esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Deep Learning for Electronic Health Records
## 4 Deep Learning Models
### Convolutional Neural Networks (CNN)  
The convolutional neural network is a representative deep learning method that was first utilized for image feature extraction. When it utilizes its capacity to extract some features from data, these features potentially contain features that humans cannot perceive. The structure, such as LeNET-5 [22], is often a combination of convolution layers and pooling layers. In addition, there are many derivative algorithms based on CNN architecture, such as AlexNet [23], VGGNet [24], ResNet [25], etc.  
In Equation (1), \(x\) represents the input, and \(w\) is the weighted function of the equivalent convolution filter for one-dimensional data. From one-dimensional to two-dimensional  
Figure 4: Neural network model with single hidden layer on left and double hidden layer on right.  
Figure 3: Shared tasks published by i2b2/n2c2. Since 2018 the i2b2 project ended the name was changed to n2c2. For each share task, a high-quality dataset has been officially published.  
data, \(X\) represents two-dimensional data in Equation (2), and \(K\) represents the convolution kernel. To obtain the feature map, the convolution process can be seen as the sliding product of the input matrix \(X\) and the convolution kernel matrix \(K\).  
\[C_{1d}=\sum_{\alpha=-\infty}^{\infty}x(\alpha)w(t-\alpha) \tag{1}\]  
\[C_{2d}=\sum_{m}\sum_{n}\ X(m,n)K(i-m,j-n) \tag{2}\]  
CNN could even extract text features, which is referred to as Text-CNN [26] to distinguish it from CNN in images. The Text-CNN network is the model architecture displayed in Figure 5. Text-CNN takes a pre-trained word vector as input and then generates the appropriate word embedding. In contrast to general CNN, the width of the convolution kernel in convolution must be the same as the word vector's dimension. For example, the convolution kernel in Figure 5 has a dimension of six and is a two-dimensional matrix. The pooling and other procedures that follow are similar to those used by CNN in general.  
When it comes to extracting text features in EHR, Text-CNN technology plays a critical role. Many technologies are aimed at optimizing the medical text features obtained by Text-CNN, reducing the privacy information features of patients, highlighting the features useful for downstream tasks, and achieving the effect of protecting private information, in order to obtain the effect of protecting private information.
# An Innovative Parabolic Trough Collector Design with a Twin-Cavity Receiver
## 3 Results and Discussion  
### Verification of the Developed Models  
Before proceeding with the optical and thermal analysis results, it is essential to present the verification results regarding the two numerical models that were developed in the present work. The verification was conducted for the whole operating temperature range using two different models from theory (see Equations (11)-(14)) as concerns the Darcy friction factor as well as the Nusselt number. To use the abovementioned formulas properly, it was necessary to define when the flow transition from the laminar to the turbulent regime took place in every PTC. It could be found by calculating the Reynolds ratio (\(Re/Re_{cr}\)), as Figure 4 suggests.  
As is depicted in Figure 4, the transition to the turbulent state was different for each PTC. More specifically, the transition happened from 140 \({}^{\circ}\)C to 160 \({}^{\circ}\)C in the PTC-TC and in the range of 160 \({}^{\circ}\)C to 180 \({}^{\circ}\)C in the PTC-F. This difference existed due to the higher temperature levels of the PTC-TC against the PTC-F. Hence, the theoretical models were applied for the whole operating temperature range by also taking into consideration the transition regions, which came from Figure 4. Figure 5 presents the verification results concerning the Nusselt number and the Darcy friction factor for the PTC-TC. The respective results for the PTC-F are given in Figure 6.  
Figures 5 and 6 show that there is an acceptable agreement between the CFD and the theoretical results. In particular, the mean declination from the theoretical values does not exceed 5.0% as regards the PTC-TC in the Nusselt number and the Darcy friction factor. As far as the PTC-F is concerned, this declination is lower than 5.6%. Moreover, Figures 5 and 6 indicate the transition from the laminar flow regime to the turbulent flow regime. It is clear that the transition regions are identical to those found in Figure 4 from the Reynolds ratio calculation.  
Figure 4: Reynolds ratio for both PTCs (the horizontal dotted red line indicates the transition limit).
## 2 Materials and Methods
### Evaluation Metrics  
The most common evaluation metrics for regression models are R-Squared (\(R^{2}\)), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These metrics are properly described in the scientific literature [60; 61]. According to [PERSON] et al., in the following formulas, \(X_{i}\) is the predicted _ith_ value, and the \(Y_{i}\) element is the actual _ith_ value.  
R\({}^{2}\) is the coefficient of determination. It measures the proportion of variance in the dependent variable that is explained by the independent variables in a regression model. It ranges from 0 to 1, where higher values indicate a better fit. However, a high \(R^{2}\) does not necessarily imply a well-performing model, especially in the presence of overfitting or multicollinearity.  
\[R^{2}=1-\frac{\sum_{i=1}^{m}(X_{i}-Y_{i})^{2}}{\sum_{i=1}^{m}\left(\overline{Y }-Y_{i}\right)^{2}}\]  
_MAE_ provides the average of the absolute difference between predicted and actual values, without considering their direction. It provides an intuitive measure of model accuracy by averaging the absolute differences, making it less sensitive to large outliers compared to squared error metrics.  
\[MAE=\frac{1}{m}\cdot\sum_{i=1}^{m}|X_{i}-Y_{i}|\]  
_MSE_ calculates the average squared differences between predicted and actual values. By squaring the errors, it penalizes larger deviations more heavily, making it useful for emphasizing substantial prediction errors. The _MSE_ is always a positive value.  
\[MSE=\frac{1}{m}\cdot\sum_{i=1}^{m}(X_{i}-Y_{i})^{2}\]_RMSE_ provides a balanced measure of model accuracy by accounting for both an average error magnitude and the impact of larger errors, making it particularly useful for comparing different models.  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(X_{i}-Y_{i})^{2}}\]
## 3 Results and Discussion
### Lipid Oxidation  
#### 3.2.1 Content of CDs  
Formation of CDs in raw ground chicken legs was highly depended on time and treatments (\(p<0.05\), test F). The CD concentrations of the samples increased with time, reaching the highest values on the 12 th day of storage at 4 \({}^{\circ}\)C, apart from the sample with nutmeg for which the maximum CD content was observed after 10 days (Figure 1). The results are in agreement with [36], as an increase of CD concentration of the raw chicken patties with mamao luang fruit was observed during 12 days of storage at 4 \({}^{\circ}\)C and also [13], reported an increase of CDs during chilled storage of raw ground pork samples. Others indicated that the CD content of the control sample of raw chicken patties was picked on 7 th day of storage and dropped thereafter [36] and that the maximum CD concentration in the control sample of cooked chicken thigh was observed on the 2 nd day of 5 days of chilled storage [37]. The decrease of CD level at the later stage of storage could be explained by the dissociation of conjugated hydroperoxides to secondary lipid oxidation products of lower molecular weight [36]. In this study, CD concentrations in all treated samples were significantly lower compared to control (Figure 1) with the black seed and cardamom extracts being the most potent antioxidant treatments. For the control sample CD content ranged from 0.30 to 0.54% while for the black seed-treated sample from 0.19% to 0.30% and cardamom-treated sample from 0.19% to 0.33%. The order of samples by increasing content of CD's was as followed: Black seed \(\approx\) cardamom \(<\) nutmeg \(\approx\) bay leaf \(\approx\) caraway \(\approx\) allspice \(<\) cloves \(<\) control. Others concluded that the inhibition of CD formation by plant extracts was due to their phenolic content [38]. However, in this study the antioxidant effectiveness of spice extracts in meat matrix measured as CD content did not correlate with their antioxidant activity nor with their phenolic contents since black seed and cardamom extracts showed the lowest ABTS\({}^{\bullet+}\) radical scavenging capacity (Table 1) and phenolic contents among tested samples, as reported previously by [13]. On the other hand, cloves despite the highest ABTS\({}^{\bullet+}\) radical scavenging activity and phenolic content [13], exhibited the lowest inhibition of CD formation (from 0.32% to 0.44%) in chicken meat among extract tested. For clove treatment, the protective effect on lipid stability was observed from the 7 th day of storage (Figure 1). Increased oxidative stability of lipids, expressed as CD marker, was observed in precooked chicken nuggets after addition of grape seed powder [39], cooked chicken thigh containing oregano, sage and honey [37], raw ground pork with persimmon peel extract [38] and raw chicken patties with mamao luang [36]. The data on the beneficial effects of black seed in muscle food system are scarce [11; 13]. In our previous study on the oxidative stability of raw ground pork during
## 3 Results and Discussion
### Solid-Liquid Mixture of Media on the Cleaning Effect of
Surface roughness and removal depth are analyzed to investigate the effect of SiO\({}_{2}\) percentage size on both. The results are shown in Figures 21 and 22. From the surface roughness, with the increase in SiO\({}_{2}\) percentage content, the surface roughness does not change much, but before the percentage of 1% and then a slightly increasing trend, from about 1 \(\upmu\)m to about 1.2 \(\upmu\)m, when the percentage of SiO\({}_{2}\) reaches 2%, there is a minimum value of about 0.8 \(\upmu\)m, with a more uniform distribution. Subsequently, the surface roughness increases gradually with the increase in SiO\({}_{2}\) percentage and rises to a maximum value of 5.7 \(\upmu\)m at 10%. Figure 19: The effect of the mixture of water and SiO\({}_{2}\) on micro-morphology: (**a**) 0%; (**b**) 0.5%; (**c**) 1%; (**d**) 2%; (**e**) 5%; and (**f**) 10%. Figure 22: Effect of SiO\({}_{2}\) proportion on removal depth. Figure 20: Effect of a water and SiO\({}_{2}\) mixture on three-dimensional morphology: (**a**) 0%; (**b**) 0.5%; (**c**) 1%; (**d**) 2%; (**e**) 5%; and (**f**) 10%. From the perspective of machining oxide removal, the use of the mixed suspension improved efficiency by 19.3% [21]. In terms of laser cleaning for rust removal, under the same parameter conditions, there was a 25% increase in the removal of rust layers, indicating that the efficiency of a solid-liquid mixture is greater than that of a single medium assistance. The cleaning mechanism is now analyzed, and the results are shown in Figure 23. The rust layer, when subjected to a solid-liquid mixed dielectric, undergoes vaporization after being irradiated by the laser, with the liquid medium absorbing heat. This vaporization serves to ablate surface contaminants. Subsequently, solid particles collide with each other and continuously impact the rust layer through mechanical motion. The cleaning mechanism of the solid-liquid mixture is a hybrid mechanism, where the ions of the removed rust layer are dispersed in the liquid and do not remain on the cleaned surface. Solid-liquid mixture-assisted laser cleaning technology is an advanced cleaning method that combines solid particles and liquid media. It enhances the absorption and transmission of laser energy by adding solid particles during the cleaning process, thereby improving cleaning efficiency and quality. Cleaning efficiency may be influenced by various factors, including laser parameters, particle characteristics, compositions of cleaning solutions, etc., which require further research for optimization.
# Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder
## 1 Introduction  
For hydraulically actuated systems such as cranes, the hydraulic cylinder is the most common actuator since it can provide a linear motion with, generally speaking, a large force to volume ratio, a high efficiency and at a modest price. For systems which require a cylinder force in both directions, a double acting cylinder is needed, and the differential cylinder is an obvious choice due to its low cost and simple design. The main disadvantage is the difference in effective hydraulic area which leads to a jump in both velocity and force gain when changing sign of direction, i.e., around zero velocity.  
For many hydraulic systems, the pressure compensated directional control valve is a practical choice due to the fact that it provides load independent flow control of the actuators. The pressure compensator senses the load pressure, and adjusts the pressure drop over the directional control valve to give a load independent flow. Since the velocity of the actuator is proportional to the hydraulic flow through the valve, this translates to load independent velocity control. For manually operated systems, the velocity control makes it easy for an operator to control systems that are subjected to large variations in external load.  
For closed loop control systems, the load independent velocity control can be utilized in a control system using feedforward [1]. In this case, both a position reference and a velocity reference are generated in the control system. An example of a typical closed loop electro-hydraulic motion control system with feedforward is shown in Figure 1. The feedback controller uses the position reference and the measured cylinder position, whereas the feedforward controller uses the velocity reference. The pressure compensator is connected to a supply line which is shared with other actuators. The red dashed lines show the hydraulic pilot lines for the counterbalance valve and the pressure compensator.  
It should be noted that feedforward control cannot be used alone. A feedback controller is also needed to help track the position reference, to eliminate steady state position error, and to counteract any drift. Normally the feedforward gain is based on system components, and is defined as theratio of valve opening to actuator velocity. With this in mind, it follows that modeling errors and model uncertainties, in addition to external disturbances and system dynamics, may yield sub-optimal performance with a fixed feedforward gain.  
This paper focuses on modeling and motion control of a hydraulic loader crane with pressure compensated differential cylinders. An adaptive feedforward controller is investigated to improve performance of the motion control system. Two different approaches to feedforward control have been implemented, the first is based on the MIT-rule [2], and the second is based on the sign-sign algorithm [3].
# Identification of Damage in Beams by Modal Curvatures Using Acoustic Beamformers
## 3 Direct Problem
### Sensitivity of Modal Quantities to Local Damage and Generation of the Replica Vector
The matrix of replica vectors includes the components of modal curvatures of the first three modes at seven equally spaced locations (Figure 1), which coincide with the strain gauges. These values of modal curvatures were derived from the longitudinal modal strains on the top flange averaging over a length of 6 mm (Figure 1), which corresponds to the length of the grid of the strain gauges used in experiments. The replica vectors include all the cases generated by scanning the ranges of possible damage parameters over discrete locations and discrete height reductions. Location increments of \(dS=~{}1\) mm were used, corresponding to a nondimensional increment \(ds=dS/L=0.002\), with positions ranging from \(s^{D}=~{}0.04\) to 0.96, for a total of 48 steps. The discrete height reduction increment was \(dh^{D}=1/32\), from a residual height equal to \(0.97H^{Ul}\) to \(0.06H^{U}\), in 30 steps. These \begin{table}
\begin{tabular}{c c c c c c} \hline  & **U** & **D1.A** & **D2.A** & **D3.A** & **D4.A** \\ \hline \(f_{n1}\) & 298.96 & 298.23 & 296.12 & 292.06 284.09 & \\ \hline \(\Delta f_{n1}\) & & 0.25 & 0.95 & 2.31 & 4.98 \\ \hline \(f_{n2}\) & 817.12 & 811.67 & 796.67 & 769.99 & 725.52 \\ \hline \(\Delta f_{n2}\) & & 0.67 & 2.50 & 5.77 & 11.21 \\ \hline \(f_{n3}\) & 1582.02 & 1572.86 & 1549.21 & 1512.15 & 1546.32 \\ \hline \(\Delta f_{n3}\) & & 0.58 & 2.07 & 4.23 & 7.39 \\ \hline  & U & D1.B & D2.B & D3.B & D4.B \\ \hline \(f_{n1}\) & 298.96 & 295.28 & 290.78 & 279.95 & 261.29 \\ \hline \(\Delta f_{n1}\) & & 1.23 & 2.73 & 6.36 & 12.60 \\ \hline \(f_{n2}\) & 817.12 & 810.97 & 809.69 & 800.44 & 785.54 \\ \hline \(\Delta f_{n2}\) & & 0.75 & 0.91 & 2.04 & 3.87 \\ \hline \(f_{n3}\) & 1582.02 & 1570.62 & 1568.89 & 1552.45 & 1525.23 \\ \hline \(\Delta f_{n3}\) & & 0.72 & 0.83 & 1.87 & 3.59 \\ \hline \end{tabular}
\end{table} 
Table 2: Numerical natural frequencies (\(f_{ni}\)) [Hz] of the first three modes (\(i=1\), 2, 3) and their percent variation (\(\Delta f_{ni}\)) due to damage. values correspond to a reduction of the bending stiffness \(\beta\), respectively, ranging from 9.10% to 99.98%.
## 3 Results
### ETLook and CMRSET AET Estimates  
The first results presented are mean daily estimates of actual evapotranspiration (AET) per month at the field scale for both sites, which are shown in Figure 4. On the left, the results of the CMRSET algorithm [51] are presented, and on the right the results of the ETLook algorithm are shown. The values of AET calculated using the energy balance approach in ETLook [37] are generally lower than the AET estimates in CMRSET. The results from ETLook also demonstrate a different temporal pattern, with the peak in  
Figure 3: Average daily reference evapotranspiration estimated for July 2017 (mm day\({}^{-1}\)) used for the evapotranspiration estimates.  
Figure 2: Case study soil geometry.  
mean daily ET occurring in February, compared with January in the CMRSET algorithm. The difference is likely explained by the fact that the crop factor approach in CMRSET underestimates the resistance due to stomatal closure and the variation in vegetation types, which the energy balance method picks up as it is more locally adjusted. As a result, the estimates of the CMRSET algorithm are mainly radiation driven. The single national regression in the crop factor approach does not directly scale to the field and would have to be adjusted for crop types.  
Scaling the mean daily values to monthly visualises the monthly spatial images (Figures 5 and 6). This clearly indicates the spatial variability of AET across the sites associated with different land cover (crops) and the irrigation scheduling. The monthly sums, for those fields that are irrigated, were much greater in January-March 2018 for the ETLook algorithm compared with the CMRSET algorithm. This is, again, because the ETLook algorithm adjusts better to local variation than the global CMRSET algorithm and is less driven by the radiation signal. The CMRSET estimates picked up some variation in AET in space due to differences in the vegetation greenness, which influences the crop factor estimate (Equation (1)). However, in August-October 2017 at site **a**, there were spatial variations in greenness, reflected in the Kc estimates, but this translates in limited spatial variations in AET due to low radiation. In contrast, the ETLook results indicated a stronger spatial pattern.
# Boosting Fraud Detection in Mobile Payment with Prior Knowledge
###### Abstract  
With the prevalence of mobile e-commerce, fraudulent transactions conducted by robots are becoming increasingly common in mobile payments, which is severely undermining market fairness and resulting in financial losses. It has become a difficult problem for mobile applications to identify robotic automation accurately and efficiently from a massive number of transactions. The current research does not propose any effective method or engineering implementation. In this article, an extension to boost algorithms is presented that permits the incorporation of prior human knowledge as a means of compensating for a training data shortage and improving prediction results. Prior human knowledge is accumulated from historical fraud transactions or transferred from different domains in the form of expert rules and blacklists. The knowledge is applied to extract risk features from transaction data, risk features together with normal features are input into the boosting algorithm to perform training, and therefore we incorporate boosting algorithm with prior human knowledge to improve the performance of the model. For the first time we verified the effectiveness of the method via a widely deployed mobile APP with 150+ million users, and by taking experiments on a certain dataset, the extended boosting model shows an accuracy increase from 0.9825 to 0.9871 and a recall rate increase from 0.888 to 0.948. We also investigated feature differences between robots and normal users and we discovered the behavior patterns of robotic automation that include less spatial motion detected by device sensors (1/10 of normal user pattern), higher IP group-clustering ratio (60% in robots vs. 15% in normal users), higher jailbroken device rate (92.47% vs. 4.64%), more irregular device names and fewer IP address changes. The quantitative analysis result is helpful for APP developers and service providers to understand and prevent fraudulent transactions from robotic automation.This article proposed an optimized boosting model, which has better use in the field of robotic automation detection of mobile phones. By combining prior knowledge and feature importance analysis, the model is more robust when the actual dataset is unbalanced or with few-short samples. The model is also more explainable as feature analysis is available which can be used for generating disposal rules in the actual fake mobile user blocking systems.  
boosting; fraud detection; machine learning; mobile payment; prior knowledge; robotic automation +
Footnote †: journal: Applied Sciences  
doi: 10.3390/app1110434