esa-sceva/satcom-chunk-collection huggingface dataset:
# Crystal Plasticity Finite Element Modeling on High Temperature Low Cycle Fatigue of Ti2 AlNb Alloy
## 2 Experiment and Modeling Theory  
### Experiment Materials and Procedures  
The combustion chamber casing is an annular thin-walled structure. The casing blank is obtained by forging, and then the finished product is obtained by machining the blank. In this paper, Ti\({}_{2}\)AlNb alloy is cut from the combustion chamber casing blank. The microstructure of the initial Ti\({}_{2}\)AlNb alloy is characterized at room temperature, and the results are shown in Figure 1. The chemical composition of as-received Ti2 AlNb alloy is shown in Table 1. According to EBSD (electron backscattered diffraction) results, alloy is composed of O phase, \(\beta\) phase and \(\alpha_{2}\) phase, accounting for 52%, 30% and 7% of the area, remainder is unresolved area, as shown in Figure 2. The different contents of Ti, Al and Nb elements and the solution elements lead to the variable phase composition of Ti2 AlNb alloy. The \(\beta\) phase is BCC structure, the \(\alpha_{2}\) phase is HCP structure, and O phase is orthorhombic structure. EBSD and EDS (energy dispersive spectroscopy) analyses are performed using a ZEISS Sigma 300 scanning electron microscope, which is equipped with an electron backscattered and an energy spectrometer. LCF tests are carried out at 550 \({}^{\circ}\)C. Figure 3a shows the sample sizes of tests. LCF tests are carried out on the Instron-8802 fatigue testing machine at a constant loading rate of 0.005 s\({}^{-1}\) with strain control loading. When the samples fracture or the stress of the testing machine decrease by 25% instantaneously, the failure of the sample was judged. As shown in Figure 3b, the loading waveforms of LCF tests are triangular wave and the strain ratio is \(-1\). And the Figure 3c is the evolution of maximum tensile stress per cycle. The specimens at different strain levels show slight cyclic hardening in the front and middle stages of low-cycle fatigue. At the end of the curve, there is obvious cyclic softening. The fatigue life of four strain range is shown in Table 2. When the total strain range reaches 1.6%, the fatigue life decreases to 482 cycles, the strain range can no longer increase. Therefore, the total strain ranges \(\Delta\epsilon_{\text{t}}\) of fatigue experiment are 1.2%, 1.3%, 1.4% and 1.6%, respectively.  
Figure 3: Fatigue experiments of Ti2 AlNb alloy: (**a**) shape and dimensions of the LCF tests specimens; (**b**) fatigue loading waveform; and (**c**) evolution of maximum tensile stress per cycle [23].  
Figure 2: Microstructure characterization and element proportion of Ti2 AlNb—base alloy: (**a**) grain morphology under OM (200\(\times\)); and (**b**) phase composition under EBSD (\(\beta\) is \(\beta\) phase, Ti2 AlNb is O phase, Ti3 Al is \(\alpha_{2}\) phase).  
\begin{table}
\begin{tabular}{c c c} \hline
**Test** & **Strain Range/\%** & **Life** \\ \hline  & 1.2 & 9660/cycle \\  & 1.3 & 1081/cycle \\ Fatigue & 1.4 & 742/cycle \\  & 1.6 & 482/cycle \\ \hline \end{tabular}
\end{table}
Table 2: Fatigue life of four strain ranges [23].
## 5 Results and Discussion
### Intra-Cluster Communication  
The simulation parameters employed throughout the experiments to evaluate the performance of the proposed cluster-head rotation method are summarised in Table 1. The proposed method is first tested using clusters composed of three and five nodes, each with heterogeneous initial energy levels ranging from 0.05 J to 0.2 J, ensuring that the total energy of nodes within each cluster is 0.5 J. Additionally, the nodes are assumed to generate data packets of varying sizes within the range of 1000 bits to 8000 bits. The proposed heterogeneity model ensures that the total data transmitted in one cycle by all nodes in a cluster equals 20,000 bits.
## 2 Materials and Methods
### Methods
#### 2.4.2 Standardized Anomaly Index  
The standardized anomaly index is a common method for quantifying dynamic changes in data and is calculated as follows:  
\[A_{i,j}=\frac{X_{i,j}-\overline{X_{j}}}{\sigma_{j}} \tag{6}\]  
where \(A_{i,j}\) is the standardized distance level index for day \(j\) of year \(i\), \(X_{i,j}\) is the original image value  
their counterparts, GOSIF and GLASS NPP have better quality and accuracy [40; 41] and can characterize SIF and NPP scientifically.  
First, the distributions of the GOSIF and GLASS NPP values were spatially compared. We calculated the 8-day average, maximum, and minimum values of GOSIF and NPP data from 2010 to 2020 (Figure 3). Comparing the three datasets, the distribution of GOSIF and GLASS NPP data essentially remained unchanged in terms of numerical distribution, with larger data values present in the southern part of the HHH Plain and smaller data values of data being scattered in the northern part of the HHH, showing an overall decreasing trend from south to north.  
Second, the correlation between GOSIF and GLASS NPP was analyzed over time. We constructed a time series of GOSIF and GLASS NPP data for the period 2010-2020 and  
Figure 3: Spatial distribution of the GOSIF maximum (**a**), GLASS NPP maximum (**b**), GOSIF mean (**c**), GLASS NPP mean (**d**), GOSIF maximum (**e**), and GLASS NPP maximum (**f**) from 2010 to 2020.  
performed a pixel-by-pixel correlation analysis using the Spearman method. The results (Figure 4) show a good correlation between the two datasets over the long time series. The overall correlation coefficient R\({}^{2}\) was >0.7, with only a low correlation observed in the small coastal water areas in the east.  
Finally, we compared the dynamic changes in the two datasets. We calculated the standardized anomaly values of the GOSIF and GLASS NPP data from 2010 to 2020 to characterize the dynamic changes in the data (Figure 5). The results showed that the dynamic changes in GOSIF and GLASS NPP were highly consistent with similar patterns and cycles.  
Based on the above results, we can preliminarily conclude that a linear relationship is present between SIF and NPP. Furthermore, SIF can, to a small extent, monitor dynamic changes in NPP. However, whether SIF can accurately monitor dynamic changes in NPP during drought events requires further evaluation and exploration.  
Figure 4: Results of the image-by-image correlation analysis of GOSIF and GLASS NPP in the HHH Plain from 2010 to 2020 (**a**) and significant values (**b**).  
Figure 5: Standardized GOSIF and GLASS NPP changes for the 8-day interval from 2010 to 2020.
## 4 Discussion
Alumina is a very efficient grain growth inhibitor capable of controlling the grain size of the TZP matrix while SA6 grains are too large and not numerous enough to avoid grain growth. 5 vol% of SA6 seem sufficient to enhance strength and toughness. Totaltoughness actually declined slightly by further increase of SA6 fraction. Transformation toughening was identified as the dominant toughing mechanism (\(\Delta\)K\({}_{\text{IC}}\)T \(\sim\)4-5.5 MPa\(\surd\)m). The contribution of other non-TT effects such as crack deflection and bridging was calculated from the difference (\(\Delta\)K\({}_{\text{IC}}\)NTT \(\sim\)1.5-2.5 MPa\(\surd\)m). Deflection and bridging effects were, however, not exclusively observed for SA6 containing materials, and also occur in TZP/alumina materials. A possible explanation for the inefficiency of the pure in situ platelet reinforcement can be found in a paper by [PERSON] et al. It is stated that, in composites with randomly oriented reinforcement elements which show incomplete debonding, it is the toughness and not the strength of the reinforcement elements which governs the toughness of the composites [37]. Occurrence of debonding depends on the interfacial strength, the fracture energy of the reinforcement and the elastic mismatch \(\alpha\) of reinforcement and matrix. Alumina has a higher Young's modulus than SA6 (400 GPa vs. \(\sim\)290 GPa). This should favor debonding of alumina/TZP interfaces compared to SA6/TZP interfaces due to higher \(\alpha\). [PERSON] stated that hexaaluminates have a low cohesive strength, so that it is very likely that the ratio between the fracture energy of the SA6/TZP interface \(\Gamma_{\text{i}}\) and the fracture energy of the SA6 \(\Gamma_{\text{f}}\) is higher than in case of an alumina/TZP combination [38]. In general, SA6 addition slightly promoted LTD, probably indirectly as the size of the matrix and gets coarser and its transformability increases. The concept to combine an yttria ceria co-stabilized matrix with alumina and hexaaluminates leaves a lot of space for variations which could lead to materials with improved properties more suitable for dental applications than the materials tested in this study. It probably makes sense to restrict toughness through the addition of higher amounts of second phase and/or stabilizer to a value at the threshold between defect size and transformation toughness control (8-9 MPa\(\surd\)m) in order to obtain composites with a more balanced mix of mechanical properties and ageing resistance.
## 2 Mathematical Formulation  
### Elastic and Plastic Strain Components  
The cyclic plasticity constitutive model consists of the [PERSON] yielding criterion, plastic flow rule, and a kinematic hardening rule. According to the [PERSON] criterion, the onset of yielding demarks the elastic and plastic domains and is defined as:  
\[f\left(\text{s.}\ \overline{a}.\ \sigma_{y}\right)=\sqrt{\frac{3}{2}(s- \overline{a}).(s-\overline{a})}-{\sigma_{y}}^{0} \tag{1}\]  
In Equation (1), \({\sigma_{y}}^{0}\) is the initial yield stress. Backstress is denoted by \(\overline{a}\) and determines the extent of the loading state outside the elastic limit. Term s is the deviatoric stress tensor and is expressed as:  
\[s=\sigma-\frac{1}{3}(\sigma.I)I \tag{2}\]  
Terms \(\sigma\), \(I\) are the applied stress and unit tensor, respectively.  
The combination of elastic strain, \(\varepsilon^{e}\), and plastic strain, \(\varepsilon^{p}\), results in the total elastic-plastic strain as:  
\[\varepsilon=\varepsilon^{e}+\varepsilon^{p} \tag{3}\]  
The elastic strain is defined by [PERSON]'s law as:  
\[\varepsilon^{e}=\frac{\sigma}{2G}-\frac{\vartheta}{E}(\sigma.I)I \tag{4}\]  
where \(G\) and \(E\) in Equation (4) are shear and elastic moduli, respectively. Term \(\vartheta\) is [PERSON]'s ratio. A flow rule was used to express relation between stress-strain within the plastic domain:\[de^{p}=\frac{1}{H_{p}}\langle ds.n\rangle n \tag{5}\]  
The unit normal vector on the yield surface is given as:  
\[n=\frac{(s-\overline{a})}{|(s-\overline{a})|} \tag{6}\]
## 2 Methodology
### Stochastic Point-Source Model
#### 2.2.2 Transformation into Time Domain  
In this study, generated Fourier spectra is transformed into the time domain to determine the PGAs of the corresponding artificial earthquakes. The following steps based on the point-source GMM are employed [6; 35]. The procedure involves calculations in both time and frequency domains. Steps (1), (2), and (5) are performed in the time domain, while steps (3) and (4) are conducted in the frequency domain:  
1. Generate Gaussian or uniform white noise for the desired time duration.
2. Apply a shaping window function to the white noise by multiplying them.
3. Transform the windowed noise into the frequency domain and normalize it by the square root of the mean square (RMS) amplitude spectrum.
4. Multiply the normalized noise with the modeled FAS as defined in Equation (1).
5. Apply Inverse Fast Fourier Transformation to obtain the predicted ground motion time histories.  
The shaping window function for time \(t\), and scaled time \(t_{\eta}\) for different window functions is defined as follows [36]:  
\[w\left(t;\epsilon,\eta,t_{\eta}\right)=a\left(t/t_{\eta}\right)^{b}\exp\left\{ -c\left(t/t_{\eta}\right)\right\} \tag{12}\]  
where \(b\), \(a\), and \(c\) are:  
\[b=-\frac{\epsilon\ln\eta}{1+\epsilon(\ln\epsilon-1)} \tag{13}\]  
\[a=\left(\exp\left\{1\right\}/\epsilon\right)^{b} \tag{14}\]  
\[c=b/\epsilon \tag{15}\]  
Following previous research [36], the values of the parameters \(\epsilon\) and \(\eta\) are set to 0.2 and 0.05, respectively. The scaled time \(t_{\eta}\) is defined as:  
\[t_{\eta}=f_{Tgm}T_{gm} \tag{16}\]  
where parameter \(f_{Tgm}\) has a value of 1 for the box window and 2 for the exponential window [37]. This study follows the recommendation to use \(f_{Tgm}=2\) to ensure accurate comparisons between response spectra computed using both window types [6]. For the duration model of ground motions in low-to-moderate seismicity regions such as ENA, the following expression is used [38]:  
\[T_{gm}=\frac{1}{2f_{c}}+hR \tag{17}\]  
where \(h\) is the slope over a given source-to-site distance. For ENA, the values of \(h\) are 0.16 for \(10\) km \(\leq R<70\) km, \(-0.03\) for \(70\) km \(\leq R<130\) km, and 0.04 for \(130\) km \(\leq R<1000\) km [38].
## 3 Methodology
### Deep-Learning-Based Citation Classification Approach
#### 3.2.2 Techniques to Handle the Class Imbalance Problem  
In this section, we tackle the problem of imbalanced classes by introducing two sophisticated classifier-level data balancing strategies that alter the model training procedure while retaining the training of the original datasets. We used the weight-balancing function and focal-loss functions at the classifier level instead of rule-based data-level balancing techniques such as random undersampling and oversampling. The mechanisms used are discussed in detail in the following paragraphs.  
_Weight balancing_ is a method for balancing datasets by altering the weightage measure for each class sample during the loss function computation. In most cases, every sample and every class are given equal and similar weightage; however, in the case of relevant classes, we might want to give certain of them high weightage. Similarly, in the case of the important and non-important binary classes of citation functions, instead of wasting time and resources looking for more data samples for the minority and relevant class that only makes up 10% of our dataset, we deployed weight balancing to both our classes (binary classes), giving the \"important\" class a higher score to reflect its greater significance.  
_Focal loss_ is a procedure in which hard-classified examples are given more weight than well-classified instances. We usually have some data samples in our dataset that are simple to classify. Although, these data samples were separated with ~99% efficacy in training, unlike the more challenging and complicated data instances, yet the outcomes were unsatisfactory. The main problem here is that the readily classified training samples provided just as much to the total cross-entropy loss value as the more difficult data points that improved our overall accuracy when correctly identified, and hence to which more emphasis should be given.  
Our multi-class data comprises seven classes, the most common of which are background and none, resulting in well-classified data instances. To deal with the data imbalance, we used a focal-loss function, which reduces the weight of well-classified samples while increasing the weight of minority-class samples, which are more difficult to classify. The generic cross-entropy function is shown in Equation (13) as follows:  
\[Cross\;Entropy=-\log(p_{t}) \tag{13}\]  
\[Focal\;Loss=-(1-p_{t})^{7}\log(p_{t}) \tag{14}\]where \(p_{t}=\left\{\begin{array}{c}p\ y=1\\ 1-p\ otherwise\end{array}\right..\)  
Equation (14) depicts the focal-loss function, which boosts the general cross-entropy function by a factor of \((1-p_{t})^{\gamma}\).
## 1 Introduction  
The problem of predicting the overturning of block structures under earthquake excitations has received continued attention over the years as it may respond to two intriguing questions of interest to seismologists, archaeosing-s, and engineers: (i) what are the physical conditions and earthquake excitation properties that may cause a rigid block structure to overturn? And (ii) given that some rocks, columns, tombstones and other man-made artefacts are reported to have overturned in the aftermath of earthquakes while others of different dimensions did not overturn, what information can be deduced from these observations about the intensity and properties of the seismic signal?  
Near-fault ground motions are characterized by single long duration strong pulses which make them particularly destructive to some structures, such as inverted pendulum structures, due to the high velocities that are imparted to above ground masses. This has prodded [PERSON] [1] to investigate the overturning potential of a near-fault earthquake on a rocking block. The equations he derived for block rocking under a half sine pulse have been adopted by the majority of researchers in the field over the last 40 years. Various numerical solutions as well as closed form solutions that depend on the shape of the adopted pulse were proposed. Many shapes and refinements were developed to represent the dominant pulse of near-earthquake ground motions to be used in lieu of the full earthquake signal when investigating the overturning stability of blocks. Experimental programs were carried out to identify which intensity measures correlate better with overturning outcomes of blocks and columns subjected to various pulse types and full earthquake signals. Robust predictive models have not, however, been obtained. In the following paragraphs, previous research relevant to the problem is reviewed along four converging avenues: (i) theoretical and numerical investigations of the stability of rocking blocks, (ii) experimental investigations, (iii) representations of near-fault earthquakes by their dominant impulses, and (iv) archaeoseismological relevance of the rocking block problem and the need for efficient scalar intensity measures is identified.
## 5 Pairwise Clock Synchronization under Unknown Random Delays
### Bootstrap Bias Correction
Moreover, another bias correction method, referred to as the Jackknifebias-correction, was reported in [45]. The Jackknife bias-corrected estimator of the exponential MLE, \(\hat{\theta}=(U_{(1)}-V_{(1)})/2\), was proposed and compared to the corresponding bootstrap bias-corrected estimator Equation (35). Simulation results in [45] illustrated that the Jackknife bias correction does a better job at reducing bias, but is generally eroded by a variance increase, which yields a larger MSE.
### Smart City Data Based Services  
With the ongoing digitalization of cities, open urban platforms can be utilized in various fields [6]. The open provisioning of data can be seen as an opportunity to improve the urban living conditions and provide city services for different topics and areas of relevance:  
* _Smart Government_: The key difference between an e-government and a smart government is the use of intelligently networked objects and cyber-physical systems [7]. Structures such as big data and open data are included in the development strategy. The change relates either to the resulting product, the process, or the prerequisites for the creation of the administrative service in question. Smart government therefore provides the means towards a data-driven digital administration [7];  
Figure 1: Abstract architecture of an open urban platform.  
* _Smart Economy:_ Within a smart economy, cities are able to provide important data for new business models and to create conditions for economic development;
* _Smart Environment:_ City data and their analytics provide new terms and conditions for environmental support. Within a large number of governmental tasks--such as waste monitoring or energy efficiency in the case of street lighting--data analytics can help to reduce CO2 emissions and even provide an improved habitat for animals and humans;
* _Smart Urban Society:_ Smart Urban Society addresses--in a data-based context--topics such as digital collective urban living and social interaction. Therefore, new incentives to live in a city can be developed and the social exchange can be supported. Topics such as smart health and education could also be the focus of this field and can be supported, e.g., through city data and self-sovereign entities;
* _Smart Mobility:_ Smart mobility increases the use of environmentally friendly mobility options. Here, data are used to give users more information about the possibilities and benefits of using cheaper, faster, and environmentally friendly mobility solutions.
# Design of an Active and Passive Control System of Hand Exoskeleton for Rehabilitation
## 4 Active and Passive Control
### Passive Control  
Because of the partial or complete loss of motor function, in general, the stroke patients cannot finish the whole rehabilitation training, i.e., they cannot actively control their hands to the functional position. Under this circumstance, the control system of the hand rehabilitation exoskeleton can detect the contact parameters by the force sensors on the fingertips and switch to the passive mode, where the fingers are actuated to the functional position by the exoskeleton. In the passive rehabilitation mode, the goal is to actuate the joints to follow the desired trajectory and move to the preset functional position. The PD control method based on the inverse dynamic compensation is adopted and the control block diagram is shown in Figure 6.  
Defining the trajectory error \(e(t)=\mathbf{\theta}(t)-\mathbf{\theta}_{d}(t)\) and velocity error \(\dot{e}(t)=\dot{\mathbf{\theta}}(t)-\dot{\mathbf{\theta}}_{d}(t)\), \(\mathbf{\theta}_{d}(t)\) is the desired trajectory of the exoskeleton in joint space, \(\mathbf{\theta}(t)\) is the actual trajectory in joint space. Assuming the desired trajectory \(\mathbf{\theta}_{d}(t),\dot{\mathbf{\theta}}_{d}(t)\) are bounded, and by inducing \(\mathbf{\
u}\) and substituting it into the dynamic equation in joint space, we have the controller as follows:  
\[\tau=\mathbf{D}(\mathbf{\theta})\mathbf{\
u}+\mathbf{h}(\mathbf{\theta},\dot{\mathbf{\theta}})+\mathbf{G} \tag{3}\]  
substituting Equation (3) into the dynamic equation Equation (2), and we have,  
\[\mathbf{D}(\ddot{\mathbf{\theta}}-\mathbf{\
u})=\mathbf{0}. \tag{4}\]  
if \(\mathbf{D}\) is invertible, then from (4) we have:  
\[\ddot{\mathbf{\theta}}=\mathbf{\
u}. \tag{5}\]  
setting \(\mathbf{\
u}\) as the following equation:  
\[\mathbf{\
u}=\ddot{\mathbf{\theta}}_{d}-\mathbf{K}(\mathbf{p})\mathbf{e}. \tag{6}\]  
substituting (5) into (6), the error equation of the system can be obtained as follows:  
\[(\mathbf{p}^{2}\mathbf{I}+\mathbf{K}(\mathbf{p}))\mathbf{e}=0. \tag{7}\]  
introducing the PD control method into (7):  
\[\mathbf{K}(\mathbf{p})=\mathbf{K}_{\upsilon}\mathbf{p}+\mathbf{K}_{p}, \tag{8}\]  
where \(\mathbf{K}_{\upsilon}\) and \(\mathbf{K}_{p}\) are positive diagonal matrices. If we substitute Equations (6) and (8) into Equation (3), the passive rehabilitation control equation can be obtained as:  
\[\tau=\mathbf{D}(\mathbf{\theta})(\ddot{\mathbf{\theta}}_{d}-\mathbf{K}_{\upsilon}\dot{\mathbf{e} }-\mathbf{K}_{p}\mathbf{e})+\mathbf{h}(\mathbf{\theta},\dot{\mathbf{\theta}})+\mathbf{G}(\mathbf{\theta}). \tag{9}\]  
Figure 6: The passive control block diagram.