esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Implementation of the Nonlinear Creep Model of AS
### Numerical Model Verification  
To verify the accuracy of the secondary development of the nonlinear creep model, a numerical calculation model that was consistent with the creep test of AS was established. The size of the simulation model was a 50 mm \(\times\) 100 mm cylinder with a total of 14,280 units and 14,686 nodes. A vertical uniform stress was applied at the top of the model, and a fixed circumferential stress was applied on both sides to simulate the confining pressure. A displacement constraint was applied at the bottom in the vertical direction. Displacement constraints, specific numerical models and boundary conditions are shown in Figure 12a. The parameters of the model were the material parameters of AS. In this simulation, the axial load was applied by means of fixed confining pressure and stepwise loading. The confining pressure was set to 18 MPa, and the creep time of each stage was 48 h. The axial load deviatoric stresses of successive stages were 39.6 MPa, 43.9 MPa, 48.2 MPa, 52.5 MPa, and 56.8 MPa.  
In the numerical simulation, the rock parameters were taken as the average values from the conventional triaxial compression tests. The variations in the specimen axial displacement with time under different loading stresses are shown in Figure 12b. The deformation curves obtained by the numerical calculation agree well with the experimental results. The good agreement between the numerical simulation and lab experiments indicates that the nonlinear creep model developed by FLAC3D can reflect the creep behaviors of the surrounding rock, which can be used in future analysis of Quadian Coal Mine 22.  
Figure 11: Flow chart of the numerical implementation process for the nonlinear creep model of AS.
# Seismic Design Procedure for Low-Rise Cold-Formed Steel-Special Bolded Moment Frames
## 4 Test Results
### Global Responses
To reuse for the test of specimen BS_03, specimen BS_02 was terminated before strength deterioration started, i.e., a storey drift ratio of 4.0%. The bolts of this specimen were snugly tightened; therefore, the load did not reach the beam yielding strength. Stable hysteresis without unexpected behaviour was observed. In BS_03, it was designed to observe unexpected behaviour. As simulated, the beam started to yield first after a storey drift ratio of 2.0%. No slippage at the bolted connection was observed until the testing was terminated. After the load reached the yielding of the beam, lateral torsional deformation accumulated, and the strength continuously deteriorated (see Figure 15). Figure 11: Applied load vs. storey drift ratio: (**a**) OS_01; (**b**) OS_02; (**c**) BS_01_03; (**d**) BS_02; (**e**) BS_03. Figure 12: Specim OS_01: (**a**) LTB (3.0% storey drift ratio); (**b**) contact of the beam and column (4.0% storey drift ratio); (**c**) local buckling of the web (4.0% storey drift ratio). After the beam reached its capacity, LTB occurred, and significant strength deterioration was observed. When a frame follows the CFS-SBFM design concept, strength deterioration only occurs after the storey drift ratio exceeds 3.0%. This acceptable storey drift ratio was determined based on the slippage and bearing of the bolted joint. Additionally, greater ductility can be achieved if the joint type meets the design requirements specified in the AISI standard (i.e., snugly tightened). While using the PT joint type makes installation easier and faster, it may result in less overstrength capacity, which affects the ductility.
## 2 Materials and Methods
### Analysis of Network Simulations  
#### 2.2.1 Firing Activity Analysis  
The first step of all analysis consisted in the detection of spiking activity of all nodes of the network. Spike timing was established as the passage of membrane potential of the nodes above a fixed threshold (\(-\)0.05, Figure 1a).  
We defined the mean firing rate for each region/node of interest (\(\mathrm{MFR}_{\mathrm{ROI}}\)) as the amount of spikes divided by the time window of observation.  
To characterize the global/population activity of the model (MFR), we averaged the \(\mathrm{MFR}_{\mathrm{ROI}}\) values across nodes (either all nodes or, when specified, the ones of the intact or the manipulated hemisphere):  
\[\mathrm{MFR}=\left\langle\mathrm{MFR}_{\mathrm{ROI}}\right\rangle_{\mathrm{ nodes}} \tag{8}\]  
Then, to quantify the impact of the local manipulation on activity, we defined the MFR difference (\(\mathrm{MFR}_{\Delta}\)) as:  
\[\mathrm{MFR}_{\Delta}=\mathrm{MFR}(\mathrm{local\ manipulation})-\mathrm{ MFR}(\mathrm{control}) \tag{9}\]  
where \(\mathrm{MFR}(\mathrm{condition})\) refers to the MFR in a condition of interest. Note that the \(\mathrm{MFR}_{\Delta}\) involved only the nodes not affected by the local manipulation (i.e., excluding the silenced nodes also in the control condition).  
Then, for each node we defined the time-dependent Instantaneous Firing Rate (IFR(t), in Hz) as the frequency of spikes in a time window of 25 ms, and the temporal dependence was obtained by sliding the time window by 10 ms on the simulated data. We varied the size of the time window (e.g., 50 ms) and found qualitatively similar results.  
In order to quantify the impact of the stimulation on the node's activity, we defined the quantities:  
\[\Delta\mathrm{IFR}(\mathrm{t})=\mathrm{IFR}(\mathrm{t},\mathrm{\ trial})-\left\langle\mathrm{IFR}(\mathrm{t},\mathrm{\ trial})\right\rangle_{\mathrm{(t}<0)} \tag{10}\]  
\[\left\langle\Delta\mathrm{IFR}(\mathrm{t})\right\rangle=\left\langle\Delta \mathrm{IFR}(\mathrm{t},\mathrm{\ trial})\right\rangle_{\mathrm{trials}} \tag{11}\]  
where <IFR(t, trial)>(t<0) is the average IFR over the time prior to stimulation, and <\(\Delta\mathrm{IFR}(\mathrm{t})\)>\({}_{\mathrm{trials}}\) is the average \(\Delta\mathrm{IFR}\) across trials.  
The level of network synchrony was quantified through the averaged pairwise cross-correlation between the spontaneous IFR signals:  
\[\text{CC}_{\text{IFR}}=\left\langle\frac{\text{Cov}\big{(}\text{IFR}_{\text{i}}, \text{IFR}_{\text{j}}\big{)}}{\sigma(\text{IFR}_{\text{i}})\ \sigma(\text{IFR}_{\text{j}})}\right\rangle \tag{12}\]  
where \(<\ldots>\) denotes the average over all possible pairs of nodes (excluding silenced nodes), \(\text{Cov}(\text{IFR}_{\text{i}},\text{IFR}_{\text{j}})\) is the covariance between two spontaneous IFR signals (\(\text{IFR}_{\text{i}},\text{IFR}_{\text{j}}\)), and \(\sigma(\text{IFR}_{\text{i}}),\,\sigma(\text{IFR}_{\text{j}})\) are the corresponding standard deviations of the IFR signals
# A Miniaturized and Fast System for Thin Film Thickness Measurement
## 4 Simulation
### Correlation Analysis of Influencing Factors
#### 4.2.2 Influence of Thin-Film Materials  
Based on the simulation signal of \(I_{r}\) (\(z\), \(\lambda\)), we change signal contrast \(K=B/A\) to analyze the influence caused by various absorption and reflection coefficients under different film materials. Figure 7a illustrates the reflection spectrum signals with of 0.1, 0.5, and 1, respectively. For a particular thickness, the amplitude of the signal gets higher when contrast increases, while the period remains nearly constant. The thickness calculation deviations under different contrasts are shown in Figure 7b. The deviations keep the value of 16.61 nm, which proves that the interference signal contrast does not relate to the thickness calculation deviation.
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 1 Introduction
### Depth Image  
In the depth image, the pixel value represents the distance from the camera to the subject. This depth image can be post-processed to add realism to the 2D image, or used in various areas, such as games, motion recognition, and 3D printing [24]. Depending on factors of interest, such as the material and hardness of the subject, various types of images, such as CT images or X-ray radiographic images, can be used to analyze the subject. Various studies, such as medicine, agriculture, and manufacturing, are actively being conducted [25; 26; 27; 28; 29; 30; 31]. These images have the advantage of seeing changes in parameters of interest (e.g., materials constituting the object, hardness, and height) from a photograph of the object at a glance [32]. A depth image is most suitable for judging tire errors, because the value of each pixel represents height, which is the most reliable criterion for detecting faults occurring in the tire surface.  
It is challenging to use a traditional computer vision algorithm to detect many errors occurring on the tire surface through simple height information. Because the tire's tread forms a finely curved shape, methods using conventional computer vision algorithms to address this error require curvature correction before error detection [33]. Additionally, to accurately detect an error occurring in a tire, it is necessary to be able to filter the noise of the height value and to be able to determine the shape of the information displayed by the height difference. In addition, excessive noise filtering to detect one error may lose features that play an important role in detecting another error. To solve this problem, in this paper, we detect the tire error using the result of training the depth image on the deep learning network model.
# Atomistic Investigation of Plastic Deformation and Dislocation Motion in Uranium Mononitride
## 1 Introduction
[PERSON] and [PERSON] [14] introduced a fully atomistic formalism to calculate the Peierls stress and applied it to straight edge dislocations in body-centered cubic iron. Alternatively, a hybrid continuum-atomistic approach based on the Peierls-Nabarro theory was used by [PERSON] et al. [15] to calculate the Peierls stresses and core widths for sodium chloride (NaCl) and lithium fluoride (LiF) and by [PERSON] and [PERSON] [13] to calculate the same parameters for uranium dioxide (UO\({}_{2}\)). Many studies have looked at the dislocation mobility in metals like aluminum [16; 17] and nickel [18], as well as in aluminum-magnesium alloys [18] and steels [19]. Additionally, [PERSON] et al. [20] conducted a density functional theory (DFT) study of dislocations in titanium nitride and determined the principal slip system. [PERSON] et al. [21] conducted a similar DFT study for NaCl as well as silver chloride. Two promising interatomic potentials of UN exist in the literature: [PERSON]'s angular-dependent potential [11], and [PERSON] et al. 's embedded-atom method (EAM) potential [22]. Both potentials have been assessed in our previous work [12], where their effectiveness in predicting thermophysical and mechanical properties was evaluated, identifying their strengths and weaknesses. Notably, while these potentials showed some capability in predicting mechanical properties and defect formation energies, their suitability for modeling dynamic phenomena remained uncertain. Given the importance of understanding plastic deformation in nuclear fuels, further investigation is needed into their ability to capture key dynamical processes, such as dislocation slip and mobility, using simplified single-crystal models. This is particularly valuable since such models usually provide a baseline to develop a multi-scale modeling approach that can predict the macroscopic mechanical behavior of polycrystalline samples from elementary atomistic mechanisms. In this work, we perform MD simulations of the stress-strain behavior of nanocrystalline UN, investigating the dynamic processes of plastic deformation and identifying the principal slip systems. The Peierls stress, threshold Schmid stress, and dislocation mobility are computed for straight edge and screw dislocations in UN, with complete mobility functions fitted. Finally, the yield stress is estimated based on two different approaches. In conducting these simulations, we carefully account for the limitations identified in our previous study [12], ensuring that the weaknesses of each potential are avoided--such as not relying on 0 K energy minimizations with the Tseplyaev potential and circumventing structures that invoke excessive U-U repulsion with the Kocevski potential.
# A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution
## 3 Knowledge Conflict Resolution Model
### Knowledge Conflict Resolution Model
Path embedding is acquired through training the structure of the knowledge graph. The output of the diagram is a knowledge graph without relationship. The confidence score is the intermediate result of a new relation, when the confidence score is greater than the conflicted relationship's confidence score, the new relationship will replace the conflicted relation. Firstly, the proposed model predicted the confidence between entities in the new event based on the semantic embedding and the path-based embedding vector. Secondly, the proposed model compared with the confidence of the original fact. If the difference between the two confidences was greater than a certain threshold, then the old facts were replaced with the newly added facts to achieve the renewal of the knowledge graph. The proposed algorithm Kgedl (knowledge graph evolution algorithm based on deep learning) is shown in Algorithm 1. ```
Input: F is the set of tuples in TKG and new facts,\(f=\)\(<\)\(s\),\(p\),\(o\),\(w\),\([b\),\(e]\)\(>\), \(f\in\) F, query Q, time constraint set C and query results F\({}_{\text{Q}C}\), \(f^{\prime}\in\) F\({}_{\text{Q}C}\);

Output: updated TKG. ```
1:for (\(j=0;j<|\)/j;\(j++\))
2:\(\{\)
3:\(\mathbf{p}=\mathbf{r}_{1}+\ldots+\mathbf{r}/\) // get the path embedding between entity \(s\) and \(o\)
4:\(s_{i}=w(W[\mathbf{s}_{i-1}\times\mathbf{p}_{i}])//\) RNN is utilized to get the concatenation of path embedding and semantic embedding
5:\(P_{embedding}(s,p,o,w,b)=f_{p,o}^{predict}(a_{e_{i},a_{o,b}},\ldots,a_{e_{i},a_{o,b +}})\)//Calculate the probability of fact to be truth from the perspective of embedding semantics
6:\(P(o|o^{\prime},R_{n})=\frac{R_{n}(o^{\prime},o)}{|R_{n}(o^{\prime},o^{\prime })|}\)// The probability that node \(o^{\prime}\) jumps to \(o\) along the edge of type \(R_{n}\)
7:\(h_{s,p}(o)=\sum\limits_{o^{\prime}\in\text{ramp}(P^{\prime})}h_{s,p}.p^{\prime }(o^{\prime})\cdot P(o|o^{\prime},R_{n})\)
8:\(P_{path}(o,s)=\sum\limits_{P\in P_{n}}h_{s,p}(o)\)\(P_{\text{f}}\) // Path score probability of node \(s\) to \(o\)
9:\(P(s,o)=\alpha P_{embedding}(s,o)+(1-\alpha)P_{path}(o,s)//\) Probability scores of semantic embedding and path embedding for nodes \(s\) to \(o\)
10:\(P(\text{G})=\frac{1}{Z}\exp\left(\sum\limits_{(f,w_{s})\in\text{F}}w_{f} \right)\)// Estimate the probability distribution of the facts to be true in the query subset
11:if \(\left(|P(\text{f})-P(f^{\prime})|>\theta\right)\) // If the confidence of the two facts is greater than the threshold
12:\(\{\)
13: deletefromTKG(\(f\));
14: addtoTKG(\(f\));
15:\(\}\)//updating TKG
16:\(\}\) ```  
**Algorithm 1** Knowledge graph evolution algorithm based on deep learning.
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 4 Simulation Results
### Scenario 1: Tracking Performance Test  
The first scenario tests the tracking performance of the presented control scheme. The transient of boost converter under the PI-PBC with GPEBO is shown in Figure 4, which contains the traces of output voltage \(v\), inductor current \(i\) and control input \(u\). \(\gamma=\) 10,000, \(\lambda=1000,\mu=1\times 10^{-6}\) are set. It is seen that bigger gains obtain a better transient. Afterward, the step change in \(x_{2\star}\) is considered. The nominal value of \(x_{2\star}\) is 12 V. After 0.2 s, it is changed to 18 V. Similarly, the reference is set to be 24 V after 0.4 s. The simulation result is revealed in Figure 5 with the selection of \(k_{p}=0.015,k_{i}=0.15\), which satisfies the condition of Equation (9). By observing this figure, one can see that the output voltage can converge to the desired values. Moreover, the estimated current also nicely approaches the real value. This can be seen by the result of the following estimation error.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol (Unit)** & **Value** \\ \hline Input voltage & \(E\) (V) & 6 \\ Reference output voltage & \(x_{2\star}\)(V) & 12 \\ Gain & \(x_{2\star}\)/\(E\) & 2 \\ Resistance & \(R\) (\(\Omega\)) & 100 \\ Inductance & \(L\) (mH) & 5 \\ Capacitance & C (\(\mu\)F) & 680 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation/experimental set—points and physical parameters.  
Figure 3: The structure of the proposed sensorless controller.
## 3 Results and Discussion
### Monitoring the Volatile Compound Profiles in Seaweed Fermentation
Most aldehydes have short chains (C \(\leq\) 7), such as furfural, hexanal, and heptanal. According to [PERSON] et al. [22], the notes of hexanal and heptanal were described as fishy odors. Aldehydes such as hexanals and heptanals were reduced after all microbial fermentations, in which BS fermentation was of the most significance. Moreover, the amount of safranal, a compound that contributes a spicy saffron-like note with herbaceous, tobacco facets, and floral undertones, was doubled after SC fermentation. As for alcohols, the aroma compound 1-penten-3-ol (green) [20] was identified in the unfermented _Ulva_ sp., and it was decreased in each microbial fermentation. The total alcohol content decreased after microbial fermentations except for SC (Table 1). Five additional alcohols were generated in the _Ulva_ sp. suspension after SC fermentation, such as 1-pentanol (fermented, oily, sweet) and 1-hexanol (fruity, floral aromatic) [20; 23]. In the metabolism of brewing yeast, the Ehrlich pathway is a metabolic route to produce high alcohol content from amino acids, involving transamination, decarboxylation, and reduction [24]. This finding might explain the alcohol production during the SC fermentation of _Ulva_ sp. Benzaldehyde (bitter almond and burnt sugar) was identified in the unfermented _Ulva_ sp., and it was converted into benzyl alcohol (floral and rose) [20] by alcohol dehydrogenase after BS or SC fermentation [25]. Our results were similar to those reported by [PERSON] et al. [26]; that is, benzaldehyde and benzyl alcohol were decreased and increased, respectively, through SC fermentation. On the contrary, benzaldehyde was oxidized into benzoic acid (sweet and pleasant odor) [27] during lactic acid fermentation, which was also observed in lactic acid bacteria-fermented dairy products [28]. In addition, the number of other aroma compounds, such as furan 2-ethylfuran (coca), was reduced after microbial fermentations, but BS fermentation increased the amount of 2,5-dimethylpyrazine (roasted, nutty) and dimethyl sulfide (boiled cabbage). The overall changes in volatile compounds for the unfermented and fermented _Ulva_ sp. were summarized in the heat map (Figure 1). Compared with the unfermented _Ulva_ sp., BS or SC fermentation could reduce aldehyde contents, which might mitigate potential unpleasant odors from seaweed. BS fermentation improved ketone production, whereas SC increased alcohol production during fermentation. By contrast, volatile acids were found in the _Ulva_ sp. suspension fermented with lactic acid bacteria (LA, LB, and LC).
## 6 Stage 1: TS Modeling
### Model Parameter Determination  
#### 6.2.1 TS Properties for the Order Range Determination  
The optimal orders of \(p\) and \(q\) for the ARIMA model or \(p\) for the ARI model without the MA part were close to \(2n\), i.e., 6 in this study. Subsequently, few choices were generated based on observations from the ACF and PACF plots of the TS segments. The same results of the TS example in Figure 8b are used in the following discussions. From the PACF plot, we concluded that lags 6 or 7 were the cut lag for this segment. From the ACF plot, autocorrelation diminished after lag 6 indicating that it was a tail-off lag. When generalizing to all segments, the order \(p\) for the AR part can be taken as 5, 6, or 7, and the order \(q\) for the MA part can be taken as 5 or 6 (0 for the ARI model). Thus, a group of models, e.g., ARI(5,1), ARI(6,1), ARI(7,1), ARIMA(5,1,6), ARIMA(6,1,6), and ARIMA(7,1,6) with \(p_{max}=7\) and \(q_{max}=6\) were initially considered.
# Failure Detection within Composite Materials in System Engineering Applications
## 4 Discussion  
Since mechanical characteristics can be used to build structures or components using predetermined materials in order to prevent unacceptable amounts of failure and/or deformation, a variety of mechanical properties must be measured and documented properly. Since the force-deformation properties of a CF composite are reliant on specimen shape, studying the stress-strain relationship will shed light on the composite's issues.  
According to expectations, the stiffness value of the CF specimens is inversely proportional to the speed with which force is applied. It has been observed that the 7-ply specimens had a far higher stiffness value than the 4-ply specimens.  
Wet lay-up samples had an average flexural strength of 566.7 MPa \(\pm\) 27.3 MPa, whereas pre-preg generated specimens had an average flexural strength of 680.8 MPa \(\pm\) 13.4 MPa, indicating that the wet lay-up samples had a 16.7% drop in flexural strength.  
This was deemed acceptable as it is very difficult to maintain a consistent resin to fabric ratio of the wet layup as the resin content is added by hand, showing how many variables there are and the difficulty of consistent repeatability in the process, even down to the single sample piece. The reduction in strength could be attributed to the lower weight across pre-preg and wet layup samples, although the reinforcement is near identical the reduction in epoxy resin matrix is reduced as seen in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Specimen Number** & **Production Method** & **Maximum Force (N)** & **Flexural Strength (Gpa)** \\ \hline  
\\ \hline \end{tabular}
\end{table}
Table 2: CF specimen’s characteristics, production methods and calculated values of flexural strength.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ply Number** & **Production Method** & **Weight (g)** \\ \hline
4 & Wet lay-up & 10.11 \\ \hline
7 & Wet lay-up & 18.06 \\ \hline
4 & Pre-peg & 13.46 \\ \hline
7 & Pre-peg & 24.05 \\ \hline \end{tabular}
\end{table}
Table 3: Carbon fiber specimens’ weights.