esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hardening Models
### Ptw
The first segment of Equation (17) (\(y_{0}-(y_{0}-y_{\infty})\) erf\(\left[\kappa\widehat{T}\,\ln\left(\frac{\gamma\dot{\xi}}{\dot{\epsilon}} \right)\right]\)) relates to plastic flow resultant from thermally activated dislocation glide. The third segment of Equation (17) (\(\varsigma_{0}\left(\frac{\dot{\epsilon}}{\gamma_{5}^{6}}\right)^{\beta}\)) relates to plastic flow occurring in an overdriven shock regime where dislocation drag is the pre-eminate mechanism for plastic flow. The second segment of Equation (17) (\(y_{1}\left(\frac{\dot{\epsilon}}{\gamma_{5}^{6}}\right)^{y_{2}}\)) allows for the potentiality of a significant upturn toward the end of the loading regime that is dominated by thermally activated dislocation glide (such as that depicted in Figure 10 of Reference [8], where \(\dot{\epsilon}\sim 10^{4}\)). With the four building blocks now in hand, we can now proceed with a statement of the dimensionless flow stress, \(\widehat{\tau}\):  
\[\widehat{\tau}= \begin{cases}\widehat{\tau}_{\text{s}}&\text{if $a$}\\ \widehat{\tau}_{\text{s}}-\left(\widehat{\tau}_{\text{s}}-\widehat{\tau}_{y} \right)\exp\left(-\frac{1}{k}\,\epsilon\right)&\text{if $b$}\\ \widehat{\tau}_{\text{s}}+\frac{1}{p}\left(s_{0}-\widehat{\tau}_{y}\right)\ln \left[1-\left[1-\exp\left(-p\,\frac{\widehat{\tau}_{\text{s}}-\widehat{\tau}_ {y}}{s_{0}-\widehat{\tau}_{y}}\right)\right]\exp\left\{-\frac{p\theta\epsilon} {\left(s_{0}-\widehat{\tau}_{y}\right)\left[\exp\left(p\,\frac{\widehat{ \tau}_{\text{s}}-\widehat{\tau}_{y}}{s_{0}-\widehat{\tau}_{y}}\right)-1\right] \right\}\right\}\right]&\text{if $c$}\end{cases}  \tag{18}\]  
where,  
a: if \(\widehat{\tau}_{\text{s}}=\widehat{\tau}_{y}\);  
b: if \(p=0\) (Voee equation applies);  
c: otherwise;  
\(k=\left(\widehat{\tau}_{\text{s}}-\widehat{\tau}_{y}\right)/\theta\) (\(\theta\) and \(p\) are dimensionless parameters). The flow stress and saturation stress are now given as:  
\[\sigma =2\,\widehat{\tau}\,G \tag{19}\] \[\sigma_{\text{s}} =2\,\widehat{\tau}_{\text{s}}\,G \tag{20}\]  
In summary, the PTW model is a simple history-independent model that is applicable to a very wide range of strain rates. It provides both flow stress and saturation stress. It has 15 input parameters: \(G_{0}\), \(M\), \(p\), \(s_{0}\), \(s_{\infty}\), \(T_{m}\), \(y_{0}\), \(y_{1}\), \(y_{2}\), \(y_{\infty}\), \(\alpha\), \(\beta\), \(\gamma\), \(\kappa\), and \(\theta\).
# Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing
## 3 Materials and Methods
### Classification Performance Metrics  
The classification results were evaluated by comparing the studied approaches with the ground-truth datasets. Different metrics were selected for a quantitative evaluation to analyze classifiers that work well with unbalanced data. These metrics were: accuracy, sensitivity, specificity, F1-score, and the Matthews correlation coefficient (MCC) [38; 39]. To compute these metrics, the following variables identify the possible outcomes in a binary classification problem:  
* True positive (\(TP\)): both positive true value and classification result,
* False positive (\(FP\)): negative true value and positive classification result,
* True negative (\(TN\)): both negative true value and classification result.
* False negative (\(FN\)): positive true value and negative classification result.  
As a result, the classification accuracy, sensitivity, specificity, F1-score, and MCC (_normalized_) are computed as:  
accuracy \[= \frac{TP+TN}{TP+FP+TN+FN},\] (6) sensitivity \[= \frac{TP}{TP+FN},\] (7) specificity \[= \frac{TN}{TN+FP},\] (8) F1-score \[= \frac{2\times TP}{2\times TP+FN+FP},\] (9) MCC \[= \frac{1}{2}\left(1+\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)( TP+FN)(TN+FP)(TN+FN)}}\right).\] (10)  
Accuracy, sensitivity, and specificity are common metrics in classification tasks that evaluate the precision with respect to positive and negative true values [38]. Meanwhile, the F1-score measures the precision of a test, where a value close to one means high precision and sensitivity [39]. The parameter MCC is used as a measure of the classification quality, and this parameter takes into account true and false positives together with negatives. The MCC is generally regarded as a balanced measure that can be used even if the classes are of very different sizes. In other words, the MCC(normalized) is a correlation coefficient between the observed and predicted binary classifications, returning a value between 0 and 1. A coefficient of 1 represents a perfect prediction, 0.5 no better than a random prediction, and 0 total disagreement between prediction and observation [39].
# Detection of Gas-Solid Two-Phase Flow Based on CFD and Capacitance Method
###### Abstract  
Multiphase flow in annular channels is complex, particularly in the region where the flow direction abruptly changes between the inner pipe and the outer pipe, as the cases in horizontal drilling and pneumatic conveying. Simplified models and experience are still the main sources of information. First, to understand the process more deeply, Computational Fluid Dynamics (CFD) package Fluent is used to simulate the gas-solid flow in the horizontal and the inclined section of an annular pipe. Discrete Phase Model (DPM) is adopted to calculate the trajectories of solid particles of different sizes at different air velocities. Also, the Two-Fluid model is used to simulate the sand flow in the inclined section for the case of air flow stoppage, for which an experiment is also conducted to verify the CFD simulation. Simulation results reveal the behaviour of the solid particles showing the dispersed spatial distribution of small particles near the entrance. On the other hand, larger particles manifest a distinct sedimented flow pattern along the bottom of the pipe. The density distribution of the particles over a pipe cross section is demonstrated at a variety of air velocities. The results also show that the large airspeed tends to generate swirls near the outlet of the inner pipe. In addition, Electrical Capacitance Tomography (ECT) technology is used to reconstruct the spatial distribution of particles, and the cross-correlation algorithm to detect velocity. Both the distribution and the velocity measurement by electric sensors agree reasonably well with the CFD predictions. The details revealed by CFD simulation and the mutual-verification between CFD simulation and the ECT method of this study could be valuable for the industry in drilling process control and equipment development.  
Discrete Phase Model (DPM); Two-Fluid model; numerical simulation; annular space; cross-correlation +
Footnote †: journal: Applied Physics Letters
## 4 Implementation in Seismic Monitoring of a Large-Scale Building Using Two Vision-Based Systems
### Seismic Behavior and System Identification of the Three-Story Building
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**System**} & \(\delta_{max}\) & \(\delta_{min}\) & \multirow{2}{*}{**System**} & \(\delta_{max}\) & \(\delta_{min}\) & \multirow{2}{*}{**(mm)**} & \multirow{2}{*}{**(\(\Delta_{max}\)**} \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(\%)** \\ \hline HS & 1118.3 & \(-\)787.4 & SD & 1113.3 & \(-\)779.2 & \(-\)28.57 & 3.63 \\ \hline \end{tabular}
\end{table} 
Table 8: Measurement difference between high-speed (HS) system and standalone DSLR (SD) system in assessing high-amplitude seismic excitation. The comparison between HS and SD systems in extracting the first mode of vibration in the terms of frequency, \(f_{1}\), and damping, \(\zeta_{1}\), is given in Table 9. The difference in measuring frequency, \(\Delta_{f}\), shows the lowest difference is computed in measuring the transverse frequency and a slightly higher difference is observed in other directions. The relative difference within the range of 3% is observed in measuring structural damping properties, \(\Delta_{\zeta}\) Overall, similar to what was demonstrated for high-amplitude displacement measurement, the structural modal properties computed from both vision systems are also very comparable.
## 3 Methodology
### Models  
The proposed model is designed to detect HWP malicious documents using a Transformer-based language model to leverage the linguistic characteristics (context) of malware. HWP malware is often encoded to evade detection; thus, it is first decoded into a human-readable code format. To embed this transformed code in a manner similar to language embedding, we implement MalCode2 Vec. Since text cannot be directly processed in language models, it must be converted into numerical representations. However, instead of arbitrary numbers, these values should encapsulate the relationships between words, embedding them into a meaningful vector space. Similarly, in malware analysis, malware code chunks are converted into numerical values that retain their semantic relationships. This study not only determines whether a document is malicious but also classifies the type of malware. However, there is a class imbalance among different malware types. In classification tasks, class imbalance results in the model failing to learn sufficient representations for the minority class, leading to reduced performance for those classes. To address this issue, we employ a data augmentation approach to generate additional samples for the minority class. The sample generation process utilizes a generative adversarial network (GAN). The rationale for using a GAN instead of a large-scale language model that generates grammatically refined text is that the generated samples do not need to function as actual malware but merely need to capture the characteristics of malware. Hence, a smaller model that effectively incorporates these characteristics is preferred.  
Figure 3: Example of (**a**) encrypted script codes in the stream and (**b**) decrypted script codes from the stream.
## 6 Practical Application  
The proposed algorithm is utilized in the Tactical Decision Support System (TDSS) being developed at the University of Defense, Czech Republic. TDSS aims at supporting commanders of the Czech Army in their decision-making at the tactical level. It has been implemented into several models of military tactics, such as optimal cooperative reconnaissance by aerial and/or ground unmanned vehicles, and optimal logistics distribution on the battlefield. More details about the information support of commanders can be found in [33; 34; 35; 36; 37; 38; 39].  
An example of the optimal logistics distribution model is shown in Figure 5. In this model, units (see blue circles labeled by numbers) deployed in the area of operations are supplied by logistic vehicles (see blue hexagons labeled by letters). Each unit may have different requirements for the amount of supplies it needs; the maximum capacity constraint of the supplying vehicles is thus taken into account. The model plans the entire logistic operation: each supplying vehicle knows the order of units it needs to visit and the shortest routes between them (see the black thick lines in Figure 5).  
The modified optimization criterion defined in Formula (2) is used mostly in this problem, as the general requirement is to conduct the operation as quickly as possible. Alternatively, the optimization criterion to minimize the fuel consumed can be selected. In such a case, the cost of an edge in the graph is computed as the fuel consumed by a vehicle which moves along the route between the nodes connecting the edge. This creates an asymmetric graph as the fuel consumed by moving between two nodes may differ, depending on the direction of travel; this is however no limitation for the proposed algorithm. More information about this model can be found in [33].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Instance**} & **BKS** & **Original ACO** & **ACO with AOP** \\  & **StDev (\%)** & **StDev (\%)** & **StDev (\%)** \\ \hline p08 & 9.393 (0.42\%) & 3.486 (0.14\%) & 0.028 (0.00\%) \\ p09 & 480.583 (26.18\%) & 8.838 (0.60\%) & 3.047 (0.22\%) \\ p10 & 114.528 (10.92\%) & 3.722 (0.35\%) & 4.720 (0.49\%) \\ p11 & 96.537 (11.94\%) & 24.424 (3.07\%) & 3.133 (0.42\%) \\ p12 & 0.000 (0.00\%) & 0.000 (0.00\%) & 0.000 (0.00\%) \\ p13 & 0.000 (0.00\%) & 0.000 (0.00\%) & 0.000 (0.00\%) \\ p14 & 9.385 (1.37\%) & 0.000 (0.00\%) & 0.000 (0.00\%) \\ p15 & 23.746 (3.67\%) & 20.894 (2.86\%) & 1.891 (0.30\%) \\ p16 & 59.136 (8.52\%) & 5.189 (0.66\%) & 0.000 (0.00\%) \\ p17 & 10.837 (1.57\%) & 24.148 (2.95\%) & 0.000 (0.00\%) \\ p18 & 209.406 (23.38\%) & 31.286 (4.11\%) & 7.135 (1.09\%) \\ p19 & 46.958 (6.76\%) & 22.779 (2.62\%) & 3.387 (0.52\%) \\ p20 & 11.839 (1.71\%) & 35.761 (3.90\%) & 4.437 (0.65\%) \\ p21 & 199.647 (21.32\%) & 41.186 (4.78\%) & 8.948 (1.36\%) \\ p22 & 69.188 (9.61\%) & 60.946 (6.33\%) & 5.530 (0.84\%) \\ p23 & 9.598 (1.39\%) & 80.569 (7.97\%) & 4.460 (0.65\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._
# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 2 Materials and Methods: Tsunami Modelling and Satellite-Based Emergency Mapping  
In this section the two main components of the proposed workflow are described, i.e., the GDACS integrated tsunami model and the SEM mechanism of the Copernicus Emergency Management Service (CEMS-RM).
## 4 Motivations  
In duty cycle OppNets, the contacts can be classified into two categories: the effective contact and the missed contact. The contact is effective when two nodes are both in the listening state at a certain time in the contact duration. As shown in Figure 1a, contacts at time \(t_{1}\) and \(t_{2}\) are the effective contacts, and the contacts at time \(t_{3}\) and \(t_{4}\) in Figure 1b are the missed contacts because the two nodes are not both in listening state for the contact duration.  
We analyzed the contact in duty-cycle OppNets in the example of Figure 1, and found that, when the duty cycle operation is randomly chosen, many contacts would be missed. As shown in Figure 2a, the states of node \(i\) and node \(j\) are randomly configured, and their contact at time \(t_{5}\) will be missed. We want to decrease the number of missed contacts, and each node can adaptively schedule its states to find its neighbours.  
As we know that knowing the characteristics of the OppNets is the key to designing effective protocols, applications and working mechanisms. Among the properties, the distribution of the inter-contact time is particularly important, since it is a good indicator of network connectivity. The inter-contact time is the duration between two consequent contacts of a certain node pair, the number of inter-contact time events indicates that the number of disconnection and reconnection events have occurred.  
As we mentioned before, it was verified from experiments that the inter-contact intervals had the distribution feature of self-similarity [17]. Since the self-similarity feature has mathematical predictability, we try to predict the future contact information based on the history inter-contact intervals. According to the possibility of future contacts between nodes, each node can choose to be in listening or sleeping state in each time slot, and the number of missed contacts will decrease. As shown in Figure 2b, the states of nodes \(i\) and \(j\) are adaptively configured based on the predicted contact information, and the contact occurs at time \(t_{5}\) will become the effective contact. Hence, in order to achieve this purpose, we aim to design an adaptive scheduling mechanism based on self-similarity in the following section.
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 2 Durability Prediction Approach for Carbonated Concrete Structures
### One-Dimensional Concrete Carbonation Model
#### 2.1.4 Cement Hydration, \(\alpha_{H}\)  
The main constituents of Portland cement include alite, belite, aluminate, and ferrite. Their formulas are C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A, and C\({}_{4}\)AF, respectively. The hydration of each constituent can be calculated by obtaining a weighted average as each compound shows different hydration behaviors even under the same curing condition. Hydration of cement can be calculated using the following Equation (6):  
\[\alpha_{H}=\alpha_{h(C_{3}S)}W_{C_{3}S}+\alpha_{h(C_{2}S)}W_{C_{2}S}+\alpha_{h( C_{3}A)}W_{C_{3}A}+\alpha_{h(C_{4}AF)}W_{C_{4}AF} \tag{6}\]  
where \(\alpha_{h(C_{3}S)}\) is hydration of alite; \(\alpha_{h(C_{2}S)}\) is hydration of belite; \(\alpha_{h(C_{3}A)}\) is hydration of aluminate; \(\alpha_{h(C_{4}AF)}\) is hydration of ferrite; and \(W_{i}\) is the percentage of the weight of the compound for the volume of concrete.  
The overall hydration reaction kinetics of concrete for each constituent according to types of cement (Table 2) can be obtained using a weighted average as shown in Figure 3.  
In this study, the degree of hydration of each compound was calculated using Equation (7) based on the Avrami equation [22]. The Avrami equation is strongly applicable to the early stage of crystallization, but not to the later stage process because the second crystallization takes place. Bayesian statistics, which shifts initial values to measurement data to fit posterior predicted values, can supplement the limitations of the Avrami equation.  
\[\alpha_{h}=1-exp[-\alpha_{i}\cdot(t-\beta_{i})]^{\gamma_{i}} \tag{7}\]  
where Table 2 can be referred to for \(\alpha_{i}\), \(\beta_{i}\), and \(\gamma_{i}\). These are constants according to compounds in cement. Table 1 is used to apply them in this model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{4}{c}{**Type of Portland Cement**} \\ \cline{2-5}  & **TYPE I** & **TYPE II** & **TYPE IV** & **TYPE V** \\ \hline C\({}_{3}\)S (\%) & 49 & 42 & 23 & 46 \\ \hline C\({}_{2}\)S (\%) & 23 & 37 & 58 & 32 \\ \hline C\({}_{3}\)A (\%) & 10 & 6 & 3 & 4 \\ \hline C\({}_{4}\)AF (\%) & 9 & 12 & 9 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of Portland cement [23].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\alpha_{i}\) & \(\beta_{i}\) & \(\gamma_{i}\) \\ \hline C\({}_{3}\)S (\%) & 0.25 & 0.90 & 0.70 \\ \hline C\({}_{2}\)S (\%) & 0.46 & 0 & 0.12 \\ \hline C\({}_{3}\)A (\%) & 0.28 & 0.90 & 0.77 \\ \hline C\({}_{4}\)AF (\%) & 0.26 & 0.90 & 0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Constants of compounds in cement [22].
## 4 Numerical Simulation of the Formation Effect of Grout Curtain in Ore-Rock Contact Zone
### Group-Hole Grouting Curtain Body Formation Effect  
#### 4.3.1 Group-Hole Grouting Calculation Model  
An engineering-scale three-dimensional porous media model is created to investigate the formation effect of the curtain body. The model has dimensions of 30 m \(\times\) 30 m \(\times\) 40 m and considers circular arrangements of the grouting holes. Eight grouting holes are set up to prevent overflow. The center of the working face is used as the starting point, and the grouting holes are positioned at equal intervals along a specific inclination and tangential angle, as illustrated in Figure 10. To ensure the quality of the curtain body formation, a grouting sequence is followed. First, the bottom plate, top plate, and two gangs are grouted in a specific order. This process involves grouting eight holes in the bottom plate to form a first-order curtain body. Next, a second-order curtain body is formed in the top plate, followed by a third-order curtain body in the two gangs at the end. This sequence helps reinforce both the top plate and the bottom plate.  
Figure 10: Multiple borehole Grouting Calculation Model. (**a**) Model structure (**b**) Model meshing.  
Figure 9: Single-hole curtain body morphology under different water pressures. (**a**) 0 Mpa; (**b**) 1.5 Mpa; (**c**) 3 MPa.
## 2 System Requirement
### System Design Overview  
The topology is shown in Figure 2. We used low-cost, flexible, and rigid dielectric substrates. The system consists of the following components: an ESP8266 EX Chip, a crystal oscillator, a Future Technology Devices International (FTDI) connector, a 4 MB flash memory, step-down regulators (3.3 V), a matching circuit, and a power supply. All the components are integrated on the PCB. A 50 \(\Omega\) connector is used for the connection to the semi-flexible button antenna. All communications between the components occur via the serial peripheral interface (SPI) protocol and universal asynchronous receiver-transmitter (UART).  
In the ESP8266 chip, the radio frequency (RF) transceiver supports the various channels of the IEEE802.11b/g/n standards. Furthermore, the impedance of the ESP8266 EX power amplifier (PA) output is \(39+j6\)\(\Omega\) (source impedance). Therefore, a matching circuit is added. The input impedance (load impedance) is \(49-j4\)\(\Omega\) (from the button sensor  
Figure 2: Design system architecture for the button sensor antenna (Point A: BSA Device, and Points B, C: WiFi Router).  
antenna to the chip) after the addition of a T-shaped matching circuit consisting of two series inductors (1 nH) and a parallel capacitor (1 pF), connected to the ground plane. A U.FL connector connects the button antenna to the wireless sensor module via the matching network. In addition, a 1000 mAh Lipo rechargeable battery with a volume of \(12\) mm \(\times\) 4 mm \(\times\) 2 mm was chosen as the power source. A wrist band cover (similar to a watch cover) was manufactured by a 3D printer using polylactic acid (PLA) material. PLA is known to be harmless to humans and was used as the enclosure material for the sensor module. The BSA was programmed using the received signal strength indication (RSSI). The button sensor antenna was connected to the internet, to transmits the on-body monitoring data to the Blynk cloud in the Android using the TCP/IP protocols.
# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 3 Materials and Methods
### Exclusive Task Layer Design  
#### 3.3.1 Conditional Random Field  
Conditional random field (CRF) is a probabilistic graph model for sequence annotation tasks. It can model the output sequence under the condition of a given input sequence and fully consider the context information to improve the accuracy of annotation. Its structure is shown in Figure 7.  
CRF directly models conditional probability \(P(Y\mid X)\), that is, the probability of predicting label sequence \(Y\) given input sequence \(X\). The model describes the relationship between the output labels and the input sequence through the feature function, as well as the dependency between the labels. By optimizing the weight parameters, CRF is able to maximize the log-likelihood function of the training data. The Viterbi Algorithm is often used to efficiently find the optimal label sequence during the push process.  
Given the input sequence \(X=(x_{1},x_{2},\ldots,x_{T})\) and the corresponding output sequence \(Y=(y_{1},y_{\cdot},\ldots,y_{T})\):  
\[P(Y\mid X)=\frac{1}{Z(X)}exp\left(\sum_{t=1}^{T}\sum_{k}\lambda_{k}f_{k}(y_{t },y_{t-1},X,t)\right) \tag{17}\]  
\[Z(X)=\sum_{Y}exp\left(\sum_{t=1}^{T}\sum_{k}\lambda_{k}f_{k}(y_{t},y_{t-1},X,t )\right) \tag{18}\]  
\[P(Y\mid X)=\sum_{t=1}^{T}\sum_{k}\lambda_{k}f_{k}(y_{t},y_{t-1},X,t)-\log Z(X) \tag{19}\]  
where \(f_{k}(y_{t},y_{t-1},X,t)\) is the characteristic function, which describes the relationship between the output tag and the input sequence, as well as the dependency between the tags, \(\lambda_{k}\) is the weight of the characteristic function, and \(Z(X)\) is a normalized factor, ensuring a normal probability distribution that sums to 1.  
The characteristic function can be divided into two categories: the state characteristic function \(f(y_{t},X,t)\) is used to describe the label \(y_{t}\) and input \(X\) t relationship in place; and the transfer characteristic function \(f(y_{t},y_{t-1},X,t)\) is used to describe the label \(y_{t}\), the previous label \(y_{t-1}\), and the relations among them.