esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning from the Past: Parametric Analysis of Cob Walls
## 3 Results and Discussions
### FEM In-Plane Analysis  
The DOE using a CCD rotatability design with four factors (length, height, thickness, and acceleration) generated 49 design points. The minimum value computed for \(F_{ys}\) was equal to 5.52. This value was obtained for a wall with 6 m length, 3.05 m height, 0.65 m thickness, and subjected to an acceleration of 0.74 m/s\({}^{2}\). It would be expected that the length of the wall would play an important role in the wall in-plane response. Bulky walls tend to fail by shear, whereas slender walls tend to top up or fail by crushing at their base. Nevertheless, as the boundary conditions adopted prevented the in-plane rotation of the wall and, since the levels of stress created by the low levels of acceleration adopted did not exceed the yielding point of the material, the parametric analysis did not capture properly the effects of wall length.  
The full quadratic model was refined by removing the non-significant terms. By taking into account only those statistically significant terms, the expression shown in Equation (9) was obtained. Equation (9) can be used to estimate the value of \(F_{ys}\) for every combination of wall height (\(H_{wall}\) in m) and lateral acceleration (\(a\) in m/s\({}^{2}\)) within the range of values explored by the analysis presented in this paper. In this equation, the length and the thickness of the wall are not included, as their effect on the response is non-significant, and this confirms the comment made at the beginning of the section regarding the fact that the thickness of the wall was expected not to influence the in-plane response of the wall.  
\[F_{ys}=24.799-10.239H_{wall}-1.798a+1.439H_{wall}^{2}, \tag{9}\]  
If Equation (9) is evaluated for a wall height of 3.05 m and an acceleration of 0.981 m/s\({}^{2}\), then, \(F_{ys}=5.19\). This indicates that even under the most adverse combination of values for the parameters explored, and based on the assumptions adopted, the material would not reach its yield point. Furthermore, it can be seen that the out-of-plane model is more critical than the in-plane model as the critical value of \(F_{ys}\) obtained for the out-of-plane model was only 1.10. Equation (9) was also used to create the response surface shown in Figure 8. This response surface shows how \(F_{ys}\) varies based on the values of height and acceleration. This response surface reflects the quadratic effect that the input parameters have on the response
## 4 Results and Discussion
### Ultimate Energy Dissipation Capacity and Prediction of Failure
Finally, the solid bold lines of Figure 19a,b were redrawn in Figure 19c (thin solid line and thin dotted line) and summed up to obtain the solid bold line of Figure 19c that represents the normalized ultimate energy dissipation capacity of the SS-TTD, characterised by parameters \(k_{pl}=1/2.5\), \(k_{p2}=1/14\), \(a=-14.66\), \(b=3900\) and the values of \(Q_{y}\), \(Q_{B}\), \(\delta_{y}\) given by Equations (1) and (2). Once the ultimate energy dissipation capacity of the SS-TTD is determined, the failure of the SS-TTD under arbitrarily applied cyclic loading can be predicted from the \(Q\)-\(\delta\) curve obtained up to each instant from numerical simulations (i.e., nonlinear time history analysis) as follows. At any instant \(i\), the values of \({}_{cp}\eta\) and \(\eta\) are calculated from the \(Q\)-\(\delta\) curve obtained up to instant \(i\) as explained above, they are named \({}_{cp}\eta_{i}\) and \(\eta_{i}\) hereafter. Tracking this pair of points (\({}_{cp}\eta_{i}\), \(\eta_{i}\)) and plotting them in Figure 19c, failure is expected to occur when the solid bold line is reached. Previous studies [6; 7; 8; 9; 10; 11] related the ultimate energy dissipation capacity of the damper to the cumulative ductility factor \(\mu_{CUM}\) defined by Equation (7). In this equation, \(\sum\delta\) is the sum of excursions of displacement experienced by the damper, normalized by the yielding displacement \(\delta_{y}\). Figure 20  
Figure 19: Ultimate energy dissipation capacity: (**a**) Bauschinger part, (**b**) skeleton part, (**c**) total. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _Specimen_ & \({}_{cp}\eta^{+}\)[\({}_{cp}\eta^{-}\)] & \({}_{cp}\eta^{-}\)[\({}_{cp}\eta^{+}\)] & \({}_{cp}\eta^{-}\)[\({}_{cp}\eta^{+}\)] & \({}_{cp}\eta^{+}\)[\({}_{cp}\eta^{-}\)] & \(\eta\)[\({}_{cp}\eta^{+}\)] & \(\eta\)[\({}_{cp}\eta\)] & \(\mu_{CUM}\) & \(\mu_{CUM}\) \\ \hline _SS-TTD5_ & 17.6 9.3 & 37 41 & 1643 1611 & 1680 1652 & 3332 & 1912 & 41.7 \\ _SS-TTD6_ & 22.5 28.4 & 54 66 & 1700 1633 & 1754 1699 & 3453 & 1728 & 27.8 \\ _SS-TTD4_ & 9.3 6.5 & 16 10 & 1884 1856 & 1900 1866 & 3766 & 2707 & 143.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ultimate energy dissipation capacity. Figure 18: Skeleton curves. shows the normalized ultimate energy dissipation capacity \(\eta\) against \(\mu_{\mathit{CLM}}\) obtained for the SS-TTDs tested in this study until failure. \[\mu_{\mathit{CLM}}\ =\ \frac{\sum\delta}{\delta_{y}} \tag{7}\]  
The pair of values (\(\eta\), \(\mu_{\mathit{CLM}}\)) at failure obtained for the SS-TTDs are plotted in Figure 21 and compared with the values obtained in previous studies [6; 7; 8; 9]. It is seen that the proposed SS-TTD has an energy dissipation capacity and an accumulated ductility notably larger than those reported for other dampers in previous studies.
## 3 Results and Discussion
### The Change in Uplift Displacement in Two Kinds of Soil
Although the grouting pressure gradually increases during the initial stage of fracturing, the attenuation of grouting pressure leads to mainly permeation and compaction effects around the grout vein. However, the contribution of permeation and compaction to uplift displacement is relatively small. Therefore, in the initial stage after splitting, a brief nearly horizontal displacement-time curve appears in the uplift displacement. With the further increase in grouting pressure, the widening of the fissure reduces the attenuation effect. Additionally, the increased grouting pressure exerts greater pressure on the soil surrounding the fissure, leading to more pronounced compaction and splitting effects in the sand. This prompts another increase in uplift displacement with the increase in grouting pressure. Once the pressure reaches the initiation pressure for secondary splitting at a certain location within the sand, a second splitting occurs promptly due to the substantial grouting pressure. Moreover, a similar near-horizontal displacement-time development curve also appears in the initial uplift displacement after splitting. This splitting process continues in a cycle until grouting is stopped. After each splitting, the rate of uplift displacement increases, as evidenced by the average slope of the uplift velocity stages in the displacement curves of each monitoring point depicted in Figure 6. The aforementioned process can be visualized through images of grout diffusion obtained at corresponding time points of uplift displacement vs. grouting time curves, as shown in Figure 6a-c. These images, respectively, depict grout diffusion during the first minor splitting, the second intermediate splitting, and the third major splitting, all occurring during the grouting process. The completion of grouting and subsequent excavation provide clear evidence of the spatiotemporal development patterns of grout diffusion, as well as the corresponding variations in uplift displacement curves, as depicted in Figure 6d. Figure 6a-d demonstrate that the uplift displacement vs. grouting time curves of the four monitoring points exhibit a typical \"stepwise\" growth pattern, with an increase in speed after each step.
# Interconnecting Haptic Interfaces with High Update Rates through the Internet +
## 2 Internet-Based Haptic Applications  
The expansion of the Internet has led to the emergence of haptic applications. Researchers in [18] try to transfer the haptic feeling over the Internet. Network performance, which is often measured through delay and jitter is playing a crucial role in QoE of the user [19]. Other factors that can deteriorate the QoE is the scaling factor in macro-micro teleoperations [20, 21] and the inertia of the haptic system [22, 23]. Some interesting techniques that can compensate the delay of the network are shown in [24, 25, 26]. Moreover Fuzzy algorithms [27, 28] can be used when the network performance is not sufficient. Computer grids [29] can also offer sufficient computational power to process the demanding Fuzzy algorithms.  
Haptic applications could be used to military operations [30], education [31], telesurgery [32], video games [33], and video enhancement [34]. Motion-copying systems (MCS) [35] also use haptics to copy movements of skill experts for mentoring. Emotional communication can be enhanced among users with the use of haptic devices [36]. The virtual reality in now evolving to augmented virtuality [37].  
Haptics can help people with visual problems. Navigation and spatial information is offered through haptic sticks to blind people [38, 39]. Tactile sensors [40] can be used for remote manipulation and tele-operations. Kinesthetic disabilities can be overcomed with the help of haptic devices [41]. Humanoid robots can imitate the human walking by using haptic sensors [42].  
In order to use haptic devices through websites HTML5 offers new java script modules called Web Graphic Library (WebGL). WebGL can manipulate 3D models in a web browser. Researchers in [43] created an HTML5 Haptics (H5H) Plugin. It is compatible with almost all browsers, and it uses \"HAPI\" to render haptics. It supports almost all haptic devices.
## 2 Defect Detection Technique Using Multi-Channel Eddy Current System
### 16 Ch Eddy Current System  
#### 2.2.1 System Introduction  
A power supply was used to apply 12 V of power to the 16-channel eddy current system. The 16-channel eddy current system distributes the supplied power and applies 7 V of power to the sensors; it is connected to the acquisition program through a LAN connection and stores the real-time monitoring data and the X and Y component signal outputs. The acquisition program can monitor 16 channels of X and Y signals and 2D images in real time, and it can create a 3D image of the section where a defect occurs. In addition, the frequency and gain value can be set. In the experiment, we applied a frequency of 300 kHz and lift-off of 1 mm.
## 4 Performance Evaluation and Analysis  
### Deep Learning Model Selection  
In this study, ResNet-50 [26], DenseNet-121 [27], and Vision Transformer (ViT) [28] models were employed to evaluate the defect detection performance of deep learning models trained on synthetic thermographic datasets when applied to real thermographic datasets. Leveraging the architectural strengths of each model, ResNet-50, DenseNet-121, and ViT were used as key application models to compare the defect detection performance on real thermographic data when trained with synthetic thermographic data.  
ResNet-50 is an architecture that effectively addresses the gradient vanishing problem, a common challenge in deep learning models, through the introduction of residual connections. A residual connection directly adds the input to the output of the subsequent layer, preserving the original input signal while enabling the model to learn transformed information simultaneously. This design ensures stable training even in deep network architectures, alleviates the gradient vanishing problem, and facilitates effective interaction between low-level and high-level information [26].  
DenseNet-121 is an architecture designed to maximize the sharing of feature information across layers by introducing a dense connection structure, where the output of each layer is connected to all subsequent layers. With dense connections, each layer receives the outputs of all preceding layers as input, reducing information loss and enabling the effective learning of complex features with fewer parameters. These structural characteristics promote efficient gradient flow, ensuring stability during training even in very deep networks [27].  
The Vision Transformer (ViT), unlike Convolutional Neural Network (CNN)-based architectures such as ResNet-50 and DenseNet-121, processes images by dividing them into patches and treating them as sequence data, excelling in learning global relationships. This capability makes ViT particularly effective for defect detection in composite materials with complex structures, as it enables the understanding of global patterns and the identification of relationships between defect regions [28].  
The optimizer for each deep learning model was the Adam optimizer, with the learning rate set to 0.001 [29]. The batch size was configured as 64, and training was conducted for up to 100 epochs. However, early stopping was applied to all three models at the 10 th epoch, as no further improvements in loss were observed. Binary Cross-Entropy Loss was utilized for binary classification in defect detection.  
This study employed transfer learning to enhance the training efficiency of ResNet-50, DenseNet-121, and ViT. Pretrained weights from ImageNet were used to initialize all three models. Fine-tuning was performed on all layers, allowing the models to effectively adapt to the features of the synthetic thermographic data.
# Structural Behavior of Floor Systems Made by Floor Plates--Mechanical Model Based on Test Results
## 3 Results
### Deflection Shape of the Slabs  
Whereas Figure 5 shows the maximum deformations at midspan, measured, and calculated, questions arise if the behavior of the beam can also reasonably be predicted along the longitudinal axis. From Euler-Bernoulli beam theory, the influence of a small reduction of the second moment of area, at the joint location, should be limited as far as the beam reacts elastically. Four experiments were executed with additional devices along the longitudinal axis to obtain an idea about the deformation shape and if this can be reasonably calculated based on classic beam theory. Results are shown in Figure 6; the solid lines with the dots are the connected linear measurements while the dashed lines are those following the theory (only given for C02 and C04).  
It can be seen that in for the C04 test, an initial crack appears at the beginning with almost straight lines to the point of a maximum deflection, after which the whole section becomes cracked; measurements and theory therefore coincidence. It deviates again in the final stage where a plastic hinge is formed in the middle.  
It might not be a surprise that for the test specimens C05 and C06, with complete delamination of the interface, the deformation shape becomes asymmetric and completely different. The location of maximum deflection moves towards the plastic hinge at the end of the overlap bars.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 5 Design of High-Resolution Multispectral Polarization Imaging Scheme
### Workflow  
The specific workflow of the system is as follows:  
1. The subsystem supplies power to the image and lighting modules in the electronics and optical detection subsystem;
2. The optical detection subsystem shoots that target to obtain the visible light polarization and infrared image of the target;
3. The servo subsystem provides image stabilization, pitch, and rotation for the optical detection subsystem to realize scanning imaging;
4. The electronics subsystem fuses and stores the acquired images.
## 3 Results and Discussion
### Antioxidant Capacity
#### 3.1.3 Comparison between Oregano and Thyme Extracts  
Comparing our oregano and thyme extracts, oregano extracts presented the highest total phenolic content (\(\sim\)2152 mg GAE/g DW vs. \(\sim\)436 mg GAE/g DW). Likewise, methanolic extracts of oregano (_O. vulgare_) showed a higher TPC than thyme (_T. capitatus_) extracts, with 55 mg GAE/g DW and 38.2 mg GAE/g DW, respectively. However, when compared to our results, the Greek plants seemed to have a lower TPC [31]. This could be related to the preparations of extracts since Greek plant extracts were prepared from dried samples and our extracts were prepared from freeze-dried samples. The freeze-dried process can preserve phenolic compounds and the other compounds responsible for the antioxidant capacity of plants [32].  
However, our thyme extracts showed the highest ferric-reducing antioxidant powers (\(\sim\)3148 \(\upmu\)mol Fe\({}^{2+}\)/g DW vs. \(\sim\)1627 \(\upmu\)mol Fe\({}^{2+}\)/g DW in oregano extracts), while similar DPPH radical scavenging abilities were found (\(\sim\)0.6259 mmol TE/g DW for thyme and \(\sim\)0.5367 mmol TE/g DW for oregano). The same high antioxidant capacity of thyme extracts was found by [PERSON] et al. [33], where ethanolic extracts (50%, \(v/v\)) of _T. vulgaris_ from Spain showed a higher antioxidant capacity by the DPPH radical assay, with 1.15 mmol TE/g DW (compared to 0.78 mmol TE/g DW for _O. vulgare_ from China). Contrary to these results, [PERSON] et al. [31] study showed that oregano extracts presented a higher antioxidant capacity than thyme in the DPPH radical assay, with 139.8 mg TE/g DW and 56.2 mg TE/g DW, respectively; and the same tendency was found in the FRAP assay [31]. Different origins, edapholicmatic conditions, harvest times, and methods of preparation of extracts could influence the antioxidant capacity and the phenolic composition of extracts. Also, the extraction solvent had an influence on the antioxidant capacity and phenolic composition of extracts, for instance, extracts from oregano and thyme with 50% ethanol showed the highest TPC when compared to ethanol and water [34].
# Influences of Different Factors and Sensitivity Analysis of Permeability of Gassy Coal
## 1 Introduction
[32] conducted unsteady state displacement tests on coal samples based on the NMR technique to establish and validate a relative permeability model considering interfacial effects. [PERSON] et al. [33] proposed and validated a new permeability model for deep coal, which incorporates matrix-fracture interaction and creep deformation, showing improved performance over classical models, with the internal swelling coefficient playing a key role in permeability evolution. [PERSON] and [PERSON] [34] studied permeability evolution in mining-disturbed coal under triaxial stress, with permeability models accounting for stress state and path, showing good agreement with experimental data and providing insights for coal mine disaster prevention and coal-CBM exploitation. [PERSON] et al. [35] proposed the use of modulus loss rate to represent the variation in elastic modulus in different directions and developed an anisotropic permeability model by relating permeability to strain based on the modulus loss rate. [PERSON] et al. [36] constructed an anisotropic permeability model for the permeability evolution induced by directional compression by deriving the fracture aperture diameter during compression and introducing the strain ratio between the matrix and fracture unit. [PERSON] et al. [37] constructed an anisotropic permeability model for a doubly porous coal body by integrating the flow and migration of compressible gases in the coal matrix and fracture system, assuming unequal thermal expansion and adsorption in each direction. [PERSON] et al. [38] defined damage by modulus loss and coupled the damage parameters into the C-B model to construct a permeability evolution model for damaged coal seams that considered the effect of damage on permeability. In summary, while numerous studies have been conducted on the permeability of gas-containing coal, yielding a wealth of valuable insights, there remains a relative lack of comprehensive analyses that specifically address the combined effects of various influencing factors on coal permeability. Moreover, few studies have delved into the sensitivity of these factors, leaving a gap in understanding how each factor individually and interactively impacts the permeability of gas-containing coal under different conditions. Therefore, in this study, seepage tests were performed using a damaged coal rock seepage test system to investigate the impact of various factors on the permeability of gas-containing coal and the sensitivity of permeability to these factors. The findings offer valuable insights to analyze the influencing factors and sensitivity of the permeability of gas-containing coal for the efficient extraction of coalbed methane.
# Deep Reinforcement Learning for Selection of Dispatch Rules for Scheduling of Production Systems
## 5 Method Implementation and Evaluation  
### DRL Agent Implementation  
The neural network used for this work is sequential. A sequential model is a linear stack of layers, where one layer is added at a time from input to output. There are four layers.  
* INPUT LAYER (1): This is the first layer of the model. It is a flatten layer, which is used to normalize the input. The shape of the input vector is (1, Ls). This means that the input is expected to be a one-dimensional array with state elements.
* MIDDLE LAYERS (2): There are two fully connected dense layers with 1024 units and a ReLU activation function in each. The ReLU activation function helps to introduce non-linearity into the model.
* OUTPUT LAYER (1): This is the final dense layer. This layer has action units, which correspond to the number of possible actions that the agent can take. The 'linear' activation function is used, which means that the output of this layer will be a linear combination of the inputs. This is used to represent action values (Q-values) directly. The shape of the output vector is (1, len(A)).  
In Table 2, the DRL agent parameters used in implementing the aforementioned DQN network are presented. The stability and convergence of the network are achieved after multiple training parameter combinations, with the best ones resulting in the following table.  
Regarding the DRL agent's DQN, it has already been mentioned that the input and output vector depend on the implementation approach. For this work, there are four assembly lines and a maximum of 60 jobs; thus,  
\[Ssub=\left\{J_{c},P_{i},Jrw_{c},Itmin_{c},Jtmax_{c},Jps_{c1},Jps_{c2},Jps_{c3}, Jps_{c4}\right\} \tag{10}\]  
For the output vector, which is the action space, the three dispatch rules over the four resources create an action space of 81 actions. Let us encode the dispatch rules as follows:  
* _Shortest Processing Time_\((SPT)=1\);
* _Longest Processing Time_\((LPT)=2\);
* _Most Tasks Remaining per Job_\((MTRI)=3\).  
The action space consists of all combinations of the three dispatch rules over the four resources, which are the assembly lines.