esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Blood Pressure and Brachial Artery Flow-Mediated Dilation  
Blood pressure (BP) and brachial artery FMD were measured in a quiet and temperature-controlled laboratory with dimmed light. All measurements were performed after at least 10 min of rest in a supine position. An air purifier with a high-efficiency particulate absorbing filter in the laboratory was used to minimize the PM\({}_{2.5}\) exposure during baseline and post-exercise measures.  
A SphygmoCor Xcel device (AtCor Medical, New South Wales. Australia) was used to measure the BP. The cuff was placed on the right upper arm, and the BP was measured at least three times. We averaged two systolic BP (SBP) and diastolic BP (DBP) values within 5 mmHg between measures.  
Brachial artery FMD was assessed using a doppler ultrasound system (Aloka Prosound \(\alpha\)7, Hitachi, Japan) with an 8-15 MHz linear array transducer (UST-5412, Hitachi, Japan). The participant's right arm was abducted in the supine position at the heart level. The pressure cuff was placed 1-2 cm distal to the antecubital fossa and connected to a rapid inflator/deflator system (E20 & AG101, [PERSON], Washington, DC, USA). The brachial artery images were obtained approximately 5-10 cm proximal to the antecubital fossa. The transducer was clamped with the flexbar (Flexbar Machine Corporation, Islandia, NY, USA), after optimizing the image, to minimize the transducer's movement during the FMD procedure. Additionally, the distance between the transducer and the antecubital fossa was measured to capture the same portion of the brachial artery in the next visit. The blood velocity was assessed with \(\leq\)60\({}^{\circ}\) of the doppler's insonation angle. The baseline diameter and velocity were obtained for 1 min. Then, the pressure cuff was promptly inflated to 250 mmHg and maintained for 5 min. The diameter and velocity were recorded for 3 min after rapid cuff deflation to assess the vascular response to the reactive hyperemia. Acquired diameter and velocity were analyzed by commercially available automated wall detection software (Cardiovascular Suite, FMD Studio, Quipu, Italy). FMD was calculated as absolute (peak diameter-baseline diameter) and relative values ([peak diameter-baseline diameter]/baseline diameter). Additionally, FMD normalized by shear rate (SR) area under the curve up to the peak diameter (FMD/SR\({}_{\text{AUC}}\)) was analyzed, and allometrically scaled FMD (scaled FMD) was assessed to eliminate any potential effects of changes in SR and baseline diameter on brachial artery FMD [20].
# Salient Object Detection via Fusion of Multi-Visual Perception
## 4 Methodology
### Single Feature Detection
**The three other features, dark channel, saturation, and hue:** The statistical histogram is combined with the background prior theory [28] to detect salient objects. In most scenarios, the background prior theory assumes that the edges of the image tend to be the background. Thus, the edges of the image are considered non-salient. Therefore, when the statistical histogram changes from the edge to the whole, the newly emerging values are also almost salient object values. In addition, there are sometimes salient objects at the edges of the image, so when the statistical histogram values increase from the edges to the overall anomaly, these values are also likely to be salient object values. To obtain the value of salient objects, first, the statistical histogram of \(\hat{f}_{m}\) is defined as \(h_{m}^{all}(\tau_{k})\), where  
Figure 3: The MSCN distribution between the salient object and background. (**a**) MSCN feature map. (**b**) MSCN distribution. (**c**) The background distribution of MSCN. (**d**) The salient object distribution of MSCN. \(m\in\{3,4,5\}\). Second, the outermost value of the four sides of \(\hat{f}_{m}\) is extracted, and its statistical histogram is obtained as \(h_{m}^{edge}(r_{k})\). The statistics of each level are then processed as a whole/edge to obtain the pixel increase multiplier for each level, and its expression is as follows:  
\[h_{m}(r_{k})=h_{m}^{all}(r_{k})/h_{m}^{edge}(r_{k}). \tag{8}\]  
Through the multiplier, the saliency map can be obtained as follows:  
\[\hat{f}_{m}(i,j)=\left\{ \begin{array}{ll}1&h_{m}^{edge}(r_{k})=0\parallel h_ {m}(r_{k})>T\\ 0&other\end{array} \right., \tag{9}\]  
where \(h_{m}^{edge}(r_{k})=0\) means a value that does not exist in \(h_{m}^{edge}(r_{k})\) but exists in \(h_{m}^{all}(r_{k})\). \(T=mean(h(r_{k}))+\lambda_{4}\times std(h(r_{k}))\) is a threshold for judging whether the value increases abnormally in the process from the edge to the whole, where \(mean(h(r_{k}))\) means to find the mean value of \(h(r_{k})\), \(std(h(r_{k}))\) means to find the variance of \(h(r_{k})\), and \(\lambda_{4}\) is set to 1.5. Here, the threshold \(T\) varies with the values of the mean and variance. If the histogram level of the feature map value satisfies the above two conditions, it is set to 1. Otherwise, it is set to 0. As one example, Figure 5 illustrates the feature of the hue detection process. Figure 5a shows the statistics histogram \(h_{m}^{all}(r_{k})\) of \(\hat{f}_{4}\). Figure 5b shows the edge statistics histogram \(h_{m}^{edge}(r_{k})\) of \(\hat{f}_{4}\). Figure 5c is the hue growth result from the edge to the whole image calculated by Equation (8). Figure 5d is the hue's final saliency map. The features of the dark channel and saturation also follow the same process. Figure 4: An example of MSCN coefficient feature detection process. (**a**) raw. (**b**) MSCN distribution. (**c**) MSCN feature map. (**d**) MSCN salient map.
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 3 Experiment 2
### Results
#### 3.2.5 Perceived Competence  
The analysis on perceived competence revealed no significant main effect of character sounds (\(F\)(1,31) = 1.98, \(p\) = 0.17, and \({\eta_{p}}^{2}\) = 0.06), ambient sounds (\(F\)(1,31) = 1.61, \(p\) = 0.22, and \({\eta_{p}}^{2}\) = 0.05), or the interaction (\(F\)(1,31) = 0.09, \(p\) = 0.77, and \({\eta_{p}}^{2}\) = 0.00).
## 2 Related Research
### Data Imbalance Nature in an Actual Experiment Involving the Collection of Patient Data  
An imbalanced dataset is a common problem, especially in the medicine and rehabilitation fields. This condition occurs when one or more groups have an extensive amount of data while another is limited [14; 15; 16]. Several studies have shown the inherent nature of the dataset imbalance problem in experiments with actual participants or patients. [PERSON] et al. encountered an imbalance between healthy and unhealthy participants in a dataset used for knee abnormality detection [14]. [PERSON] et al. proposed a abnormal electrocardiography (ECG) classification method to address the dataset imbalance problem [15]. [PERSON] et al. developed a classification algorithm based on SVM to address the imbalance between normal and neuropathy-myopathy samples in an EMG dataset [16]. The studies mentioned above showed that imbalances are evident in datasets when experimenting with normal and abnormal participants.  
The FMA is prone to produce imbalanced datasets. Ideally, a dataset will have balanced data for each impairment level. In actual situations, the number of data depends on the availability of patients in a rehabilitation facility. This situation highlights the tradeoff in terms of the number of patients across different impairment levels in data collection experiments, with some levels having an abundance of patients and others fewer. Additionally, a patient may exhibit multiple levels of impairment in repetitive tasks, which may pose a risk of reducing the number of data for minority classes. Furthermore, this condition limits further evaluation of patient characteristics if information such as age and gender is imbalanced.  
This study addresses the subjectivity issue of the FMA by proposing an improved method for recognizing finger movement impairment levels. By fulfilling the identified research gap, the method utilized a higher sampling frequency of EMG data and incorporated more EMG channels to capture the various muscles involved in finger movement. An extensive number of patients was recruited to enhance the reliability of the machine learning model, resulting in improved recognition performance. Additionally, the study highlighted the challenges of an imbalanced dataset and suggested effective processing methods for the collected data.
## 2 Experimental Setup and Procedure
### Measurement
#### 2.3.2 Measurement Method  
The new generation ADV instrument \"Vectrino+\" (Vectrino+, Nortek AS, Hedmark, Norway) was adopted to measure the three-dimensional time varying velocities (longitudinal velocity \(u\), transverse velocity \(v\), and vertical velocity \(w\) along the \(X\), \(Y\), \(Z\) axis, respectively); subsequently, the three-dimensional mean velocities and turbulent flow fields were obtained. The ADV was fixed on a platform which could freely move along the tank longitudinally, transversely and vertically. The downward-looking probe of the ADV was used; i.e., the direction of the measurement probe was vertically downward and perpendicular to the flow. There were four measuring jaws on the probe with an audio frequency of as high as 10 MHz, one of which was tied with a red coil to identify the direction of the velocity component \(u\). The three orthogonal dimensions of the velocity component implied with the right-hand screw rule. The measuring range of the ADV was 0-1.0 m/s, with a measuring accuracy of within \(\pm 0.5\%\pm 1\) mm/s. The sample frequency of the velocity was 200 Hz and the duration was 30 s, which meant that, for every single-point, the hits in the measurement time series were as high as 5700-6000. The ADV signal quality, represented by the signal to noise ratio (SNR) and correlation coefficient (COR), was tested prior to each set of measurements. The majority of the samples had an SNR \(>\) 20 dB and COR \(>\) 90, which indicated that the measurement data were reliable. In order to further increase the accuracy of measurement data, the raw ADV data were de-spiked by the acceleration threshold method which was proposed by [PERSON] and [PERSON] [32], which was capable in identifying and substituting spikes. The relative acceleration threshold was taken as 1.2, and the factor of velocity standard deviation was taken as 1.5 according to experience.
# Quick Calculation of Magnetic Flux Density in Electrical Facilities
## 4 Case study
For the study of the MFD produced on the sidewalk of the main facade, the MFD will be studied at a height of 1 m above the ground from the coordinate x=0, y=2.6 (a wall thickness of 10 cm is assumed) to the coordinate x=4, y=4. Figure 11: Original conductors’ layout (left) and the proposed alternative (right). This is the detail A of Figure 10 for both alternatives. The axes directions are indicated in the figure. Figure 10: Floor plan (left) and 3D view (right) of the DTS to study with the existing cable routing (dimensions in meters). Detail A is shown in Figure 11.  
The calculation plane mesh has been plotted with points every 10 cm in the directions of both axes (X and Y). A simulation with 400 wire sections, using a calculation grid with 1600 points and using 100 discrete elements per section takes 36 seconds. After analyzing the obtained results, which are shown in the next section, the study is repeated using an alternative conductors' layout, which shows much lower impression levels for this case. Other paths have been tested and intermediate MFD values between the studied scenarios have been obtained. The new proposal is to group conductors mixing the different phase. Among all the existing possibilities, the one that produces the least MFD has been chosen. In this way, along the tray, the original phases sequence of the nine conductors, 0-0-0-4-4-4-8-8-8, will be compared to the proposed one, 0-4-8-8-0-4-4-8-0. In the same way, in the wiring harness that goes down the wall, the conductors will be arranged so that each triplet has one cable of each phase. In addition, each triplet will be a transposition of the previous triplet in order to minimize the induction produced in the surroundings. This distribution of the different conductors of each phase will produce greater compensations between them than the original one, due to the better-balanced distances to the calculation points. Therefore, the proposed alternative will produce smaller MFDs. In this way, the phases' sequence in the triplets (following the X-axis direction) will change from the original one, 8-8-8-4-4-4-0-0, to the proposed alternative, 4-0-8-0-8-4-8-4-0. Figure 11 shows a detail of both cases in these zones, extracted from the CRMag(r)software. The isolated conductors are placed in different order in the new alternative, as shown in Figure 11b. This Figure is a schematic representation obtained from the software. Nonetheless, all conductors are insulated and they do not touch each other. Finally, the buried cables that leave the center towards the low-voltage facility will have the same arrangement as the one used along the tray. After studying both cases, the highest MFDs will be compared by making some cuts for a fixed value of the y-coordinate or the x-coordinate. The curves of both alternatives will be represented to draw conclusions. The rest of the alternatives studied, consisting of the transpositions of the selected alternative, have been rejected since they do not provide any improvement.
# P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
## 1 Introduction
However, there is no guarantee that all present P-waves have been labeled, nor that all labels are correct. In the literature, P-waves have been automatically detected using different techniques. In [31; 32; 33; 34; 35; 36; 37], the authors presented an automatic P-waves detection technique based on wavelet transform. However, the authors in [31; 32; 33] did not validate the algorithms with any standard databases. In [33], no P-wave delineation criterion is explained. In [38; 39; 40], the authors proposed an automated P-wave detection method based on second order derivatives and low-pass differentiation. In [41; 42], other algorithms for automatic P-wave detection based on adaptive threshold are presented. Although the algorithm in [42] achieved high sensitivity, it was not validated with any standard database. In [43; 44; 45], the authors introduced deep neural networks for P-wave detection. The authors of [43], used a neural network with asymmetric basis functions for the purpose of extracting the features of P-waves in the MIT-BIH Arrhythmia Database [27], but the detection rate is not mentioned. Over the past few years, deep convolutional neural networks have gained attention in the field of visual recognition [46; 47]. However, in biomedical image processing, the desired output should include localization and pixel labels. In [48], the authors trained a deep convolutional network using a sliding-window to label each pixel in the image by providing a patch around that pixel as an input. This network is good at localizing, but suffers from redundancy, due to overlapping patches. In [49], the authors introduced the U-Net fully convolutional neural network architecture. This network provides good classification, as well as localization, using very few training images. To our knowledge, this paper is the first to introduce the manual annotation of P-waves in the MIT-BIH AF database [29; 30] to be used as a benchmark for validation and evaluation purposes. In addition, it introduces an automated prediction algorithm for P-waves, with their variable morphologies, in the aforementioned database. This should help clinicians in diagnosing several cardiac arrhythmias. Furthermore, the proposed algorithm works on printed ECG signals, which are the most commonly used in developing countries, without the need for digitalization, as in [2]. The proposed automated algorithm uses U-Net deep fully convolutional networks. The proposed algorithm is validated over the well-known QT database [26] and the performance is compared to other algorithms in the literature. This paper is organized as follows; in Section 2, we provide information on datasets used in this article. In addition, we introduce the proposed algorithm. In Section 3, we present the experimental results obtained, and our conclusions are given in Section 4.
## 3 Results
### Comparison with In Situ River Stage Data  
The total wetted area measured by RadWet-L was highly correlated with the river stage (Figures 14 and 15) with Pearson R values ranging between 0.78 and 0.94 (\(p\) values \(<\) 0.01). This gives confidence that RadWet-L is able to capture temporal patterns of inundation. At all gauge stations, RadWet-L measured the total wetted area tracks river stage height closely (Figure 15). It was able to capture isolated changes in inundation, such as the reduction in river height observed at the Estiarodo Repouso gauging station in February 2022 and the increase in river stage at the Acanaui gauging station in November 2019.  
Figure 14: Comparisons between RadWet-L total wetted area, and in situ river stage height at [59]. 5 locations within the Amazon River Basin.  
Figure 13: Comparison between RadWet-L total wetted area classified outputs with G-REALM satellite altimetry measurements [58] over 6 candidate sub-basins within the larger Amazon Basin.
## 4 Full-Flow Field Simulation
### Pressure Distribution  
To illustrate pressure distribution of the flow field evaluated by pressure difference across the test section, streamwise pressure measurement were performed using static taps set on the sidewall. Two rows of nine pressure taps were set: one centered along with the primary flow, and another centered along with the secondary flow. Results of pressure measurements are shown in Figure 10 and compared with those of the large eddy simulation. The numerical data shows the same trend as the experimental results, which is a slight decrease in the pressure along the streamwise direction.  
As seen in Figure 10, the pressure of the primary and secondary flow varied greatly along the streamwise direction due to the existence of shock waves, and the location of the shock waves was predicted well by the numerical simulation compared with the experimental measurement. Pressure mismatch occurred along the streamwise direction from both the experimental and numerical results, as shown in Figure 10, even though the pressure of the primary and secondary flow matched well at the start of the test section. The larger convective Mach number caused the larger pressure mismatch. This is probably due to the differences in transmission angles and intensity of the oblique shock waves, as shown in Figure 9.  
Deviation of the experimental and simulation data can be seen in Figure 10. The experimental data seems to be larger than the simulation data and the smaller convective Mach number makes the deviation more significant. Although the inlet condition of the simulation was modified according to the experiment, it could not be eliminated. From Figure 10, the experimental pressure seemed to go up, which was due to the limited detecting points. When the flow passes the shock, the pressure should decrease abruptly. However, we set only nine pressure tabs on the test section, so some trough values of the pressure were not obtained and we could see the abrupt decrease of the pressure. If the trough values were included, the pressure would fluctuate around 22, 17, and 18 Kpa in the three experiments, respectively. As for the simulation, although the peak pressure values decreased, the trough value of the pressure stayed almost constant all the time, which is consistent with reference [14]. This means that the pressure decreased slightly in the test section and the pressures fluctuated around 18, 14, and 14 Kpa in the simulations, respectively. Hence, in both the simulations and experiments, the trends were almost the same.
## 5 Conclusions  
In a nutshell, exogenous NO treatment improved the color and firmness of blood orange pulp, as well as the physical properties of the fruit. Astringency was minimized by reducing the content of tannin in the pulp. NO treatment increased the content of limonene (aroma) in the peel and soluble solid content (sweetness) in the pulp, and improved physical properties of the fruit. Therefore, NO treatment enhanced blood orange fruit qualities and economic values. Although SNP is an inexpensive chemical and has been widely used in mammals and plants, it has been reported to release toxic cyanide and reduce the photochemical activity of photosystem II during its photolysis [71; 72]. New NO donors with both high releasing efficiency and high safety should be developed. NO is tightly correlated with nitrate metabolism. Appropriate NO treatments combined with nitrogen management are worth further studies.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11152218/s1](https://www.mdpi.com/article/10.3390/foods11152218/s1)]([https://www.mdpi.com/article/10.3390/foods11152218/s1](https://www.mdpi.com/article/10.3390/foods11152218/s1)), Table S1. Measured values of Figure 2; Figure S1. Bagging and NO+Bagging treatments to blood orange fruits; Figure S2. Effects of different treatments on polygalacturonase (PG; A), pectinase (PE; B), and cellulase (C) activities in blood orange fruits; Figure S3. Effects of different treatments on the activities of chalcone synthase, CHS (A), chalcone isomerase, CHI (B), dihydroflavonol-4-reductase, DFR (C) anthocyanin synthase, and ANS (D) in anthocyanin biosynthesis pathway of blood orange pulp.  
**Author Contributions:** Conceptualization, S.Y.; methodology, Z.-W.Z., H.L. ([PERSON]) and H.L. ([PERSON]); formal analysis, S.Y., Z.-W.Z., H.L. ([PERSON]) and H.L. ([PERSON]); investigation, X.-Y.Y., Y.-F.F. and Q.K.; data curation, C.-Q.W., M.Y. and Y.-E.C.; writing--original draft preparation, S.Y.; writing--review and editing, all authors; funding acquisition, S.Y. and Z.-W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Sichuan Province Youth Science and Technology Innovation Team (20 CXTD0062) to S.Y. and the Applied Basic Research Program of Sichuan Province (2020 YJ0410) to Z.-W.Z.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** We thank LeftPub for its linguistic assistance during the preparation of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.