-casein matrix (Figure 4B) [41]. Coalescence of fat globules occurs during manufacturing because of the combined effects of shear stress on the fat globule membrane and shrinkage of the surrounding para-casein matrix, which forces the occluded globules into close contact [41].
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 4 Results and Discussion
### Response Prediction for Nonlinear Structure
(**d**) \(u_{b}\) under the action of the seismic wave in cluster 1. (**e**) \(u_{b}\) under the action of the seismic wave in cluster 2. (**f**) \(u_{b}\) under the action of the seismic wave in cluster 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Index** & \(\mathbf{u_{d}}\) & \(\mathbf{\bar{u}_{d}}\) & \(\mathbf{u_{b}}\) & \(\mathbf{\bar{u}_{b}}\) & \(\mathbf{u_{66}}\) & \(\mathbf{\bar{u}_{66}}\) \\ \hline \multirow{2}{*}{Cluster 1} & \(E_{WMAPE}\) (\%) & 7.110 & 4.274 & 6.377 & 4.480 & 9.132 & 10.366 \\  & \(E_{PEAK}\) (\%) & 6.456 & 17.949 & 3.750 & 10.625 & 17.978 & 31.579 \\ \hline \multirow{2}{*}{Cluster 2} & \(E_{WMAPE}\) (\%) & 15.238 & 13.773 & 6.129 & 10.322 & 9.392 & 14.566 \\  & \(E_{PEAK}\) (\%) & 2.343 & 28.696 & 2.144 & 20.236 & 9.053 & 20.556 \\ \hline \multirow{2}{*}{Cluster 3} & \(E_{WMAPE}\) (\%) & 7.388 & 4.444 & 3.031 & 7.698 & 5.234 & 7.893 \\  & \(E_{PEAK}\) (\%) & 8.772 & 18.421 & 1.993 & 38.889 & 22.143 & 7.826 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Error values of the LSTM model for the nonlinear structure.
## 4 Experimental Results and Analyses
### Contrast Experiments  
In order to further verify the effectiveness of the method, the results of this paper are compared with those obtained in references [14; 16]. Since both references [14; 16] and this paper use static tensile method to measure elastic modulus, experimental data are relatively comparable. The comparison results are shown in Table 4.  
In Table 4, in terms of the measurement of sample micro-shape variables under external forces, the minimum step length of micro-shape variables measured by digital laser speckle method in literature [16] is about 0.088 mm. Although the measured results are consistent with the standard values, the relative uncertainty is large. The main reasons for this result are that the laser beam intensity in the experiment is not uniform in space, there are stray light on the scattering object surface and the defects of the algorithm. In literature [14], Fraunhoffer single-slit diffraction is used to measure micromorphic variables. Although the uncertainty of measurement results is not evaluated in literature [14], the measurement accuracy of the shape variable is the highest, which benefits from the minimum force increase interval applied to the sample.  
In this paper, the minimum step size of the self-made air-coupled ultrasonic transducer is about 0.43 mm, and the relative uncertainty of the measurement results is better than that of literature [16]. Different from references [14; 16], this paper adopts the method of symmetry measurement of tension increase and tension decrease on the sample to reduce the measurement error. The experimental results show that the method and the device used have high linearity and symmetry, good stability and repeatability.
# Acoustic Emission/Seismicity at Depth Beneath an Artificial Lake after the 2011 Tohoku Earthquake
## 1 Introduction  
Acoustic emission (AE)/seismicity is important for evaluating the stability of geological structures; for instance, the delineation of fracture systems from AE activity in geothermal reservoirs is indispensable for thermal energy extraction [1]. The stability evaluation of fractures during hydraulic stimulation in geothermal reservoirs is necessary to avoid inducing large AE events, where the relationship between large AE events and the pore-pressure distribution in reservoirs is studied [2]. The stability of geological structures is also important from the viewpoint of disaster prevention. The dynamic behavior of geological structures has been studied by many seismologists who have examined repeating earthquakes and slow slip at seismic zones [3; 4; 5].  
The 11 March 2011 Tohoku earthquake and its many aftershocks brought widespread destruction to eastern Japan [6; 7; 8]. This seismic activity occurred on both offshore and inland faults. Although thrust events dominated seismicity before and during the Tohoku earthquake, eastward movement of the shallow crust of the overriding tectonic plate caused extension that relaxed the subduction-related horizontal compressional stress locally, resulting in normal movement on some inland faults after the Tohoku earthquake [9].  
Close observations of seismic activity are indispensable for the monitoring of the stability of geological structures. This paper describes a seismic swarm that occurred beneath an artificial lake near the plane of an active fault in Sendai city, Japan, after the Tohoku earthquake. It has long been known that changes in the water level of reservoirs can trigger substantial earthquakes, and it is shown that the seismic activity was sensitive to the small change of tectonic stress caused by the water level of the artificial lake.
## 3 Computational Results
### Uniform Discretizations
The significant error from the phase function 3 case implied that a more complex phase function would significantly increase the error during calculations. Furthermore, the maximum error  
Figure 4: Absolute relative error of Markov chain angular distribution predictions compared to Monte Carlo simulations. Panel (**a**). Phase function 1, OD = 2; Panel (**b**). Phase function 1, OD = 5; Panel (**c**). Phase function 2, OD = 2; Panel (**d**). Phase function 2, OD = 5; Panel (**e**). Phase function 3, OD = 2; Panel (**f**). Phase function 3, OD = 5. Red dash lines indicate an error threshold of 1.0%, and blue dash lines indicate a threshold of 0.1%. would increase with a higher OD. More specifically, when the OD increased from 2 to 5, the maximum error (occurring around the transmission angle of 90\({}^{\circ}\)) increased from 0.6% to 6.0% in this investigation.
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 4 Experiments and Results
### Experimental Evaluation  
For the sake of the evaluation of the proposed algorithm, we choose to follow both a qualitative and a quantitative approach. The former aims to investigate whether real-life users are satisfied (a) with the regions (geo-clusters) extracted by the geo-clustering process and (b) with the proposed description (ideally name) of each.
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 5 Discussion  
According to the findings of the study, users generally viewed that current synchronous applications are better, compared to asynchronous applications in terms of collaborative experiences and system usability. However, some features in the application were found to be underutilized, such as the record feature, the reactions feature, the raise hand feature, the breakout room feature, the file sharing feature, the whiteboard feature, the chat feature, the annotation feature, and the camera feature. These features were essential for collaborative purposes, but participants did not use them frequently enough. From the study, users believed that the file sharing, whiteboard, and annotation features were not easy to use, which perhaps contributed to their underutilization.  
The study also gathered feedback on the web camera usage during the virtual conferences. Most of the participants preferred to turn off their web cameras unless specifically instructed not to do so. According to the results of the survey, most of the participants were unconcerned about their other members not turning on their cameras during an online engagement. This was further supported by the findings of the focus group discussion, in which participants discussed the issues they faced while performing collaborative activities on the current virtual conference application. Qualitative data extracted from the transcription of the focus group discussion is labeled using the G[Number]M/F[Number] arrangement, where G[Number] denotes the group number, e.g., Group 1 to 5, M/F denotes the sex of the participants, e.g., Male or Female, and [Number] denotes the participant's identification number, e.g., 1 to 16. The indented text was the feedback from the participants.  
Figure 9: The system usability score (SUS) of the current virtual conference application.