esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
## 2 Methodology
### Flood Risk Index for Building (FRIB)  
In the field of disaster management, risk is composed of three elements: vulnerability, hazard, and exposure (UNDDR, [15]). _Vulnerability_ refers to the degree to which damage can be exacerbated, _hazard_ indicates the extent of potential damage caused by flooding, and _exposure_ represents the degree to which an area or entity is subjected to flood risk. In this study, FRIB (Flood Risk Index for Building) is defined--by considering these three properties--as \"the extent of loss that a particular building may incur due to urban flooding and its overall likelihood of being exposed to such risk\". Figure 1 illustrates this definition, representing the relationships and definitions for each component.  
Various indicators for evaluating the three components that constitute FRIB have been identified in previous research (Figure 2). From among these indicators, this study selected those that are publicly available government data within Korea (excluding paid or private company data) and can be applied at the building level. The data below were used as they were, or additional indicators were generated using those indicators.  
Figure 1: Definition FRIB and each element: vulnerability, hazard, exposure.  
Figure 2: Evaluation indicators by risk component [16; 17; 18; 19; 20; 21; 22; 23].
# Print Quality Analysis of Stone Paper and Coated Sticker Paper Used in Screen Printing
## 4 Results and Discussions
### Surface Contact Angle  
The changes in the surface contact angles of the stone paper and the coated sticker paper over time are presented in Figure 4. Both materials' surface contact angles were measured at the dripping moment and every five seconds after that. For the stone paper, the surface contact angle was \(117.6^{\circ}\) at the time of the first drop, \(108.5^{\circ}\) by the 5 th second, \(104.1^{\circ}\) by the 10 th second, \(102.4^{\circ}\) by the 15 th second, and \(101.7^{\circ}\) by the 20 th second. For the coated sticker paper, the surface contact angle was \(90.3^{\circ}\) at the time of the first drop, \(83.7^{\circ}\) by the 5 th second, \(80.2^{\circ}\) by the 10 th second, \(74.6^{\circ}\) by the 15 th second, and \(72.9^{\circ}\) by the 20 th second. At the end of the 20 s average measurement period, the surface contact angle was \(106.9^{\circ}\) for the stone paper and \(80.3^{\circ}\) for the coated sticker paper.  
Coated sticker paper absorbs and spreads water drops due to its cellulosic structure. Thus, its surface contact angle decreases. Due to the polymer structure of stone paper, it is hydrophobic, resulting in a high surface contact angle (Figure 4).
# Correlation of Gravity and Magnetic Field Changes Preceding Strong Earthquakes in Yunnan Province
## 3 Results
### Calculation of the Correlation Coefficient of Gravity and Magnetic Field Data  
According to the analysis of gravity anomaly indicators, the range of gravity anomalies for earthquakes with magnitudes \(M_{s}\)6.0 to 7.0 is 220 to 350 km [17]. The data gridding and normalization of the seismic anomaly area were performed on Yingjiang \(M_{s}\)6.1, Ludian \(M_{s}\)6.5, Jinggu \(M_{s}\)6.6, and Yangbi \(M_{s}\)6.4, and the correlation analysis of the annual change data of the gravity and magnetic field was performed. According to Formula (4), the correlation coefficient of annual variation data of gravity and magnetic field from 2011 to 2021 is shown in Figure 4.  
Figure 3: The histogram and nuclear density map of the uniform number of grid point changes in the gravity and magnetic field after normalization from 2011 to 2021. (**a**) Histogram of uniform number of grid points of gravity and magnetic field after normalization (the ordinate represents the quantity and the abscissa represents the normalized interval value); (**b**) The normalized gravity and magnetic field grid points change uniform number nuclear density map (the ordinate represents the kernel density value, and the abscissa represents the normalized interval range value).  
In Figure 4a, Yingjiang is located on the China-Myanmar border, surpassing China's border without gravity magnetic field data, and the amount of data used in the calculation of the correlation coefficient is relatively small. The absolute value of the correlation coefficient in the year before and the second year before the earthquake is relatively large. One year before the occurrence of the four strong earthquakes in Figure 4, the correlation coefficient of the annual variation of the gravity and magnetic field in the area covered by the anomaly reached the maximum, and the correlation coefficient decreased sharply in the year of the earthquake.
## 2 Materials and Methods
### Production
#### 2.2.4 Dynamic Modulus Test  
To describe the viscoelastic properties of the colored asphalt mixtures, the dynamic modulus test was conducted using a modular asphalt tester manufactured by IPC Global. Basically, the dynamic modulus test simulates various combinations of testing temperatures and loading frequencies to obtain the test specimen's response in a wide array of pavement loading scenarios. From this collection of responses, the dynamic modulus curve at a certain temperature is produced, which represents the asphalt mixture's elastic modulus at a certain temperature and loading frequency.  
In accordance with AASHTO T342 [31], the dynamic modulus test in this study was performed as follows:  
1. Three cylindrical specimens, one per each asphalt mixture to be tested, with a height of 150 mm and a diameter of 100 mm were fabricated.
2. Hexagonal gauge points were installed at four sides of each test specimen, where the linear variable displacement transducers (LVDT) were placed. The LVDT was used to acquire the vertical strain response induced by the load. To ensure that the placement of the LVDTs was perfectly aligned and evenly spaced, a gauge point fixing jig device was used.
3. After placement of the hexagonal gauge points and attachment of the LVDTs, the test specimen was placed in the modular asphalt tester. The test specimens were subjected to sinusoidal loading with a strain rate between 50 and 70 \(\upmu\)e.
4. Five testing temperatures (\(-\)5, 5, 20, 40, and 54 \({}^{\circ}\)C) were considered in this experiment. To achieve a uniform temperature of the test specimens, the test specimens were preconditioned according to the testing temperature by storing the test specimens in a chamber set at the testing temperature before each test. For each testing temperature, six loading frequencies were implemented: 25, 10, 5, 1, 0.5, and 0.1 Hz.
5. After calculating the dynamic modulus and phase angle for each combination of testing temperature and loading frequency from the obtained strain response and applied compressive load, a dynamic modulus master curve and phase angle graph of each test specimen at 5 \({}^{\circ}\)C reference temperature was constructed using the time-temperature superposition principle.
## 3 Results and Discussion
### Physicochemical Parameters
A regression analysis of this relationship was performed and a linear Equation (5) with a high correlation coefficient was obtained:  
\[\text{y}=33.986-0.258^{\text{*}}\text{x},\text{R}^{2}=0.954 \tag{5}\]  
Kinematic viscosity is determined through calculations from the ratio of dynamic viscosity and density, and is represented by the ratio of viscous force to inertial force. The relationship between dynamic and kinematic viscosity is presented in Figure 5.  
The kinematic viscosity was determined to be 31.125 mm\({}^{2}\)/s (at 20 \({}^{\circ}\)C), 28.707 mm\({}^{2}\)/s (at 30 \({}^{\circ}\)C), 26.285 mm\({}^{2}\)/s (at 40 \({}^{\circ}\)C), 21.416 mm\({}^{2}\)/s (at 50 \({}^{\circ}\)C), 18.617 mm\({}^{2}\)/s (at 60 \({}^{\circ}\)C), 17.163 mm\({}^{2}\)/s (at 70 \({}^{\circ}\)C), and 16.552 mm\({}^{2}\)/s (at 80 \({}^{\circ}\)C). The literature reports data for kinematic viscosity of 24.994 mm\({}^{2}\)/s at 40 \({}^{\circ}\)C [17], which is close to the values in our study. The lower slope of the lines for both viscosities indicates that the oil has a relatively low stability, explained by the composition of the lipid fraction. Similar dependencies of the physicochemical parameters on temperature have been established for other vegetable oils, which is in agreement with the data we obtained. [PERSON] et al. [53] investigated the kinematic viscosity of liquids, including sunflower, soybean, and coconut oils, at varying temperatures (50, 55, 60, 65, 70, 75, and 80 \({}^{\circ}\)C). According to them, the kinematic viscosity of all liquids decreased as their temperature increased. [PERSON] et al. [54] determined the physical properties, such as density, dynamic and kinematic viscosity, and fluidity, at a temperature range of 5-32 \({}^{\circ}\)C. They found that the density dependencies of samples on temperature were characteristic of decreasing linear function within a measured temperature range. [PERSON] [55] determined the dynamic viscosity of sunflower oil at temperatures between 313 K and 373 K and shear between 3.3 s\({}^{-1}\) and 120 s\({}^{-1}\). The results obtained in this study showed that all investigated physicochemical indicators decrease with an increase in temperature, which is an indication of its influence on the properties of the oils, including their stability. This property should be considered when including flaxseed oil in different food products, depending on the temperature conditions during individual technological operations.
## 4 Cases Study
### RUL Prediction of PCCS
#### 4.2.2 The RUL Prediction Result of Sample 2  
The failure threshold of Sample 2 is w = 14.6, the actual failure LM \(L_{\text{fail}}\) = 112 thousand kilometers, and the initial parameters are given as \(\Theta\) = [0.12,0.6,0.01,0.1]T, the predicted and actual wear path of sample 2 are shown in Figure 14.  
Figure 14 shows that the predicted wear trend is very close to the actual wear loss, and the average MSE between the actual predicted wear is 0.0597, which is small. It proves that the proposed model can fit the wear loss well. The EM algorithm is applied to estimate the model parameter. The restoration results of estimation at each mileage point are shown in Figure 15.  
Figure 14: The actual wear path and predicted wear path of sample 2.  
Figure 15: Results of model parameter estimation, (**a**) the restoration results of estimated \(\mu_{\Theta}\); (**b**) the restoration results of estimated \(\sigma_{\Theta^{\prime}}^{2}\); (**c**) the restoration results of estimated Q; (**d**) the restoration results of estimated \(\sigma^{2}\).  
Figure 15 demonstrates that the model parameters can converge quickly with the wear data accumulates. The model parameters can be updated at each mileage point, and then the PDF of the RUL is calculated by the Equation (12). The PDF of RUL prediction at the last eight monitoring points is shown in Figure 16.  
As is shown in Figure 16, every real RUL point falls within the PDF of the RUL, and the PDF curves become narrower with wear data information increases, which indicates the result of RUL prediction is more and more accurate.  
In order to validate the performance of the proposed method, the prediction result is compared to the real RUL at each mileage point. The results are shown in Figure 17.  
Figure 17 shows that the result of the RUL prediction is very close to the real RUL. The average RE between the real RUL and predict RUL is 0.0702. For example, the RE at the last mileage point is 0.0170. It shows that the RUL prediction of PCCS is very precise.  
Figure 16: The PDF of RUL prediction.  
Figure 17: The expectation of RUL prediction and real RUL.
###### Abstract
The development of technical characteristics such as advanced sensor technology, large-range wireless communication protocols, robust computational capacity, and low-power consumption have allowed taking SHM from small-scale experimental testing to the structural monitoring of massive infrastructure projects, including historic buildings and industries, footbridges, and highways [9]. This paper proposes the design, implementation, and validation of a low-cost SHM system capable of measuring the structural responses and identifying the dynamic properties of different structural systems using inexpensive technological devices and standard dynamical identification techniques. Thus, the extraction of the modal parameters found by this system provides accurate information for, subsequently, assessing the health condition of a civil structure and detecting damage scenarios. The present study provides an attractive cost-benefit alternative and effortless methodology for SHM regardless of the complexity of civil infrastructure. The development of the low-cost SHM system represents a relevant contribution to interdisciplinary research between SHM and electronic communications.
# An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
## 3 Proposed Scheme
### Improved Energy-Efficient LEACH Protocol
#### 3.2.4 Data Transmission Scheme of the IEE-LEACH Protocol
Since the distance of a single hop is less than 70 m as mentioned above, the single hop will be adopted when the distance between CH and BS is less than 70 m. Otherwise, the multi-hop method will be adopted. This improvement is beneficial for the average energy dissipation that enhances the lifetime of the sensor network. Thus, the proposed IEE-LEACH protocol shows flexibility over conventional LEACH protocols in terms of selecting cluster heads, cluster formation, and the data transmission.
# Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering
## 6 Results
### The Impact of the Number of Augmentations  
As Figure 2b in Section 3 shows, parallel augmentation enables the enlargement of augmented dataset size. By defining the number of augmentations (N), we obtain an N-times larger augmented dataset. The objective of this subsection is to examine the impact of N on the performances of the recognizers. Because of a larger training corpus, the number of mixtures for the HMM recognizer is increased from 16 to 32.  
Figure 13 depicts the average accuracy of HMM and CNN recognizers for the number of augmentations from one to five. The best augmentation strategy from the previous subsection (VC for HMM and PS + VC for CNN in the OWP scenario) was exploited. The results of particular experiments for each speaker are given in the Supplementary Document accessible in the repository [32].  
Figure 12: Average training time (**a**) and Real Time Factor (**b**) for HMM and CNN recognizers. The corresponding accuracies are given in parentheses.  
As evident from Figure 13, unlike the CNN, the HMM recognizer shows a small drop in accuracy after N = 2. In that way, enlargement of the training part does not lead to performance improvement for the HMM recognizer.  
On the other hand, the accuracy for the CNN recognizer notably increases as N goes higher. When N goes from 1 to 3, the accuracy roughly increases by 1 percentage point (88.06 to 89.03). Afterward, the improvement slowly decreases with a tendency for saturation of performance. The final examined case (N = 5) gives an accuracy of 89.45%. The next subsection deals with the relative _WER_ improvement.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 3 The Architecture Proposal
### Architecture Components
#### 3.2.2 Policy Manager  
This component is responsible for applying or repealing the policies of the domain that deploys the proposed architecture. It acts with the _BGP Speaker_ and the _Path Computer_ components to implement the domain policies. Thus, if an _AS app_ needs to explore multiple AS-Paths for a given prefix, the _Policy Manager_ will allow, or not, the use of multiple next hops for that prefix. The policies are generated inside the _Network Administrator_ component to be executed in the _Policy Manager_.  
Moreover, the _Policy Manager_ is responsible for avoiding two (or more) _AS apps_ creating rules that go against each other or the AS policies. Then, this component makes routing decisions based on the policies provided from the domain and from the _Network Administrator_ component.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.2 Icing Severity Classification  
The problem of classifying the icing degree of stay cables was solved by image classification. For this reason, the mask generated by the icing segmentation of stay cables was used as the input of image classification, and the output was the current icing degree grade. In the cable icing data set, the F1 value and IOU of the U-Net model were better than the other models, and the comprehensive evaluation index was the highest.  
As shown in Table 7, the mask after image segmentation by U-Net was input into several current popular image classification networks. Among them, ResNet50 was used as a classifier to achieve the highest accuracy of 95.71%, 9.39% higher than VGG16, which had the worst relative accuracy. The second stage used the ResNet50 model as a classifier superior to other classification networks.
# An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
## 2 Materials and Methods
### The Building Method of Simple Stable Feature Database  
This section builds simple stable feature databases using the various local invariant features that were introduced in the previous section. A simple stable feature database is created in two steps: first, relevant features are extracted from a reference image that is geographically accurate, and their useful information is stored in the initial feature database. Next, training images are continuously matched with the feature database, and the content of the feature database is updated based on the matching results, such as by adding and removing feature points. This feature database construction process can be seen in Algorithm 1. In the process of continuous matching iterations, relatively stable invariant feature points are obtained. The results of the test image registration depend heavily on the pretraining of the feature database. Because of this, we pre-align the input remote sensing training images to make sure that the pixel offset between the reference image and the training images is not too large. We also geometrically correct the training image during the training matching process to make sure that the feature points in the feature database and the reference image are geometrically consistent.
## 4 Discussion
### Water Management
#### 4.2.1 Hydropower  
There are four primary hydropower stations in the SAM, with a total capacity of about 336 MW and an annual generating capacity of 1.26 billion KWH, or about 44.7% of the average electricity consumption in 2018. The electricity consumption rates increased by 13.62% and 19.62% in 2018 and 2019, respectively, with higher energy demand in summer than in other seasons. Hydropower production potential is expected to increase in spring and decline in summer due to the earlier snowmelt and reduced water discharge from glaciers, which will further aggravate the imbalance between regional power supply and demand. Moreover, alternative energy sources must be sought to meet the rapidly increasing demands on the current hydropower generating capacity.
## 5 Discussion  
The results show that the multilevel surgery shifted (Figure 4) apneas that cause more severe O\({}_{2}\) desaturations (Figure 2) to hypopneas that allow some passing airflow (Figures 3 and 4). The surgery increased the portion of hypopnea in AHI. Although we believe apnea reduction is the key to improve O\({}_{2}\) saturation, apnea index after the surgery easily reaches the floor (floor effect, i.e., close to 0, see Figure 2) and distorts the pre- and post-operative comparison. That is why we selected hypopnea/AHI ratio as the measurement. The improvement of postoperative O\({}_{2}\) saturation in very severe OSA patients was seen in several ways: 1. Reduced desaturation frequency by desaturation index; 2. Reduced desaturation severity by mean SpO\({}_{2}\) desaturation; and 3. Improved mean SpO\({}_{2}\).  
The surgery improved mean SpO\({}_{2}\) in the 19 \"non-surgical success\" patients, regardless of the qualification of surgical success. Although the improvement of mean SpO\({}_{2}\) in the eight \"surgical success\" cases did not reveal statistical significance (\(p=0.0625\)), it needs to be aware that this could be the limitations of a small number or the ceiling effect--these 8 patients had higher preoperative mean SpO\({}_{2}\) of 93.8%, and it is harder to get better than 95% after the surgery.  
Classical surgical success--AHI \(<\) 20 events/h and AHI reduction \(>\) 50%, first proposed by [PERSON] et al. [36]--was frequently reported in associated studies with less severity and might serve as an outcome measure for comparison. The surgical success of the present study was 29.6% (8/27), which was worse than [PERSON]'s 30% (3/10) [35] but better than [PERSON]'s 17% (1/6) [34] and [PERSON]'s 9% (1/11) [23] (calculated from detailed subject information provided in available literature for non-framework sleep surgeries in very severe OSA patients). The mean AHI reduction in the present study was 40.5 events/h, which was worse than [PERSON]'s 42.2 events/h [35] but better than [PERSON]'s 38.2 events/h [34] and [PERSON]'s 14.4 events/h [23].  
If improving O\({}_{2}\) saturation to reduce consequences is the goal, we may need to redefine \"success of surgery\", at least for very severe OSAs. The results suggest that factors evaluating surgical success could include a combination of AHI reduction and hypopnea ratio, desaturation index, mean SpO\({}_{2}\) desaturation, and mean SpO\({}_{2}\). Future studies are needed to clarify the connection between the O\({}_{2}\) desaturation, including level and frequency, and various consequences. These results may intrigue future works to investigate a better classification of surgical success, taking into account initial, but not only, AHI.  
Figure 9: Mean SpO\({}_{2}\) improved 2.63% in “non-surgical success” patients (19/27), \(p=0.0087\).