esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridge Crack Detection Based on SSENets
## 1 Introduction
The conventional convolution used in the above studies is aggregated simultaneously in the spatial dimension and the channel dimension of the feature map, which ignores the relationship between channels and fails to establish the connections among the channels. Therefore, it would block the network from studying the features of images. In order to deal with the problem of high complexity and low detection accuracy in the conventional convolution, Inception [26; 27; 28; 29], Xception [30], MobileNet [31], Squeeze-and-Excitation Networks (SENets) [32] used depthwise separable convolution, separating the aggregation in spatial dimension and channel dimension. These methods verified that the depthwise separable convolution can improve the performance of the network while reducing the parameters of the model. Compared with other networks, SENets used global information to establish the relationship among channels, and recalibrated the value of the feature map. Though it could improve the network performance, if the input of the SE module is a single feature map, the network performance will be greatly reduced when the vanishing gradient appears with the increase of network depth. In order to solve above problems, we proposed a convolutional neural network named Skip-Squeeze-and-Excitation Networks (SSENets), which based on the embedded SSE module. Our main contributions are as follows:  
* We designed an embedded module with skip-connection strategy, which was called Skip-Squeeze-and-Excitation (SSE) module. By inserting the SSE module into the existing network, the detection accuracy can be improved without increasing the computational complexity. * Considering the large span of crack size in the crack detection task, we introduced the Atrous Spatial Pyramid Pooling (ASPP) module into our model. It can effectively improve the detection accuracy by capturing the context of images in multiple scales. * Based on the above-mentioned modules, we proposed SSENets, which was applied to the bridge crack detection task. The detection accuracy of SSENets can reach 97.77%, which is higher than the traditional classification models and the model proposed by [PERSON] et al. [25] under the same model complexity.
# Seismic AVOA Inversion for Weak Anisotropy Parameters and Fracture Density in a Monoclinic Medium
## 1 Introduction
The prestack AVAZ inversion method is used to extract the anisotropy parameters or shear-wave splitting factor, \(\gamma\), which is related to fracture density [25]. The inversion algorithm has also been deeply studied by many scholars [26; 27; 28]. However, with indirect substitution there exists intrinsic errors, and the inversion methods based on simplified models such as the HTI medium can be misleading when applied to shale fractured reservoirs with a complex medium. These limit the improvement to the prestack inversion accuracy. Therefore, it cannot satisfy the accuracy requirements for obtaining fracture density. In this paper, based upon previous research, and combined with rock physics theories, assuming that the fracture reservoir has monoclinic symmetry, and under the condition that the fracture radius, width, and orientation are known, we derived a new form of P-wave reflection coefficient, consisting of the WA parameters, and the fracture density. This was achieved by substituting the stiffness coefficients of the VTI background, and normal and tangential fracture compliances. The new form of P-wave reflection coefficient was then used to build a theoretical framework of linear AVOA inversion, based on Bayesian theory, that can avoid the assumption that the WA parameters of VTI background media are known, and avoid the intrinsic errors introduced from the indirect substitution. Finally, synthetic data, which contain Gaussian random noise with different signal-to-noise ratios (SNRs), were utilized to verify the stability of the proposed approach.
## 2 Materials and Methods
### Estimation of Phenolic Content and Antioxidant Potentials
#### 2.4.4 2,2-Diphenyl-1-picrylhydrazyl (DPPH) Assay  
A modified DPPH assay generated by [PERSON] et al. [14] was applied to evaluate the free radical scavenging activity in the fermented and unfermented coffee beans. In total, 40 \(\mu\)L of coffee extract was mixed with 260 \(\mu\)L of diluted 0.1 mM DPPH in a methanol solution in the plate and incubated in the dark for 30 min at room temperature. Trolox in an ethanol solution, with concentrations ranging from 0 to 200 \(\mu\)g/mL, was used as the standard to produce the calibration curve. Additionally, 40 \(\mu\)L of the sample in the same reagent, along with an equal amount of water, was used as the blank. The reduction in absorbance was measured at 517 nm at the end of incubation, and the results of this assay were expressed as milligrams of Trolox equivalents (TE) per gram of coffee bean powder on a dry weight basis (mg TE/g) with the standard deviation (SD) reported.  
4.5 2,2\({}^{\prime}\)-Azino-bis(3-ethylbenzothazoline-6-sulfonic acid) (ABTS\({}^{\bullet+}\)) Radical Cation-Based Assay  
A modified ABTS\({}^{\bullet+}\) decolorization assay, as described by [PERSON] et al. [15], was conducted with some modifications to assess the antioxidant capacity of the fermented and unfermented coffee bean extracts. In this assay, ABTS\({}^{\bullet+}\) solution was prepared by mixing 12.5 mL of 7 mM ABTS in water solution with 220 \(\mu\)L of 140 mM potassium persulfate, and it was incubated in the dark for 16 h at room temperature before use. The prepared ABTS\({}^{\bullet+}\) solution was then diluted with analytical-grade ethanol to achieve a final absorbance of 0.7 at 734 nm, indicating that it was ready to be used. Briefly, 10 \(\mu\)L of sample or standard was added and mixed with 290 \(\mu\)L of final prepared diluted ABTS\({}^{\bullet+}\) dye inside 96-wellplate. This mixture was then incubated at room temperature in the dark for 6 min. After incubation, the absorbance of sample was recorded. Trolox in ethanol solution, with concentrations ranging from 0 to 500 \(\upmu\)g/mL, was used as the standard for the calibration curve, and sample in same reagent amount of water was used as the blank. Similarly, the results were expressed as mg TE/g \(\pm\) SD.
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 4 Application to Industrial Data  
Using data from vehicles operating in industrial conditions, it was decided to conduct a simple simulation to verify the conclusions obtained during laboratory tests. The machines have a built-in on-board monitoring system that measures different types of operational parameters, such as vehicle speed. Additionally, the machine was equipped with an IMU, which allows the obtainment of a signal from an accelerometer. The speed and \(Z\)-axis accelerometer (direction: up-down) signals made it possible to create a three-state classification of road quality [39, 41]. The results of this classification will be used as a signal to test the possibility of recognizing road patterns. Sample results for selected cycles of haulage process from one shift are presented in Figure 11. Subsequent cycles are performed on the same routes. Additionally, in each cycle, the machine traveled to a set point and returned along the same route. However, separate patterns were created for both of these drives. Similarities between the signals can be seen; however, there are different lengths of the same patterns, and time shifts between different sections are also visible.  
When comparing two of the presented paths by using DTW implementation in the DTW library in R language, a cumulative cost density with an over-imposed warping path can be plotted using the dtwPlotDensity function. This, applied to cycles 13 and 14 from Figure 12, is presented in Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pattern Length** & **5 Panels (5 \(\times\) 18 cm = 90 cm)** & **3 Panels (3 \(\times\) 18 cm = 54 cm)** & **1 Panel (18 cm)** \\ \hline \% of correctly recognized patterns & 100\% (3 out of 3 drives) & 100\% (3 out of 3 drives) & 0\% (0 out of 3 drives) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the laboratory stand results.  
Figure 11: DTW distance for selected training and test drives for pattern (c)—isolated panel 3.  
The blue line represents an optimal alignment on a field, where the high cost of an assignment has been presented with a red-orange color and low cost with a green color.
## 4 Land-Streamer vs. Conventional Data
### Trace Comparison in Time-Offset Domain  
One conventional and one land-streamer shot gathers are shown in Figure 3a,b, respectively. Traces of both shot gathers show a very good agreement with each other. Both first arrivals and later arrivals are very similar. The land-streamer traces (Figure 3b) show a slightly lower signal-to-noise ratio (SNR), especially before the first break and at large source-receiver offsets (traces 1 to 6 at offsets 52.3 to 60 m from the first trace); however, the first break and later arrivals are easily recognized in both shot gathers.  
Figure 3: Shot gather no. 74 for both (**a**) conventional and (**b**) land-streamer data set.
## 5 Conclusions  
In conclusion, we have successfully demonstrated an off-axis digital holographic interferometry technique integrated with a Mach-Zehnder interferometer based setup for precise temperature measurement and profiling of transparent media. This technique offers numerous advantages, including its simplicity, cost-effectiveness, and independence from precise optomechanical adjustments and accurate frequency carrier mask definition. Furthermore, it does not require high-quality optics or specific two-dimensional gratings.  
The core principle of the technique lies in measuring the phase difference between two digitally reconstructed wave fields and leveraging the temperature coefficient of the refractive index. Through this approach, we derived Equation (12) that relates temperature to phase changes and the temperature coefficient of the refractive index. Equation (12) simplifies the calculation process and eliminates the need for complex mathematical inversions, such as the inverse Abel transformation. It also removes the requirement for additional computations involving the Lorentz-Lorentz equation and Gladstone-Dale relation. Moreover, the technique has the potential to be extended for 3D measurements. In order to use Equation (12), which relates the temperature to the phase changes, it is essential to have the thermal coefficient of the refractive index. This coefficient is provided by Equation (16), which incorporates the necessary information regarding the corresponding temperature, pressure, and wavelength. Thus, Equation (16) plays a crucial role in obtaining the thermal coefficient required for accurate temperature calculations using Equation (12).  
By estimating the uncertainty of the measurement to be less than 1 \({}^{\circ}\)C, we have demonstrated the high precision achievable with this technique. The measurement speed depends primarily on the camera's frame rate and the laser's power, enabling the capture of fast-changing phenomena.  
Overall, this off-axis digital holographic interferometry technique integrated with a Mach-Zehnder interferometer based setup offers a reliable and efficient solution for temperature measurement in transparent media, opening up possibilities for various applications in research and industry, such as combustion studies, emissions control, fire safety, aerospace and propulsion systems, and environmental research.  
Conceptualization, D.K.; Methodology, D.K.; Formal analysis, D.K.; Data curation, D.K.; Writing--original draft, D.K.; Writing--review & editing, D.K., B.T., A.S. and S.P.; Visualization, D.K., B.T., A.S. and S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 4 Hybrid Trust-Based Congestion-Aware Cluster Routing (HTCCR)  
In this section, the proposed HTCCR protocol using hybrid KHM and the EGSA is detailed below. The complete flow of the proposed work is shown in Figure 2.  
The proposed HTCCR protocol considers various QoS parameters like energy, trust, distance, and traffic load for fitness selection as a multiple-objective system. Once the fitness parameters are computed, it discovers k-probable paths between the source and destination. The primary objective of the propounded algorithm is to choose the best route depending on several objectives that are considered to support priority-based data delivery along with secure routing. Priority-based data delivery is explained in detail in the ensuing sections. Further, to ensure security, data are encoded using XOR encoding before they are transmitted to the destination, and XOR decoding is performed at the destination to obtain data.
# In-Vitro Simulation of the Blood Flow in an Axisymmetric Abdominal Aortic Aneurysm
## 3 Results and Discussion
WSS in a vessel is the tangential force per unit area acted by the blood flow and is, by definition, calculated as the product of the spatial gradient of the velocity component normal to the wall and the fluid viscosity (see the Appendix A for further details). Normalized WSSs \(\tau_{wu}\) for varying \(Re\) are shown in Figure 6; the normalization is obtained considering the WSS of the healthy aorta \(\tau_{h}\), i.e., \(\tau_{wn}=\tau_{w}/\tau_{h}\), where \(\tau_{w}\) is the WSS. For all the examined \(Re\), the recovered trend shows that the WSS values rapidly approach zero in correspondence of the proximal neck, i.e., around the detachment point, and maintain a near zero value in the aneurysal cavity; a sharp peak occurs in correspondence of the reattachment point. A decrease of the WSS is expected after the peak values, but we were not able to recover it since the framed area in the performed measurements did not extend to a long enough portion of the aorta downstream of the bulge. In Figure 6, the dashed lines have then to be considered as representative of the WSS recovered just in correspondence of the distal neck. The peak value is found to decrease with \(Re\) and to be about  
Figure 6: Normalized wall shear stress (WSS) \(\tau_{wn}\) for varying \(Re\). The red line represents the left aorta boundary. Data are from the second set of experiments (ID 1\({}^{\prime}\) to 6\({}^{\prime}\), Table 1). twice the upstream value; this ratio is in agreement with [20; 32], while several authors found a greater value [19]. As highlighted by several authors, the peak strongly depends on the bulge shape and on the geometry in correspondence of the distal neck [20], as well as on the discretization considered in the numerical scheme [33]. In particular, [PERSON] et al. [20] proved that in the presence of the downstream bifurcation with a stenosed common iliac artery, the time-averaged WSS is lowered with respect to the values observed in a healthy aorta. Moreover in [11; 18; 19], the authors found very low values of WSS in correspondence of the recirculation zones, a peak in correspondence of the distal neck, as well as a shift towards the exit section of the aneurysm and a reduction of the recirculation zone as the Reynolds number increases. We remark that the rupture risk, as well as its location, are still debated issues. On the one hand, [PERSON] et al. [10] have shown that in most cases the rupture does not occur where pressure and WSS are maximal but in recirculation zones where low WSS and ILT deposition predominate and in areas of reduced flow velocity; on the other hand, elevated wall stress seems to be associated with rupture events [34]. Additionally, in [9] the authors emphasized how the spatial distribution of the WSSG may be considered as a hemodynamic force causing damage to the arterial wall at an intermediate stage of growth of the aneurysm.
# Hemodynamic Interference of Serial Stenoses and Its Impact on FFR and iFR Measurements
## 1 Introduction  
When serial stenoses are present within one coronary artery, the hemodynamics of each stenosis is influenced by the existence of the other. This, in turn, affects the pressure measurement used for identifying the hemodynamic significant stenosis. Fractional flow reserve (FFR), a common clinical tool, measures the distal-to-proximal pressure ratio of a single stenosis. The FFR is performed under induced hyperemia by an infusion of adenosine (140 \(\upmu\)g/kg/min), which generally enlarges the lumen by mitigating the flow resistance [1, 2]. An FFR value less than 0.8 usually suggests an revascularization [34]. However, it has been shown that the FFR was not valid for serial stenoses, since each stenosis was affected by the presence of the other [5, 6]. [PERSON] et al. investigated FFR measurements to evaluate the hemodynamic interference between serial stenoses, and showed significant errors between the measured FFR and the true FFR in 74% of cases [7]. The instantaneous wave-free ratio (iFR) has been proposed recently as an adenosine-free alternative to FFR [8, 9]. The iFR outputs the distal-to-proximal pressure ratio of an single stenosis at 75% of diastolic phase, which iscalled the wave-free period [10; 11]. The vessel resistance and the interference between serial stenoses at the diastole are expected to be significantly smaller than those under the hyperemic condition [12; 13].  
Numerical studies have also been used to investigate hemodynamics and interference in serial stenoses. The extent of the flow recirculation and its influence on the flow field downstream were strongly associated with the spacing distance between two serial stenoses [14]. A large spacing distance mitigated the impact of the interference on the hemodynamic parameters, i.e., pressure gradient and wall shear stress (WSS) [15; 16]. The existing approach uses velocity streamlines for estimating the hemodynamic interference qualitatively. However, the hemodynamic interference has never been defined quantitatively. The role of the hemodynamic interference on FFR and iFR measurements in serial stenoses has never been examined.  
To address this knowledge gap, the goal of the present study is to define the hemodynamic interference in serial stenoses with various spacing ratios (spacing distance divided by the reference diameter of the artery) and to quantify the role of hemodynamic interference on the pressure measurements. We will utilize computational fluid dynamics (CFD) to evaluate the pressure distribution, velocity streamlines and profiles, and Q-criterion, which identifies vortex dominant regions [17]. Moreover, we will quantify the hemodynamic interplay at rest and its influence on the iFR measurement. The obtained quantitative results are expected to provide an enhanced understanding of the hemodynamic interference and its role on pressure measurements techniques, and to illuminate its potential for optimal surgical planning and treatment of serial stenoses.
## 3 Experiment and Results  
In this paper we present the EEG signal feature analysis with four entropy values. SVM is used for feature classification, and the steps are shown in Figure 1. At first, the subject goes through driving training in a virtual environment under the instruction of the operator while the EEG signal is collected. This original signal then goes though the pre-processing step, which includes filtering, signal baseline correction, segmentation and manual check. The original EEG records of the two states (normal state and fatigue state) are converted into 1-s segmented data sets. The next step is computation of entropy value for the segmented EEG signals, in which spectrum entropy, approximate entropy, sample entropy and fuzzy entropy are used. Once the sets of entropy value are obtained, the analysis on electrode selection and combination of entropy features can be performed. Feature combination is carried out to differentiate the dataset of the two states.
###### Abstract  
The long continuity of Interferometric Synthetic Aperture Radar (InSAR) can provide high space and resolution data for ground deformation investigations. The ground deformation in this paper appeared in the city's development, although it is close to the Erhai region, which is different from a water-deficient city. Therefore, the analysis and prediction of ground deformation using a new method is required. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) images from 2015 to 2018 were used to study the characteristics of ground deformation in the Erhai region using the Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique. The results were cross-validated using ascending and descending direction images to ensure the accuracy. In addition, the results showed that there was little ground deformation in the northern part of the Erhai region, while there was obvious ground deformation in the southern part. Four influencing factors--including the building area, water level, cumulative precipitation, and cumulative temperature of the southern Erhai region--were used together to predict the cumulative ground deformation using back-propagation (BP). The R of all the involved data was 0.966, and the root mean square errors (RMSEs) between the simulated values using BP and the true measured values were 3.063, 1.003, and 1.119, respectively. The results showed that BP has great potential in predicting the change tendency of ground deformation with high precision. The main reason for ground deformation is the continuous increase of building area; the water level followed. The cumulative precipitation and cumulative temperature are the reasons for the seasonal ground deformation. Some countermeasures and suggestions are given to face the challenge of serious ground deformation.  
SBAS-InSAR; ground deformation; back-propagation; Erhai region +
Footnote †: journal: 18 March 2019; Accepted: 15 May 2019; Published: 19 May 2019