esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Dynamic Game-Based Pricing Model for Cloud Environment
## 1 Introduction
In simpler terms, their decisions can be presumed to occur simultaneously. However, in dynamic games, the agents' decisions have an order, and the decision-maker in each stage of the game is aware of the decisions of other agents in the previous stages. Dynamic games are commonly depicted using a decision tree, wherein each node defines a decision state and each edge signifies a potential action in that state for a player. The optimal decision for each player is determined by finding a situation where no player is willing to change his decision if the other players' decisions are stable. This situation is called the Nash equilibrium point [7]. To tackle dynamic games, we begin by addressing the sub-games at the lowest level in the tree. Through solving these sub-games, we eventually reach the tree's root. This way of solving a tree is called backward induction. Therefore, the main contributions of this research can be stated as follows:  
* Providing a comprehensive model for cloud pricing based on dynamic games in game theory. * Taking into account a broader range of factors that impact the price, including energy usage, the likelihood of a job being successfully completed, and the influence of one job on the execution of others. For instance, if a CPU-intensive task is run during a time when the cloud is experiencing a high computational load, it can result in higher costs for the service provider. This aspect should be taken into consideration when determining the pricing. * A comprehensive comparison of the proposed method compared to related tasks with different scenarios. The remaining part of the paper is structured in the following manner: Section 2 presents a thorough examination of the most recent literature on pricing methods. Section 3 provides a detailed description of the proposed game-based pricing method and the preliminaries required. The experimental results and performance evaluation of the proposed method are discussed in Section 4. The performance of the proposed method is evaluatedin comparison with other recent pricing methods based on the simulation study. Finally, Section 5 concludes the paper with a summary.
### Image-Based Methods to Evaluate Chemotherapy-Induced Cardiotoxic Effects  
Clinical protocols and diagnostic methods routinely use echocardiographic imaging and have reasonable sensitivity and specificity in comprehensively monitoring and detecting DOX-mediated functional changes [8], while biopsy sampling is invasive and sparse, often missing structurally damaged tissue. Thus, the irreversible injury evolves undetected, worsening in time and leading, within months and years, to cardiomyopathy, and eventually to heart failure. Therefore, there has been a critical need to develop more accurate methods to detect earlier the cardiotoxic effects in sub-chronic phases following DOX-based therapies. With this respect, MR imaging offers excellent tissue contrast. For example, one group recently utilized MRI to assess cardiotoxicity in breast cancer survivors, showing that most patients had significantly different values of ejection fraction and global strain post-DOX compared to the baseline, and some presented evidence of diffuse fibrosis [9]. While 2D Cine and Late Gadolinium Enhancement methods are able to characterize post-DOX functional and structural changes [9], these scans typically use protocols with large slice thickness (\(\sim\)8-10 mm), similarly to those in acute or chronic infarct scars [10]. Current T1 and T2 mapping methods can differentiate, overall, the injured myocardium post-chemotherapy [11]; however, these low-resolution images (voxel size 2 \(\times\) 2 \(\times\) 10 mm\({}^{3}\)) might miss subtle structural alterations. Thus, advanced methods using high-resolution 3D imaging should be adapted for imaging protocols of post-DOX evaluation.
## 2 Methodology
### CFD Modeling  
The CFD code FLUENT19.2 was used as a numerical tool to evaluate the effects of the ship's motion on diffusion in the event of a hydrogen leak. The governing equations employed to numerically simulate the hydrogen release and dispersion include the continuity, momentum, energy, species transport, turbulent kinetic energy, and dissipation rate equations of turbulent kinetic energy [18].  
The conservation of mass is provided by:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\left(\overrightarrow{\rho v} \right)=0, \tag{1}\]  
where \(\rho\), \(t\), and \(\overrightarrow{v}\) represent the density, time, and overall velocity vector, respectively.  
The conservation of momentum is expressed as:  
\[\frac{\partial}{\partial t}\left(\rho\overrightarrow{v}\right)+\
abla\cdot \left(\rho\overrightarrow{vv}\right)=-\
abla p+\
abla\cdot\overline{\tau}+ \rho\overrightarrow{g}+\overset{\rightarrow}{F}, \tag{2}\]  
where \(p\), \(\overset{\rightarrow}{\tau}\), \(\overset{\rightarrow}{g}\), and \(\overset{\rightarrow}{F}\) represent the pressure, stress tensor, gravitational acceleration, and force vector, respectively.  
The conservation of energy is provided by:  
\[\frac{\partial}{\partial t}\left(\rho E\right)+\
abla\cdot\left(\overset{ \rightarrow}{v}\left(\rho E+p\right)\right)=\
abla\cdot k_{eff}\
abla T-\sum _{j}h_{j}\overset{\rightarrow}{j_{j}}+\left(\tau_{eff}^{=}\overset{ \rightarrow}{v}\right)), \tag{3}\]  
where \(E\), \(k_{eff}\), \(T\), \(h_{j}\), and \(\overset{\rightarrow}{j_{j}}\) represent the total energy, effective conductivity, temperature, sensible enthalpy of species j, and diffusion flux of species j, respectively.  
The species transport equation is:  
\[\frac{\partial}{\partial t}\left(\rho Y_{i}\right)+\
abla\cdot\left(\rho \overrightarrow{v}Y_{i}\right)=-\
abla\cdot\overset{\rightarrow}{J_{i}}+R_ {i}+S_{i}, \tag{4}\]  
where \(Y_{i}\), \(R_{i}\), and \(S_{i}\) denote the mass fraction of each species, net rate of the product of species i by chemical reaction, and the rate of creation by additions from the dispersed phase plus any user-defined sources, respectively.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 4 Results
### Kalman Filter Algorithm Implementation  
The Kalman Filter algorithm is an autoregressive filtering model. Therefore, the optimal estimate of the system state at the current time, can be obtained by the optimal estimate of the system state at the previous moment and the observation of the system state at the current time.  
Firstly, the state of the KF is represented by the following two variables:  
* \(\hat{x}_{k\mathrm{J}}\) represents an estimate of the system state at time \(k\);
* \(P_{k\mathrm{J}k}\) represents the covariance matrix of the state estimation error at time \(k\), which measures the accuracy of the estimation.  
The KF estimates a process by using a form of feedback control: the filter estimates the process state at some time and then obtains feedback in the form of (noisy) measurements. As such, the equations for the KF fall into two groups: _time update equations_ and _measurement update equations_[48]. The time update equations project forward (in time) the current state and error covariance estimates to obtain the a priori estimates for the next time step. The measurement update equations are responsible for the feedback--i.e., for incorporating a new measurement into the a priori estimate to obtain an improved a posteriori estimate.  
The time update equations can also be thought of as predictor equations, while the measurements update equations can be thought of as corrector equations. Indeed, the final estimation algorithm resembles that of a _predictor-corrector_ algorithm for solving numerical problems as shown in Figure 5.  
Figure 4: Basic structure of Kalman Filter algorithm.  
Figure 5: Iterative process of prediction and correction phase of the Kalman Filter.
## 4 MultiDS Model
### Ridge Regression Classifier  
Here, we also use ridge regression [24] as a classification function due to its superiority in preventing overfitting. We let the final representation of the support set and query set be \(s\in\mathbb{R}^{NK\times h_{1}}\) and \(q\in\mathbb{R}^{\text{NQ}\times h_{1}}\), which are processed by hierarchical attentions and calibration vectors.  
Firstly, we train the ridge regression with the annotated support set in Equation (22).  
\[W_{rr}=s^{T}(ss^{T}+\phi I)^{-1}\widehat{y}^{s}, \tag{22}\]  
where \(\phi\) is a trainable parameter, and \(\widehat{y}^{s}\) represents the one-hot label of the support set.  
Secondly, as shown in Equation (23), the classifier is optimized by the following regularized squared loss.  
\[\mathcal{L}_{rr}=\|W_{rr}s-\widehat{y}^{s}\|_{F}^{2}+\phi\|W_{rr}\|_{F}^{2}, \tag{23}\]  
where \(\|\cdot\|_{F}\) denotes Frobenius norm.  
Thirdly, the well-trained classifier is applied to categorize the query set samples. The classification loss based on cross entropy is derived in Equation (24).  
\[\mathcal{L}_{cls}=CE(\alpha\cdot W_{rr}q+\beta,y^{q}), \tag{24}\]  
where \(CE(\cdot)\) represents the cross entropy function; \(\alpha\) and \(\beta\) are trainable parameters; \(y^{q}\) means the true label of the query set.  
The training procedure of MultiDS is concluded in Algorithm 1.  
```
0: Model parameter \(\theta\); Meta-training episode \(epi\); Dataset for meta-training \(D_{train}\); Number of class \(N\); Number of instance for each class in support set \(K\); Number of instance for each class in query set \(Q\); An N-way-K-shot task \(t=\{t^{s},t^{q}\}=\{(x_{nr}^{s},y_{n}^{s})_{n=1}^{N\times K},(x_{nr}^{q},y_{nr }^{q})_{n=1}^{N\times Q}\}\); Precomputed general word importance matrix \(s^{a}(\cdot)\);
0: Trained model parameter \(\theta\);
1: Randomly initialize model parameter \(\theta\);
2:for each \(i\in[1,epi]\)do
3: Randomly sample an N-way-K-shot task \(t=\{t^{s},t^{q}\}\) from \(D_{train}\);
4: Compute BERT embeddings of \(t\) by Equation (2);
5: Compute domain-agnostic attentions via general word importance matrix \(s^{a}(\cdot)\) in Equations (4)-(7);
6: Compute domain-specific attentions by Equations (8)-(11);
7: Compute class-specific attentions by Equations (12)-(15);
8: Fuse three kinds of attentions and derive hierarchical attentions by Equations (16) and (17);
9: Construct domain calibration vector via word embeddings and domain-specific attentions by Equations (19) and (20);
10: Train the classifier with support set \(t^{s}=\{(x_{nr}^{s},y_{n}^{s})_{n=1}^{N\times K}\}\), and classify query set instance \(t^{q}=\{(x_{nr}^{q},y_{nr}^{q})_{n=1}^{N\times Q}\}\) by Equations (22)-(24);
11: Update \(\theta\) by minimizing classification loss in Equation (24);
12:endfor
```  
**Algorithm 1** The training procedure of MultiDS
###### Abstract  
Due to the irreversible energy substitution from fossil fuels to clean energy, the development trend of future power systems is based on renewable energy generation. However, due to the incompatibility of converter-based non-dispatchable renewable energy generation, the stability and reliability of traditional power systems deteriorate as more renewables are introduced. Since conventional power systems are dominated by synchronous machines (SM), it is natural to utilize a virtual synchronous generator (VSG) control strategy that intimates SM characteristics on integrated converters. The VSG algorithm developed in this paper originates from mimicking mathematic models of synchronous machines. Among the different models of implementation, the second-order model is simple, stable, and compatible with the control schemes of current converters in traditional power systems. The VSG control strategy is thoroughly researched and case studied for various converter-interfaced systems that include renewable generation, energy storage, electric vehicles (EV), and other energy demands. VSG-based integration converters can provide grid services such as spinning reserves and inertia emulation to the upper grids of centralized plants, distributed generation networks, and microgrids. Thus, the VSG control strategy has paved a feasible way for an evolutionary transition to a power electronics-based future power grid. By referring to the knowledge of traditional grids, a hierarchical system of operations can be established. Finally, generation and loads can be united in universal compatibility architecture under consolidated synchronous mechanisms.  
virtual synchronous control; renewable energy integration; high penetration renewable generation; distributed generation; microgrid
## 4 Performance Estimation  
### General Idea  
The goal of the performance estimation function is to estimate classification performance of each partition. With our estimation function, we want to determine which partition is better. We employ a training set for this function. At the same time we need a test set in order to decide which partition is better. We use a part of the training set for this, and the other part remains for training purposes.  
We build a classifier for each partition. Each cluster represents one class. We classify the documents and get the performance measure that shows how good a particular partition is. Various measures can be used in our algorithm. The goal of our algorithm is to build a hierarchy with an optimum performance measure. For example, for partition into \(2\) clusters we get the classification performance measure \(0.97\) (Figure 5). For partition into \(3\) clusters the measure is \(0.93\) and for partition into \(4\) clusters it is \(0.88\).  
We believe that on further layers of the hierarchy, the partition into \(k\) clusters will be the same as in the performance table (Figure 5). We also know various features of clusters, such as size, diversity, _etc._ Therefore, it is possible to explore all possible partitions as well as final hierarchies and to estimate their performance. For example, one may want to make estimation for partition into 3 clusters (Figure 3).  
Cluster \(\{l_{1}\}\) has only one class and we assume that its performance is \(1\). Cluster \(\{l_{2},l_{3}\}\) has 2 classes and its performance is believed to be similar to the performance from the table, which is \(0.97\). Cluster \(\{l_{4},l_{5},l_{6}\}\) can be clustered further in \(2\) different ways (Figure 4). We calculate the number of labels at different levels for the sake of simplicity. So we do not distinguish which labels are in the cluster of size 2: \(\{l_{4},l_{5}\}\), \(\{l_{4},l_{6}\}\) or \(\{l_{5},l_{6}\}\). Finally, we have only two different partitions (Figure 4).  
We make performance estimation for the first partition based on the table from Figure 5. It is \(0.93\). The performance of the second partition cannot be estimated using the referenced table since it contains a sub-hierarchy. We propose a certain approach for doing this.
# Moon Sensor Station to Improve the Performance of Lunar Satellite Navigation Systems
## 6 Evaluation on a Possible Lunar Constellation
CRLB/WLS horizontal and vertical accuracy for the descending trajectory. Focusing on Figure 4, it is possible to note that the positioning error is greatly reduced when using the local sensor, especially the vertical one, obtaining the best performance when using DRS+ISL. Furthermore, also when the local sensor is used only for differential corrections there is a great improvement especially for the horizontal error, where the DRS performs even better than (or equal to) the other methods. Summarizing, DRS+ISL always has the best performance: both the horizontal and vertical errors are about 2 m in the initial part of the trajectory and in the last part around 1 m for the horizontal error and well below 1 m for the vertical error. Figures 5 and 6 show that when fewer than four satellites are visible, exploiting the ISL or the PLS solutions, it is still possible to solve the positioning problem. Moreover, ISL+DRS gives the best results, similar to the previous case, also when only one satellite is in visibility. Finally, to have a term of comparison with possible navigation requirements, Table 2 reports the comparison of the obtained performances with the requirements proposed by ESA [52] for descent approach and terminal landing phases of a lunar mission. This comparison is performed for the three landing scenarios in terms of the percentage of epochs for which the requirements are satisfied. During the descent phase, from 28 km of altitude down to about 3 km, the horizontal requirement is satisfied with any method (if available) and the availability of the service improves from 33% to 77% with the ISL solution. Concerning vertical error, more complex solutions, such as ISL + DRS, are essential to increase availability from 0% to 56%. In the ESA Terminal Landing phase, from about 3 km of altitude to the lunar surface, the requirements are quite stringent: the vertical error requirement is never satisfied, and the horizontal requirement can be satisfied only if the local sensor is exploited (ISL + DRS). This  
Figure 5: Landing #2. CRLB/WLS horizontal and vertical accuracy for the descending trajectory. Figure 6: Landing #3. CRLB/WLS horizontal and vertical accuracy for the descending trajectory. result calls for additional investigations on autonomous techniques for vertical guidance, such as laser altimeters, cameras, etc., that should be conducted on board for the landing phase. Previous results are also confirmed from other scenarios, such as the one reported in the following example, where a spacecraft moves from a low orbit parking position to the Moon surface following a typical descending and landing trajectory (Figure 7--top). The 3D RMS errors (Figure 7--bottom) of the most promising configurations (DRS, ISL, and ISL+DRS) confirm the previous simulation results, with DRS + ISL obtaining the best performance.
## 5 Experimental Analysis
### Performance on Different Sentence Length  
We want to study the impact of sentence lengths. The ID and OOD test sets of the three formalisms are split with 10 tokens range. The ID test set has six groups and OOD has seven groups. GNNSDP(GCN) and biaffine parser are evaluated on them. The results for different groups are shown in Figure 3.  
The results show that GNNSDP outperforms the biaffine parser on different groups with the same embedding settings, except for being slightly lower on the first group (0-10 tokens) on ID test set of PAS formalism. Furthermore, GNNSDP that only uses POS tag embeddings outperforms the biaffine parser that uses POS tag, character-level, and lemma embeddings when sentences get longer, especially when sentences are longer than 30. It turns out that higher-order information is favorable for longer sentences since higher-order dependency relationships are more prevalent in longer sentences.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  &
\begin{tabular}{c} **Chinese** \\ **PAS** \\ **ID** \\ \end{tabular} & \multicolumn{2}{c}{**Czech**} \\  & **PSD** & **ID** & **SOD** \\ \hline Turku[42] & 79.6 & 75.3 & 63.7 \\ Riga [43] & 82.5 & 75.3 & 61.3 \\ Peking [10] & 83.4 & 78.5 & 64.4 \\ Lisbon [39] & 82.0 & 79.3 & 63.5 \\ D\&M [12]: Basic & 87.4 & 86.9 & 77.8 \\ \hline GNNSDP(GCN): Basic & **88.3** & **88.2** & **79.1** \\ GNNSDP(GAT): Basic & **88.0** & **87.8** & **78.9** \\ \hline D\&M [12]: +Char+Lemma & 87.8 & 87.6 & 78.9 \\ \hline GNNSDP(GCN): +Char+Lemma & **88.5** & **88.8** & **80.2** \\ GNNSDP(GAT): +Char+Lemma & **88.3** & **88.9** & **80.2** \\ \hline GNNSDP(GCN): +Char+Lemma+BERT\({}_{\text{\tiny{New}}}\) & **90.1** & **89.6** & **80.7** \\ GNNSDP(GAT): +Char+Lemma+BERT\({}_{\text{\tiny{New}}}\) & **90.4** & **89.3** & **80.4** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of labeled F1 scores achieved by our model and previous state-of-the-art on Chinese and Czech datasets. The bold numbers indicate the current best scores. The results of D&M on Chinese and Czech are not reported in their paper; therefore, we reproduce it. The F1 scores of baseline and our models are averaged over five runs. Only the PAS formalism and in-domain test set is available for Chinese; the PSD formalism, in-domain, and out-of-domain test sets are available for Czech.
# Design and Testing of a Wearable System for Monitoring Car Drivers
## 2 Materials and Methods
### Testing Layout and Modes
Threshold values for each sensor, dividing non-aggressive driving from aggressive driving, have been selected by integrating the existing literature, as shown in the Introduction Section, with observations from repeated experiments. Overall, the proposed ADC index formulates a comprehensive assessment of the driver's aggressive behavior by integrating multiple physiological and movement indicators into a single parameter, which can be used for both monitoring and intervention through sound or haptic warnings. The criterion is used by indicating an aggressive driving behavior when any one of the parameters overpasses its threshold, whereas the criterion value gives an overall estimation.
# A New Spatial Distance Metric for Verification of Precipitation
## 3 Results
### Analysis of Real-World Examples
The gray bar in the lower-left corner represents the estimate of displacement provided by PSD. The value of the \(Q\) parameter is also provided in each panel title. show a clear improvement in the forecasting ability of the ECMWF model, with regards to spatial displacement of precipitation, despite the analyzed period of 8 years being rather short. The 1- and 5-day forecasts also exhibit a noticeable seasonal dependence of PSD values, with the forecast being better during the cold part of the year. The seasonal variation of the PSD value is likely linked to the frequency of occurrence of convective precipitation (which is more challenging for the model to predict since convection is a sub-grid process), which appears more frequently in the warmer part of the year. In order to determine if the ECMWF forecast possesses some minimal measure of skillfulness, its PSD values can be compared to the PSD values of the corresponding persistence forecasts. The persistence forecast assumes that the future state of the atmosphere (in this case, the precipitation that falls in 6 hours) is assumed to be identical to the current state of the atmosphere (the analysis precipitation). In the meteorological community, the persistence forecast is frequently used as a benchmark for evaluating forecast skill. As long as the error of the model's forecast is smaller than the error of the persistence forecast, the model could be considered to possess some minimal measure of skillfulness, whereas if the reverse is true, the model can be considered to possess no skill. Current state-of-the-art NWP prediction models tend to beat the persistence forecast by a large margin at short lead times. However, at longer lead times (e.g., 10-days or more), the model's error can become comparable to or larger than the error of the persistence forecast. In our case, the mean PSD values for the corresponding 1-, 5-, and 9-day persistence forecasts are 40, 59, and 61 grid points. As expected, for the 1-day forecast, the ECMWF model is much better than the persistence forecasts, with PSD being approximately ten times smaller for the ECMWF model. For the 5-day forecast, the ECMWF model is still significantly better, with the model's PSD being about half of the PSD of the persistence forecast. For the 9-day forecast, the ECMWF model is still superior, but the difference is smaller since the model's PSD is only about 20% smaller than the persistence forecast's. This indicates that the model possesses only a limited amount of skill when used to perform the 9-day forecasts.