esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of Stone Masking Asphalt Mixtures Fabricated by Different Compaction Methods
## 3 Results
### Fatigue Performance Comparison  
#### 3.5.1 Fatigue Test  
Table 16 lists the fatigue test results of the SMA-13 mixtures designed by the three methods. The stress ratio (S) defines the ratio of the maximum load stress to the ultimate failure strength, and the fatigue life (N) is the total load time to failure of the mixture, and the C\({}_{\rm v}\) represents the coefficient of variation of each test.  
As the stress ratio changed, the fatigue lifetimes of the mixtures designed by the three methods exhibited the same trend. Specifically, increasing the stress ratio gradually reduced the fatigue life of the mixture. In addition, the large dispersion between the number of fatigue times \(N\) and the stress ratio \(S\) is problematic for analyzing the fatigue data. In previous studies, the fatigue life of mixtures has been effectively analyzed by the Weibull distribution. Therefore, the two-parameter  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Design Methods**} & \multicolumn{4}{c}{**Fatigue Life (N) Under the Following Stress Ratios S**} \\ \cline{2-6}  & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** \\ \hline \multirow{4}{*}{Marshall} & 17,119 & 3577 & 1217 & 716 & 306 \\  & 18,130 & 4229 & 1770 & 818 & 357 \\  & 19,831 & 4697 & 2074 & 940 & 445 \\  & 21,697 & 5098 & 2346 & 1135 & 559 \\  & 23,136 & 5766 & 2849 & 1216 & 675 \\ \hline \multirow{4}{*}{SGC} & C\({}_{\rm v}\) & 11\% & 16\% & 26\% & 19\% & 28\% \\ \hline \multirow{4}{*}{SGC} & 19,339 & 3745 & 1527 & 834 & 427 \\  & 21,218 & 4641 & 2013 & 1058 & 479 \\  & 23,048 & 5089 & 2590 & 1225 & 534 \\  & 24,831 & 5823 & 3004 & 1373 & 598 \\  & 26,696 & 6138 & 3407 & 1528 & 684 \\ \hline \multirow{4}{*}{VVTM} & 11\% & 17\% & 27\% & 20\% & 17\% \\  & 20,451 & 4258 & 1715 & 1004 & 485 \\  & 22,454 & 5259 & 2025 & 1110 & 545 \\  & 24,323 & 6110 & 2520 & 1259 & 622 \\  & 26,463 & 6658 & 3376 & 1475 & 743 \\  & 28,291 & 7190 & 3751 & 1588 & 802 \\ \hline \multirow{2}{*}{C\({}_{\rm v}\)} & 11\% & 18\% & 29\% & 17\% & 18\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Fatigue test results of SMA-13s designed by different methods.  
Weibull distribution was applied to the present relationship between the fatigue number \(N\) and the stress ratio S. The failure probability (\(P\)) is given by the following fatigue equation:  
\[P=\text{F}\big{(}\overline{N}\big{)}=1-\exp-\frac{\overline{N}^{m}}{t},\overline {N}\geq 1,m,t>0. \tag{2}\]  
After a logarithmic transformation, Equation (2) becomes:  
\[\ln\ln\frac{1}{1-\text{P}}=\text{m}\ln\overline{N}-\ln t=m_{0}\ln N-\ln t, \tag{3}\]  
where \(m_{0}=(1-R)\times m\) is the shape parameter, and \(t\) is the scale parameter.  
The regression coefficients \(m_{0}\), \(\ln t\), and \(R^{2}\) were obtained by incorporating the fatigue test data in Table 16 into Equation (3). Their values are listed in Table 17.
# An Inverter Topology for Wireless Power Transfer System with Multiple Transmitter Coils
###### Abstract  
This paper presents an inverter topology for a wireless power transfer (WPT) system that is intended to reduce the component counts and complexity of the conventional excitation circuit for multiple transmitter coils. The proposed inverter topology requires only (n+2) power switches, where \"n\" is the number of transmitter coils. An excitation of a proper transmitter coil pattern with regard to the receiver coil position is determined. The output voltage can be regulated through the primary-side control by adjusting the duty cycles of the inverter switches. A detection method of the receiver coil position is presented using the detection switches on the secondary side. The detection algorithm is based on the reflected impedance knowledge and requires only a current sensor on the primary side. A proper transmitter coil pattern is energized to ensure maximum transfer efficiency throughout the operation. The proposed system is experimentally validated on the created 500-watt WPT multi-coil system. After the receiver coil is placed in a designated area, the proper transmitter coil pattern can be automatically selected and energized. The output voltage can be regulated to a desired value under the typical operation conditions, including load change.  
Keywords:inverter topology; wireless power transfer; multiple transmitter coils; primary-side control; output voltage regulation; detection of receiver coil position +
Footnote †: journal: Applied
## 3 Data Extraction and Documentation
### Machine Learning Methods
By stacking memory cells, information of previous inputs are stored in the output with weights, carried by cell state, which makes **Long Short-Term Memory (LSTM)** an outstanding tool to mimic time series [79]. **Auto Encoder (AE)** is an unsupervised learning algorithm that extracts features from input data without labelling information [80]. It generally consists of two parts including encoder and decoder. The encoder type compresseses data when dealing inputs of high dimensionality by mapping variables in the hidden layer. The decoder reconstructs approximation in inputs. Supposing the activation function is a linear function and there are fewer hidden layers than the dimensionality of input data, then the linear auto encoder is similar to a principle component analysis (PCA). Several variants of AE have been developed: **Denoising Auto Encoder (DAE)** is an extension version of the basic auto encoder, which is trained to reconstruct the stochastically corrupted input data by adding isotropic Gaussian and by forcing the hidden layer to explore robust features; **Sparse Auto Encoder (SAE)** imposes sparse constraints on the hidden layers while imposing activation closing to zero. This is used for fault diagnosis and intelligent fault signature. **Variational Auto Encoder (VAE)** is a deep generative architecture constructing a probability distribution for a latent variable. It generates new input data by sampling distribution. Currently, the generative model is widely used for reliability and risk analysis [81]. To evaluate the performance of the models, the most important metrics in publications are accuracy, precision, recall, F-measure, mean square error, mean absolute percentage error, and the failure capture rate, or in machine learning terms the true positive rate. The latter one is generally analysed next to true negative rate (i.e., percentage of assets that will not fail and the model will predict them correctly) as we can reach a complete true positive rate whenever all occurring failures are predicted but we are able to gain information about risky assets [82]. The current proposal is performing, as per relevant literature review, a comparison of main class of ML. We are focusing on specific performance, but in the specific applicability with the intent to construct a round mat per choosing the right approach for the right asset. The best evolution or validation studies are than selected for a specific asset in a particular content. In answering **RQ6** methods, the learning algorithm or models are used as synonymous and alternative in the filtering database.
## 3 Methods
### Feature Extraction
#### 3.2.1 Electrocardiography (ECG)  
Electrocardiography (ECG) represents a non-intrusive technique to monitor the heart's electrical dynamics, proving indispensable for deciphering emotional states. This method involves the application of electrodes on the skin to capture the heart's electrical signals during its contraction and relaxation phases, thus offering a detailed view of cardiac electrical activity. Such detailed cardiac measurements are crucial for understanding the physiological underpinnings related to various emotional states.  
The utility of ECG in categorizing emotional states is attributed to the integral connection of the heart with the autonomic nervous system, which orchestrates emotional reactions. Emotional stimuli cause specific physiological changes, such as variations in heart rate, heart rate variability (HRV), and other ECG-derived metrics, all of which are instrumental in identifying different emotions.  
Moreover, the analysis extended to the morphological aspects of ECG signals, particularly the QRS complex width. This parameter, representing the time span from the QRS complex's start to its conclusion, reflects the duration of ventricular depolarization and is susceptible to alterations under different emotional conditions. Figure 2 illustrates ECG traces corresponding to various scenarios, with the R peaks formally annotated, providing visual information on the cardiac responses elicited by various emotional stimuli.  
The Teager energy operator was used to detect R peaks [38; 39].  
\[\psi_{c}[x(t)]=\dot{x}(t)\cdot\dot{x}(t)-\frac{1}{2}[\dot{x}(t)\cdot\ddot{x}(t)+x (t)\cdot\dot{x}(t)] \tag{1}\]  
where \(x(t)\) is our ECG signal.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 3 Proposed Architectures
### CCC Architecture  
The CCC architecture is the current architecture used in 4G systems. In this architecture, the service request of the user passes through the local level base station and backhaul network and is then delivered to core functions, such as a serving gateway or a public data network gateway. The request then goes through the internet before finally arriving at the central cloud to complete the service process. In the actual mobile communication network, the physical transmission distance for a related service could be several hundred kilometers, causing high latency. However, the CCC architecture features the benefit of centralized pooled computing resources as shown in Figure 2.
# Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
###### Abstract  
Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine-MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.  
deep learning; MRI; heart; segmentation; annotated data set +
Footnote †: journal: Medical Image Analysis
# Dynamic Network Formation for FSO Satellite Communication
## 1 Introduction
### Related Works
Based on this bi-objective function, they created a new crossover, a local search scheme, a mutation and a new selection operator. Their algorithm converges with probability one to a globally optimal solution. Another method, suggested by [PERSON] and [PERSON] [20], uses variable neighborhood search, where the search was guided to the shaking phase while using skewed functions. A hybrid algorithm based on tabu search and ant-colony optimization was proposed by [PERSON] et al. [21] for the \(k\)-minimum-MST problem, which is a minimum cost subtree of formed with at least \(k\leq n\) vertices. In this paper, we extend the ant-colony method to produce a cost-effective bounded degree spanning tree on a dynamic scenario (dynamic graph).
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 4 Experimental Results
### Determining the Optimal Parameters of the Log-Distance Path Loss Model  
For the proposed method, experiments are conducted to select the optimal parameters \(PL_{0}\) and \(n\) for the LDPLM representing the relation between the RSSI and distance. To determine \(PL_{0}\), the distance between the beacon and the receiver is set to 1 m. We collected 1000 signals. The RSSI measurement unit is dBm and ranges from 0 to -100. The RSSI value is closer to 0 as the signal is stronger. Figure 11 shows the histogram of RSSI collected at a distance of 1 m. RSSI fluctuates irregularly between \(-18\) dB and \(-42\) dB. The \(PL_{0}\) value is selected as \(-23\) dB, which is the mode among the collected RSSI values.  
To obtain the path loss exponent \(n\), RSSI is collected at different distances of 3 m, 5 m, 10 m, and 15 m in LOS environments. Additionally, RSSI is collected at 10 m and 15 m distances in NLOS environments. A total of 1000 signals are collected for each distance. To determine the optimal \(n\), we calculate \(n\) from 1000 data points collected at each distance and then compute the average. \(n\) can be calculated through Equation (1).  
The total average of \(n\) for each distance is calculated. For this experiment, an optimal path loss exponent of 4.68 is chosen for the relationship between RSSI and distance. Table 3 presents the average path loss exponent collected for each distance and the final selected path loss exponent. In this paper, the parameters for calculating the threshold in Equation (3) are set as follows: \(\alpha=-23\), \(n=4.68\), and \(d=15\).  
Figure 10: MAF example: (**a**) Initial state when data is collected below the window size; (**b**) General operating state.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **3 m** & **5 m** & **10 m** & **15 m** & **10 m NLOS** & **15 m NLOS** & **Total** \\ \hline \(n\) Average & 5.52 & 4.35 & 4.35 & 3.83 & 5.05 & 4.96 & 4.68 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distance-based path loss exponent average.
## 4 Discussion
It can be concluded that the rearing temperature did not have a significant effect on the protein quality, and the decrease in protein content did not necessarily mean that the content of every amino acid was decreasing. Similar results were obtained in a previous study by [PERSON] et al. [19]. The calculated EAAI of _T. molitor_ reared at three different temperatures using egg as a reference protein [43] was 37, 45, and 41%, respectively. Statistically significance differences were found between the of temperatures 22 and 25 \({}^{\circ}\)C. The differences in the calculated values were due to the different AAS values of the amino acids at different temperatures. The calculated EAAIs of _T. molitor_ were close to those of lentils (41), beans (47), peas (50), corn (55), wheat (68), rice (74), rye (75), and poultry (78). Beef (82), pork (84), and milk (95) have higher EAAIs [43]. Based on the requirements recommended by the FAO and WHO, edible insects contain almost all essential amino acids at concentrations sufficient to fulfil the dietary requirements of healthy humans [15; 34; 36]. Thus, the obtained results support the claim that _T. molitor_ larvae can be an alternative protein source with a satisfactory amino acid composition, providing almost the daily required amounts of essential amino acids according to the FAO and WHO. At all temperatures, the insects obtained adequate amounts of threonine, valine, histidine, phenylalanine, and tyrosine. Larger amounts of isoleucine were obtained at 25 \({}^{\circ}\)C, and sufficient amounts of cysteine and methionine were obtained at 28 \({}^{\circ}\)C [15]. The essential amino acid profile was comparable in quantity to that of conventional resource-intensive foods of animal origin [15]. There have already been studies dealing with the effects of temperature on fat and protein contents, but our research also provided detailed information on the effect on protein quality in terms of amino acid composition and changes in the AAS and EAAI. Even though our results bring some interesting outputs that might be useful for insect-rearing farmers, future research focused on accounting for larval density, feed effects, improved rearing methodology, and the completion of results with the fatty acid profiles of the tested larvae would be required.
# Measurement of Super-Pressure Balloon Deformation with Simplified Digital Image Correlation
###### Abstract  
A super pressure balloon (SPB) is an aerostatic balloon that can fly at a constant altitude for an extended period. Japan Aerospace Exploration Agency (JAXA) has been developing a light-weight, high strength balloon made of thin polyethylene films and diamond-shaped net with high tensile fibers. Previous investigations proved that strength requirements on SPB members are satisfied even though the net covering the SPB sometimes becomes damaged during the inflation test. This may be due to non-uniform expansion, which causes stress concentration, however, no method exists to confirm this hypothesis. In this study, we tested a new method called Simplified Digital Image Correlation method (SiDIC) to check if it can measure the displacement of the SPB by using a rubber balloon. After measuring the measurement accuracy of the Digital Image Correlation method (DIC) and SiDIC, we applied both DIC and SiDIC to a rubber balloon covered just with the net. Interestingly, SiDIC entailed a smaller amount of data but could measure the deformation more accurately than DIC. In addition, assuming the stress concentration, one part of the net was bonded to the balloon to restrict the deformation. SiDIC properly identified the undeformed region.  
super pressure balloon; stress concentration; strain; non-contact measurement; digital image correlation; large deformation Article
## 2 Materials and Methods
### Feature Engineering
#### 2.4.1 Vegetation Indices  
A total of eleven vegetation indices (VIs) were generated, which served as candidate features in the feature selection method. Table 2 provides a comprehensive description of the derived VIs with the corresponding formulas. The list included normalised difference indices, ratio indices and triangular indices. We derived VIs that combined near-infrared and red wavelengths to strengthen the presence of photosynthetically active vegetation; VIs that leveraged green and red edge spectra, which are more sensitive to higher chlorophyll concentration [72]; and VIs that exploited the shortwave infrared spectrum to highlight vegetation moisture. Moreover, we calculated VIs with less sensitivity to soil background and atmospheric effects.
###### Abstract
The measurement results showed that increasing the temperature by 10\({}^{\circ}\) Fahrenheit could reduce the load borne the bolt by 4.5 KN [16]. [PERSON] et al. reported that with an increase in the excavation depth and the arrangement of the additional struts, the thermal load coefficients of the supports gradually decreased; in addition to soil stiffness, the thermal load coefficient was related to the stiffness of the support-soil system and the excavation stage [17]. [PERSON] et al. proposed an analytical formula for the thermal stress on multi-layer horizontal supports. However, it cannot distinguish the difference in the axial force on each support layer, which makes it unsuitable for deep foundation pit engineering with temperature gradients [18]. [PERSON] et al. analyzed a steel-concrete mixed structure and found that the internal force on and the deformation of the structure were larger under the effect of the annual temperature difference; they also reported that the variation in the temperature had a greater influence on the middle span than on the side span. Therefore, the cross-sectional area and the restraint form of the braces should be adjusted reasonably to reduce this influence [19]. Most of the above studies have taken the influence of thermal stress on concrete supporting structures into account, but few are concerned with steel supporting structures. A steel assembled supporting structure, composed of a steel H-section and a rigid panel, was proposed in this work for the foundation pit. Steel has high temperature sensitivity, and its performance is significantly affected by surrounding temperature. However, there is few studies on the influence of thermal stress on steel support structure. The full-scale test was conducted to study the effects of the temperature on the deformation of and the stress on the retaining pile, the crown beam, and the waist beam. Then, a finite element model was established, and its accuracy was verified by key parameters. Finally, the mechanical properties of the supporting structure were analyzed comprehensively by a numerical simulation. The research on the stability of this kind of foundation pit supporting structure is of great significance.
## 3 Results and Discussions
### Data Transmission Test  
The experimental evaluations of OTs for bidirectional multi-wavelength communication were conducted by two scenarios, in which the OT_Tx was used to perform multi-wavelength transmissions either with or without micro-projection function simultaneously. In order to receive multi-wavelength transmitted from free-space, physical bandpass optical filters were used in the configuration of the receiver part. When multi-wavelengths were transmitted, while micro-projection function was also  
Figure 5: Received lights at OT_Rx without adding physical bandpass optical filters.  
Figure 6: Received lights at OT_Rx with the aid of physical bandpass optical filters.  
performed, the measured results of red, green and blue wavelengths as illustrated in Figures 7-9, demonstrated that the maximum data transmission rate at a BER of \(10^{-3}\) composed by these three wavelengths using the modulation scheme of NRZ_OOK, 8-PSK and 16-QAM are 182 Mb/s, 255 Mb/s and 305 Mb/s respectively. When multi-wavelength were transmitted, while micro-projection function was not performed, the measured results of red, green and blue wavelengths as illustrated in Figures 10-12, shown that the maximum data transmission rate at a BER of \(10^{-3}\), composed by these three wavelengths using the modulation scheme of NRZ_OOK, 8-PSK and 16-QAM are 162 Mb/s, 330 Mb/s and 380 Mb/s respectively.  
From the comparisons of the measurement results, it is clearly that the data transmission performance of all the modulation schemes except the amplitude modulation scheme, i.e., NRZ_OOK, has improved when data were transmitted while micro-projection was not performed. The major problem that the amplitude modulation scheme, i.e., NRZ_OOK was not improved when no image data was presented was resulted from the low contrast ratio (CR) of the LCoS device when using RGB-LEDs. Since NRZ_OOK is a pulse amplitude modulation, a low CR of LCoS device will not be able to perform intensity/amplitude modulation efficiently. The data rate improvement of NRZ_OOK, are therefore not absolutely positive. The low CR of the LCoS device resulted from the fact that the RGB-LEDs light source used is a non-coherent light source and therefore the problem of low CR can be improved while coherent light sources, i.e., laser diodes are used.  
From the comparisons of the measurement results, which all satisfy the Forward Error Correction (FEC) limitations [18], the 16-QAM can achieve the highest data transmission rate either when micro-projection was performed with data transmission simultaneously or not. However, when the micro-projection was performed simultaneously with data transmission, it is obvious that the performance degradation in terms of a lower data transmission rate has resulted. Nevertheless, the proposed OT based on micro-projection architecture for bidirectional multi-wavelength VLC still offers a better data transmission performance than using CMOS optical camera-based VLC.