esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Evaluation of Shock Wave Interaction with a Liquid Droplet
## 2 Computational Model
### CFD Analysis
#### 2.2.1 VOF Method  
For the transient tracking of the liquid-gas interface during the interaction process, the VOF method was used, which allows the approximation of the boundary between the two different phases on a fixed numerical mesh. The VOF technique was shown to be more efficient and flexible than other methods when treating the complex interface between two immiscible fluids [21], and has been very often employed to study the SW/droplet interaction [6; 12].  
Practically, the compressible Navier-Stokes equations are written for volume-averaged fields that are shared by the two different phases. The governing equations are solved for an effective fluid, whose averaged properties are evaluated according to the volume fractions. For example, the averaged density and viscosity are determined by:  
\[\rho=\alpha\rho_{l}+(1-\alpha)\rho_{g} \tag{5}\]  
and  
\[\mu=\alpha\mu_{l}+(1-\alpha)\mu_{g}, \tag{6}\]  
where \(\rho_{g}\) and \(\mu_{g}\) correspond to the gaseous phase, while \(\alpha\) stands for the volume fraction of liquid water. The flow field variable \(\alpha\) is evaluated throughout the computational domain by solving the corresponding continuity equation, while the volume fraction of the ideal gas is plainly computed. In any computational cell, depending on the local volume fractions, the fluid properties and the flow variables are representative of either one of the two phases (\(\alpha=0\) or \(\alpha=1\)) or a mixture of them in the transitional regions (\(0<\alpha<1\)). In the following presentation of the results, the air/liquid interface is assumed to correspond to the isosurface at \(\alpha=0.5\) for the liquid volume fraction. Note that, due to the explicit formulation that was adopted to compute the time-dependent VOF solution, the time step size was practically limited by stability constraints. Furthermore, the sharp interface modeling type, along with interfacial antidiffusion, was used [16].  
Finally, one of the advantages of the VOF method is that the effect of surface tension forces at the air-water interface can be simply simulated. Specifically, the present analysis for the SW/droplet interaction was carried out by employing the continuum surface force  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Symbol** & **Value** \\ \hline Mach number & Ma & 0.577 \\ Reynolds number & Re & \(1.06\times 10^{5}\) \\ Ohnesorge number & Oh & \(1.64\times 10^{-4}\) \\ Weber number & Wee & \(7.33\times 10^{3}\) \\ Density ratio & \(\varepsilon\) & 459 \\ Viscosity ratio & \(N\) & 44.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristic dimensionless groups.  
(CSF) model proposed in [26], with the momentum equation being supplied with an additional source term due to surface tension [18].
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 2 Materials: Optimal Power Flows Considering Uncertainty Cost Functions
### Analytical Algorithm: Interior Point Method
#### 3.3.3 MIPS Based Optimization  
The optimization problem under study is redefined below [18]:  
\[\text{Min}\ f(x) \tag{56}\] \[\text{Subject to: }G(X) =0\] (57) \[H(X) \leq 0 \tag{58}\]  
This optimization problem then becomes a problem where there are only equality restrictions through the inclusion of slack variables \(Z\) and the sum of a barrier function to the objective function:  
\[\text{Min}\ f(x)-\gamma\sum_{m=1}^{n_{i}}log(Z_{m}) \tag{59}\] \[\text{Subject to: }G(X) =0\] (60) \[H(X)+Z =0\] (61) \[Z >0 \tag{62}\]  
As the perturbation parameter \(\gamma\) approaches zero, the solution of the previous problem approaches the solution of the original problem. The Lagrangian for a given value of \(\gamma\) from the previous optimization problem is then:  
\[L^{\gamma}(X,Z,\lambda,\mu)=f(x)+\lambda^{T}G(X)+\mu^{T}(H(X)+Z)-\gamma\sum_{ m=1}^{n_{i}}log(Z_{m}). \tag{63}\]  
From expression (63), the gradient to each of the variables is obtained:  
\[L^{\gamma}_{X}(X,Z,\lambda,\mu)=f_{X}+\lambda^{T}G_{X}+\mu^{T}H_{X} \tag{64}\]  
\[L^{\gamma}_{Z}(X,Z,\lambda,\mu)=\mu^{T}-\gamma e^{T}[Z]^{-1} \tag{65}\]  
\[L^{\gamma}_{\lambda}(X,Z,\lambda,\mu)=G(X)^{T} \tag{66}\]  
\[L^{\gamma}_{\mu}(X,Z,\lambda,\mu)=H^{T}(X)+Z^{T}. \tag{67}\]  
It should be noted that \(e\) is a vector of ones and \([A]\) represents a diagonal matrix with vector A on the diagonal.  
To satisfy the Karush-Kuhn-Tucker conditions, the values of \(X,Z,\lambda\) and \(\mu\) must be found which make expressions (64) to (67) equal to zero as \(\gamma\) approaches zero. To determine these values, the Newton-Raphson method is used, which produces the following matrix equation:  
\[\begin{bmatrix}L^{\gamma}_{XX}&0&G^{T}_{X}&H^{T}_{X}\\ 0&[\mu]&0&[Z]\\ G_{X}&0&0&0\\ H_{X}&I&0&0\end{bmatrix}\begin{bmatrix}\Delta X\\ \Delta Z\\ \Delta\lambda\\ \Delta\mu\end{bmatrix}=-\begin{bmatrix}L^{\gamma\ T}_{X}\\ \mu[Z]-\gamma e\\ G(X)\\ H(X)+Z.\end{bmatrix} \tag{68}\]  
Since it is possible that the variations \(\Delta X\), \(\Delta Z\)\(\Delta\lambda\) and \(\Delta\mu\) take the variables to a new infeasible point, they are usually truncated by \(\alpha\) coefficients as shown below:  
\[\begin{array}{l}X_{i+1}=X_{i}+\alpha_{p}\Delta X\\ Z_{i+1}=Z_{i}+\alpha_{p}\Delta Z\\ \lambda_{i+1}=\lambda_{i}+\alpha_{d}\Delta\lambda\\ \mu_{i+1}=\mu_{i}+\alpha_{d}\Delta\mu.\end{array} \tag{69}\]There are different ways to determine the values of \(\alpha\) such as functions of merit, confidence regions, and filtering methods [31]. Finally, the value of \(\alpha\) must be updated which, as mentioned, should decrease as the number of iterations increases.
## 4 Performance Evaluation
### Simulation Setup
#### 4.2.7 Discussion  
As presented above, we aim through extensive simulations to show the efficiency of our approach by achieving low errors in estimating inter-node distances in a WSN, as well as its stability when varying different propagation parameters in conditions simulating a realistic model. Moreover, our technique outperforms the benchmark anchor-free approach in the same propagation models and same assumptions.  
Hence, our anchor-free technique relies on using the contextual network information consisting of connectivity, network density, and node number to overcome any variation in the WSN deployment environment. In other words, our technique adjusts and adapts the estimation of the main unknown parameters, \(\hat{R}\) and \(\hat{\tau}_{\prime}\) by using derived equation functions of propagation model parameters as well as deployed network parameters. After that, \(\hat{R}\) and \(\hat{\tau}_{\prime}\) are used in estimating the inter-node distance based on the received power model, allowing us to accurately find the relation between distance and received power without using any extra device or known nodes. Then, the nodes are localized based on the estimated inter-node distances. This will drastically decrease the cost of the WSN since no anchor nodes are needed and the aim is to achieve a relative positioning in some WSN applications.
## 1 Introduction  
Food waste is a growing and increasingly worldwide concern. Currently, about a third of the food produced is wasted [1]. In addition to the serious economic and environmental impacts that this problem entails, food waste also has a social impact, as it highlights and accentuates the gap between developed and underdeveloped societies. On one hand, the observed food waste in developed societies is growing consistent with consumerist attitude. Conversely, the food shortages seen in underdeveloped countries and in developing countries is often associated with the consumption of unsafe food.  
However, the consumption of food whose security is not guaranteed does not represent a phenomenon exclusive to underdeveloped countries, despite their greater incidence. It is estimated that in 2019, 9.7% of the world population was, at some point, exposed to foodstuffs in an improper state for consumption [1].  
To reduce these numbers, it becomes imperative for all players in the horticultural products supply chain to be aware of the specificities related to good hygiene practices in the handling and conservation of this type of perishable food, combining them with appropriate inventory management policies. Among others, sales strategies that adopt dynamic pricing systems according to the remaining shelf life of a food item, while its safety is not yet compromised, will inevitably dictate a greater demand and respective outflow of that product to the final consumer.  
The decision support system (DSS) proposed in this article is expected to meet the mentioned guidelines. It aims to help with food waste reduction for traditional food retailers while selling fresh horticultural products, but also to promote food safety and quality. The computational tool predicts the remaining shelf life of fresh horticultural products by determining when the doses of bacteria that affect these products reaches the infectious threshold. The time to reach the infectious dose threshold is used to dynamically calculate the daily price. Thus, this decision support system contributes to the sustainable development by promoting information to retailers that encompasses sustainable consumption, based on perishable horticultural safety and quality, and fair price depending on that quality. The DSS aims to balance the safety and quality of the food product sold, while promoting innovation in the processes of small entities in the sector, thereby enhancing their competitiveness.  
This paper is structured as follows. Section 2 includes a brief literature review on predictive microbiology. Section 3 describes the materials and methods used and applied along the research. The shelf life prediction method is described as well as the dynamic pricing method. Section 4 the analysis and discussion of results of several case studies. Section 5 encompasses an overall discussion of the results. Conclusions and future works are given in Section 6.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 5 Experimental Evaluation
### Computational Efficiency
Moreover, there are datasets, such as ElectricDevices (with about 16,500 time series of length 96 each), where our framework is able to run successfully in our computational infrastructure and to produce clusterings with average ARI higher than that reported in [22], while it is impossible to successfully run the standard \(k\)-means algorithm due to the quadratic computational complexity of DTW and/or the large size of the dataset (see also Table 5 and Section 5.6). In a nutshell, we observe that the total CPU utilization of our framework becomes comparable to that of applying \(k\)-means to the original data for datasets with \(NT^{2}\) ranging from \(10^{8}\) to \(3\cdot 10^{8}\), while the benefits of our framework's significantly improved asymptotic computational complexity become apparent for datasets with \(NT^{2}\) larger than \(4\cdot 10^{8}\). On the other hand, if we focus on \(k\)-means only, for datasets with \(T\) at most 100, applying \(k\)-means to sparsified datasets is about 10-20 times faster than its application to the original data. As \(T\) grows larger to 200-400, the speed-up factor of \(k\)-means increases to 60-80, and reaches values above 120-150 for a time-series length \(T\) above 600. As an additional note regarding the high computational overhead due to Sparse Gaussian Process Regression, we should mention that (i) SGPR could run offline, independently of \(k\)-means (or any other shape-based time-series clustering algorithm) and only once per time series, with the resulting sparse time series stored for any future use; and (ii) that one could arrange for SGPR to run in parallel (and completely independently) for each different time series, which would result in a completion time about two orders of magnitude faster, without increasing the total CPU utilization.
# A Review of Time-Scale Modification of Music Signals +
## 5 TSM Based on the Phase Vocoder (PV-TSM)
### Tricks of the Trade  
The phase vocoder technique highly benefits from frequency estimates that are already close to the instantaneous frequencies as well as from a small analysis hopsize (resulting in a small \(\Delta t\)). In PV-TSM, the frame length \(N\) is therefore commonly set to a relatively large value. In practice, \(N\) typically corresponds to roughly 100 ms in order to achieve a high frequency resolution of the STFT.  
To reduce the loss of vertical phase coherence in the phase vocoder, [PERSON] and [PERSON] proposed a modification to the standard PV-TSM in [6]. Their core observation is that a sinusoidal component may affect multiple adjacent time-frequency bins of a single analysis frame. Therefore, the phases of these bins should not be updated independently, but in a joint fashion. A peak in a frame's magnitude spectrum is assumed to be representative of a particular sinusoidal component. The frequency bins with lower magnitude values surrounding the peak are assumed to be affected by the same component as well. In the phase propagation process, only the time-frequency bins with a peak magnitude are updated in the usual fashion described in Equation (26). The phases of the remaining frequency bins are _locked_ to the phase of the sinusoidal component corresponding to the closest peak. This procedure allows to locally preserve the signal's vertical phase coherence. This technique, also known as _identity phase locking_, leads to reduced phasiness artifacts and less smearing of transients.  
Another approach to reduce phasiveness artifacts is the _phase vocoder with synchronized overlap-add_ (PVSOLA) and similar formulations [19; 20; 21]. The core observation of these methods is that the original vertical phase coherence is usually lost gradually over the course of phase-updated synthesis frames. Therefore, these procedures \"reset\" the vertical phase coherence repeatedly after a fixed number of frames. The resetting is done by copying an analysis frame unmodified to the output signal and adjusting it in a WSOLA-like fashion. After having reset the vertical phase coherence, the next synthesis frames are again computed by the phase propagation strategy.  
To reduce the effect of transient smearing, other approaches include transient preservation schemes, similar to those for WSOLA. In [22], transients are identified in the input signal, cut out from the waveform, and temporarily stored. The remaining signal, after filling the gaps using linear prediction techniques, is modified using PV-TSM. Finally, the stored transients are relocated according to the stretching factor and reinserted into the modified signal.
## 4 Sensitivity Analyses on Boundary Conditions and Model Closures  
### Model Validation  
The results of the numerical model are displayed in Figures 2-6 for the different gas superficial velocities, \(U_{G}\). In Figures 2-6, the labels on the \(x\)-axis correspond to the mean value of \(d_{eq}\) in every class. The predicted BSDs are unimodal with a peak of frequency within the class \(d_{eq}=(2;3]\) mm and are, generally, in agreement with the experimental data. It is worth noting that for every \(U_{G}\), the model underestimates the relative frequency of the smallest bubble class (0; 1) mm and overestimates one of the classes (2; 3) mm. At \(U_{G}=0.0188\) m/s, the shape of the BSD progressively changes (i.e., see the difference between Figures 2 and 6), which is in agreement with the onset of the flow regime transition. In our point of view, the decrease in the Sauter mean diameter from 0.0037 m/s to 0.0111 m/s and its subsequent rise is caused by a change in the prevailing local fluid dynamic phenomena. Possibly, two prevailing mechanisms, determining BSD equilibrium, exist and this behavior is king of a blending between them. Ongoing measurements of BSDs at different axial and radial positions and local liquid velocity profiles aim to clarify this statement. Generally, the BSDs obtained from the model are in good agreement with the experimental BSDs. In addition, the results for the Sauter mean diameter are summarized in Table 1 and show good agreement between the model results and the experimental data. In conclusion, the proposed PBM approach is able to predict bubble size distributions in the bubble column, in the \"pseudo-homogeneous\" flow regime (which is typical of industrial applications). The model seems to provide reasonable results in terms of bubble size distributions and Sauter mean diameter; therefore, the approach can be applied to estimate the interfacial area for the heat and mass transfer at the interface. In addition, this model may be used to perform sensitivity analyses on operating conditions (i.e., temperature and pressure) for scaling-up purposes. Finally, the proposed approach may be used to predict the flow regime transition, especially if coupled with a lift-force approach. It is worth noting that the mathematical formulation of the model is quite simple and may be easily improved.  
Figure 3: Comparison between PBM results and experimental data: \(U_{G}=0.0074\) m/s.  
Figure 2: Comparison between the population balance model (PBM) results and experimental data: \(U_{G}=0.0037\) m/s.  
Figure 4: Comparison between PBM results and experimental data: \(U_{G}\) = 0.0111 m/s.  
Figure 5: Comparison between PBM results and experimental data: \(U_{G}\) = 0.0149 m/s.  
Figure 6: Comparison between PBM results and experimental data: \(U_{G}\) = 0.0188 m/s.
## 3 Experimental Results and Analysis
### Fracture Modulus  
In the fracture mechanics experiment involving coal samples, the fracture modulus (\(E_{S}\)) reflects the capacity of coal to resist elastic deformation. The fracture modulus of coal samples varies with the immersion time and loading rate of mine water, as illustrated in Figure 8.  
From Figure 8a, it is evident that the fracture modulus gradually decreases with increasing immersion time in mine water. The fracture modulus for non-immersed samples and those immersed for 30 days, 60 days, and 120 days are 166.89 MPa, 108.12 MPa, 90.65 MPa, and 77.81 MPa, respectively. After immersion for 30 days, the fracture modulus significantly decreased by 35.22% compared to non-immersed samples. Subsequently, immersion for 120 days results in a 53.38% decrease compared to non-immersed samples. Thus, the trend in the decreasing fracture modulus gradually becomes flatter. Generally, a higher elastic modulus in rocks indicates increased brittleness [38], as well as a greater ability to resist induced crack closure [39]. Conversely, in the case of coal samples, the weakening of the fracture modulus due to mine water indicates a diminished ability to resist crack instability. According to Figure 8b, the fracture modulus of coal varies from 87 MPa to 121 MPa at different loading rates. The fracture modulus increases significantly,  
Figure 8: Fracture modulus curve of coal samples ((a) fracture modulus versus immersion time (loading rate is 0.002 mm/s); (b) fracture modulus versus loading rate).  
by 23.53%, with an increase in the loading rate from 0.0002 mm/s to 0.002 mm/s. As the loading rate increases to 0.005 mm/s, the fracture modulus rises by 11.80% relative to the 0.002 mm/s loading rate. Therefore, within this range of loading rates, the loading rate has minimal impact on the ability of coal to resist elastic deformation during fracture.
## 2 System Identification Methods  
In this study, two systems identification models are applied to investigate the applicability of using these models for predicting the structures behavior with attached passive control of Tuned mass damper (TMD), as described in Section 3. The two systems are nonlinear AutoRegressive with eXogenous inputs (NARX), and Adaptive neuro-fuzzy inference system (ANFIS). In this study, the used input-output model is divided into single-input single-output (SISO) and multi-input single-output (MISO). The SISO model is designed based on seismic signals as an input parameter and response of structure as output parameters. While the MISO model depends on the seismic signals and time-delayed output of structural response as input parameters and the response of structure as output parameters.
## 3 The Proposed PHM System
### The Functional Architecture  
The functional architecture includes all the activities within the framework that fulfill the defined requirements of machine producers.  
The proposed functional architecture is shown in Figure 4. The functions have the same objectives as those introduced in [8], i.e., DA, DP, FDA, PA, HM. However, some modifications based on the previous observations and producers' requirements are here proposed.  
First, the DP function is split into two sub-functions: the system-level feature extraction and the component-level feature extraction. The former deals with the extraction of system-level features, which can reveal the machine operating condition. The latter deals with the extraction of component-level features that can reveal the health condition of the components. In particular, component-level features can be used for two aims: diagnostics and prognostics. In the first case, the features allow establishing if a fault condition has occurred. In the second case, the features are HIs and allow us to predict the RUL of the component. These sets of features change from one component to another. Thus, they need to be selected and computed for each critical component of the machine.  
Second, the Operating Condition Recognition (OCR) function is introduced between the DP function and the FDA. In general, the producer wants to know the machine's working condition at any point in time. The operating condition can be known or unknown. If data collected during the machine functioning under one setting are available, then it means that the setting is known and the data can be used for diagnostic and prognostic model training. Thus, the so-built predictive models can be applied to make inferences on streaming data. Otherwise, if the operating condition is unknown, the collected data cannot be associated with a specific setting. Hence, existing models cannot predict the operating condition with high accuracy. However, these data can be assigned to the same unknown group that corresponds to the same novel situation. Then, the new group can be used to train the existing models and include the new class. Hence, the OCR function aims to recognize the setting under which the machinery works at any point in time. It can be seen as a _novelty detection problem_. Its output is system-level information that determines the choice of existing predictive models. Thus, it can also be considered a function that automatically labels the collected observations as belonging to an existing class or novel behavior.  
Finally, the proposed functional architecture also includes anomaly detection in the FDA and PA functions. In the first case, an anomalous behavior could correspond to a sudden or unknown fault, while in the second case, the irregular behavior could mean that the component started a new HS. Thus, it can be considered as the point for starting the RUL prediction [82].