esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Heat Transfer Efficiency Evaluation with Mouromtseff Number
There is an agreement between both experimental and FOM values in reporting a worsening of the thermal performance of the nanofluid in comparison to the base fluid. This serves to confirm that the Mouromtseff number ratio can be used to envisage the convective heat transfer performance of nanofluids. However, there is a deviation between the FOMs and the experimental values. The mixing process within the dispersion is more crucial for convective heat transfer performance than the inherent properties of the nanofluid under turbulent flow conditions. This effect, along with the uncertainties related to the experimental measurements, can be the cause of these discrepancies. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Temperature (K)** & **Re** & \(\frac{k_{nf}}{k_{bf}}\) & \(\left(\frac{Mo_{nf}}{Mo_{bf}}\right)\) & _[PERSON]–Boelet_ & \(\left(\frac{Mo_{nf}}{Mo_{bf}}\right)\) & _[PERSON]–Boelet_ & \(\left(\frac{Mo_{nf}}{Mo_{bf}}\right)\) \\  & & & 930 & 0.945 & 0.993 & 0.992 & 0.991 \\  & & & 6604 & 0.939 & 0.991 & 0.990 & 0.990 \\  & & & 8271 & 0.940 & 0.992 & 0.991 & 0.991 \\  & & & 9921 & 0.938 & 0.991 & 0.990 & 0.990 \\  & & & 11,629 & 0.939 & 0.992 & 0.991 & 0.991 \\ \hline  & & & 6981 & 0.945 & 0.994 & 0.994 & 0.994 \\  & & & 9300 & 0.945 & 0.993 & 0.992 & 0.993 \\
308 & & & 11,684 & 0.952 & 0.996 & 0.996 & 0.996 \\  & & & 13,951 & 0.946 & 0.993 & 0.993 & 0.993 \\  & & & 16,513 & 0.945 & 0.997 & 0.998 & 0.998 \\ \hline  & & & 8963 & 0.954 & 0.992 & 0.991 & 0.991 \\  & & & 11,958 & 0.954 & 0.992 & 0.992 & 0.992 \\
318 & & & 14,917 & 0.957 & 0.992 & 0.992 & 0.992 \\  & & & 17,888 & 0.953 & 0.992 & 0.991 & 0.991 \\  & & & 20,885 & 0.953 & 0.992 & 0.992 & 0.992 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Mo_{nf}/Mo_{bf}\) ratios obtained through Equations (3)–(5) and experimental \(h_{nf}/h_{bf}\) ratio at 298.15, 308.15, and 318.15 K within the studied flow rate for the three analysed nanofluids at 0.25, 1.0, and 2.0 wt%. Thus, the heat transfer performance of A-TiO\({}_{2}\) in a PG:W 30:70 vol% nanofluids was evaluated by determining both the experimental convective heat transfer coefficients and the Mouromtseff number ratios. The benefit of the Mo ratio to assess the nanofluid performance was evidenced and supported by the experimental results, but with maximum deviations between \(Mo_{nf}/Mo_{bf}\) and \(h_{nf}/h_{bf}\) of 15% related to the experimental uncertainties and the effect of the mixing phenomena. This implies that experimental heat transfer tests are still needed to accurately determine the convective heat transfer performance of nanofluids in heat exchangers.
# Sport Performance Analysis with a Focus on Racket Sports: A Review
## 3 Results
### SR1: An Overview of Sport Performance Analysis in General
#### 3.1.3 Methods of Performance Analysis  
There are many different performance analysis methods that can be used depending on what is being measured, what data is being analyzed, and what sport is being investigated. Following are some of the techniques that have been identified:  
* Statistics methods including ANOVA, one-way ANOVA, two-way MANOVA, linear model, cluster analysis, k-means clustering, logistic regression, Chi-square analyses, Mann-Whitney U-test, discriminant analysis, Matched Paired \(t\)-test, mixed linear method and Markov chain: [PERSON] and [PERSON] [4], [PERSON] et al. [70], [PERSON] et al. [71], [PERSON] et al. [72], [PERSON] et al. [73], [PERSON] et al. [74], [PERSON] et al. [75], [PERSON] et al. [16], [PERSON] et al. [76], [PERSON] and [PERSON] [77], [PERSON] et al. [78], [PERSON] and [PERSON] [79], [PERSON] et al. [80], [PERSON] et al. [81], [PERSON] et al. [82], [PERSON] et al. [83], [PERSON] and [PERSON] [84], [PERSON] et al. [85], [PERSON] et al. [86], [PERSON] et al. [87];
* Machine learning: [PERSON] et al. [37], [PERSON] et al. [88], [PERSON] et al. [89], [PERSON] et al. [90], [PERSON] and [PERSON] [91], [PERSON] [92], [PERSON] et al. [93], [PERSON] et al. [94];
* Neural networks including recurrent neural network (RNN) with long short-term memory (LSTM): [PERSON] and [PERSON] [95], [PERSON] et al. [96], [PERSON] et al. [97], [PERSON] and [PERSON] [98], [PERSON] et al. [99], [PERSON] et al. [100];
* Decision tree including partial decision tree (PART) and random forest: [PERSON] et al. [101], [PERSON] et al. [5], [PERSON] et al. [102; 103];
* Social network analysis: [PERSON] et al. [31], [PERSON] et al. [32], [PERSON] and [PERSON] [33], [PERSON] et al. [104], [PERSON] et al. [105];
* Notational analysis: [PERSON] et al. [8], [PERSON] et al. [106], [PERSON] et al. [107], [PERSON] et al. [108], [PERSON] et al. [109], [PERSON] et al. [110], [PERSON] and [PERSON] [111];
* Self-organising maps: [PERSON] et al. [78], [PERSON] et al. [112], [PERSON] et al. [113];  
**Summary:** In general, a significant amount of work has been done in the area of performance analysis methods. Statistical methods have been implemented across various sports and data sources, followed by large implementations of notational analysis. Three regions can be explored further: neural network analysis, social network analysis, and the use of self-organizing maps.
## 5 Conclusion and Future Work  
In SGs, current research work primarily focuses on optimization techniques of power scheduling. However, prior to scheduling, an accurate load forecasting model is needed, because accurate load forecasting leads to enhanced management of resources, which in turn directly affects the economies of the energy trade. Furthermore, lower similarities (high randomness) and non-linearity in history load curves make the SG's DLF more challenging as compared to long-term load forecasting. Thus, the aforementioned reasons lead us to investigate the SG's DLF models. From a literature review, we found that many DLF models are proposed for SGs; however, these models trade off between accuracy and execution time. Thus, we focus on the development of an accurate DLF model with reduced execution time. In this regard, this paper has presented an ANN-based DLF model for SGs. Simulation results show that the newly-proposed DLF model is able to capture the non-linearity(ies) in the history load curve, such that its accuracy is approximately \(97.11\%\), such that the average execution time is improved by \(38.50\%\).  
As the multi-variate auto-regressive training model minimizes the forecast error to some extent, so our future directions are focused on either the improvement of this model or its replacement with a better model.
## 5 Validation
### Algorithmic Accuracy
Since this was the result of a qualitative, manual tuning process, the results are not described in detail on account of their limited generality. Overall, special consideration should be taken when selecting reference points to avoid the adverse effects of data bias. The effect of oscillations around a result was reduced with the dampening factor \(\alpha\) introduced in Equation (7) in Section 3.4. Convergence to local minima can be avoided by selecting the starting values of the iteration in the same general area as the expected result.
# Experimental Flow Performance Investigation of Francis Turbines from Model to Prototype
## 3 Experimental Results and Discussions
### Pressure Pulsation Impacts on Hydraulics Performance
A vortex rope was observed at the draft tube cone, exhibiting smooth behavior from the runner outlet to the draft tube cone. The presence of the rotating vortex rope was evident with low amplitude values, and it did not collapse within the draft tube cone. No amplitude dampening was observed in the rotating vortex rope (RVR), and no vibration or cavitation affected hydraulic efficiency. At the monitoring points near the exit of the draft tube elbow, \(P_{4}\), minor differences in pressure fluctuation amplitudes under various working conditions were observed. The pattern of pressure pulsation was essentially the same across these points, with similar main frequencies and amplitudes of pressure pulsation. The frequency amplitudes of pressure pulsations at the monitoring points were found to be 0-0.20 (\(f/f_{n}\)), as shown in Figures 10-12, which is consistent with the calculated and measured values. At 100% load, the pattern of pressure pulsation amplitude fluctuation at the minimum (38.10 m), design (38.50 m), and maximum (37.50 m) head levels showed minor differences at the draft tube cone and draft tube elbow, with frequencies measured at 0-0.1 (\(f/f_{n}\)). However, the spiral inlet and head cover showed high pressure fluctuations due to the water flow direction and varying guide vane opening angles, with pressure frequency amplitudes measured at 0-0.3 (\(f/f_{n}\)). These pressure fluctuations did not affect hydraulic performance, and the Francis turbine model demonstrated stability with no vibration or sound. This indicates that the experimental method used in this paper can accurately predict the hydraulic performance and design validity of the turbine. Figure 11: _Cont._  
Figure 10: Frequency spectrum at the **(a)** spiral case inlet, **(b)** head cover, **(c)** draft tube cone, and **(d)** draft tube elbow for the model runner with the head of 38.10 m.
## 5 Results
### Comparative Study  
#### 5.2.1 Attention Mechanism Experiment  
In order to verify the attention of the attention mechanism in the deep semantic feature fusion strategy to the object area, an attention comparison experiment was conducted with the previous research, and the results are shown in Figure 9.  
As depicted in the figure, the basic network without employing a feature fusion strategy exhibits issues such as semantic ambiguity and feature confusion in the object regions, which leads to inadequate attention towards the graspable regions of the objects. As shown in Figure 7, the cup in line 1 is expected to possess the optimal grasping area within its body. The basic network without the attention mechanism neglects to allocate sufficient focus on the cup's body, while the LGAR-Net2 network proposed in this paper can effectively direct attention towards the central region of the cup's body and suppresses excessive emphasis on grasping the handle, thereby enhancing overall grasping quality. The optimal grasping position for the scraper in the third row is at the handle. The basic network without an attention mechanism has an even distribution of attention to the whole scraper, failing to accurately identify the best grasping area at the handle. The LGAR-Net2 network proposed in this paper effectively focuses on the grasping area of the handle andsuppresses the ungrasping blade area. Compared to other networks, LGAR-Net2 with an added attention mechanism demonstrates heightened focus on graspable object areas.
# Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint
## 1 Introduction
### Contributions  
Inspired by Ref. [38], we choose active power as signature and propose a GSP-based hybrid NILM algorithm. We first model the power disaggregation as a Matrix Factorization (MF) problem and formulate the objective function. Then, in order to reduce the training complexity and corresponding overhead, we integrate the technical idea of the event-based approaches into the MF problem, thus a novel hybrid NILM is formed, where GSP is used for event detection through adding constraint to the MF problem. In this paper, we construct a graph with the active power measurements and introduce the graph shift quadratic form as a new regularization term of the MF problem. Finally, we design a two-step iterative optimization method to solve the problem, the first step minimizing the regularization term to find the signal with minimum variation, and based on the total graph variation minimizer, the second step uses the simulated annealing (SA) algorithm to iteratively minimize the objective function and constraint.  
We refer to the proposed algorithm in this paper as Graph Shift Quadratic Form constrained Active Power Disaggregation (GSQF-APD). The major contributions of this paper are 4-fold, which are listed as follows:  
* We pose the power disaggregation as a MF problem and integrate the technical idea of the event-based approaches into the MF problem, i.e., GSQF-APD is actually a hybrid NILM, which can reduce the training complexity.
* The graph shift quadratic form is introduced as a new regularization term, which outperforms the Laplacian-based approach in classification accuracy.  
* We separate the appliances from the aggregate power signal one by one in a descending order, which reduces the coverage to low power appliances, and obviously improves the disaggregation accuracy.
* Compared with other algorithms that require a training process, GSQF-APD is less dependent on training data.  
The rest of the paper is organized as follows. Section 2 makes a statement of the problem and elaborates on the proposed GSQF-APD algorithm. Section 3 describes the experimental design and results. The final section concludes the paper and discusses future work.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Metrics in Human Mobility Data Network
#### 2.2.2 Human Mobility Metrics  
Common data-driven human mobility metrics could be useful for observing population mobility. They are generated from CDR, GPS, wi-fi or LBSN by data fusion and analytics. Therefore, the following six metrics are used to comprehensively evaluate people's behaviors.  
Social-based categorization: a unit of measurement that contributes to data analysis such as unique users or unique devices. It is also related to contacts of people since it considers ICT, recontact rate, contact duration, node density properties;
* Spatial-based categorization: a unit of measurement that categorizes the dimensions of the user locations. For example, tile grids, GPS from POIs, or tower catchment areas define unique locations that are visited by a user for internal analysis;
* Spatial-based aggregation: a unit of measurement that aggregates data of the regions of interest before being shared. It also describes the size of these regions. For example, all users in a region of interest, such as county or neighbourhood, can be aggregated so that the metrics calculate the across population;
* Temporal-based categorization: a unit of measurement that categorises every user's location in specific time periods. For example, the modal location a that a user logs data in every day;
* Temporal-based aggregation: a unit of measurement that aggregates data of all users in a region into a time window. For example, we might be interested in the average numbers of locations visited by users in region over the course of an 8 h time window;
* Temporal thresholds: a unit of measurement where providers may decide to calculate locations where the user spends at least a certain amount of time, rather than to calculate locations by top location every 8 h;
* Movement-based: a unit of measurement that results from individuals' mobility traces. It considers flight length, pause time, visit frequency, mean-squared distance, number of places visited, or speed;
* Link-based: a unit of measurement that analyzes the similarities or spatio-temporal relations of people movements. Contact duration, node density, intercontact time, recontact rate/count, the temporal variance of node density, pairwise distance, relative velocity are examples of this metric;
* Network performance-based: a unit of measurement that analyzes the effects of human mobility on the performance of the networks. For example, message delay, data loss ratio, transmission count, energy consumption are properties of this network-related metric.  
Researchers have utilized such data to understand individual human mobility patterns, evaluate human mobility behaviors and explore social ties in several contexts, e.g., on extreme events [43, 44, 45].
## 6 Applications to Cardiovascular Medicine
### Heterogeneous Models
Myofiber orientation was prescribed following the Laplace-Dirichlet rule-based algorithm by [PERSON] et al. [55]. Measured left ventricular pressure was applied to the endocardium of the stress-free early diastolic model while constraining axial displacements on the basal plane. Boundary loads consisted of right ventricular pressure at the septum and a traction-free condition at the epicardium. Passive and active material parameters were defined in terms of a scale of the infarction and ischemia severity. This ischemia effect factor modulated different responses with regions identified with zero severity behaving like healthy tissue and becoming stiffer and less actively contractile with larger lesion severity. Material parameters and the ischemia effect factor were fitted for each one of the 17 regions by minimizing a weighted function of the mean square error of diastole-to-systole volume change and the region-wise average strain. Results agree with previous studies on predicting the stiffening of regions corresponding to infarcted tissue and border zone. Additionally, the model allowed the estimation of the ischemia effect on tissue stiffening and the recovery of compliance after revascularization treatment. One of the main limitations of the above studies is the assumption of either spatial material homogeneity or segment-wise heterogeneity, however, material properties are likely to vary continuously throughout the myocardium. To address this, [PERSON] et al. proposed an iterative inverse method to resolve the heterogeneous distribution of mechanical properties on an LV model from a 64-year-old heart with systolic heart failure, LBBB, coronary artery disease, and chronic infarction in the inferior section of the LV [141]. 4D echocardiography was used to obtain the anatomic model and FEM mesh at early atrial systole. Speckle tracking was used to estimate systolic strain averaged over the 17 standard regions, and pressure was measured by catheterization. Gadolinium-enhanced MRI was used to identify the location of infarcted fibrotic tissue. The Holzapfel-Ogden material model was implemented allowing spatial variations of the scalar material parameters with a piece-wise linear representation with fiber orientation following the rule-based algorithm proposed by [PERSON] et al. [55]. Active tension was neglected, and end-diastole configuration was assumed stress-free. Rigid body motion was constrained by impeding axial displacement at the basal plane and apex, and by an in-plane elastic foundation at the base plane imposed as a collection of linear springs with uniform elastic constant. A sequential quadratic programming algorithm was applied to estimate the almost 3000 spatially distributed material parameters. To favor convergence to smooth distributions, optimization was constrained by a first-order Tikhonov functional. Results show that estimated strains were lower, and the material stiffer, in regions corresponding to infarcted tissue and its immediate surroundings identified by gadolinium-enhanced MRI.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 2 Photoacoustic Signal Generation
### Laser-Induced Breakdown
Such contrast is because [PERSON] used large focusing angles between \(8^{\circ}\) to \(16^{\circ}\) and assumed diffraction limited conditions to calculate \(I_{th}\) while [PERSON] used very small focusing angle \(\leq\)1.7 where \(I_{th}\) is influenced by self-focusing of the laser beam. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Pulse Duration, \(\tau_{2}\)** & **Wavelength, \(\lambda\)(nm)** & **Focusing Angle, \(\theta\)** & \(f\)**- Number, \(f\#\)** & **Measured Spot** & **Energy** & **Irradiance Threshold,** & **Source** \\  & & & **Diameter, \(2\omega_{0}\) (\(\mu\)m)** & **Threshold,** \(I_{th}\) (\(10^{11}\) Wcm\({}^{-2}\))** & **Source** \\ \hline
76 ns & 750 & \(19^{\circ}\) & 2.99 & 20 & 5497.78 & 0.23 & [54] \\  
{\circ}\) & 3.56 & 4.4 & 0.17 & 111.0 & [54] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental optical breakdown thresholds for water for different pulse durations, wavelengths, and focusing angles. The spot size is experimentally measured while the \(f\)-number is calculated using Equation (4).
## 5 Applications in Control Theory  
Controlling chaos via different control schemes has received great interest after it was first introduced in 1990 by Ott, Grebogi, and Yorke [108]. The Lozi map is often used in this domain and in control theory-based chaos.
# Demand Prediction Using a Soft-Computing Approach: A Case Study of Automotive Industry
## 3 Proposed Model
### Time Series Artificial Neural Network  
The network inputs include the variables obtained in the Delphi method and the fuzzy inference system. In addition, this contemplates the standardized information. That is, to homogenize the values that are integrated into the network database [58]. Also, some of the parameters to contemplate in the network construction are the inputs number, topology, number of neurons for each hidden layer and the output values [59].  
The mathematical interpretation network is represented in the following way, input value \(x_{j}\), weights values \(w_{ij}\), bias values \(b_{i}\) and output value \(y_{i}\), as in the Equation (4).  
\[y_{i}=\phi_{i}\left(\sum_{j-1}^{n}w_{ij}x_{j}+b_{i}\right) \tag{4}\]  
with \(i=1,...,h\) activation function of the neuron \(i\) is represented as \(\phi_{i}\). This represents the operations performed between the input layer and the hidden layer, while the relationship that saves the hidden layer and output layer is calculated as the Equation (5).  
\[y_{s}=\phi_{s}\left(\sum_{i-1}^{h}w_{is}y_{i}+b_{s}\right) \tag{5}\]  
with \(s=1,...,g\). In the training process different algorithms are proposed to the experimentation. Also, it is important to mention that this can be modified based on the results obtained from what stands out the algorithms Levenberg-Marquardt (TRAINLM) and scaled conjugate gradien (TRAINSCG)[60; 61].