esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The results of all smoothing techniques are presented in Figures 3-5. The most accurate smoothing technique showing the strongest relationship with the Vicon acceleration data (closest approximation to the real-world) was GNSS data processed with a fourth-order (zero lag) low pass Butterworth filter with a cut-off frequency of 2 Hz (mean bias 0.00 m\(\cdot\)s\({}^{-2}\), 95% LoA \(\pm\) 1.55 m\(\cdot\)s\({}^{-2}\), RMSE 0.79 m\(\cdot\)s\({}^{-2}\)).  
The validity of the 2 Hz Butterworth smoothed GNSS data across all movements is presented in Table 4. No significant differences were found between the GNSS and Vicon acceleration within each movement category, nor were the movements different from each other. The average deviation (RMSE) between the GNSS acceleration data and Vicon was the largest for the decelerating jumping movement (average deviation of 1.21 m\(\cdot\)s\({}^{-2}\)) and smallest for linear movement and stop of motion (average deviation of 0.52 m\(\cdot\)s\({}^{-2}\)).  
Figure 3: Measurement indices of validity indicating how closely the GNSS acceleration data resemble the Vicon acceleration data using a Butterworth filter with different cut-off frequencies. (**A**) Mean bias with 95% LoA for the difference between GNSS acceleration minus Vicon acceleration. (**B**) Precision/RMSE for the difference between GNSS acceleration minus Vicon acceleration. Manf Smo = manufacturer smoothed acceleration data. Raw = not smoothed in any way by the manufacturer software.  
Figure 4: Measurement indices of validity indicating how closely the GNSS acceleration data resembled the Vicon acceleration data using exponential smoothing with different smoothing constants. (**A**) Mean bias with 95% LoA for the difference between GNSS acceleration minus Vicon acceleration. (**B**) Precision/RMSE for the difference between GNSS acceleration minus Vicon acceleration. Manifest Smo = manufacturer smoothed acceleration data. Raw = not smoothed in any way by the manufacturer software.  
Figure 5: Measurement indices of validity indicating how closely the GNSS acceleration data resembled the Vicon acceleration data using the moving average smoothing over different sliding windows. (**A**) Mean bias with 95% LoA for the difference between GNSS acceleration minus Vicon acceleration. (**B**) Precision/RMSE for the difference between GNSS acceleration minus Vicon acceleration. Manifest Smo = manufacturer smoothed acceleration data. Raw = not smoothed in any way by the manufacturer software.  
The GNSS manufacturer smoothed data deviated from the Vicon data by 1.21 m\(\cdot\)s\({}^{-2}\) on average. The raw GNSS data had the largest deviation across all conditions and deviated from the Vicon data by 2.24 m\(\cdot\)s\({}^{-2}\).  
Overall, the GNSS data processed with exponential smoothing provided the strongest smoothing, and therefore the biggest discrepancy with the Vicon acceleration data, followed by the moving average and Butterworth filter.
## 2 Method  
### Radar-Based Heartbeat Rate Estimation  
A Doppler radar mainly measures the fluctuation of the human chest; the sampled radar signal \(\mathrm{r}=[r[0],r[1],r[2],\ldots,r[K-1]]\) is modeled according to  
\[r[k]=\sum_{n=1}^{N_{s}}\sigma_{n}e^{(\frac{4\pi i}{\lambda}d_{n}[k])}, \tag{1}\]  
where \(N_{s}\) is the number of scatters on the human body, \(\sigma_{n}\) is the amplitude of the signal from the \(n\)th scatter, and \(d_{n}[k]\) is the displacement of the \(n\)th scatter in the \(k\)th sample [22]. Though the sampled signal is a very complex mixture of respiratory and heartbeat-induced phase changes, it was demonstrated that the heartbeat information is distinguishable in the frequency domain, where multiple respiratory and heartbeat harmonics can be observed [18]. The respiratory rate is commonly between 0.1 and 0.3 Hz, and its second-, third- and higher-order harmonic spectral lines may appear at around 1 Hz. The heartbeat frequency is also about 1 Hz, and its fundamental frequency is hard to distinguish from the respiratory harmonics, while its harmonic frequencies may be more than 2 Hz and hardly affected by respiration.  
Therefore, we propose to use a high-pass filter to simultaneously attenuate the respiratory components and the heartbeat fundamental frequency, while maintaining the high order harmonics of the heartbeat. The filtered radar signal \(\mathrm{x}=[x[0],x[1],x[2],\ldots,x[K-1]]\) is periodic with unknown waveform and modeled as  
\[x[k]=s[k\mathrm{mod}P_{s}], \tag{2}\]  
where \(P_{s}\) is the period length of the signal, and \(\mathrm{s}=[s[0],s[1],s[2],\ldots,x[P_{s}-1]]\) is the waveform in one period.  
The maximum likelihood estimation of \(P_{s}\) with the signal model (2) in white Gaussian noise is [20]  
\[\hat{P}_{s}=\underset{P}{\mathrm{argmax}}\left\{\frac{1}{\left\lceil\frac{K}{ P}\right\rceil-1}\sum_{k=0}^{P-1}\left|\sum_{l=0}^{\lfloor\frac{K}{P}\rfloor-1}x[k +IP]\right|^{2}\right\}, \tag{3}\]  
where \(\hat{P}_{s}\) is the estimated signal period, and the heartbeat rate was output as 60 \(f_{s}/\hat{P}_{s}\) beats per minute, with \(f_{s}\) being the sampling rate. The symbol \(\lfloor\rfloor\) means rounding down and returns the maximum integer not greater than the number inside. The flowchart of the proposed heartbeat estimation method is shown in Figure 1.
## 4 Experimental Results and Discussion
### Results
#### 4.4.3 WM Adaptability  
As mentioned before, a watermarking scheme's capability to accommodate any WM data is essential because any user can use the scheme with his WM data, even though some of the previous works do not have this WM adaptability [10,12]. Our scheme makes it possible by using newly generated random data as the WM information for every mini-batch in training. We have verified this adaptability of our scheme by experimenting with various WM information. Table 4 shows two examples of the results. The one marked as 'Random (average)' means the average values for all the WM data used, while WM2 and WM3 are the two examples of the case using two specific WM data described in the table. From those columns' values, it is confirmed that our scheme applies to any WM data without losing similar robustness.
# Machine Learning Sorting Method of Bauxite Based on SE-Enhanced Network
## 1 Introduction  
Bauxite, as the main raw material of alumina and metal aluminum, plays an irreplaceable role in the manufacturing fields of spacecrafts [1; 2], automobiles [3; 4], and so on. At the same time, due to the thermostability and wear resistance of bauxite, it has a wide range of application value in the fields of refractory [5; 6], polishing powder [7], advanced grinding wheel, and so on.  
At present, ore separation mainly depends on manual beneficiation and machine learning methods. In the traditional beneficiation process, ore separation mainly depends on the experience of professionals. Now, we use machine learning for ore separation. The intervention of professionals in ore separation is reduced, which not only improves the beneficiation capacity, but also reduces the process abnormality and equipment failure rate. The combination of convolution neural network and spectral technology [8; 9; 10], ore image segmentation [11], ABC-BP (Artificial Bee Colony-Back Propagation) neural network [12], and other improved methods [13; 14; 15] are used to realize the ore classification of image recognition and effectively solve the problem of manual separation in the process of ore production.  
Traditional manual beneficiation has low separation efficiency and a serious waste of resources, which cannot meet the development needs of modern industry. The ore separation method based on machine learning improves the ore separation ability and solves some problems of traditional manual beneficiation, but the detection speed is not high and cannot achieve real-time detection in industry. For ore detection based on theconvolutional neural network, the corresponding region proposals on the bauxite photo are first generated and then a feature extraction and classification on the region proposals are carried out, which reduces the speed of ore detection. In order to improve the ore detection speed, the ore positioning and identification can be processed at the same time, so as to realize end-to-end optimization and significantly improve the detection speed.  
Therefore, a new bauxite separation method is proposed in this paper. Aiming at the problem of insufficient detection accuracy of self-built bauxite datasets, an improved YOLOv4 network combining the SE (Squeeze-and-Excitation) attention module and the K-means clustering algorithm is proposed. The K-means clustering algorithm clusters bauxite in the datasets to determine the length-width ratio of bauxite. By adding the SE attention module to the YOLOv4 network, we can enhance the ability of the network to learn the characteristics of bauxite, automatically learn the importance of different channel characteristics, and improve the accuracy of bauxite target detection. It has potential application value in the fields of mining intelligence and protection of precious resources and provides a theoretical reference for further practical application.
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## Methodology
### Mosquito Human Interaction (SEI-SEIR Model)
The values of all the parameters and their sources are listed in Table 2.  
\[ \begin{array}{rcl}p_{htv}&=&\begin{cases}0.0729T-0.9037&(12.4\ ^{\circ}\text{C} \leq T\leq 26.1\ ^{\circ}\text{C}),\\ 1,&(26.1\ ^{\circ}\text{C}\leq T\leq 32.5\ ^{\circ}\text{C}),\end{cases}\\ \\ p_{vh}&=&0.001044t(T-12.286)\sqrt{(32.461-T)}(12.286\ ^{\circ}\text{C}\leq T \leq 32.461\ ^{\circ}\text{C}),\\ \\ b_{s}&=&0.0043T+0.0943\ \ \ \ \ \ \ \ \ \ \ (21\ ^{\circ}\text{C}\leq T\leq 32\ ^{\circ}\text{C}).\end{array}  \tag{4}\] \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Transmission Probability (human to vector), \(p_{htv}\) & Temperature dependent [17] \\ Transmission Probability (vector to human), \(p_{vh}\) & Temperature dependent [17] \\ Bite Rate of susceptible mosquitoes(in bites per day), \(b_{s}\) & Temperature dependent [17] \\ Bite Rate of infectious mosquitoes(in bites per day), \(b_{i}\) & \(2b_{s}\)[30] \\ Latent Period for humans(in days), \(\frac{1}{\alpha_{H}}\) & random (3–15) [1] \\ Recovery Period for humans(in days), \(\frac{1}{\gamma}\) & 7 [1] \\ Latent Period for mosquitoes(in days), \(\frac{1}{\alpha_{V}}\) & 15 [31] \\ \hline \end{tabular}
\end{table} 
Table 2: Disease Parameters for Zika.
## 3 Modelling, Parameter Adjustment, and Results
### Geometric Model  
The envelope of the room was considered uniform in the model from the point of view of the surface geometry and material characteristics (Figure 2). Elements, such as the door, window, or other details, such as the joining method of the panels, doorknob, etc., were not taken into consideration in the numerical model. The interior dimensions of the room in the model were those determined by direct measurements on the experimental booth: 3.00 \(\times\) 6.00 \(\times\) 2.70 m\({}^{3}\). The envelope has a similar thickness to that of the experimental stand: 0.10 m. The dimensions of the hot air exhaust outlet were determined by direct measurements: 0.25 \(\times\) 0.25 m\({}^{2}\). The dimensions of the cold air input grids were also determined by direct measurements: 0.40 \(\times\) 0.10 m\({}^{2}\).  
The analysis domain was extended by 0.50 m, outside the outer limit of the experimental booth walls.  
The thermocouples were mounted inside the container: 15 thermocouples in the vertical longitudinal plane, 15 thermocouples in the horizontal upper side plane (red crosses in Figure 2), and two thermocouples on the walls of the container.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 3 Methodology  
### Normal Behavior Model  
In this subsection, the selected normal behavior model based on an artificial neural network (ANN) is comprehensively described. Note that this model is designed at a turbine level, thus each WT in the park will have its associated normality model trained with only its own historical SCADA data. First, the data preprocess is detailed to deal with out-of-range values, missing data, and sensors with different magnitudes. Second, a one-year time window is selected to define the training dataset including all operating conditions of the WT. This will ensure that the detected anomalies are not just a change in seasonality and will allow the methodology to be used across all regions of operation of the WT. Third, the ANN set-up is detailed, including a brief explanation of the optimization  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Wind Speed [m/s]**} & \multicolumn{4}{c}{**Main Bearing Temperature [\({}^{\circ}\)C]**} \\  & & _min_ & _max_ & _std_ & _mean_ & _min_ & _max_ & _std_ & _mean_ \\
**WT** & **Timestamp** & & & & & & & & & \\ \hline WT01 & 2018-01-01 00:00:00 & 3.8 & 11.8 & 1.369 & 8.346 & 33.0 & 33.0 & 0.000 & 33.000 \\ WT02 & 2018-01-01 00:00:00 & 3.8 & 11.5 & 1.234 & 8.065 & 32.0 & 33.0 & 0.221 & 32.051 \\ WT03 & 2018-01-01 00:00:00 & 5.6 & 11.5 & 0.976 & 8.283 & 29.0 & 30.0 & 0.500 & 29.505 \\ WT01 & 2018-01-01 00:10:00 & 3.7 & 11.1 & 1.323 & 7.794 & 33.0 & 33.0 & 0.000 & 33.000 \\ WT02 & 2018-01-01 00:10:00 & 3.5 & 10.3 & 1.152 & 7.178 & 32.0 & 33.0 & 0.386 & 32.182 \\ WT03 & 2018-01-01 00:10:00 & 4.6 & 10.9 & 0.984 & 7.858 & 29.0 & 30.0 & 0.499 & 29.532 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample of SCADA data for a limited selection of turbines and signals. Each signal is stored completed with its minimum (_min_), maximum (_max_), mean (_mean_), and standard deviation (_std_) values. The frequency of the data is 10 min.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**WT** & **Timestamp** & **Component** & **Comments** \\ \hline WT11 & 2017-04-28 11:08:00 & Gearbox Bearing & Gearbox Bearing repair \\ WT06 & 2018-06-11 08:40:00 & Main Bearing & Replacing Main Bearing and Main Shaft \\ WT07 & 2019-07-17 07:40:00 & Main Bearing & Inspection required, condition-based. \\ WT03 & 2020-02-25 09:10:00 & Main Bearing & Main Bearing inspection, due to rate of worn \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample of work order data.  
and regularization methods, as well as the selection of the ANN structure. Finally, a specific purposely build indicator for the normal behavior model is stated.
# Exploring the Health Information Management System of Kuwait: Lessons and Opportunities
## 3 Results
### Problems in the Existing Medical Records Management Systems  
Respondents were asked to identify the top five problems faced while managing medical records from a pre-established list of 12 problems. The most prevalent problems cited by the respondents were: missing medical records, lack of storage facilities, slow retrieval of records, and duplication of patients' records. Figure 1 represents the major problems in the existing manual management system, as indicated by the respondents.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{6}{c}{**Hospital Type**} \\ \cline{3-6}  & & **Government** & **Government** & **Private** & **Oil Sector** & **Total** \\ \multicolumn{1}{c}{**Storage, Retention, and Disposal of Medical Records**} & **General** & **Tritary** & **Hospitals** & **Hospitals** & **Hospitals** & **Total** \\ \cline{3-6}  & & **n (\%)** & **n (\%)** & **n (\%)** & **n (\%)** & **n (\%)** \\ \hline \multirow{3}{*}{How long were inactive Kuwaiti patients’ records kept in the department?} & 2 years & 0 (0.0) & 1 (5.3) & 0 (0.0) & 0 (0.0) & 1 (3.3) \\  & 5 years & 1 (16.7) & 0 (0.0) & 0 (0.0) & 0 (0.0) & 1 (3.3) \\  & 10 years & 5 (83.3) & 13 (68.4) & 4 (100.0) & 0 (0.0) & 22 (73.3) \\  & More than 10 years & 0 (0.0) & 5 (26.3) & 0 (0.0) & 0 (0.0) & 5 (16.7) \\  & NA & 0 (0.0) & 0 (0.0) & 0 (0.0) & 1 (100.0) & 1 (3.3) \\ \hline \multirow{3}{*}{How long were in-active Non-Kuwaiti patients’ records kept in the department?} & 2 years & 1 (16.7) & 1 (5.3) & 0(0.0) & 0 (0.0) & 2 (6.7) \\  & 5 years & 5 (83.3) & 15 (78.9) & 4 (100.0) & 0 (0.0) & 24 (80.0) \\  & 10 years & 0 (0.0) & 0 (0.0) & 0 (0.0) & 0 (0.0) & 0(0.0) \\  & More than 10 years & 0 (0.0) & 3 (15.8) & 0 (0.0) & 0 (0.0) & 3 (10.0) \\  & NA & 0 (0.0) & 0 (0.0) & 0 (0.0) & 1 (100.0) & 1 (3.3) \\ \hline \multirow{3}{*}{Do you have enough storage facilities} & Yes & 0 (0.0) & 4 (21.1) & 2 (50.0) & 1 (100.0) & 7 (23.3) \\  & No & 6 (100.0) & 15 (78.9) & 2 (50.0) & 0 (0.0) & 23 (76.7) \\ \hline \multirow{3}{*}{Method of Disposal of patient in-active records} & Burned then disposed Archived then burned \& 1 (16.7) & 2 (10.5) & 0 (0.0) & 0 (0.0) & 3 (10.0) \\  & disposed & 3 (50.0) & 0 (0.0) & 1 (25.0) & 0 (0.0) & 4 (13.3) \\ \cline{1-1}  & Stacked then stored on-site & 2 (33.3) & 13 (68.4) & 0 (0.0) & 0 (0.0) & 15 (50.0) \\ \cline{1-1}  & Stored off site facility & 2 (33.3) & 3 (15.8) & 1 (25.0) & 0 (0.0) & 6 (20.0) \\ \cline{1-1}  & Other methods & 0 (0.0) & 2 (10.5) & 1 (25.0) & 1 (100.0) & 4 (13.3) \\ \cline{1-1}  & NA & 0 (0.0) & 0 (0.0) & 1 (25.0) & 0 (0.0) & 1 (3.3) \\ \hline \multirow{3}{*}{Availability of MR disposal policy} & Ministry of Health policy & 4 (66.7) & 13 (68.4) & 4 (100.0) & 0 (0.0) & 21 (70.0) \\ \cline{1-1}  & Hospital policy & 2 (33.3) & 1 (5.3) & 1 (25.0) & 0 (0.0) & 4 (13.3) \\ \cline{1-1}  & Department policy & 3 (50.0) & 5 (26.3) & 0 (0.0) & 0 (0.0) & 8 (26.7) \\ \hline \end{tabular} Abbreviations: NA, Not Applicable; MR, Medical Records.  
\end{table}
Table 4: Storage Retention, and Disposal of Medical Records.
# Discrimination of Spatial Distribution of Aquatic Organisms in a Coastal Ecosystem Using eDNA
## 2 Materials and Methods
### Sampling, Data Collection and Primer Selection  
A survey permitted by Ha-dong local government (permission number: 2010-0165) was conducted in June 2018. We sampled the surface water (approximately the top 50 cm) in this study. In total, nineteen sampling sites were selected, which covered an extensive area from the Seomjin River estuary to the outer Gwangyang Bay (Figure 1). According to [PERSON] et al. [25], at Gwangyang Bay, higher water temperatures corresponded to lower salinity, and vice versa. Phosphorus and nitrogen concentrations were spatially similar across the Bay. Based on the divisions indicated in [PERSON] et al. [25], Zone I covered the inner Bay (sites 3-7, and 9), Zone II represented the main channel of the Bay (sites 8, 10, and 11), and Zone III mostly belonged to the outer Bay (sites 12-21) (Figure 1). Water samples for meta-barcoding analysis (more than 1 L per sample) were obtained at the same time and moved with dry ice to laboratory to filter (0.45 \(\upmu\)m pore-size membrane; Advantec M USA) and stored at \(-80\)\({}^{\circ}\)C before NGS analysis. Negative controls were included for every study site to prevent cross contamination. Water temperature and salinity were measured on-site using portable equipment (Model: YSI Professional Plus, OH, USA), while the nutrient and chlorophyll-\(a\) concentrations were analyzed in the lab. Phosphorus and nitrogen concentrations were measured using an UV spectrophotometer based on standard analytical methods proposed by the Korean Ministry of Oceans and Fisheries. Chlorophyll-\(a\) measurements were also based on UV spectrophotometry. In contrast to nutrient measurements, chlorophyll-\(a\) samples were filtered through a 0.45 \(\upmu\)m pore-size membrane (Model: Advantec MFS membrane filter). The filter membrane was then homogenized after acetone extraction prior to spectrophotometry. Organic and inorganic carbon concentrations were measured using a carbon analyzer (Model: vario TOC cub, Langenselbold, Germany) using 850 \({}^{\circ}\)C combustion catalytic oxidation methods.  
We selected the V9 region of the 18S rDNA gene (18S V9), primarily because of its broad range among eukaryotes [27; 28]. NGS approaches using the 18S V9 region have recently allowed the characterization of marine planktonic biodiversity in the oceans [29] and prompted biomarker establishment initiatives [30].
## 3 Results and Discussion
### Skin Parameter Estimation
#### 3.4.1 Analysis of the Skin Hydration and the Transepidermal Water Loss Measurement  
The degree of epidermis moisturization and the level of transepidermal water loss are the coupled parameters of the skin characterizing the ability to bind and retain water in particular skin components [31]. The true effect of skin moisturization as a result of the regular application of cosmetic are preparations can be reached only if the cosmetic product used is also able to preserve the sufficient barrier properties of the epidermis protecting against transepidermal water loss [32; 33; 34]. The expected desirable effects of cosmetic product application were described by (i) an increase in the epidermis moisturization to above 45 CM [35; 36] and (ii) a reduction in the level of transepidermal water loss (TEWL), preferably below 10 g/h\(\cdot\)m\({}^{2}\)[37; 38].  
The analysis of changes in the degree of epidermis moisturization and the coupled changes in the TEWL (Table 1) should be performed in two aspects. For both macroemulsions, a beneficial decrease in the TEWL of 6.6% and 7.7% was observed for macroemulsions 1 and 2, respectively, at \(p<0.05\); however, their epidermis moisturizing effects were different. The application of macroemulsion 1 resulted in an increase in the stratum corneum  
Figure 3: Delta backscattering profiles recorded for macroemulsions 1 (**A**) and 2 (**B**), stored at 40 \({}^{\circ}\)C for 30 days.  
moisturization of about 7% (\(p<0.05\)). The effect of this cosmetic preparation on the skin expressed by the TEWL value and moisturization was described as positive; moreover, it was additionally enhanced by the strengthening of the epidermis barrier functions. The results obtained for macroemulsion 2 evidenced that its application improved the moisturization of the epidermis by over 70% (\(p<0.05\)). After the 4-week application of macroemulsion 2, the skin of the volunteers at the site of application was classified as of the correct degree of moisturization (>45 CM), while at the beginning of this study, it was classified as very dry (~30 CM). This really substantial increase in the epidermis moisturization was accompanied by a significant decrease in the TEWL, which permits the conclusion that macroemulsion 2 (serum for the body) shows much stronger moisturizing properties than macroemulsion 1 (face cream). Moreover, the analysis of the results indicated that the ability to bind and retain water in the skin structures, which is the effect assigned to _Solidago virggaurea_ extract, was much greater in macroemulsion 2, in which this extract was much more compatible with the other components of the cosmetic formulation, as evidenced by the earlier results of physicochemical studies and stability evaluation. Similar results were also obtained by Prof. [PERSON]'s research group, which, in the course of studies on extracts of _Lacarius piperatus_ and _Ribes rubrum_, confirmed the beneficial effect of facial creams containing them in increasing skin hydration levels [39].