esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The contributions of ROI\({}_{\text{myoc}}\), ROI\({}_{\text{ref}}\), and ROI\({}_{\text{excl}}\) were 1.5 \(\pm\) 0.2%, 2.7 \(\pm\) 0.4%, and 2 \(\pm\) 0.3%, respectively, for 3\(\sigma\), and 1.1 \(\pm\) 0.1%, 1.6 \(\pm\) 0.4%, and 1.3 \(\pm\) 0.3%, respectively, for FWHM (Figure 4). We found that the selection of ROI\({}_{\text{ref}}\) was the largest source of variability in both quantification methods because it presented the highest average LGE extent variability, as shown in Figure 4. However, only the difference between ROI\({}_{\text{freq3}\sigma}\) and ROI\({}_{\text{myoc3}\sigma}\) was statistically significant. Furthermore, the average LGE extent variability was smaller when using the FWHM method compared to the 3\(\sigma\) method; however, statistical significance was found only in ROI\({}_{\text{myoc}}\) and ROI\({}_{\text{excl}}\) but not in ROI\({}_{\text{ref}}\) (Figure 4).  
The summation of the variabilities of the three ROIs studied resulted in a total variability equal to 6.2 \(\pm\) 0.6% in the case of the 3\(\sigma\) quantification method and 4.0 \(\pm\) 0.6% in the case of the FWHM method. We obtained similar values (6 \(\pm\) 2% and 3.6 \(\pm\) 0.8% for the 3\(\sigma\) and FWHM methods, respectively) when variability was evaluated directly from the measurements. The FWHM quantification method generally yielded lower LGE extent values. In three out of five slices, we found that the LGE extent quantified with FWHM was significantly lower than when quantified with the 3\(\sigma\) method (Figure 5).  
The interobserver variability was higher in the 3\(\sigma\) method, 0.56 (0.15-0.86), compared to the FWHM method, 0.43 (0.13-0.75). The intraobserver variability was higher in the 3\(\sigma\) method for all four observers (Observer 1: 0.60 (0.41-1) for 3\(\sigma\) and 0.22 (0.13-0.70) for FWHM; Observer 2: 0.22 (0.13-0.70) for 3\(\sigma\) and 0.20 (0.12-0.66) for FWHM; Observer 3: 0.34 (0.21-0.89) for 3\(\sigma\) and 0.20 (0.12-0.65) for FWHM; Observer 4: 0.49 (0.32-1) for 3\(\sigma\) and 0.07 (0.04-0.28) for FWHM, see Figure 6). However, the 95% CIs for the 3\(\sigma\) and FWHM methods overlapped for all but the fourth observer, and so the statistical significance of the difference was not confirmed.  
Figure 4: LGE extent variability of each ROI with corresponding standard errors for the 3\(\sigma\) (blue) and FWHM (red) quantification methods. We evaluated the statistical significance of the differences between the 3\(\sigma\) and FWHM methods (marked with an asterisk), and the statistical significance of the differences within the same method (marked with a cross). Abbreviations as in Figures 1 and 2.  
Figure 5: LGE extents with corresponding standard errors as obtained with the 3\(\sigma\) (blue) and FWHM (red) quantification methods. Statistically significant differences are marked with an asterisk. Abbreviations as in Figures 1 and 2.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 4 Results and Discussions
### Case 5: AC/DC Power Flow Considering Changes of Droop Parameters and Converter Outage  
In the fifth case, to demonstrate the impact of changes in the droop parameters of each converter station, the same test as Case 4 is evaluated by changing the droop parameters as shown in Table 17. Tables 18-21 depict the results of PF calculations both in all AC and DC buses and in branches in case of the outage of converter station #1 and considering the changes in droop parameters according to Table 17. It is worth mentioning that the algorithm is converged in 0.43 s and in three iterations.  
Changing the droop parameters is led to an increase in the total active power generated by the generator connected to bus 2. As a consequence, there are some slight changes of the PF on the AC lines. However, compared to the previous case, those changes are negligible. In addition, changing the droop parameters is causes an increase in the DC PF on the DC lines (compared to Case 4), but as the converter station #1 is not connected to the grid, the summation of the PF between the DC lines is equal to zero.
## 3 AMB Rigid Rotor System Based on Fuzzy Control Strategy
### Fuzzy Inference and Defuzzification of Fuzzy Controllers  
The Mamdani inference type is used, and the value obtained by this method cannot be calculated directly as a specific value; thus, it is necessary to defuzzify the obtained value and process it using the common method of clearing the center of gravity so that the obtained value is the specific value of the output coefficients to be rectified.  
The fuzzy controller generates fuzzy outputs based on the input variables (error \(E\) and rate of change of error \(EC\)). For instance, for the proportional coefficient correction \(\Delta K_{p}\), a specific rule in the fuzzy rule base may produce the following fuzzy output:  
\[\Delta K_{p}=\{(u_{1},\mu_{1}),(u_{2},\mu_{2}),\ldots,(u_{n},\mu_{n})\}, \tag{4}\]  
where \(u_{i}\) is the possible value of \(\Delta K_{p}\), and \(\mu_{i}\) is the membership degree corresponding to \(u_{i}\).  
The centroid method determines the specific output value by calculating the weighted average of the fuzzy set. Its mathematical expression is as follows:  
\[u^{*}=\frac{\sum_{i=1}^{n}\mu_{i}\cdot u_{i}}{\sum_{i=1}^{n}\mu_{i}}, \tag{5}\]  
where \(u^{*}\) is the specific output value after deblurring (such as the specific value of \(\Delta K_{p}\)), \(\mu_{i}\) is the membership degree of the \(i\)-th output value, and \(u_{i}\) is the \(i\)-th output value. The calculated \(u^{*}\) is used as the correction value to adjust the parameters of the PID controller, as shown in Equation (2), \(K_{p}=K_{p0}+\Delta K_{p}\).  
Figure 4: The triangular membership functions of the input and output variable domain and the characteristic surface plot. (**a**) Membership function of \(E\). (**b**) Membership function of \(EC\). (**c**) Membership function of \(\Delta K_{p}\). (**d**) Characteristic surface plot of \(\Delta K_{p}\). (**e**) Membership function of \(\Delta K_{i}\). (**f**) Characteristic surface plot of \(\Delta K_{i}\). (**g**) Membership function of \(\Delta K_{d}\). (**h**) Characteristic surface plot of \(\Delta K_{d}\).  
By reasonably designing the membership functions and optimizing the calculation methods, the centroid method can effectively support the high-performance operation of the control system. In the AMB rigid rotor system, the centroid method ensures the dynamic adjustment of PID parameters, thereby improving the control accuracy and stability of the system.
# Fatigue Reliability Analysis of Rib-To-Deck Joints Using Test Data and In-Situ Measurements
###### Abstract  
In this paper, the fatigue performance of rib-to-deck joints in orthotropic steel decks (OSDs) using thickened edge U-ribs (TEUs) and the OSD using conventional U-ribs (CUs) are investigated based on the fatigue test result and in-situ monitoring data. Firstly, comparative fatigue tests were carried out with full-scale rib-to-deck specimens. Probability-stress-life (P-S-N) curves were derived through the test data measured from a total of 18 specimens, including 7 CU specimens and 11 TEU specimens. According to the results, the TEU can lead to a notable enhancement in the fatigue strength of rib-to-deck joints, i.e., 21.4% in terms of nominal stress and 21.1% in terms of hot spot stress. After that, a typical OSD steel bridge was selected as the prototype to investigate the fatigue performance of rib-to-decks in the OSD using TEUs under actual applications. In the analysis, the uncertainty in both fatigue strength, and vehicle loads were considered. A multi-scale finite element model of the prototype bridge was established, and numerical analysis conducted to derive the vehicle-induced stress spectra of the rib-to-deck joints in critical positions. In the derivation, a stochastic traffic model was employed, through which the in-situ measurement was incorporated. Finally, fatigue reliability analysis was carried out for the prototype bridge based on the above works. The result showed that a notably higher fatigue reliability can be expected in the rib-to-deck joint in OSDs using TEUs when compared with the joints in OSDs using CUs, which in turn can lead to notable improvement in fatigue life. For instance, under the target reliability of 2.3, the fatigue life of the two critical rib-to-deck joints were, respectively, increased by 153% and 155% when using TEUs.  
orthotropic steel deck; thickened edge U-ribs; flux cored arc welding; fatigue test; in-situ measurement; fatigue reliability +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Infrared and Visible Image Fusion Based on SWGF
#### 3.2.2 Weight Mapping-Based Detail Layer Fusion  
Infrared images capture critical yet invisible details, while visible images provide rich background information. To leverage both, we directly integrate bright, prominent targets from the infrared image into the visible image. A simple saliency detection method is used to extract a saliency map from the infrared detail layer, as follows:  
\[P_{IR}=\left\{\begin{matrix}|D_{IR}|-|D_{VIS}|,&if\ |D_{IR}|-|D_{VIS}|>0\\ 0,&\text{otherwise}\end{matrix}\right. \tag{11}\]  
where \(P_{IR}\) represents the saliency map of the detail layer within the infrared image.  
Saliency maps often contain artifacts and noise. To mitigate this, we propose a weight map optimization method based on a SWGF, which reduces noise while preserving detail transmission. The SWGF is applied to the saliency map of the detail layer, using the corresponding infrared and visible source images as guides. The optimization is defined as follows:  
\[w_{d1}=SWGF(I_{IR},P_{IR},r,eps) \tag{12}\]  
\[w_{d2}=SWGF(I_{VIS},P_{VIS},r,eps) \tag{13}\]  
where _SWGF_ (0) represents the side window guided filter function, \(w_{d1}\) and \(w_{d2}\) are the weight maps of the detail layer, respectively. Then, perform linear weighted summation to obtain the following detail layer fusion results:  
\[D_{F}=w_{d1}D_{IR}+w_{d2}D_{VIS} \tag{14}\]
# Towards a Heat-Resistant Tethered Micro-Aerial Vehicle for Structure Fire Sensing
## 3 UAV Design
### Material Selection  
Material selection is fundamental. The tensile strength must be sufficient to withstand small collisions and impacts that may occur inside a structure that has caught fire.  
The operating temperature target is relatively low compared to the temperatures present in modern aeronautical propulsion systems, which can easily reach more than 1000 degC. Therefore, many common materials are suitable for this application. However, if we restrict the material selection to those with low density and low thermal conductivity, most metals and alloys are excluded. Carbon-fiber-reinforced polymer (CFRP) offers a high specific strength (tensile strength over density), only surpassed by some much-higher-cost ceramics. The higher the specific strength, the less the amount of material necessary to achieve the same properties and, therefore, the lower the weight. This, combined with high availability compared to materials such as metallic matrix composites (MMCs) and ceramic matrix composites (CMCs) [34], makes it desirable for this application.  
The main disadvantage of CFRP is its high thermal conductivity. The CFRP must be coupled with thermal insulation material to avoid excessive thermal conduction and component overheating on the inside. Some alternatives are explored in Table 2, which compares their thermal conductivity values.  
Figure 2: Diagram of the selected components, sensors, and their connections.
## 2 Experimental Setup and Raw Observations Analysis
(a) Continuity and the C/N0 of Xiaomi 8 A and Xiaomi 8 B; (b) Continuity and the C/N0 of OPPO and HUAWEI; (c) Continuity and the C/N0 of iRTK2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**OPPO**} & \multicolumn{2}{c}{**HUAWEI**} & \multicolumn{2}{c}{**Xiaomi 8 A**} & \multicolumn{2}{c}{**Xiaomi 8 B**} \\ \hline  & **Pseudorange** & **Carrier** & **Pseudorange** & **Carrier** & **Pseudorange** & **Carrier** & **Pseudorange** & **Carrier** \\ \hline G10 & 10.042 & 4.562 & 6.712 & 2.582 & 4.601 & 1.151 & 3.915 & 1.108 \\ G12 & 22.681 & 12.047 & 17.241 & 9.184 & 12.021 & 6.848 & 5.064 & 3.395 \\ G21 & 15.746 & 8.174 & 11.203 & 5.723 & 7.497 & 3.486 & 4.138 & 3.069 \\ G29 & 9.845 & 5.741 & 8.607 & 3.363 & 5.452 & 2.105 & 4.913 & 1.947 \\ R03 & 19.835 & 12.765 & 17.754 & 8.306 & 13.333 & 3.646 & 12.984 & 3.620 \\ R04 & 24.629 & 13.451 & 19.328 & 11.136 & 16.763 & 8.667 & 14.029 & 8.019 \\ R05 & 11.284 & 9.563 & 10.416 & 8.184 & 9.796 & 7.756 & 8.910 & 6.924 \\ R15 & 15.206 & 10.843 & 14.094 & 9.356 & 12.166 & 8.661 & 10.549 & 7.940 \\ C16 & 13.219 & 10.712 & 7.652 & 4.318 & 6.111 & 2.887 & 5.092 & 2.193 \\ C25 & 11.271 & 9.007 & 8.492 & 6.477 & 5.317 & 3.371 & 3.749 & 3.065 \\ C32 & 16.816 & 11.054 & 12.592 & 9.710 & 8.476 & 6.455 & 6.013 & 5.201 \\ C33 & 10.480 & 8.183 & 9.705 & 7.659 & 6.429 & 3.622 & 5.481 & 2.872 \\ \hline \end{tabular}
\end{table} 
Table 2: The RMSE of combinative observation of each smartphone (Based on the results of iRTK2). Figure 4: The rate of change of pseudorange and carrier obtained by smartphones and iRTK2 after processing. (**a**) The rate of change of pseudorange and carrier of G10. (**b**) The rate of change of pseudorange and carrier of R03. (**c**) The rate of change of pseudorange and carrier of C16.
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 7 Conclusions and Future Works  
In this paper, a novel traffic sign detection and recognition method was presented, which aimed to address the problem of real time traffic sign detection and classification. To achieve this goal, we proposed a faster method for traffic detection and recognition. The first stage was to segment the image into ROIs by MSERs, on a simplified Gabor filter feature map. Then, we filtered out several negative samples based on defined filter rules, such as size and aspect ratio, etc. In the second stage, circular and triangular shapes were classified by the SVM on the HOG feature, and we obtained the super class to which the traffic signs belonged. In the recognition stage, the traffic signs were classified by the CNN  
Figure 19: Examples of the detection and recognition results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Steps**} & **GTSDB Average Processing** & **CSTD Average Processing Time** \\  & **Time (ms/Frame)** & **(ms/Frame)** \\ \hline Simplified Gabor Filter & 17 & 15 \\ MSERs & 29 & 26 \\ HOG & 93 & 79 \\ SVM & 15 & 13 \\ Classification & 5 & – \\ Total processing time & 159 & – \\ \hline \hline \end{tabular}
\end{table}
Table 8: Process time of every stage of our method.  
with eight channel inputs of simplified Gabor filter maps with high accuracy. Our method completed the whole process of detection and classification at a speed of close to 6.3 frames per second, with an image size of 1360 \(\times\) 800, on a regular notebook computer, with an accuracy of 99.43%. Therefore, our method is feasible for use in real time traffic sign detection and classification.  
In the future, the recognition phase can be sped up using dimension reduction of feature vectors. The number and parameters of Gabor filters will greatly influence the results of the classification results. It is necessary to research the optimization of Gabor parameters using optimization algorithms, to improve the efficiency and accuracy of traffic sign detection and classification. Finally, we accelerated our method by a GPU and further improved the efficiency.  
The first author, [PERSON], and the corresponding author, [PERSON], were responsible for the overall work, proposed the idea and experiments of the method in the paper, and the paper was written mainly by the two authors. The third author, [PERSON], performed part of the experiments and contributed to many effective discussions in both the ideas and paper writing. The fourth author, [PERSON], provided many positive suggestions and comments for the paper. The fifth author, [PERSON], performed part of the experiments and gave many good suggestions. The sixth author, [PERSON], performed part of the experiments  
This research was funded by the National Natural Science Foundation of China (grant number: 61671470), the Key Research and Development Program of China (grant number: 2016 YFC0802900), and the National Natural Science Foundation of China (grant number: 51705531).  
The authors declare no conflict of interest.
## 1 Introduction  
The mammalian retina continuously grows after birth, which is the so-called postnatal retinal development. Most of the cell differentiation, structure building, and synaptogenesis is complete at the time point of eye opening [1], while the retinal neurite circuit is still under development after eye opening [2; 3; 4]. For mice, the dendrites of retinal ganglion cells (RGCs) ramify across the whole inner plexiform layer (IPL) at the time of birth and refine into different stratifications during postnatal retinal development [5]. After eye opening, a light-stimulation-regulated, RGC dendritic lamination was observed. Around 40% of bistratified RGCs turned into monostratified [3; 6]. With stratification refinement, the density of both ribbons and conventional synapses in the IPL continuously increases after eye opening and reaches the peak level by around postnatal day (P) 21 [7]. However, the receptive field center size of RGCs of the mouse at P28 reduced from the mouse at P17 (3 days after eye opening) [8]. All these studies indicated that the postnatal retinal development after eye opening is important for visual function. However, the study of developing mouse retinas is primarily limited to endpoint histological examination, which limits dynamic observation of developing retinal architecture. Thus, an in vivo imaging method for longitudinal monitoring of postnatal retinal development is desirable.  
This study is to validate optical coherence tomography (OCT) for longitudinal monitoring of postnatal retinal development in developing mouse eyes. OCT has been widely used in ophthalmology clinics and research for its excellent depth-resolved capability [9]. For instance, the retinal thickness, as a reflection of total cell number and retinal structure complexity, has been used as a biomarker for retinal degeneration [10; 11; 12] and damage recovery [13; 14] studies. Retinal layer thickness abnormity has also been associated with retinopathy of prematurity (ROP). Compared with the normally developed retina, for ROP, the photoreceptor layer was thinner, and the inner nuclear layer (INL) was thicker [15]. As a new OCT modality, OCT angiography has been used to monitor vascular changes in normal-developing and diseased animal models [16; 17; 18].  
In this study, longitudinal OCT of developing mouse eyes was conducted from eye opening, i.e., P14 up to P56. Quantitative analysis of individual retinal layers was analyzed and discussed. Three-dimensional (3D) frame registration and super averaging [19; 20; 21] were adopted to investigate the fine structure of the retina. Interestingly, a hyporeflective layer (HRL) between the nerve fiber layer (NFL) and inner plexiform layer (IPL) was observed in developing eyes and gradually disappeared with aging. On the contrary, the IPL stratification structure was not clear at an early age but gradually developed with aging. To interpret the observed retinal layer development, a model based on eyeball expansion, cell apoptosis, and retinal structural modification is discussed.
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 5 Experiments and Results
### Experimental Setup
#### 5.2.5 Evaluation Metrics  
In order to evaluate the performance of the proposed AB-LSTM, contrasting experiments were conducted on AB-LSTM and traditional LSTM, at various SNR levels and for utterances with various lengths. As a classic evaluation metric, EER was used to compare different algorithms. EER is the value where the false alarm rate (FAR) is equal to the false reject rate (FRR). False alarm is defined as the situation where the system incorrectly verifies or identifies a non-target speaker. FAR is the number of false alarms divided by the number of total target trials. False reject is defined as the false rejection of a true target speaker. The FRR is the number of false rejections divided by the total of non-target trials [21]. The CQCC-GMM is adopted from the official MATLAB code in [22]. EER was calculated using the MSR Identity Toolkit Version 1.0 [23].  
Figure 6: An illustration of the bagging approach used. \(\overline{F}_{a1}\) denotes the bagging result of the three segments from the same utterance. \(\overline{F}_{a1}\) to \(\overline{F}_{a4}\) denote bagging results after adding four different kinds of noises to the original utterance.
## 5 Implementation and Simulation Setup
### Modifying Cooja to Support Multi-Interfaced Simulations
\begin{tabular}{l|l l} \(\overline{PL(d_{max})}\) & the average path loss at distance \(d_{max}\) from the transmitter & \([\text{dBm}]\) \\ \(d_{max}\) & the maximum transmitting range & \([\text{m}]\) \\ \(P_{t}\) & signal transmit power & \([\text{dBm}]\) \\ \(S_{r}\) & sensitivity of the receiver & \([\text{dBm}]\) \\ \end{tabular}   
Finally, by putting (6) into (7) and isolating for the transmission range \(d_{max}\), we obtain (8). Since we are trying to model an industrial environment, which is typically indoors, we went with a reference distance \(d_{ref}=1\) m, a common practice when modeling indoor environments [34] (p. 109). \[d_{max}=d_{ref}\times 10^{\Big{(}P_{t}-S+AG_{r}+AG_{r}-20\times log_{10}\Big{(}4 \pi\frac{f}{c}d_{ref}\Big{)}\Big{)}/(\alpha\times 10)}\, \tag{8}\]  
Table 2 lists all relevant constants used to configure the LogisticLoss radio medium during our simulations (see Section 5.3). Note that we loosely based the values for \(\alpha\) and \(\sigma\) on values found in literature [34-38]. However, since \"it is better to utilize parameter values accurately characterizing the path loss for the specific scenario\" [38] (p. 23), these values are likely not a good match for your specific use-case, and you should empirically determine more appropriate values [34] (Chapter 4.9.2). Nonetheless, to assess the effectiveness of DRiPLOF, it suffices that \(\alpha\) and \(\sigma\) create a scenario wherein nodes have one interface with a relatively bad transmission success rate towards their closest neighbors and another interface for which the opposite is true.