esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Ficin Use as an Exfoliant Agent in Cosmetic Applications  
### General Profile and Ficin Source  
Similarly to bromelain, fiction, belongs to a complex family of cysteine proteases and their isoforms, mainly originating from the _Moraceae_ plant family [68; 69]. Ficin, from the cysteine protease enzyme family, is obtained from fig trees' latex and the _Ficus_ genus, and is characterized by laticifer cells that contain and secrete this milky fluid in cases of tree injury, serving as a defense mechanism [68; 69]. While fiction can be found in various _Ficus_ species, its most common sources are _Ficus carica_ and _Ficus glabrata_[69] or commercially available _Ficus carica_ cell cultures [70]. Ficin complex encloses multiple thiol-based endopeptidases, along with peroxididases, phosphatases, amylases, and glycoproteins [68; 69].  
The composition of fiction extracts is influenced by the species used and the environmental conditions under which they are obtained [68; 69]. Ficin is present in the unripe fruit, stem, milky fig latex fluid, root, leaves, and bark, and is extracted in concentrations ranging depending on the plant part and environmental factors [68; 69; 71]. Interestingly, unripe fig latex has been claimed to be the primary source of fiction extraction as it contains higher protease activity [72]. However, more qualitative information about fiction's concentrations in distinct fig plant parts is required, in order to create valid comparative analyses. Ficin crude extract from fiction latex, produced by _Ficus carica_, has been shown to have high proteolytic activity of 209,000 units/mg [73]. However, alternative sources have been explored, like fig by-products and agricultural waste (i.e., leaves, unripe fruit residues), to develop cost-effective, sustainable extraction techniques. The variability of ficin content due to factors like fig variety, climate, and collection methods has posed challenges in standardizing its commercial cosmetic, medicinal, and food processing applications [68; 69; 71].
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 3 Data Collection II: Stationary Target Reflection
### Binary Target Detection: Experimental Results
#### 3.5.2 MF Response  
As can be seen from Figure 7, there is a stark contrast between the sampled data with and without the presence of a target in the far field; thus, as expected, the MF responses closely follow the sampled data from the receive antenna. Figure 8 shows the binary target detection MF response calculated using Equation (9) at \((R,\theta)=(6.70\) m, \(0^{\circ})\). There exist differences of approximately 13.2 dB and 13.4 dB, respectively, between the MF responses of the positive and negative tests at this location. These increases in MF strongly suggest the presence of a target in the far field when compared to the cases when no target is present.
# Data Privacy and Ethical Considerations in Database Management
## 4 Case Study  
We used a dataset that mimics an example of a real-world health dataset, given that authentic patient healthcare data cannot be used due to privacy concerns. The dataset \"Healthcare Dataset\" was acquired from Kaggle, a popular data science competition platform and online community for data scientists and machine learning professionals. This dataset contains patient-specific information, making it an essential target for privacy-related concerns.  
To manage the dataset data, we used pgAdmin4 v8.2, which is a database administration tool for PostgreSQL.  
The dataset contains the following columns:  
* Name: This column indicates the patient's name associated with the healthcare record.
* Age: This column indicates the age of the patient at the time of admission, expressed in years.
* Gender: This column indicates the gender of the patient, either \"Male\" or \"Female\".
* Blood Type: The patient's blood type, which can be one of the common blood types (e.g., \"A+\", \"O-\", etc.).
* Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as \"Diabetes\", \"Hypertension\", \"Asthma\", etc.
* Date of Admission: This column specifies the date the patient was admitted to the healthcare facility.
* Doctor: This column indicates the name of the doctor responsible for the patient's care during their admission.
* Hospital: This column identifies the healthcare facility or hospital where the patient was admitted.
* Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including \"Aetna\", \"Blue Cross\", \"Cigna\", \"UnitedHealthcare\", and \"Medicare\".
* Billing Amount: This column specifies the money billed for the patient's healthcare services during admission. This is expressed as a floating-point number.
* Room Number: This column specifies the room number where the patient was accommodated during admission.
* Admission Type: This column specifies the type of admission, which can be \"Emergency\", \"Elective\", or \"Urgent\", reflecting the circumstances of the admission.
* Discharge Date: This column specifies the date the patient was discharged from the healthcare facility based on the admission date and a random number of days within a realistic range.
* Medication: This column identifies a medication prescribed or administered to the patient during admission. Examples include \"Aspirin\", \"Ibuprofen\", \"Penicillin\", \"Paracetamol\", and \"Lipitor\".  
Test Results: This column describes the results of a medical test conducted during the patient's admission. Possible values include \"Normal\", \"Abnormal\", or \"Inconclusive\", indicating the outcome of the test.
## 4 Optimal Trajectory
### Optimization Method
The optimal choice for \(\Delta_{xy,LT}\), \(\|\dot{\mathbf{p}}_{T}\|\), \(z_{C}\) and \(\varepsilon\) is the one that minimizes \(F\). When optimizing the objective function \(F\), we may also wish to set some constraints on the trajectory. First, we limit the EE motion within a rectangular cuboid workspace defined as  
\[ \begin{split} X_{min}&<x<X_{max}\\ Y_{min}&<y<Y_{max}\\ Z_{min}&<z<Z_{max}\end{split}  \tag{34}\]  
In a practical application, we also have to limit the torques and velocities at the robot's actuators: these are proportional, respectively, to the cable tensions and cable velocities, if the robot uses cable winches controlled by rotary motors (as is common for CDPRs [34]). So we set constraints directly on the tensions and velocities, as  
\[ \begin{split}\max\left\{\tau_{1},\tau_{2},\tau_{3}\right\}& <\tau_{max}\\ \max\left\{\dot{\rho}_{1},\dot{\rho}_{2},\dot{\rho}_{3}\right\}& <\dot{\rho}_{max}\end{split}  \tag{35}\]  
where \(\tau_{max}\) and \(\dot{\rho}_{max}\) depend on the robot's motors; for instance, denoting the maximum torque and maximum angular speed of the motors as, respectively, \(T_{M}\) and \(\Omega_{M}\), and the radius of the winches that actuate the cables as \(R\), one has \(\tau_{max}=T_{M}/R\) and \(\dot{\rho}_{max}=\Omega_{M}R\) (assuming that the motors and the winches of all cables are identical). The cable tensions in Equation (35) are found from Equations (4) and (5), while the velocities are found by differentiating Equation (1) with respect to time. Finally, when a CSPR moves along a dynamical trajectory, cable tensions must also be ensured to be always positive. For a given \(\Gamma\), the feasible range \([\omega_{min},\omega_{max}]\) for the trajectory frequency \(\omega\) can be calculated by the method presented in [7]. If (and only if) the value of \(\omega\) is in this range, then cable tensions \(\tau_{i}\) are positive both before and after launch, since the method in [7] does not depend on the EE mass \(m\), which in this case changes instantaneously at \(t=t_{L}\). Therefore, the optimization problem also requires the constraint  
\[\omega_{min}<\omega<\omega_{max} \tag{36}\]
## 4 Case Study and Discussion
### Case Study  
#### 4.2.1 Case Setup  
The proposed MILP optimization problems were implemented in GAMS 25.1.2 and solved using GUROBI 8.0. The dual simplex algorithm terminated after reaching a 1% duality gap. This case study was conducted to compare the discharging power, the amount of excess power, and the total net revenue for the wind farm operator to determine whether to consider the REC multiplier in off-peak periods. When considering the REC multiplier, its values were listed by size, as shown in Table 4.  
Case 1 is the reference case describing the current state of the wind farm operation without imposing the REC multiplier. Case 2 was the first control case where the REC multiplier was 1 for validating condition 1 as described in Section 3.2. The REC multiplier in case 2-1 was calculated by rounding up the reciprocal of charging, discharging, and turnaround efficiencies to two decimal places based on condition 2 as described in Section 3.2. Cases 2-2 and 2-3 were additional control cases for comparing the results with the increasing REC multiplier.
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 2 Methodology
### External Heterogeneous Data Fusion Network  
Through previous experiments, it was found that one of the main reasons for the low accuracy of traffic flow prediction results is the fact that multi-source heterogeneous data are left out of consideration and data of only one type are acquired. According to the speed-flow equation for traffic, the nonlinear relationship between speed, flow, and road occupancy rate is so complex that we cannot confirm one variable by any other single variable. For instance, when at a flat peak, the speed of the vehicle is high but the road occupancy is low, so the flow is quite low. However, in traffic congestion, though the occupancy rate is high, the speed stays at a low level and the flow remains at a small value. Therefore, the current traffic flow condition cannot be analyzed according to only one aspect of the traffic status, as the forecast result will have a large initial difference from the actual data.  
Furthermore, weather is a significant factor that affects the traffic conditions to a great extent. Whether the weather is sunny or not affects the driving safety factor, which leads to higher vehicle speeds when the weather is sunny, and the saturation flow of the lane can be higher at this time. In the case of bad weather such as snowstorms and fog, the driver takes the initiative to slow down due to the low visibility, and reaching the saturation level is unlikely.  
The above shows that there are many determinants of traffic conditions. Considering only single-source traffic flow parameters leads to the traffic conditions being oversimplified and the complexity being ignored. To be rigorous, the model requires vehicle speed, road occupancy, minimum visibility, and other data as a supplement.  
We apply an external heterogeneous data fusion network to solve the problem of analysis error caused by a lack of information. Figure 6 shows the architecture of the network. The neural network adopts an attention mechanism and reasonably allocates the weights of various heterogeneous characteristic parameters.  
Attention scores are calculated as:  
\[e_{l}^{p}=V_{l}^{in}\cdot\sigma(W_{l1}^{in}\cdot[d_{l-1}^{en};s_{l-1}^{en}]+W_ {l2}^{in}\cdot m_{l}^{p}+b_{l}^{in}) \tag{9}\]  
\[\alpha_{l}^{p}=\frac{\exp(e_{l}^{p})}{\sum_{j=1}^{f}\exp(e_{l}^{j})} \tag{10}\]  
where \(W_{l1}^{in}\), \(W_{l2}^{in}\), and \(V_{l}^{in}\) are trainable weight matrices, \(b_{l}^{in}\) is the bias, \([d_{l-1}^{en};s_{l-1}^{en}]\) is the result of connecting hidden layers with cell state layers in the GRU units of the decoder, \(m_{l}^{p}\) is the \(p\)-th heterogeneous characteristic parameter at moment \(l\), and \(a_{l}^{p}\) is the allocated weight. The final result \(\tilde{x}_{l}^{out}\) is calculated as:  
\[\tilde{x}_{l}^{out}=\left(\alpha_{l}^{1}m_{l}^{1},\alpha_{l}^{2}m_{l}^{2}, \ldots,\alpha_{l}^{f}m_{l}^{f}\right)^{\mathrm{T}} \tag{11}\]  
Figure 6: External heterogeneous data fusion network.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 1 Introduction
(2018) proposed a method combining envelope order spectrum and convolutional neural network. The envelope order spectrum extracted from the original signal contains a wealth of information about the order of fault characteristics. Subsequently, the CNN model is employed to extract these representative features, which can better identify the type of bearing defects. [PERSON] et al. (2018) proposed a fault diagnosis method based on a one-dimensional convolutional neural network (1 DCNN) and support vector machine (SVM). The improved 1 DCNN is employed to extract the features of fault signals, and SVM is employed as the classifier for fault classification. The advantage of this is that 1 DCNN has a stronger extraction ability for vibration signals. [PERSON] et al. (2018) combined the multi-scale CNN-LSTM module with the deep residual module, which has the advantage of stronger fault feature extraction ability, and the use of residual can improve the feature expression ability of the model. From the research of the above scholars, fault signal analysis and machine learning models can effectively improve the accuracy of fault diagnosis, while the deep learning method in bearing fault diagnosis faces the limitations of data dependence, noise sensitivity, poor interpretability, and high computational cost. Based on this problem, this paper proposes a fault diagnosis method based on improved VMD and parallel hybrid neural network, using the sliding window overlapping sampling method to increase the number of samples, improving VMD to remove noise, and using the gap layer to reduce the amount of calculation in parallel hybrid neural network and using the confusion matrix graph to display the results. The main contributions of this paper are as follows:  
1. An improved VMD algorithm is proposed by combining VMD with reweighted kurtosis. Using the reweighted kurtosis value as the evaluation index, the influence of some strong shocks in the fault signal can be eliminated, and the accuracy of fault diagnosis can be improved. 2. A parallel hybrid neural network model is proposed, which fuses the features extracted by the two parallel networks to achieve feature enhancement and complementarity, which can improve the classification accuracy of the diagnosis model. 3. The proposed fault diagnosis method is compared with several latest bearing fault diagnosis models. The proposed method has certain advantages in the accuracy of fault diagnosis, which reflects the positives of the method.
Using ABRF neural network and OLHS to establish the first generation metamodel, the NSGA-II optimization algorithm is then used to obtain the Pareto, and the MLPA is combined to dynamically improve the accuracy of this surrogate model until it converges. 2. Different from the traditional method of adding points, this paper mainly considers the maximum-min distance (MMD) point, the nearest boundary (NB) point and the high-quality points on the optimal front edge obtained by ARBF-MLPA. The selection of these three points helps to improve the local accuracy of the model and leads the optimization results being closer to the real front edge. 3. After test functions of three different frontier features testing, the method proposed in this paper has more advantages than the traditional RBF-UDPA method in the overall convergence speed and the distribution of the final optimal front. 4. The head, chest, abdomen and pelvis of the driver were corresponding to the corresponding position of the B-pillar of the car, and the amount of invasion of each part was measured indirectly by setting the spring unit at the B-pillar, so as to simulate the invasion injury suffered in the side impact of the car. The amount of chest invasion was included as one of the design variables. 5. Combine the ARBF-MLPA method with the NSGA-II optimization algorithm to optimize the design for vehicle-side crash safety The optimized model reduces chest intrusion by 4.32%, peak collision force by 2.11% and reduces mass by 14.05%. The method proposed in this paper with higher accuracy and converges faster than the traditional point-adding method. It can be used in the multi-objective optimization design of the side crashworthiness of the vehicle, which can reduce the optimization process time on the premise of ensuring the correct calculation.
# Background Subtraction for Moving Object Detection in RGBD Data: A Survey
## 6 Comparisons
### Comparisons on the MULTIVISION Stereo Dataset  
Performance comparisons on the MULTIVISION Stereo dataset are reported in Table 5. Here, values for the DECB-LF algorithm by [PERSON] et al. [15], the DECB and the CB4D algorithms by [PERSON] et al. [58], and for the Codebook algorithm using only color (CB) and only depth (CB-D) are those reported in [15]. Values for the RGBD-KDE algorithm by [PERSON] et al. [80] and the KDE algorithm using only color (C-KDE) and only depth (D-KDE) are those reported in [80]. Values for the DEOR-PCA algorithm by [PERSON] et al. [70] are those reported by the same authors.  
It can be observed that, for all the videos, the combined use of both color and depth information (i.e., DEOR-PCA, DECB, DECB-LF, and RGBD-KDE methods) allows achieving results better than those obtained by color alone (i.e., CB and C-KDE methods) or depth alone (i.e., CB-D and D-KDE methods). Moreover, the difficulty in estimating and discriminating disparities in case of flickering lights (e.g., in LCDScreen and LabDoor videos) and in case of depth camouflage (e.g., in Crossing video) leads depth alone-based methods to obtain worse results as compared to color alone-based methods. Only for sequence Suitcase (see Figure 3), where the main issue is color camouflage, depth alone allows achieving results better than color alone, due to the high accuracy of the estimated depth information.  
Figure 3: Suitcase video from the MULTIVISION Stereo dataset. (**a**) RGB image; Disparity estimated using: (**b**) Var [103]; (**c**) Phase [104]; and (**d**) SGBM [105]; (**e**) GT.  
Figure 2: Wall video from the MULTIVISION Kinect dataset. (**a**) RGB image; (**b**) Depth image; (**c**) GT.
# High-Gain Waveguide-Fed Circularly Polarized Spidron Fractal Aperture Antenna
###### Abstract  
A high-gain rectangular waveguide-fed aperture antenna that uses a Spidron fractal structure to produce circular polarization is proposed. The antenna consists of a Spidron fractal aperture etched onto the ground plane of a dielectric substrate that is directly excited by a WR (Waveguide Rectangular)-90 waveguide-to-coax adapter. A superstrate was implemented at an appropriate distance above the antenna to enhance the broadside gain significantly. An antenna prototype was fabricated and tested to validate the design. The measured impedance bandwidth for \(|S_{11}|\leq-10\) dB is 9.89-11.58 GHz (15.74%). The corresponding measured 3 dB axial ratio (AR) bandwidth is 10.68-11.00 GHz (2.95%), and within the measured 3 dB AR bandwidth, a maximum realized gain of 9.59 dBic is achieved. The radiation patterns of the proposed antenna are presented and discussed.  
c 2019 9 691 15 February 2019 18 February 2019
## 2 Methodology
### Initial Input Data  
The initial data gathering, along with the empirical study, has been performed, taking into account the available dataset (FB bridges), with both macro (global) and micro (local) approaches. The micro approach was focused on the components' condition state within each bridge, whereas the macro level corresponds to the quality index as an indicator for the condition state of each bridge in an overall view. This is an adequate tool for the comparison of the bridges in terms of quality in a railway network. The quality feature measurement method based on the Mahalanobis distance is used to evaluate the reliability of data samples [36]. The standard deviation concept is capable of calculating the highest valuable variable for comparison and maintenance decision making. Obtaining the quality index for condition rating of the bridge requires analyzing raw data with regard to valuable variables.  
Two types of raw datasets have been considered to define the quality level of the bridge and its operation risk estimation, namely structural identification (SI) and probable defects (PD).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Name** & **Description** \\ \hline D & \begin{tabular}{c} D \\ \end{tabular} & \begin{tabular}{c} This type of bridge is made of concrete with a rectangular shape and width of fewer than 4 m. \\ \end{tabular} \\ \hline FB & \begin{tabular}{c} Fer béton \\ \end{tabular} & \begin{tabular}{c} Deck and column in these bridges are made of reinforced concrete. \\ \end{tabular} \\ \hline AV & \begin{tabular}{c} Aqueduc vötte (masonry \\ and concrete) \\ \end{tabular} & \begin{tabular}{c} Arch bridge with one span constructed by \\ concrete and masonry. \\ \end{tabular} \\ \hline Avs & Aqueduc vötte Surbaisse & \begin{tabular}{c} Arch bridge with a single span constructed with \\ concrete and masonry with low height arch. \\ \end{tabular} \\ \hline Viaduct & Viaduct & \begin{tabular}{c} This type is constructed with the several spans \\ with arch shape on a deep valley. \\ \end{tabular} \\ \hline PV & \
\begin{tabular}{c} Poly vötte \\ \end{tabular} &
\begin{tabular}{c} These bridges are constructed with steel arch, \\ truss, arch, steel beam, and plate as well as beam \\ and plate. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bridge types.  
Figure 2: Relative frequency distribution (%) of bridges in the case study sample.
## 4 Discussion
### Limitations  
Our study has several limitations. First, our scoping review collects information from a broad spectrum of study designs and methods, without an accurate evaluation of the quality of evidence. Most of included studies are observational studies with a very low quality of evidence (mostly case reports and case series, no cohort studies). A limited number of RCTs, the heterogeneity of the proposed treatments (combining instrumental therapy with different physical exercise protocol and drugs) and the lack of detailed descriptions regarding intervention (duration and treatment parameters) do not allow us to reach definite conclusions. Second, only three studies [18; 22; 26] diagnosed CRPS-I according to Budapest criteria, while others did not clearly report the diagnostic criteria. Third, the outcome measures assessed in the included studies are very heterogeneous. Most of the available research about CRPS-I treatment with physical modalities focused on pain as the main outcome assessed by unidimensional tools, such as VAS and NRS. However, these outcome measures investigated the intensity of pain only, without evaluating qualitative aspects. Some authors [18; 22; 24; 27] used more specific scores, such as DN-4 and LANSS scores (for neuropathic pain). Moreover, few studies analyzed functional outcomes such as muscle strength (HGS), upper and lower limb function (DASH and MFS), kinesiophobia (TSK), and independence (Barthel index and FIM), albeit not specific for CRPS-I. Finally, all studies failed to detail the ethics approval and only four studies reported the acquisition of informed consent [18; 22; 24; 26]. Only in three studies [18; 22; 24] did authors declare that they had no conflict of interest.