esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Method for Named Entity Recognition and Its Application to CEMR+
## 3 Method
### Attention-Based ID-CNNs-CRF Model
#### 3.3.1 Iterated Dilated CNNs  
The Dilated CNN model proposed by [PERSON] et al. [30] is different from the normal CNN filter. Normal CNN acts on a continuous position of the input matrix and continuously slides to do convolution and pooling. The dilated CNN model adds a dilation width to the filter. When it acts on the input matrix, it skips the input data in the middle of all the dilation width, and the size of the filter matrix itself remains the same, so the filter achieves a wider input, as is shown in Figure 4.  
For the input sequence \(X=\{x_{1},x_{2},...,x_{t},...,x_{n}\}\), we denote the \(j\)-th dilated convolutional layer of dilation with width \(\delta\) as \(D_{\delta}^{j}\). We applied a dilation-1 convolution \(D_{1}^{0}\) to transform the input as a representation \(i_{t}\) and took it as the beginning of the stack layers.  
\[c_{t}^{(0)}=i_{t}=D_{1}^{(0)}x_{t} \tag{1}\]  
Next, the stacked layers are represented as follows:  
\[c_{t}^{(j)}=r(D_{2^{l-c-1}}^{(j-1)}c_{t}^{(j-1)}) \tag{2}\]  
where \(c_{t}^{(j)}\) denotes the output of the \(j\)-th dilated convolutional layer of dilation, \(L_{c}\) denotes the layers of dilated convolutions, and \(r\) denotes the activation function of the ReLU activation function.  
Figure 3: Attention-based ID-CNN-CRF architecture. We stacked 4 Dilated CNN blocks, each as shown in Figure 5. To simplify the drawing, we used _Stacked Blocks_ to represent all the layers in the middle. For the output \(b_{t}^{L}\) at position \(t\) (the orange unit with four circles), we calculated its similarity with all the output units and took them as weights \(\{\alpha_{t,1},...,\alpha_{t,t},...,\alpha_{t,n}\}\). The output was multiplied by the corresponding weight and then summed as the attention vector \(a_{t}\). Concatenate \(a_{t}\) with \(b_{t}^{L}\), and add a full connection layer to map it into the category space. Finally, the optimal tag sequence was obtained by CRF.  
Each block had three layers of expansion convolution (excluding input), so the output of each block was \(c_{t}^{(3)}\). Figure 5 illustrates the architecture of a block. Our model stacks four blocks of the same architectures, and each is a three-layer dilated convolution layer with the dilation width of \([1,1,2]\). We define these three layers of expansion convolution as a block \(B\).  
\[b_{t}^{(1)}=B(x_{t}) \tag{3}\]  
\[b_{t}^{(k)}=B(b_{t}^{(k-1)}) \tag{4}\]
## 4 Verification
### Verification of the Solution for a Saturated Half-Space  
To further verify the validity of the proposed solutions, a comparison of the complex dynamic stiffness of the pile in frozen and saturated soils is provided in this section. The complex dynamic stiffness of the pile in saturated soil was calculated according to the rigorous formulation of the porous elastic mechanics integral through a virtual pile method described by [PERSON] and [PERSON] [15]. When the temperature in the analysis was set as \(T=-0.15\ ^{\circ}\mathrm{C}\), the ice content was approximately zero, and the frozen soil was equivalent to the saturated soil. Figure 2 shows the results obtained from the simplified model of the present solutions and the [PERSON]'s model of [PERSON] and [PERSON] [15] within the low-frequency range, where the horizontal axis represents the dimensionless frequency \(b_{1}=\omega/\omega_{\rm g}\), and the vertical axis denotes the dimensionless complex dynamic stiffness of the pile top \(k_{d}^{\prime}\), which can be decomposed into a real part and an imaginary part. \(\omega_{\rm g}\) is the natural frequency of the free vibration of an elastic bearing pile, meeting the following characteristic equation \(\frac{\omega_{\rm g}}{v_{\rm p}}=k_{\rm b}\cot(\frac{\omega_{\rm g}}{v_{\rm p }}H)\) in which \(v_{\rm p}\) is the longitudinal wave velocity of the pile. To ensure consistency with the calculation conditions provided in reference [15], the bearing coefficient of the pile bottom was taken as \(k_{\rm b}=4\mu a/(1-\
u)\), according to the analytical solution for the rigid disk in an elastic half-space through theoretical derivation and experimental data inversion [15]. It can be seen in Figure 2 that under the conditions of two different seepage forces (i.e., \(b^{*}=0\) indicates a free support condition, and \(b^{*}=1000\) indicates a fixed support condition), the curves corresponding to the real and the imaginary parts of the simplified model and the reference curves [15] are in good agreement. This confirms that when the temperature is close to the freezing point, the solution proposed in this paper can be reduced to the strict solution for the vertical vibration of the pile in saturated soil. Furthermore, Figure 3 shows a comparison of the reflected signals obtained for the two aforementioned models in the time domain, and it can be seen that these curves are almost identical. In Figure 3, \(\bar{t}=tc/2H\) denotes the dimensionless time.
# Wireless Power Transfer Approaches for Medical Implants: A Review
## 2 Different Approaches for a Wireless Power Transfer System
### Microwave-Based Wireless Power Transfer
The authors assert that the calculations of SAR are within the limits suggested by IEEE and claim that the proposed leadless pacing method is safer, and eliminates the battery, lead, and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Frequency**} & \begin{tabular}{c} **Output** \\ **Power** \\ **(mW)** \\ \end{tabular}  & \multirow{2}{*}{ \begin{tabular}{c} **Efficiency** \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Active** \\ **Range** \\ **(mm)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Transmitter** \\ **Dimension** \\ **(mm)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Receiver** \\ **Dimension** \\ **(mm)** \\ \end{tabular}  } \\ \hline
[72] & 2020 & 915 MHz & - & 1.93 & 40–50 & - & \(30\times 30\) \\
[73] & 2020 & 5.8 GHz & 0.01 & \(1.2\times 10^{-5}\) & 1 & - & \(0.116\times 0.116\) \\
[7] & 2019 & 4  
device pocket. [PERSON] et al. [84] provided a miniaturized implantable antenna with three frequency bands (902-928, 2400-2483.5, and 1824-1980 MHz) operating at the industrial, scientific, and medical (ISM) band and at the midfield band. A capsule-shaped and a flat type antenna were fabricated with a volume of 647 mm\({}^{3}\) and 425.6 mm\({}^{3}\), respectively. This triple band antenna was complemented with microelectronics, sensors, and batteries for stimulation in different applications. The system was examined in different tissues, including the scalp, heart, colon, large intestine, and stomach. [PERSON] et al. [85] took advantage of a microwave-based WPT technique to charge deep medical implants like cardiac pacemakers. Their novel wideband numerical model (WBNM) was to provide an RF power source of a leadless pacemaker while using a metamaterial-based antenna operating at 2.4 GHz. They used tissue simulating liquid (TSL) mimicking the human body to prove the performance of their design for implantable applications. A wireless powering technique was introduced by [PERSON] et al. [82], which overcomes the difficulty of miniaturizing the power source via adaptive electromagnetic energy transport. This method is designed for micro-implants like micro-electromechanical system sensors and opto-elements. Figure 5 shows the wireless electrostimulator inserted into the lower epicardium of a rabbit. Recent works of microwave-based WPT systems are reviewed and shown in Table 3.
## 3 Fatigue Tests at Low Temperature
### Determination of Hot-Spot Stress  
The use of S-N curves based on the hot-spot stress was an effective approach for the fatigue design of welded components and structures [33; 34]. The hot-spot stress was the stress at a weld toe obtained by a linear or quadratic extrapolation of stresses over two points in front of the weld toe under consideration [35]. The stress state of the fatigue tests was measured using strain gauges. According to the guidance document given by the International Institute of Welding [36], strain gauges were placed at a distance of 0.4t and 1.0t from the weld toe (Figure 8), and the linear stress was extrapolated to determine the hot-spot stress at the weld [36]. The hot-spot stress was calculated using Equation (1):  
\[\sigma_{hot}=1.67\cdot\sigma_{0.4t}-0.67\cdot\sigma_{1.0t} \tag{1}\]  
where  
\(\sigma_{hot}\) is the hot-spot stress,  
\(\sigma_{0.4t}\) is the stress at 0.4t,  
\(\sigma_{1.0t}\) is the stress at 1.0t,  
\(t\) is the thickness of steel.  
Figure 7: Detail of the test equipment: (**a**) testing set-up, (**b**) calibration of low environmental chamber, (**c**) diagrammatic sketch of half-bridge circuit, and (**d**) physical sketch of half-bridge circuit.
## 5 Performance Evaluation
### Task Arrival Prediction Using LSTM  
To predict the number of tasks arriving at each queue during a time slot, we used a single-layer LSTM with five hidden units for a given sequence input. We add Table 1, which shows the important parameters and their values used for the LSTM training.  
To compare the prediction performance according to the length of the input sequence (i.e., \(h\)), we measured the prediction error in terms of \(\zeta_{i}(t)=((w_{i}(t)-\hat{w}_{i}(t))/w_{i}(t))^{2}\). Then, we compared the mean and the standard deviation of the root-mean-squared (RMS) prediction error in Table 2. If we denote the number of time steps used to calculate the RMS as \(n\), the RMS for a class \(i\) queue is calculated as \(\sqrt{\frac{1}{n}\sum_{t=1}^{n}\zeta_{i}(t)}\).  
We observe in Table 2 that the best performance is obtained when the input size is four. Figure 2 shows the differences in the actual and predicted values over time for each class when the input size is four. The accuracy of the predicted values is quite high compared to the actual values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Learning rate & 0.01 \\ Number of epochs & 3000 \\ Input size & 3 to 5 \\ Hidden size & 5 \\ Activation function & ReLU \\ Optimization function & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters for LSTM training.
## 5 Results  
The first evaluation of the proposed model was conducted by measuring the suitability of the proposed approach for generating new samples from a given dataset. In this evaluation, the retention time (RT) was measured against the molecular weight (MW) for different molecular formulae (MFs) in terms of area and then plotted as shown in Figure 4. It can be seen that the different accessions of Taif rose have variable metabolomic profiles. For example, those collected from location 3 show that most of the components are concentrated below 400 MW and have a lower percentage area, whereas this is twice the case for site 2. The most important point to consider when developing the model is to provide sufficient layers to account for the different distributions in the dataset.  
Figure 3: A schematic diagram of the proposed cSGAN model used in this study.  
Figure 4: Bubble figures of RT vs. Mw for all of the locations. It is clear that Taif rose accessions are different in their oil composition characteristics.  
To further quantify the diversity that must be implemented in the proposed model, a cluster model was created for the provided dataset and used to assess the extent to which the different aspects of the datasets matched. The results of this assessment, as shown in Figure 5A,B, indicate that it is difficult to group all sites into three or four clusters. The optimal number of clusters, as shown in the cluster gap analysis in Figure 5B, indicates that an optimal value is achieved when each location is placed in its own cluster.  
The accuracy of the proposed model was assessed by measuring losses as a function of the number of epochs. From Figure 6, it can be observed that as the number of epochs increased, the losses for both the generator and discriminator networks decreased during the training and testing phases. This result demonstrates the ability of the proposed model to perfectly handle a dataset.  
Figure 5: (**A**) Cluster analysis of the dataset provided and an assessment of how closely the different aspects of the datasets are matched. (**B**) Cluster gap analysis clarifies the optimal number of clusters and indicates that the optimal value is achieved when each location is placed in a separate cluster.
## 3 Results
### CO\({}_{2}\)-Sequestration-to-Emission Ratios
Values are especially low for the mixes incorporating the commercially available cement CEM II/B-V 42.5 N (\(\pm\)6%) and CEM I 42.5 N (\(\pm\)4%). This is mainly to be attributed to the fact that within a 100-year timeframe there has been only limited progression of the carbonation front, leaving large amounts of carbon material uncarbonated and thus not taking part in CO\({}_{2}\) sequestration. At this point, the way of assigning impact to the fly ash for the CEM II/B-V 42.5 N mix becomes quite irrelevant. The same statement holds true when it comes to the underlying carbonation test method (natural vs. slightly accelerated). However, in the case of the HVFA mix, both effects still matter. Depending on the way of assigning impact to the fly ash, the CO\({}_{2}\)SP/EM ratio still covers quite a broad value range. For C\({}_{\rm e}\) values up to 4.30% (scenario Tr(DE)+Ce(CA)), the HVFA mix keeps on showing higher CO\({}_{2}\)SP/EM values than the Portland cement reference. Accelerated carbonation as an underlying method for potential CO\({}_{2}\) sequestration assessment gives substantially lower CO\({}_{2}\)SP/EM values than natural carbonation. The profound underestimation of the natural carbonation rate when estimated from accelerated tests is mainly causing this. This effect is dominant over the fact that accelerated carbonation is usually responsible for significantly higher carbonation degrees. Regardless of the adopted approach for calculating the CO\({}_{2}\)SP/EM indicator, the HVFA mix keeps on holding the benefit over the Portland cement reference by far. One could perhaps make the same statement for the CEM II/B-V 42.5 N mix consisting of a commercially available fly ash based cement. However, the corresponding benefits observed relative to the Portland cement reference are much more limited. From this perspective, it seems worthwhile to aim at high fly ash contents in binder systems. A 21-35% fly ash content seems to give only modest value when it comes to the simultaneous consideration of CO\({}_{2}\) sequestration potential and production related greenhouse gas emissions. Furthermore, it should be emphasized that the two considered fly ash based binders are to be preferred over the Portland cement mix. This may not be immediately expected given the lower CO\({}_{2}\) buffering capacity of fly ash based binders. Yet, their resulting susceptibility to carbonation ensures larger carbonated binder volume within a typical structure lifespan of 100 years. Furthermore, these binders were found to have substantially larger carbonation degrees within this carbonated volume. Figure 5: CO\({}_{2}\)-sequestration-to-emission ratios for the binder fraction of a 100-year timeframe, dimensions specific 2.24 \(\times\) 2.24 \(\times\) 0.20 m\({}^{3}\) = 1 m\({}^{3}\) of the HVFA mortar (**a**) and CEM II/B-V 42.5 N mix (**b**) in relation to the one of reference CEM I 42.5 N mortar, using various approaches for assigning impact for their fly ash content: No impact (No), impact of basic treatment (Tr(DE)), impact of basic treatment and economic allocation (Tr + Ce).
# OpticalTrust: A Sensor-to-Blockchain Framework Using Free-Space Optical Communication
## 3 Methodology  
The proposed approach is categorized into three different sub-sections, such as sensor node, FSOs model design and a decentralized storage system, which are discussed as follows (see Figure 1):  
Figure 1: Methodology of the proposed framework.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 3 Idealized Model of Mimblewimble-Based Blockchain
### Aggregate Transactions  
Transactions can be aggregated into bigger transactions. A single transaction can be seen as the sending of money between two parties. The following definition represents multiple parties:  
**Definition 6** (Aggregate Transaction).: _An aggregate transaction \(tx\) is a tuple of type:_  
\[\mathit{TransacAgg}\stackrel{{\mathrm{def}}}{{=}}\{i:\hat{1^{*} },\,o:\hat{0^{*}},tk:TxKernel^{*},tko:KOffset\}\]  
_with \(X^{*}\) representing the lists of \(X\) elements detailed in definition 3._  
Transactions can be merged non-interactively to construct an aggregate transaction.  
**Definition 7** (Transaction Join).: _Given a valid transaction \(t_{0}\) and an aggregate transaction \(tx\):_  
\[t_{0}=\{i_{0},o_{0},tk_{0},tko_{0}\}\text{ and }tx=\{i,o,tk,tko,\}\]  
_a new aggregate transaction can be constructed as:_  
\[tx=\{i_{0}\mid\mid i,o_{0}\mid\mid o,tk_{0}\mid\mid ik,tko_{0}+tko,\}\]  
_where \(\mid\mid\) is the list concatenation operator and \(+\) is the scalar sum._  
This process can be applied recursively to add more transactions into one aggregate transaction. A single transaction (Definition 3) can be seen as a particular case where the transaction kernel list contains a single element. Besides, the ownership of the coins is between two parties.  
The CoinJoin mechanism [36] makes it possible to combine all inputs and outputs from separate transactions to form a single transaction and the signatures can be composed by the parties. A Transaction Join can be understood as a simple way to perform CoinJoin with no composite signatures.  
The validity of an aggregate transaction is guaranteed by the validity of the transactional parties during the construction process.  
**Lemma 1** (Invariant: CoinJoin Validity).: _Given a valid transaction \(t_{0}\) and a valid aggregate transaction \(tx\) as in Definition 6. Let \(tx^{\prime}\) be the result of aggregating \(t_{0}\) into \(tx\). Then, \(tx^{\prime}\) is valid._In our model, aggregate transactions and blocks (Definition 9) are the same (without considering headers). We are interested in distinguishing them because the unconfirmed transaction pool will contain aggregate transactions.  
Notice that, in an aggregate transaction, an adversary could find out which input cancel which output. They could try all possible permutations and verify if they summed to the transaction excess. The property of transaction unlinkability will be proved over blocks, as we will see in Property 3.
## 3 Results and Discussion
### Effect of Temperature  
Photocatalytic processes are generally reported to be poorly influenced by the temperature, as the electron-hole generation mainly depends on the radiation intensity [57]. However, the temperature may improve the activity of the system, being able to (i) increase the reaction rate of the hydrogen generation and (ii) improve the product desorption from the catalyst [58]. As a result, according to different authors [59; 60; 61], temperature helps the  
Figure 4: Hydrogen evolution rate at varying the pH of the suspension. Experimental conditions: V = 0.30 L; P = 1 atm; T = 35 \({}^{\circ}\)C; [Methanol] = 2.5 M; C\({}_{(15\times 0.1)\text{Cu}_{3}\text{O}/\text{TiO}_{2}}\) = 700 ppm; visible range.  
Figure 5: Schematic illustration of adsorption conditions of methanol on the photocatalytic Titania surface at different pH values.  
hydrogen generation reaction compete more successfully with charge-carrying recombination. For this reason, some experimental runs are performed varying the temperature of the suspension between 20 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, in presence of methanol as a scavenger. The main results are reported in Figure 6. As clearly sown by the diagram, when the temperature increases from 20 \({}^{\circ}\)C to 80 \({}^{\circ}\)C, the hydrogen production rate achieves a value about 4.5 times higher than that obtained at the lowest temperature, thus proving the beneficial effect of temperature on the system behaviour.  
By considering that the photocatalytic hydrogen production follows a pseudo-first-order law for all the temperatures tested, it is possible to plot the Arrhenius-type equation from 293 K to 353 K (See Figure 7). A value of 21.51 KJ/mol is easily estimated for the apparent activation energy E\({}_{\text{a}}\) from the slope of the Arrhenius plot. Such a value is consistent with those reported in the literature survey for similar systems [61,62].
## 3 Results and Discussion
### Pore Structure of PolyHIPE Membrane
#### 3.2.3 Constrictivity  
The constrictivity (\(\beta\)) is defined as the ratio of \(A_{min}/A_{max}\) = \(\pi\mathrm{r_{min}}^{2}/\pi\mathrm{r_{max}}^{2}\), where \(A_{min}\) is the pore cross section area at the constriction, and \(A_{max}\) is the cross section at nonconstricted \"bulges\" in the pore [31]. Since the constrictivity is defined not for a single pore, but as the parameter of the entire pore space considered, several methods have been suggested to estimate the constrictive factor of porous material [32]. In this study, the constrictivity is statistically estimated directly from the SEM observation of the diameters of void and window throat. In order to obtain acceptable constrictivity measurement, the SEM images were taken from various sections of PolyHIPE membrane and the diameter of five visually identifiable pores and throats were determined from each SEM image (Figure 3a). The averaged pore diameter is 10 \(\pm\) 2.5 \(\mathrm{\SIUnitSymbolMicro m}\), and the averaged throat diameter is 0.6 \(\pm\) 0.2 \(\mathrm{\SIUnitSymbolMicro m}\).  
Figure 3: (**a**) SEM of PolyHIPE membrane (30 wt % St, 60 wt % EHA, and 10 wt % DVB) cross section. (**b**) Binarized SEM image by built-in SEM program, with black lines to indicate the \(L_{actual}\).  
Therefore, the estimated constrictivity is about 3 \(\times\) 10\({}^{-3}\)\(\pm\) 0.3 \(\times\) 10\({}^{-3}\). The diameter of pore and window throat is close to the range of 1000-9000 nm as determined by the mercury porosimetry.