esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 3 Results
### Changes of Electron Density during Magnetic Storms  
Figure 7 shows the electron density change during a magnetic storm in September 2011 as reproduced by the EDG-DNN model. It can be seen from Figure 7b that the electron density changed dramatically during the magnetic storm period, and the electron density at noon in local time increased greatly compared with that in the quiet period. The vertical electron density data are superimposed in Figure 7a, and it can be seen that the peak value of the electron density during the magnetic storm is higher than the peak value in the quiet period, and the trough value is lower than the trough value of the electron density in the quiet period. This shows that EDG-DNN can reproduce the characteristics of the electron density changing with time during the magnetic storm to a certain extent.
## 4 Application
### Field Data Collection Endeavor  
Our field research was orchestrated over a strategic five-day period, commencing on 17 July and concluding on 22 July 2023. This expedition was conducted within the influential ambit of the LIDAK Cement Factory's operations, focusing on 10 pivotal intersections that form the arterial conduits of urban mobility in the area. The operational rigor in our data collection involved both manual and camera-assisted vehicle counting methodologies, ensuring a holistic capture of the traffic flux across designated time intervals: the bustling morning hours, the transient afternoon period, and the peak of the evening rush. The resulting observed traffic data, meticulously encoded as _Dij_ and _D'ij_, provided a nuanced snapshot of vehicular movements. This snapshot was invaluable in establishing a robust foundation for the subsequent estimations our model sought to generate. The systematic regimentation of our field data collection--divided into three-hour observational blocks and punctuated by two-hour intervals of critical data validation and recalibration--ensured the integrity and reliability of the empirical inputs into our model.
# Borrow from Source Models: Efficient Infrared Object Detection with Limited Examples
## 4 Experiments
### Comparison with State-of-the-Arts  
Our frameworks (SMG-C and SMG-Y) are compared with some recent state-of-the-art detectors on the FLIR benchmark. The compared trackers are divided into two categories, visual and infrared detectors. The visual detectors such as SSD [9], YOLOv3 [18], Faster-RCNN [8], CenterNet [19], and RefineNet [39] are designed for RGB object detection and finetuned on the training set of FLIR. The infrared detectors, including MMTOD-CG [1], MMTOD-UNIT [1], Effi-YOLOv3 [40] and Pesudo-two-stage [14], are applied for thermal images directly.  
We present the qualitative results in Table 7. It is remarkable that two proposed detection frameworks achieve outstanding performance. Specifically speaking, SMG-Y obtains the highest mAP with 77.0% and the AP of person, car, and bicycle are 78.5%, 86.6%, and 65.8%, respectively. It outperforms advanced detectors in mAP, and the speed of it maintains 40 frames per second (FPS), keeping the balance of accuracy and speed. Despite the slightly lower mAP (75.6%) in comparison with SMG-Y, SMG-C runs at the speed of 107 FPS, which is five times faster than other infrared detectors. Compared to the high-speed detector CenterNet [19], SMG-C gains 4.5% improvement in mAP, which shows that SMG-C is an efficient real-time detector.  
More importantly, SMG-Y with 1/4 training data also achieves 74.5% mAP, surpassing all visual detectors and most infrared detectors trained on full FLIR. The bicycle accuracy in SMG-Y-1/4 is 62.5% AP, which is on par with that of Pseudo-two-stage [14]. Note that the training dataset of SMG-C-1/4 only contains 928 bicycle instances, while Pseudo-two-stage [14] is trained with 3986 examples for bicycle detection.  
Figure 9: The comparison of SMG-C and SMG-Y in terms of mAP.
## 3 Results and Discussion
### Volatile Profile
Visual and Aroma Profile of Volatile Phenols-Spiked Wines after Treatments with Solid and Dissolved Chitosan  
Figure 3 outlines the mean scores for each sensory attribute of samples spiked with ethyl phenols aiming to assess the sensory impact of _Brett_ character. Since the level of addition of ethyl phenols greatly disturbed the taste analysis only an olfactory and visual description was carried out. Triangular test revealed that panelists were able to distinguish untreated samples among chitosan treated wines, while no differences were individuated between KT and KTS (data not shown). Judging by the results of Figure 3, very similar sensory profiles were obtained between the three samples, with only a slightly significative greater limpidity and herbaceous character in wines treated with dissolved chitosan (KTS). For this latter finding, the reduction in \(n\)-hexanol content after KTS addition (Table A1) may have played a role. In spite of the observed diminution of volatile phenols in chitosan-treated wines (Figure 2), the sensory attributes related to those volatiles (rubber and animal) were found not significantly different among samples, confirming that in highly contaminated wines, the removal capacity of chitosan towards ethyl phenols is not sufficient to reduce negative sensory impact imparted by them. Moreover, contrarily to what discussed for CIELab parameters showed in Table 3, panelists were not able to detect any difference in color intensity among samples. Figure 2: (**A**) Quantification of volatile compounds grouped by chemical families and (**B**) quantification of 4-ethyl phenol (4-EP) and 4-ethyl guaiacol (4-EG) in wines after 7 days of treatment. In the same row, different letters indicate significant differences according to [PERSON]’s test (\(p<0.05\)). \(n=3\). C: no addition; KT: 0.5 g/L of solid chitosan; KTS: 0.5 g/L of chitosan in solution.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods
### Preparation of Training and Test Datasets for Deep Convolutional Learning  
The OSSE data were simulated starting from year one of the primitive equation model daily output described above. The original input images cover the entire Mediterranean domain at \(1/24^{\circ}\) spatial resolution, leading to an individual image size of 380 \(\times\) 1000 pixels. We randomly chose 40 dates (-11% of the total) to be kept aside as independent test data, and we successively resampled the original images, extracting much smaller tiles (\(76\times 100\)), which were later used as input to the network training. Despite the random holdout strategy being a standard, different choices of the test dataset could also be done. However, the test on OSSE prediction only serves here to assess the relative performance of the different network architectures, not its absolute performance, which is not relevant per se, when looking at simulated input data. The tiles are then extracted by going through a double loop on latitude and longitude, imposing a spatial overlap of 50%, so that a total of 42,250 samples is finally available for the training. The dimension of the tiles has been chosen to simplify the pre-processing and reduce the memory required by the training steps. In fact, all tiles are normalized before entering the network. In the case of the SST, ADT and \(\partial\)SST/\(\partial\)t, they are first transformed into anomalies estimated with respect to the tile spatial mean and are successively scaled by dividing the anomalies by the maximum value (in absolute value) recorded throughout the series. The \(\Delta\)ADT only goes through the normalization step. As the tiles cover an area of approximately 300 km \(\times\) 400 km, the anomaly computation is indeed serving as a high-pass filter, removing the background variability associated with basin scale processes and seasonal variations (e.g., steric and thermal variations driven by large-scale, air-sea interactions), which are not relevant to reveal the impact of mesoscale processes on SST evolution related to horizontal advection. This filtering is consistent with the tests described in [44]. After the test/prediction, the tiles are merged together to compute a weighted average on overlapping areas.
## 3 Proposed Washout Algorithm  
### New Tilt Coordination Algorithm with Proportional Gain \(\alpha\)  
Concept  
In the classical washout algorithm, continuous translational acceleration is enhanced using a high pass filter, and the specific force component generates a low-frequency translational acceleration by passing the translational acceleration through a low pass filter and then by gradient transformation. However, since the actual high and low pass filters are not ideal filters, the sum of the specific force applied with these filters is distorted differently from the original specific force. Therefore, the new gradient transformation model is proposed by using the difference between the translation acceleration and the translation acceleration past the high pass filter to solve the problem caused using the low pass filter during gradient transformation [3].  
However, the new tilt coordination algorithm improves the translational motion sensation, but the rotational motion sensation is extremely deteriorated. Therefore, this paper supplemented the side effect of the new tilt coordination by applying a proportional gain to the specific force component generated from the new tilt coordination. The proportional gain \(\alpha\) has a value between 0 and 1, and a new gradient transformation is fully reflected in the case of 1, but a value less than 1 prevents a decrease in rotational motion sensation. However, it should be noted that taking the gain too small can limit the improvement of translational motion sensation, which is an advantage of the new tilt coordination algorithm.
## 4 Model Verification Using the Real Data
### The H/T Rotating ShaftLength  
In Figure 5, the coordinates of eight check points are shown as follows.  
\(ff1(625.05,123.58,600.41)\)\(ff2(598.67,150.63,594.12)\)\(ff3(650.93,151.37,606.57)\)\(ff4(624.70,177.33,600.32)\)\(gg1(651.01,123.77,493.51)\)\(gg2(623.60,150.68,486.98)\)\(gg3(676.13,149.86,499.50)\)\(gg4(649.70,177.24,493.20)\)  
Accordingly,  
\[\begin{array}{l}\overline{ff1 gg1}=\sqrt{\left(651.01-625.05\right)^{2}+ \left(123.77-123.58\right)^{2}+\left(493.51-600.41\right)^{2}}\cong 110\text{mm}\\ \overline{ff2 gg2}=\sqrt{\left(623.60-598.67\right)^{2}+\left(150.68-150.63 \right)^{2}+\left(486.98-594.12\right)^{2}}\cong 110\text{mm}\\ \overline{ff3 gg3}=\sqrt{\left(676.13-650.93\right)^{2}+\left(149.86-151.37 \right)^{2}+\left(499.50-606.57\right)^{2}}\cong 110\text{mm}\\ \overline{ff4 gg4}=\sqrt{\left(649.70-624.70\right)^{2}+\left(177.24-177.33 \right)^{2}+\left(493.20-600.32\right)^{2}}\cong 110\text{mm}\end{array}\]  
The average length (L) is obtained as:  
\[\text{L}=\frac{\overline{ff1 gg1+ff2 gg2+ff3 gg3+ff4 gg4}}{4}\cong 110\text{mm}\]  
As above, the calculated value is confirmed equal to the theoretical value.
###### Abstract  
This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of the original signal. In the feature extraction and selection stage, windows are set at a time interval of 5 s in the preprocessed signal, while autocorrelation, scaling, and discrete cosine transform (DCT) are applied to extract and select features. Thereafter, the window removal method is applied to all of the generated windows to remove those that are unrecognizable. Lastly, in the classification stage, the NN, SVM, and LDA classifiers are used to perform individual identification. As a result, when the NN is used in the Normal Sinus Rhythm (NSR), PTB diagnostic, and QT database, the results indicate that the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM is used, all of the subject identification rates are 100%, while the window identification rates are 96.92%, 95.82% and 98.32%. When the LDA is used, all of the subject identification rates are 100%, while the window identification rates are 98.67%, 98.65% and 99.23%. The proposed method demonstrates good results with regard to data that not only includes normal signals, but also abnormal signals. In addition, the window removal method improves the individual identification accuracy by removing windows that cannot be recognized.  
electrocardiogram; ECG identification; biometrics; window removal method; non-fiducial technique
## 4 Experimental Results
### Propeller Measurement Results
#### 4.2.4 Stability in Single-Point Measurement Mode  
The one-way ANOVA homogeneity test was conducted to assess the measurement stability of the system under the single-point measurement mode. The results revealed nonsignificant differences between repeated measurements for the first, second, and third propeller blades (\(F\) = 0.378, \(p\) > 0.05; \(F\) = 0.394, \(p\) > 0.05; and \(F\) = 0.575, \(p\) > 0.05). These results confirm that the developed system is highly stable and that no observable measurement bias is introduced by repeated sampling (Table 7).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **Mean** & **Variance** & **Levene Statistic** \\ \hline \multirow{2}{*}{First blade} & Second decimal place & 2.11 & 1.064 & \\ \cline{2-5}  & Third decimal place & 4.92 & 8.028 & 403.551 *** \\ \hline \multirow{2}{*}{Second blade} & Second decimal place & 1.76 & 0.802 & \\ \cline{2-5}  & Third decimal place & 4.52 & 9.226 & 672.297 *** \\ \hline \multirow{2}{*}{Third blade} & Second decimal place & 1.90 & 0.915 & \\ \cline{2-5}  & Third decimal place & 4.36 & 8.603 & 567.765 *** \\ \hline \end{tabular}
\end{table}
Table 6: Homogeneity test for measurement precision in the single-point measurement mode (\({}^{***}p\) < 0.001).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Sum of Squares** & **df** & **Mean Square** & **F** \\ \hline \multirow{4}{*}{Third blade} & \multicolumn{2}{c}{Sum of squares} & df & Mean square & F \\ \cline{2-5}  & Between-group & 0.068 & 3 & 0.023 & \\ \cline{2-5}  & Within-group & 325.870 & 396 & 0.823 & 0.027 \\ \cline{2-5}  & Total & 325.937 & 399 & & \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._
# Uncovering Hidden Community Structure in Multi-Layer Networks
## 4 Hidden Community Detection Method  
In this section, we propose a hidden community detection (HCD) method to detect multiple layers of hidden or weak communities in a network. The reason behind introducing this scenario of detecting communities is because for several applications in a large variety of scientific disciplines, the weak, hidden structure is of great interest and deserves to be explored as it may provide some useful information which can go undetected due to its presence beneath the stronger communities in a network.  
Let us consider an example of a government agency aiming to detect a group of terrorists' social accounts within a social network. This structure of a community may be much weaker than other community structures, such as the ones based on family and friends. It can be expected that there exist some social accounts of terrorists' groups in a network but weaker to be appeared as a dominant structure, so it can be challenging for algorithms to locate them. Somehow, that structure is a hidden organization, but it is important. Detecting such a structure, especially in a network with multiple layers or overlapping layers, can be challenging.  
This method uses an existing overlapping community detection algorithm (Louvain, CNM, Girvan-Newman, etc.) as a baseline algorithm to weaken the structure of layers detected to demonstrate the hidden structure inside the network.  
Figure 3 illustrates the flowchart of the whole process of the proposed method. It can be seen that first, it normally detects the communities much like any other community detection algorithm. There are many techniques to weaken the layers. We chose edge removal as it weakens the communities' structures to expose the weaker or hidden communities if any exist. This can be determined by comparing the reduced structure of the resulting communities with the set of communities obtained by applying the baseline algorithm. Later, the iterative process goes to the overall network layers including the weak structure of communities to double check if any structure or layer weaker than the others is left. Finally, the baseline method is applied again to form a better and this time more meaningful community structure of the network.
## 6 Numerical Results  
In this section, numerical results of the proposed LRT-BER for BPSK, DPSK and 2 FSK modulations under the Rayleigh, Ricean and Nakagami-\(m\) fading envelope are given according to the performance evaluation in Section 5. Furthermore, the performance comparison with the existing MRC, EGC and LRT-CS rules is carried out under the N-P criterion. For the case of identical local detection performance indices, we first give the detection performance of the proposed fusion rule by using the closed-form system detection performance (Equations (29) and (30)). Then, the system detection performance of the proposed fusion rule is compared to the existing fusion rules.
# Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
## 1 Introduction
[18] established the mixed-lubrication model of the planetary roller screw considering contact load, contact geometry of thread, surface roughness and transient effects, and the influences of the rotational speed of the screw, axial load, and surface roughness on lubrication were discussed. [PERSON] et al. [19] proposed an explicit friction torque model composed of an applied load and viscous friction terms for two-point contact DNBSs, which can obtain the optimization design parameters of BSs by minimizing the friction model. Nevertheless, the elastohydrodynamic contact stress of BSs considering the asymmetry of the contact region of the helix raceway is not explored in the above-mentioned research, but it is essential that the non-Hertzian elastohydrodynamic contact stress can more accurately analyze the wear mechanism and the accuracy retention of the high-speed BSs. In order to eliminate the calculation error of the Hertzian elastohydrodynamic contact stress due to the asymmetry of the contact region of the helix raceway, a non-Hertzian elastohydrodynamic contact stress calculation method based on the minimum excess principle was proposed. According to the contact mechanics, the normal contact stresses of the screw raceway and the nut raceway were calculated by the Hertzian contact theory and the minimum excess principle, respectively. Furthermore, the Hertzian solution and the non-Hertzian solution of the elastohydrodynamic contact stress can be determined by the Reynolds equation under different helix angles and screw speeds. The non-Hertzian elastohydrodynamic contact stress can more accurately analyze the wear mechanism and the accuracy retention of the high-speed BSs. Additionally, a friction torque test was designed and implemented on a self-designed bed, and the proposed method was verified.
## 3 Results and Discussion
### Modified Tait Equation  
In order to ease the application of our method in practical engineering, the following modified Tait equation was used to correlate the measured densities of each CO\({}_{2}\) + \(n\)-decane mixture:  
\[\rho(T,p)=\frac{\rho_{0}(T)}{1-C\ln((B(T)+p)/(B(T)+0.1))} \tag{1}\]  
with  
\[\rho_{0}(T)=A_{0}+A_{1}T+A_{2}T^{2}+A_{3}T^{3} \tag{2}\]  
\[B(T)=B_{0}+B_{1}T+B_{2}T^{2} \tag{3}\]  
where the \(A_{i}\), \(B_{i}\), and \(C\) parameters correlated with the experimental density data.  
To test the deviation between the experimental data and the calculated values, the Absolute Average Deviation (\(AAD\)), the Maximum Absolute Deviation (\(MAD\)), and the Standard Deviation (\(\sigma\)) were evaluated:  
\[AAD/\%=\frac{100}{N-m}\sum_{i=1}^{N}\left|\frac{\rho_{i}^{\text{exp}}-\rho_{i} ^{\text{calc}}}{\rho_{i}^{\text{exp}}}\right| \tag{4}\]  
\[MAD/\%=Max\left(100\left|\frac{\rho_{i}^{\text{exp}}-\rho_{i}^{\text{calc}} }{\rho_{i}^{\text{exp}}}\right|\right) \tag{5}\]  
\[\sigma=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(\rho_{i}^{\text{exp}}-\rho_{i} ^{\text{calc}}\right)^{2}}{N-m}} \tag{6}\]  
Figure 4: Comparison of the deviations in the experimental data with the results calculated using Refprop 10.0 [43] for CO\({}_{2}\) + \(n\)-decane mixtures. (**a**) Original (0–110 MPa); (**b**) partially enlarged (0–30 MPa); (**c**) partially enlarged (30–110 MPa). \(\square\), This work; \(\blacksquare\), [PERSON] and [PERSON] [16]; \(\circ\), [PERSON] et al. [17]; \(\bullet\), [PERSON] et al. [18]; \(\triangle\), [PERSON] et al. [19]; \(\blacktriangle\), [PERSON] et al. [11]; \(\triangledown\), [PERSON] et al. [21]; \(\blacktriangledown\), [PERSON] et al. [22]; \(\varoquoise\), [PERSON] et al. [23]; \(\varoquoise\), [PERSON] et al. [24].  
where \(N\) and \(m\) represent the amount of experimental data and the number of modified Tait equation parameters, respectively. \(\rho_{\rm i}^{\rm exp}\) and \(\rho_{\rm i}^{\rm calc}\) denote the experimental density and the density values calculated using Equation (1), respectively. The modified Tait equations' parameters and statistical deviations are shown in Table 4.
## 6 Experimental Verification
### Frequency Adaptive Performance Tests  
In order to verify the frequency adaptive performance of the ANF, the speed of HPMSM was suddenly reduced from 8000 rpm to 6000 rpm, and the current waveforms and FFT analysis results are illustrated in Figure 20. Figure 20 shows that when the frequency of HPMSM is changed from 667 Hz to 500 Hz, the high-frequency harmonics of the stator currents are successfully suppressed with an extremely low harmonic content of 5.29%. Importantly, there is no need to adjust any parameters, and the resonance peak is still precisely filtered out. The experimental results show that the resonant peak is able to be suppressed when the HPMSM is running at various speeds due to the introduction of speed feedback.  
Figure 19: Experiment results of three-phase current waveforms and FFT analysis at 8000 rpm with 0.1 N\(\cdot\)m load [1 A/div].  
Figure 18: Experiment results of three-phase current waveforms and FFT analysis at 8000 rpm with 0.5 N\(\cdot\)m load: (**a**) without LC filter [2 A/div]; (**b**) with LC filter [2 A/div]; (**c**) with LC filter and ANF [2 A/div].