esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Optimization of GC-MS Analysis Conditions  
Chromotographic column selection: To achieve the best analytical characteristics of GC-MS analysis, all analysis and sample preparation conditions were selected and optimized. Since a large number of polar organic compounds were assumed in the studied samples, it was proposed to separate the components on a column VF-624 (60 m \(\times\) 0.25 mm \(\times\) 1.8 microns) with a fixed phase based on cyanopropyl polydimethylsiloxane. Optimization of the separation conditions of the components was carried out by analyzing standard solutions of VOCs (content of each component: 1 mg/L). The criterion for optimization of the temperature program was the separation of peaks of all substances. Since the components to be determined have low boiling points (20-190 \({}^{\circ}\)C), headspace analysis is suitable for their determination. In order to achieve the best sensitivity, the injected sample volume was 1 mL, and the sample heating temperature was 90 \({}^{\circ}\)C (this value is limited by the boiling point of water).  
Selection of temperature control conditions: To determine the optimal time of heating of the sample, standard solutions with the content of each component in the solution of 1 mg/L were studied. The solutions were thermostated at 90 \({}^{\circ}\)C for 5, 10, 15, 20, and 30 min. It was found that the equilibrium in the vapor-condensed phase system occurs at 20 min of sample heating.
## 4 Case Study and Analysis
### Results of the Sensitive Meteorological Feature Selection Strategy  
Table 2 presents the NWP feature selection results from the source domain. For wind power meteorological features, wind speed has the largest contribution, while large-scale precipitation has the smallest. For solar power features, sunshine intensity contributes the most, while air pressure contributes the least, indicating that the selection strategy results are reasonable. This is because wind speed has a cubic relationship with wind turbine power generation, while precipitation has minimal impact, only indirectly affecting equipment like turbines or sensors. Solar power generation is determined by sunshine intensity--the stronger the sunlight, the higher the power generation. However, changes in air pressure usually accompany the movement of weather systems, and while low-pressure areas may be associated with more clouds or rain, the direct impact of air pressure on solar power generation is minimal, thus resulting in a lower contribution.  
In addition, meteorological features contributing over 85% to wind and solar power generation, as shown in the tables, were selected as inputs for the subsequent prediction models. The analysis shows that, by selecting key meteorological features, this approach not only reduces the complexity of input features but also ensures the preservation of critical information, thereby reducing the model training time to a certain extent.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Structure** & **Layer** & **Input** & **Kernel Size** & **Stride** & **Dilation** & **Output** \\ \hline \multirow{2}{*}{TCN block \#1} & Dilated Causal Conv-1 & 4 \(\times\) 24 & 3 & 1 & 1 & \(16\times 24\) \\  & Dilated Causal Conv-2 & 16 \(\times\) 24 & 3 & 1 & 1 & \(16\times 24\) \\ \hline \multirow{2}{*}{TCN block \#2} & Dilated Causal Conv-1 & 16 \(\times\) 24 & 3 & 1 & 2 & \(16\times 24\) \\  & Dilated Causal Conv-2 & 16 \(\times\) 24 & 3 & 1 & 2 & \(16\times 24\) \\ \hline \multirow{2}{*}{TCN block \#3} & Dilated Causal Conv-1 & 16 \(\times\) 24 & 3 & 1 & 4 & \(16\times 24\) \\  & Dilated Causal Conv-2 & 16 \(\times\) 24 & 3 & 1 & 4 & \(16\times 24\) \\ \hline \multirow{2}{*}{TCN block \#4} & Dilated Causal Conv-1 & 16 \(\times\) 24 & 3 & 1 & 8 & \(16\times 24\) \\  & Dilated Causal Conv-2 & 16 \(\times\) 24 & 3 & 1 & 8 & \(16\times 24\) \\ \hline \multirow{2}{*}{LSTM} & LSTM layer & 16 \(\times\) 24 & - & - & - & \(1\times 16\) \\  & Fully connected & 1 \(\times\) 16 & - & - & - & \(1\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The hyperparameters for the TCN-ATT-LSTM model.
# Monitoring Rainfed Alfafa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
## 2 Material and Methods  
### Study Area  
Bardenas Reales comprises 41,845 ha located in the Ebro Basin (NE Spain, SW Navarra) (Figure 1). The climate is Mediterranean with an average annual precipitation of 427.5 mm. This area is characterized by a 3 month dry season from June to August (74.4 mm) with a mean annual temperature of 13.3 \({}^{\circ}\)C and sharp contrasts between winter (5.7 \({}^{\circ}\)C) and summer (21.5 \({}^{\circ}\)C) [49].  
The main land use is for sheep grazing, and approximately half of the area is cultivated by dryland farming [50]. Dry grasslands, garrigues, and scrub are among the natural habitats in this area [51]. Bardenas Reales is a fragile, unique area that is quite sensitive to environmental changes due to the fact of its erodible materials and water stress conditions [52]. Therefore, it has been an MAB Biosphere Reserve since 2000.  
In the first experiment (E1), eight rainfed alfalfa plots were selected in the area of Bardenas Reales (195, 199, 221, 257, 11, 63, 59, and 347). These plots reported differences in slope and orientation. In the second experiment (E2), 28 rainfed alfalfa plots and 1 irrigated alfalfa plot were monitored (Table 1).  
Figure 1: Chartered community of the situation of Navarra in the Iberian Peninsula. The reserve of Bardenas Reales is located in the southern area of Navarra. Location of the Experiment 1 plots (195, 199, 11, 221, 257, 59, 63, and 347) in the Bardenas Reales area.
# Deep Learning Based Airway Segmentation Using Key Point Prediction
## 4 Discussion
Nevertheless, in the present study, the distance of CV4 had a larger error, which may be due to the shape of the spine CV4 appearing in various ways in the sagittal plane compared to CV1 or CV2. It is necessary to set an additional reference point to define the hypopharynx that appears to be constant in the midsagittal plane. The limitation of most airway segmentation research is possibly due to an inconsistent patient head position [23; 27; 37]. Since patients underwent CBCT in the natural head position in this study, errors may occur. It has been reported that the shape of the airway can vary greatly depending on the angle of the head [38]. However, as concluded in most research, it is not a significant error when comparing the volume of the airway rather than evaluating the volume itself [25]. When performing CBCT, the patient's head position is consistently adjusted to a natural head position by the examiner through the head strap, chin support, and guide light. In addition, the natural head position has been proven to be reproducible [39], and, hence, there should be no major error when comparing. Due to breathing and tongue position, errors may occur in volumetric measurements [35; 37]. Therefore, for each variable, controlled and consistent scanning is required. This study divided the airway volume using 5 points in the 2D mid-sagittal image. The accuracy of these points affects the accuracy of airway segmentation. Therefore, bigger data is needed for clinical application of our algorithm to raise accuracy of coordinate determination. In the agreement analysis, according to [PERSON] et al. [40], \"Based on the 95% confident interval of the ICC estimate, values less than 0.5, between 0.5 and 0.75, between 0.75 and 0.90, and greater than 0.90 are indicative of poor, moderate, good, and excellent reliability, respectively.\" In the present study, oropharynx, hypopharynx, total volume, PNS(y), CV1(y), CV2(x), and CV4(x) indicated excellent reliability, and all other variables indicated good reliability based on the [PERSON] et al. report [40]. These results indicate that fully automatic segmentation of the airway is possible through training via deep learning of artificial intelligence. In addition, high correlation between manual data and deep learning data was estimated. To improve the accuracy, validity, and reliability of auto-segmentation, further data collection and optimum training with big data will be required for future clinical application. Furthermore, to raise the robustness of our algorithm, bigger data is needed for accurate coordinate determination. Transfer learning with other datasets, such as facial coordinates, can also be useful. We plan to develop more robust algorithms with bigger data.
## 4 Results and Discussion
### P300 Detection Results  
In this work, two channels are used for detecting P300, including Cz and Pz. P300 potentials of the five subjects are plotted in Figure 7. Classification rate of P300 and non-P300 is an index for evaluating the accuracy of target element detection. Therefore, several methods are compared, including _K_-neighbor neighbor (_K_-NN) method, SVM, LDA + SVM, and the proposed P300 detector, Kernel FDA + SVM.  
Figure 8 shows the average classification rates (CR) over the five subjects. As can be observed, the classification rates (CR) of all the methods increase with the number of rounds \(N\). The proposed P300 detector outperforms the widely used P300 detector SVM in all cases. When \(N\) = 10, the classification rates of _K_-NN, SVM, LDA + SVM, and Kernel FDA + SVM are 89.1%, 93.1%, 93.6%, and 95.5%, respectively. The classification accuracies of the four methods are all very high as \(N\) = 10. However, the BCI system needs to spend \(10\pmod{}\times 1.675\) (\(\frac{\text{seconds}}{\text{round}}\)) = 16.75 seconds accomplishing the 10-round intensification process for online EEG data collection. A user could therefore feel vision fatigue. To allow the users to feel more comfortable and speed up the online target element detection in the  
Figure 6: Plot of average relative change (_RC_) values and standard deviations of each subject under flashing and no-flashing conditions.  
Figure 7: P300 potentials of five subjects obtained from the EEGs of 10-round intensification process recorded at Pz.  
healthcare function selection mode, the number of rounds should be as small as possible in practical use. If \(N=2\), the two-round intensification process needs only 3.35 s, which is comfortable for a user to concentrate. As \(N=2\), the classification rate of Kernel FDA + SVM is already above 80% (81.3%), while the one of SVM is only 72.5%. The comparison results have demonstrated that the proposed Kernel FDA + SVM is more suitable than the widely used SVM in P300 detection.
## 5 Conclusions  
This study demonstrates that PEF can release growth factors from three types of PRP that cover a wide range of cell content and platelet concentrations. The described experiments used bovine thrombin activation for the positive control and non-activated PRP for the negative control. Data also include whole blood measured growth factor release with and without bovine thrombin. While previous studies [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 32; 33] have examined electrical stimulation of the Harvest PRP and the resulting levels of EGF, VEGF and PDGF-AA post electrical stimulation, no published study has performed a multi donor comparison for these three different types of PRP (Harvest, EmCyte, and Eclipse) in terms of composition and activation via bovine thrombin. Growth factor release for all PRPs with PEF is on the same order of magnitude as bovine thrombin activation for PDGF-AA and VEGF, but significantly higher than bovine thrombin for EGF. The study does not reveal dramatic differences between growth factor release for pulses A (2 \(\upmu\)s) and F (5 \(\upmu\)s). Previous studies have shown robust growth factor release for Harvest PRP with 5 \(\upmu\)s pulses [28]; therefore, reducing pulse durations (e.g., 2 \(\upmu\)s) at higher voltages may not provide practical benefits.  
Conceptualization, B.N.; methodology, B.N.; formal analysis, B.N., A.L.G., S.K. and E.A.L.; investigation, S.K.; resources, B.N. and S.K. writing--original draft preparation, B.N. and A.L.G.; writing--review and editing, B.N., A.L.G., S.K. and E.A.L.; supervision, B.N. and A.L.G.; project administration, B.N.; funding acquisition, B.N. All authors have read and agreed to the published version of the manuscript. This research was funded by Santise Medical and GE. Not applicable. This study was conducted at the Pennsylvania Biotechnology Center and all subjects provided informed consent. The data presented in this study are available on request from the corresponding author. The authors of this paper thank [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for contributing to this work. Also, the authors thank [PERSON] and [PERSON] for important suggestions and comments. The support, encouragement, and critical feedback for the research of in vitro electrical activation of platelets from [PERSON] and [PERSON] are very much appreciated.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Methodology and Band Indicators
Considering a given demodulation band \(\Delta f\), a waterfall of \(ESx_{\{\ell,\Delta f\}}[k]\bigm{/}\underset{k\in f_{int}}{\mathrm{max}}ESx_{\{ \ell,\Delta f\}}[k]\) for increasing center frequencies produces a bispectral map displaying the demodulated amplitude value against the center frequency of demodulation (\(f\)) and the spectral content (\(k\)). When this bispectral map \(NA^{\Delta f}(f,k)\) is integrated along the center frequencies axis (i.e., averaged along \(f\)), a peak in the \(f_{d-int}\) interval can be used to highlight the exact characteristic frequency of interest \(f_{d}\), occurring at sample \(k_{d}\). Finally, \(NA\) and \(SNR\) (Equations (9) and (10)) can be used to quantify the effectiveness of the demodulation process itself, as well as the effectiveness of each of the proposed indicators. In fact, when setting a fixed \(\Delta f\), the mean value of \(NA^{\Delta f}(f,k_{d})\) shows a relation with the performance of the demodulation: if the value is near to 1, the damage characteristic frequency is the main spectral peak in most of the demodulations. Hence it can be considered as a demodulation effectiveness:  
\[I(f,\Delta f)=\tfrac{1}{2}(N(f,\Delta f)+SN(f,\Delta f))=\tfrac{1}{2}\Big{(} ne\Big{\{}SE\Sigma x_{\{\ell,\Delta f\}}[n]\Big{\}}+ne\Big{\{}SESx_{\{\ell, \Delta f\}}[k]\Big{\}}\Big{)}\]0  
Clearly, the smaller the \(dE\) value, the weaker the damage signature in the demodulated spectra and the higher the importance of a good band indicator for the demodulation. Therefore, to evaluate the goodness of the reported band indicators, a different metric can be defined. In particular, the optimal demodulation band given a certain band indicator corresponds to the diad \(\left(f^{*},\Delta f^{*}\right)_{ind}=\mathrm{argmax}(indicator(f,\Delta f))\). Hence, a second metric called indicator effectiveness is proposed:  
\[I(f,\Delta f)=\tfrac{1}{2}(N(f,\Delta f)+SN(f,\Delta f))=\tfrac{1}{2}\Big{(} ne\Big{\{}SE\Sigma x_{\{\ell,\Delta f\}}[n]\Big{\}}+ne\Big{\{}SESx_{\{\ell, \Delta f\}}[k]\Big{\}}\Big{)}\]1  
Note that for the sake of simplicity, in this work a given value for the demodulation band \(\Delta f\) will be used, while the center frequency \(f\) is increased considering a 50% overlap for consecutive bands. In particular, \(\Delta f\) is selected considering that the resulting spectral content in \(ESx_{\{\ell,\Delta f\}}[k]\) is limited by the demodulation band. Hence, this must be large enough to contain the upper bound of the interval \(f_{int}\) (i.e., 3.05 \(\cdot\)_BPFI_). According to common knowledge [6; 10; 29], this choice seems to be advisable while not affecting much the demodulation performance. The main steps of the methodology are summarized in \[I(f,\Delta f)=\tfrac{1}{2}(N(f,\Delta f)+SN(f,\Delta f))=\tfrac{1}{2}\Big{(} ne\Big{\{}SE\Sigma x_{\{\ell,\Delta f\}}[n]\Big{\}}+ne\Big{\{}SESx_{\{\ell, \Delta f\}}[k]\Big{\}}\Big{)}\]2
# A New Tooth Profile Modification Method of Cycloidal Gears in Precision Reducers for Robots
## 4 Meshing Contact Characteristics of the Modified Profile  
### Mesling Contact Model of a Cycloid-Pin Gear  
Through the spatial coordinate transformation, the position vector and the normal vector of the cycloidal gear and the pin gear can be, respectively, represented in the fixed coordinate system \(S_{f}\). According to the gear meshing principle and the continuous contact condition of the tooth surface, at any time, the position coordinates of the contact points on the cycloidal surface should be consistent with the position coordinates on the pin profile and the normal directions of the contact points should  
Figure 6: First cycloid relationship between the modification and pressure angle.  
Figure 7: Second cycloid relationship between the modification and pressure angle.  
also be the same [14]. Therefore, the meshing contact analysis model of a cycloid-pin gear can be established as follows:  
\[\left\{\begin{array}{l}\mathbf{M}_{fc}(\phi_{2})\mathbf{\cdot R}_{c}(\varphi)= \mathbf{M}_{fp}(\phi_{1})\mathbf{\cdot R}_{p}(\beta)\\ \mathbf{M}_{fc}(\phi_{2})\mathbf{\cdot n}_{c}(\varphi)=\mathbf{M}_{fp}(\phi_{1 })\mathbf{\cdot n}_{p}(\beta)\end{array}\right., \tag{10}\]  
where \(\varphi\) and \(\beta\) are the parametric angles of the cycloidal gear and the pin gear; \(\phi_{1}\) and \(\phi_{2}\) are, respectively, the rotation angles of the pin gear and the cycloidal gear. \(\mathbf{M}_{fc}=\left[\begin{array}{cccc}\cos\phi_{2}&-\sin\phi_{2}&0&0\\ \sin\phi_{2}&\cos\phi_{2}&0&-a\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\) and \(\mathbf{M}_{fp}=\left[\begin{array}{cccc}\cos\phi_{1}&-\sin\phi_{1}&0&0\\ \sin\phi_{1}&\cos\phi_{1}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\) are the transformation matrices.  
Equation (10) is the non-linear contact analysis system of equations for the cycloid-pin meshing. There are three equations and four unknown parameters in the system of equations. Given any unknown parameter, the remaining unknown parameters can be iteratively solved.
## 4 Planning and Design of Studies of Driver Behavior at Pedestrian Crossings Using Simulators
### Defining the Research Context and Assumptions  
The research problem and context determine the planning, design, and implementation processes. Very often, a precise and accurate formulation of the research context makes it possible to significantly reduce the scenarios and conditions, and thus the scope of the research, and allows a significant shortening of the execution time while maintaining the quality of the measurement results obtained. Two key research problems were identified as factors affecting drivers' distractions (distractors inside the vehicle, such as operating touch panels) and systems for signaling drivers when approaching a pedestrian crossing, including interactive innovative signs and pictograms.  
In this study, the research problem is the behavior of drivers in the vicinity of different pedestrian crossings with different factors influencing drivers' perception and focus. The research problem defined in this way made it possible to define a research context, which involved following drivers' behavior and perceptions when approaching pedestrian crossings under different driving conditions, defined by vehicle speed, weather conditions, and the type of surrounding infrastructure. In addition, the context included various assumed specific behaviors of vulnerable road users (turning, accelerating step, etc.) and different types of pedestrian crossing infrastructures and equipment with warning systems. The influence of internal and external attentional distractors on driver behavior, such as talking on the phone or operating a screen in the vehicle or on a tablet (phone), as well as roadside advertising or unusual objects outside the vehicle, was also identified as an important component of the research context. The general types of such attention distractors, based on [31], are shown in Figure 2.  
As a result, the research assumptions related with detailed research problems were defined as:  
* Analysis of the impact of the type of pedestrian crossing;
* Analysis of the impact of the method (system) of warning;
* Analysis of the impact of surrounding infrastructure conditions;
* Analysis of the impacts of driving speed and weather conditions (including time of day);
* Analysis of the impact of the behavior of vulnerable traffic participants (pedestrian and cyclist);
* Analysis of the impacts of internal and external distractors on attention.  
Additional assumptions limiting the scope of the study were:  
* Elimination of the effect of driver fatigue during the study (short testing time);  
Figure 2: Visual, manual, and cognitive distractors affecting the driver.  
* Elimination of the effect of additional medical conditions on driving ability;
* Eliminating the impact of driving experience;
* Eliminating the impact of road infrastructure lighting and driver glare;
* Eliminating the impact of additional vehicle safety systems (including \"assistances\" and obstacle detection sensors).
# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
## 3 Proposed Autoscaling Framework
### Autonomic Manager
#### 3.2.3 Planning Phase  
The resource estimation service calculates the number of resources (VMs or containers) based on the predicted network received throughput of the previous step, \(network_{t+1}\), as shown in Algorithm 1. The autoscaler has to deal with oscillation problems that frequently perform opposite scaling actions in a short time. This problem will result in a wastage of resources and budget. We applied the control period time (CPT) between each scaling action to deal with this problem. In addition, we set the CPT value to 1 min, which is fine grained for the VM setting. \(VMs_{t+1}\) and \(network_{VM}\) are the number of estimated VMs in the next time step and the maximum network workload that a VM can handle in a minute, respectively. \(VMs_{min}\) is the minimum number of VMs, and the system cannot scale down the number of VMs to a number lower than this value. For each CPT (Line 3), we calculated \(VMs_{t+1}\) (Line 4). If \(VMs_{t+1}\) is higher than the current number of VMs \(VMs_{current}\) (Line 5), for the next time step, we scheduled the scale-up command to provision the number of VMs required to fulfill the resource requirement (Line 6). Otherwise, if \(VMs_{t+1}\) is lower than \(VMs_{current}\) (Line 7), we obtained the maximum value between \(VMs_{t+1}\) and \(VMs_{min}\)  
Figure 2: Proposed Bi-LSTM neural network architecture.  
(Line 8), and then scheduled the scale-down command to deprovision unneeded VMs (Line 9). Otherwise, the autoscaler sleeps and waits until the next interval to keep recalculating the number of needed VMs and performing scaling actions (Line 11).  
```
1:Input:\(network_{t+1}\) // predicted workload in the next interval
2:\(VMs_{min}\) // minimum number of VMs that the system has to maintain
3:Output: Scheduled scaling actions
4:Initialization
5:while system is running do
6:for each CPT do
7:\(VMs_{t+1}=\frac{network_{t+1}}{network_{VM}}\)
8:if\(VMs_{t+1}>VM_{current}\)then
9:\(SCHEDULE\_SCALE\_UIP\_COMMAND\) (\(VMs_{t+1}\))
10:elseif\(VMs_{t+1}<VMs_{current}\)then
11:\(VMs_{t+1}=max(VMs_{t+1},VMs_{min})\)
12:\(SCHEDULE\_SCALE\_DOWN\_COMMAND\) (\(VMs_{t+1}\))
13:else
14: Sleep
15:endif
16:endfor
17:endwhile
```  
**Algorithm 1** Adaption strategy service algorithm.