esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Performance Metric Definition  
The actual beam power can be expressed as \(\mathbf{Y}=\{\boldsymbol{y}_{1}\boldsymbol{y}_{2},\cdots\boldsymbol{y}_{m}\}\), and the predicted value as \(\boldsymbol{\overset{\Lambda}{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{\boldsymbol{\boldsymbol{ \boldsymbol{   }}}}}}}}}}}}=\{ \boldsymbol{\hat{y}}_{1},\ldots\boldsymbol{\hat{y}}_{m}\}\). Then, the alignment-probability expression can be obtained as \(P_{A}=\frac{1}{m}\sum_{i=1}^{m}\left(\text{argmax}(\boldsymbol{y}_{i})=\text {argmax}(\boldsymbol{\hat{y}}_{i})\right)\), where \(\text{max}\{\cdot\}\) represents the largest element in the vector vector(\(\cdot\)); \(\text{argmax}\{\cdot\}\) represents the index of \(\text{max}\{\cdot\}\); and (\(\cdot\)) is the indicator function. Thus, the throughput rate can be expressed as \(\text{R}_{T}\) as:  
\[R_{T}=\frac{\sum_{i=1}^{m}\log_{2}(1+\boldsymbol{y}_{i}[\text{argmax}\{ \boldsymbol{\hat{y}}_{i}\}])}{\sum_{i=1}^{m}\log_{2}(1+\text{max}\{\boldsymbol {\hat{y}}_{i}\})}, \tag{9}\]  
The actual throughput RT is only related to the learned model, and not to the beam training.
# The Hyperloop System and Stakeholders: A Review and Future Directions
## 3 Analysis and Results
### Infrastructure
#### 3.5.5 Interfaces  
The first hyperloop concept proposed the use of air-bearings for levitation combined with a linear induction motor [17], which requires high maintenance, tight integration between the track and the pod, and significantly increasing the pod weight with the use of fans, motors, and hover-pads [15; 20]. Subsequent efforts focused on magnetic levitation (maglev) that can be coupled with electromagnetic propulsion for higher efficiency [23]. According to recent levitation comparison studies [5; 20], the most dominant technology for ultra-high speeds is the electromagnetic suspension (EMS) and the electrodynamic suspension (EDS), called \"active levitation\" and \"passive levitation\", respectively.  
* The EMS technology is based on the attractive properties of the magnets, and it uses pod-side electromagnets and ferromagnetic materials on the guideway [5; 7; 16; 20; 52]. The EMS works with magnetic forces giving a lower levitation of about 10-20 mm above the guideway [11]. Optimization solutions have been recently reported using hybrid options (H-EMS) [114; 115] or high-temperature superconductivity (HTS-EMS) [116; 117].
* The EDS technology is based on electromagnetic induction, and it uses pod-side permanent magnets or superconducting electromagnets and a highly conductive guideway infrastructure that generate opposing magnetic fields through induction [5; 7; 16; 20; 52]. In EDS, the pod can be levitated 10-100 mm, using permanent magnets (PM-EDS) or superconducting magnets (SC-EDS) [11]. Another technology that uses embedded conducting wire loops to minimize the eddy current from the moving magnetic array is the Inductrack [118; 119]. Using high-temperature superconductivity for EDS (HTS-EDS), it supports speeds of up to 620 km/h [120]. It should be noted that a hybrid solution using both EMS and EDS has also been proposed [121].
# Temporally Programmable Hybrid MOPA Laser with Arbitrary Pulse Shape and Frequency Doubling
## 4 Second Harmonic Generation  
To obtain green lasers with arbitrary pulse shapes, second harmonic generation was accomplished using the output of the above-mentioned MOPA system. The temporal parameters of the fundamental laser (1064 nm) and the output power of the green laser are listed in Table 2. The pulse shapes of the green laser are demonstrated in Figures 7 and 8 with green lines. The maximum conversion efficiency of 42.6% was achieved when peak power intensity was 163 MW/cm\({}^{2}\) at a PRF of 40 kHz.  
Comparing the output power of the green laser with the same pulse energy but with different pulse shapes in Table 2, it was deduced that a smaller pulse duration, namely a higher peak power intensity, contributes to a higher conversion efficiency from a fundamental laser to the second harmonic wave. Moreover, the conversion efficiency is not linear to the peak power intensity, leading to pulse shape distortion from the fundamental laser. The portion with a higher peak power in the fundamental pulse converts to a green laser to a much greater extent than that with a lower peak power, resulting in a pulse duration narrowing effect.  
The comparison between the 1064 nm and 532 nm lasers in Figure 7a evidently shows that the conversion efficiency differs for different peak power levels of fundamental lasers. The lower peak power in the pulse is 0.25 for the fundamental laser, while it is smaller than 0.25 for the green laser. As shown in Figures 7b and 8c, the leading edge and the falling edge of the green laser become smaller than the fundamental laser after the SHG process, which results in a narrower pulse duration. The comparison between the 1064 nm and 532 nm lasers in Figures 7d and 8a indicates that pulse shapes with a uniform distribution of peak power could alleviate the narrowing effect and obtain a green laser pulse similar to the fundamental one. For the Gaussian shape, a higher peak power is helpful to keep the original fundamental pulse shape, as the pulse shape narrowing percentage in Figures 7b and 8b are 22% and 15%, respectively.  
To obtain a desired pulse shape from the SHG process, the non-linear conversion efficiency must be taken into account. For example, to obtain a multiple-step green pulse just like in Figure 7a (the lower peak power is 1/4 of the higher peak power for green laser), the lower peak power of the fundamental laser must be larger than one-fourth of the higher peak power.
# Activated Carbons as Effective Adsorbents of Non-Steroidal Anti-Inflammatory Drugs
## 3 Results
### Zeta Potential Measurements  
Figure 4 presents a comparison of the electrokinetic potential of pure GAC activated carbon as well as the hybrid materials containing the tested pharmaceuticals. As can be seen, the zeta potential of pure GAC activated carbon in the tested pH range is from +7.9 to -41.3 mV. The electrokinetic potential of activated carbon after adsorption varies under test conditions from +14.7 to -41.9 mV, from +11.5 to -41.9 mV, and from +8.3 to -37.8 mV for samples with NPX, IBP, and D, respectively. Additionally, it can be concluded that the zeta potential value is lower for samples after the adsorption of naproxen sodium, ibuprofen sodium, and diclofenac sodium. The observed decrease is the result of the presence of negatively charged sodium salts of the tested non-steroidal anti-inflammatory drugs originating from their dissociation. Moreover, the largest decrease in zeta potential was recorded for the sample with NPX, which confirms its highest efficiency of removal from solutions using activated carbon. The least visible effect was observed for the sample from D, which is probably caused by the presence of the -NH group in the diclofenac sodium molecule.
## 1 Introduction
The long-lasting and laborious nature of common stationary or non-stationary diffusion tests shifted the focus towards accelerated migration methods, based on the induction of an electrical potential difference. In this case, the diffusion coefficient is estimated considering the similarity between diffusion and migration, by introducing the notion of ionic mobility [7; 10; 15]. Due to its simplicity and celerity, these methods are preferable in approximately characterising the chloride penetration resistance of concrete. [PERSON] [16] proposed the rapid chloride permeability test (RCPT), which measures the amount of electrical current that passes through a specimen for a given time period. This test, adopted by AASHTO T 277 [17] and ASTM C 1202 [18], can provide an idea of the permeability of the concrete in just a few hours. However, various limitations have been pointed out in this test [6; 19; 20; 21]. On the one hand, this test does not allow the direct assessment of chloride diffusion coefficients, establishing only a qualitative relationship between the chloride penetration resistance and the electrical current that passes through a concrete specimen [3; 22]. On the other hand, for concrete with different types of cementitious materials, the pore composition is altered, affecting the passing charge and the microstructure differently. In fact, the RCPT only provides a measure of the concrete electrical conductivity that depends on the porous structure and composition of the pore solution, altered by mineral admixtures. In addition, the ion mobility is affected by the significant temperature increase during testing [2; 22]. Alternatively, the rapid chloride migration test (RCMT), first proposed by [PERSON] [23], and standardised through NT Build 492 [24], is a short-term non-steady-state method allowing the expeditious determination of the chloride migration coefficient (\(D_{cl,RCMT}\)) of concrete [25]. This methodology is essentially based on the migration of chloride ions into previously lime-saturated concrete specimens by applying an external electrical voltage, which is adjusted according to the electrical current in the system [9; 26; 27; 28]. The chloride ions are forced towards a downstream solution, and, after a short period, the chloride penetration depth is determined by a colorimetric indicator (AgNO\({}_{3}\)) that changes colour at nearly 0.07 mol/dm\({}^{3}\)[24; 26]. Owing to its simplicity, short test duration (from 6 to 96 h)and high repeatability, this method has been increasingly considered for the determination of chloride penetration resistance of concrete [26; 28]. The penetration of chloride ions in the RCMT involves the combined mechanisms of diffusion and migration, where the flux follows the Nernst-Plank law [26; 28; 29]. Based on this, Equation (1) may be derived by simply assuming that the electrical field is constant across the specimen, that the chemical reactions with the hydrated paste are neglected, that there is no interaction with other ions and that the convection mechanisms are not significant [2; 5; 22; 30].
## 3 Materials and Methods
### Regression Techniques  
Random Forest (RF)  
The random forest technique [37] was employed for regression and forecasting in this research. RF is a supervised machine learning algorithm that builds a large collection of de-correlated trees, \(T_{1}\), \(T_{2},\ldots,T_{K}\), and then averages them. RF is a modification of the bagging method, where the main idea is to average many noisy, but approximately unbiased models. For the regression task, let \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),...,(x_{N},y_{N})\}\) be a training dataset and \(\hat{f}(x)\) be the prediction for an input \(x\). The bagging procedure averages this prediction over a collection of bootstrap samples, thereby reducing its variance. For each bootstrap sample \(D^{*b}\), \(b=1,2,\ldots,B\), the regression model is fit, obtaining the prediction \(\hat{f}^{*b}(x)\). Finally, the bagging estimate is defined by  
\[\hat{f}_{bag}(x)=\frac{1}{B}\sum_{b=1}^{B}\hat{f}^{*b}(x) \tag{1}\]  
In this sense, decision trees are ideal for bagging because they are intrinsically noisy and can benefit from averaging. The idea in random forests is to improve the variance reduction of bagging by reducing the correlation between the trees, without increasing the variance too much. In other words, this approach eliminates the tendency of decision trees to overfit their training data [23]. Essentially, this method generates multiple independent decision trees, each representing a decision-making pathway in tree-like graphical form in a randomized manner during training to form a forest. Each decision tree contributes to the final prediction. In regression, the result is the average prediction of the outcomes from each tree in the forest [38]. The RF approach is depicted in Algorithm 1.  
```
Input :Training dataset \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),...,(x_{N},y_{N})\}\), n_estimators Output : Prediction Step 1: forb =1 to Bdo (a) Take a bootstrap sample \(Z^{*}\) of size \(N\) from the training data. (b) Grow a tree \(T_{b}\) to the bootstrapped data, by recursively repeating the following steps for each terminal node of the tree, until the minimum node size \(n_{min}\) is reached. 1. Select \(m\) variables at random from the \(p\) variables. 2. Pick the best variable/split-point among \(m\). 3. Split the node into two daughter nodes. Step 2: Output the ensemble of trees \(\{T_{b}\}_{1}^{B}\) to predict new \(x\): \(\hat{f}_{rf}^{B}(x)=\frac{1}{B}\sum_{b=1}^{B}T_{b}(x)\)
```  
**Algorithm 1**Random forest for regression.
# Review of Advancement in Variable Valve Actuation of Internal Combustion Engines
## 1 Introduction
### Combustion and Need for Electronic Control of Gas Exchange  
The combustion in an internal combustion engine involves three key components: in-cylinder fuel, air, and ignition. In gasoline engines, the fuel injection process evolved from a pure mechanical process such as via a carburetor to an electronically controlled process such as via port fuel injection or the recently adopted direction injection (see Figure 1). Spark ignition via a spark plug is completely electronically controlled for spark energy and timing. With its gaseous state, low density, and, thus, large volume, the electronic control of air exchange was a slow evolution process, from variable valve time (VVT) to more sophisticated systems such as discrete variable valve lift (DVVL), continuous variable valve lift (CVVL), cam-based variable valve actuation (VVA), and camless VVA, which are reviewed later in this article. There were also efforts to develop camless VVA to have complete electronic control of the air exchange process, which is a major enabler for advanced combustion such as homogeneous charge compression ignition (HCCI) to improve engine fuel economy and reduce emissions [4; 5; 6; 7]. To improve engine fuel economy, engine downsizing techniques are widely used with the help of turbochargers. In this case, the VVT, VVL, or VVA system is able to improve engine system transient responses.  
In diesel engines, fuel injection evolved from pure mechanical pumping and injection systems to common-rail fuel injection via electronically controlled fuel pressure and injectors. Note that injection timing directly controls the ignition because of the compression ignition. There is less demand or development on controlling gas exchange in traditional diesel engines because of its compression ignition and lean combustion process. More sophisticate air charge management is needed for more advanced combusions such as the Miller cycle and PCCI. However, this review is limited to valve systems for gasoline engines.
# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 3 Conventional and Deep Learning-Based Approaches
### Deep Learning Applied to FER
#### 3.3.2 RNN-LSTM  
A Recurrent Neural Network (RNN) is a type of advanced artificial neural network commonly used in speech recognition. While in a traditional neural network all inputs and outputs are independent of each other, RNNs use sequential information. This class of neural networks shows temporal dynamic behavior and uses its internal memory to calculate output depending on the earlier computations.  
A variation of the recurrent net, the so-called Long Short-Term Memory (LSTM), was proposed in the mid-90s by the German researchers [PERSON] and [PERSON] [96]. These networks use three gates to regulate and control the cell state and thus overcome the issue of the vanishing gradients and exploding problems that are common in training RNNs. LSTMs can keep the memory for a more extended period than RNNs, enabling them to model long-term dependencies in a sequence; common areas of application include speech recognition, language modeling, and video analysis (video-based expression recognition tasks).
## 1 Introduction
In particular, with the introduction of machine learning algorithms such as support vector machines and BP neural networks, the effectiveness of the DBA-ML algorithm has been verified by further numerical simulation examples and engineering applications. Support vector machines have been employed for displacement back-analysis and can replace the time-consuming numerical simulation forward calculations and improve the efficiency of inverted calculations [26; 27]. Neural networks can efficiently establish nonlinear mapping relationships between displacement and physical quantities to directly predict target parameters, which does not require forward- and back-iterative computations [28; 29]. In addition, optimization methods such as PSO (particle swarm optimization) and genetic algorithms for displacement back-analysis have also been shown to be effective in improving the learning efficiency and computational accuracy of DBA-ML [30; 31]. In recent years, after deep learning research was introduced by [PERSON] et al. in _Science_[32], deep learning algorithms have developed rapidly and provided powerful means for landslide research, and have been satisfactorily used in landslide deformation predictions [33] and sensitivity assessments [34; 35], but have not yet been used for landslide parameter inversion. In this study, we constructed a DBA-LSTM model, and we used it to develop a method to calculate the landslide warning threshold based on DBA-LSTM and the numerical simulation algorithm. Taking the Shangtan landslide in Zhaoping County, Guangxi Province, China as an example, the deformation mechanism of this landslide was analyzed based on GNSS and rainfall data, and then the failure warning threshold of the landslide was investigated using the proposed method. Before the numerical simulation work, the DBA-LSTM algorithm was used to correct the shear strength parameters of the numerical model, and the displacement warning thresholds of this landslide under metastable state and unstable state were then obtained through a series of simulations; finally, the effectiveness of the warning thresholds in this paper was verified by comparing the empirical warning models.
# Study of Root Canal Length Estimations by 3D Spatial Reproduction with Stereoscopic Vision
###### Abstract  
Extended Reality (XR) applications are considered useful for skill acquisition in dental education. In this study, we examined the functionality and usefulness of an application called \"SR View for Endo\" that measures root canal length using a Spatial Reality Display (SRD) capable of naked-eye stereoscopic viewing. Three-dimensional computer graphics (3 DCG) data of dental models were obtained and output to both the SRD and conventional 2D display devices. Forty dentists working at the Kanagawa Dental University Hospital measured root canal length using both types of devices and provided feedback through a questionnaire. Statistical analysis using one-way analysis of variance evaluated the measurement values and time, while multivariate analysis assessed the relationship between questionnaire responses and measurement time. There was no significant difference in the measurement values between the 2D device and SRD, but there was a significant difference in measurement time. Furthermore, a negative correlation was observed between the frequency of device usage and the extended measurement time of the 2D device. Measurements using the SRD demonstrated higher accuracy and shorter measurement times compared to the 2D device, increasing expectations for clinical practice in dental education and clinical education for clinical applications. However, a certain percentage of participants experienced symptoms resembling motion sickness associated with virtual reality (VR).  
X reality; XR; cross reality; virtual reality; augmented reality; spatial reality; 3D display; stereoscopic display; 3D-CG; medicine; dentistry; endodontics; root canal length; education; CT; DICOM; CAD/CAM
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## Appendix A Derivation of Lower Bound  
In this section we will show \(\mathcal{L}_{\text{KIV}}\) is a lower bound estimation of \(\log p(\mathbf{y}|\mathbf{c},\mathbf{k})\). The loss function of CVAE [27] can be formulated as maximizing by the Evidence Lower Bound (ELBO) of logarithm likelihood \(\log p(\mathbf{y}|\mathbf{c},\mathbf{k})\) as follows:  
\[\mathcal{L}_{\text{CVAE}} = -\text{KL}(q_{\phi}(\mathbf{z}|\mathbf{y},\mathbf{c},\mathbf{k})||p_{\theta}(\mathbf{ z}|\mathbf{y},\mathbf{c},\mathbf{k})) \tag{1}\] \[+\mathbb{E}_{\mathbf{z}\sim q_{\phi}(\mathbf{z}|\mathbf{c},\mathbf{k})}\log p_{ \theta}(\mathbf{y}|\mathbf{c},\mathbf{k},\mathbf{z})\] \[\leq \log p_{\theta}(\mathbf{y}|\mathbf{c},\mathbf{k}).\]  
By injecting the interpolated latent variable, the loss function of our model \(\mathcal{L}_{\text{KIV}}\) now is:  
\[\mathcal{L}_{\text{KIV}} = -\lambda\text{KL}(q_{\phi}(\mathbf{z}_{\mathbf{k}}|\mathbf{y},\mathbf{k})||p_{ \theta}(\mathbf{z}_{\mathbf{k}}|\mathbf{k})) \tag{2}\] \[-(1-\lambda)\text{KL}(q_{\phi}(\mathbf{z}_{\mathbf{c}}|\mathbf{y},\mathbf{c})||p _{\theta}(\mathbf{z}_{\mathbf{c}}|\mathbf{c}))\] \[+\mathbb{E}_{\mathbf{z}_{\mathbf{c}}\sim q_{\varepsilon},\mathbf{z}_{\mathbf{k}} \sim q_{\varepsilon}}\log p_{\theta}(\mathbf{y}|\mathbf{c},\mathbf{k},\mathbf{z}).\]  
To verify the loss function \(\mathcal{L}_{\text{KIV}}\) still remains the lower bound of \(\log p(\mathbf{y}|\mathbf{c},\mathbf{k})\), we only need to show that  
\[\mathcal{L}_{\text{KIV}}\leq\mathcal{L}_{\text{CVAE}}. \tag{3}\]  
To verify Inequality (3) holds, we notice that the reconstruction loss function in Equations (1) and (2) is the same term. We only need to verify the sum of first two negative KL-divergence terms in Equation (2) is a lower bound of negative KL-divergenceterm in Equation (A1). We leverage the convex property of KL-divergence [57] to derive it, i.e., if \((p_{1},q_{1})\) and \((p_{2},q_{2})\) are two pairs of probability mass functions, then  
\[\begin{split}&\text{KL}(\lambda p_{1}+(1-\lambda)p_{2}\parallel \lambda q_{1}+(1-\lambda)q_{2})\\ &\leq\lambda\text{KL}(p_{1}\parallel q_{1})+(1-\lambda)\text{KL} (p_{2}\parallel q_{2}),\end{split}\] (A4)  
for any \(\lambda\in[0,1]\). By replacing \(\mathbf{z}=\lambda\mathbf{z}_{\mathbf{k}}+(1-\lambda)\mathbf{z}_{\mathbf{c}}\) in the negative KL-divergence term in \(\mathcal{L}_{\text{CVAE}}\). We can easily verify that:  
\[-\text{KL}(q_{\phi}(\mathbf{z}|\mathbf{y},\mathbf{c},\mathbf{k})||p_{\theta}( \mathbf{z}|\mathbf{y},\mathbf{c},\mathbf{k}))\leq\] \[-\lambda\text{KL}(q_{\phi}(\mathbf{z}_{\mathbf{k}}|\mathbf{y},\mathbf{k})||p_{ \theta}(\mathbf{z}_{\mathbf{k}}|\mathbf{k}))+\] \[-(1-\lambda)\text{KL}(q_{\phi}(\mathbf{z}_{\mathbf{c}}|\mathbf{y},\mathbf{c})||p_ {\theta}(\mathbf{z}_{\mathbf{c}}|\mathbf{c})).\] (A5)  
Therefore \(\mathcal{L}_{\text{KIV}}\) is the lower bound estimation of logarithm likelihood \(\log p(\mathbf{y}|\mathbf{c},\mathbf{k})\).