esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer-Assisted Detection of Cemento-Enamel Junction in Intraoral Ultrasonographs
### Validation  
The CEJ detection results by the proposed method were validated by comparing the detected CEJ locations with the manually labeled ground truth by two raters. Raters R1 (a biomedical engineer) and R2 (an experienced orthodontist) have about 8 years and 2 years of experience with dental ultrasound, and 10 years and 20 years of experience in their field, respectively. To evaluate the intrarater reliability, each rater performed the measurements twice with one week apart to avoid bias.  
The impact of image preprocessing including homomorphic filtering, adaptive median filtering, and contrast enhancement was investigated. We also examined the effect of the bounding box size on the accuracy of the method by comparing three sizes: small (SBB, 31 \(\times\) 41 pixels), medium (MBB, 51 \(\times\) 71 pixels), and large (LBB, 81 \(\times\) 101 pixels) with the same center pixel (Figure 3). For example, 31 \(\times\) 41 pixels, 51 \(\times\) 71 pixels, and 81 \(\times\) 101 pixels correspond to 1.4 mm\({}^{2}\), 3.9 mm\({}^{2}\), and 8.9 mm\({}^{2}\) respectively. Therefore, the areas of MBB and LBB were approximately 3 and 6 times of SBB's area, respectively.  
The locations of the CEJ identified by the proposed method for the three bounding boxes with and without image preprocessing scenarios were compared with the two raters' labels. The discrepancies or differences in locating the CEJ between the proposed method and each rater for 120 ultrasound images were measured in Euclidean distances. A descriptive analysis was performed using the mean difference (MD), standard deviation (SD), and box plots. The intraclass correlation coefficients (ICC) were calculated to assess the intrarater, interrater, and intermethod reliabilities based on a single-rating (\(k=1\)), absolute-agreement, and 2-way mixed-effects model. ICC values < 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and \(>\) 0.90 are indicative of poor reliability, moderate reliability, good reliability, and excellent reliability, respectively [32]. A two-tailed power analysis was performed to determine the sample size using the following parameters: minimum acceptable reliability = 0.90, expected reliability = 0.95, significance level = 0.05, and power = 80%.
## 4 Results
### Change Identification Results  
Based on ZY-3 images in 2013 and 2018, our previously proposed binary CD method could obtain the unchanged and changed areas in the study area. The overall accuracy of the proposed CD method was 93.51% [36] and it could meet the reliability of subsequent wetland change identification. According to the flowchart of change identification shown in Figure 2, wetland classification results in 2018 could be obtained. The wetland classification results in 2013 and 2018 are shown in Figure 4, the overall accuracy was 98.51% and 91.16%, respectively. This could meet the requirement of actual wetland monitoring.  
The change transfer matrix between six different wetland categories from 2013 to 2018 is shown in Table 5. The increase of salt marsh was mainly from the sea area (2.27 km\({}^{2}\)) because salt marsh had strong adaptability and fertility and could expand rapidly throughout coastal areas. The increase of the aquaculture pond was from the sea area (10.58 km\({}^{2}\)) and salt marsh (5.24 km\({}^{2}\)) because the aquacultural industry had considerable economic benefits. Some new aquaculture ponds emerged under a reclamation project, and some salt marshes in original natural landscapes were developed as aquaculture ponds. The increase in farmland was from salt marshes because the agricultural industry also developed in coastal areas. The increase of building was from salt marshes and farmland because of population migration to wetland areas which occupied agricultural land and salt marshes to build residential and industrial land.
## 4 Results
### Descriptive Results of Questionnaires  
A total of 423 risk episodes were collected from the 21 users over a period of about two months. Average perceived dangerous level, average level of accident, and frequency of each task type are shown in Table 3.  
Figure 3: Preprocessing flow for the “description of the situation” item.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Episode** & **Average PDL** & **Average Level of the Accident** & **Frequency** & **Ratio [\%]** \\ \hline Driving & 3.12 & 1.66 & 310 & 73.29 \\ Maintenance & 2.30 & 1.43 & 56 & 13.24 \\ Inspection & 3.37 & 1.81 & 43 & 10.17 \\ Others & 1.86 & 1.36 & 14 & 3.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Episode distributions relying on the “type of episode”.  
As seen in Table 4, 36 accidents occurred during the response collection period. Even though no accident actually occurred in the other instances, in these episodes, the participants perceived danger while riding, doing maintenance, or inspecting their scooters.  
Figure 4 presents the results of the respondents selecting product factors as the cause of their risk episode. Except for \"none\" (occurring when the participants did not select a product factor as the cause of the risk), \"wheels\" was the most selected item, followed by \"brake\" and \"main body\".  
Table 5 reflects the product factor status for the episodes where the respondents chose a product factor as the cause of risk. Based on the selected subfactor, the episodes were classified according to whether the product was in a normal state or was malfunctioning.  
Figure 5 shows the results of the respondents' choices of environmental factors as the cause of risk episodes. \"none\" (occurring when participants did not select an environmental factor as the cause of the risk), \"road type\" was the most selected item followed by \"obstacles\", and \"road surface\". Among these, \"road type\" was chosen when participants felt danger due to the nature of the road environment depending on the type of road, such as along curves, on sidewalks, or in alleys.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Accident Occurred or Not** & **Average PDL** & **Average Level of the Accident** & **Frequency** & **Ratio [\%]** \\ \hline Just feel dangerous & 2.93 & 1.62 & 387 & 91.49 \\ Accident & 3.78 & 1.81 & 36 & 8.51 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Episode distributions relying on “the accident occurred or not”.  
Figure 4: Episode distributions and average scores relying on the “product factor”.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product Factors** & **Average PDL** & **Average Level of the Accident** & **Frequency** & **Ratio [\%]** \\ \hline Normal & 3.09 & 1.64 & 161 & 64.66 \\ Defect & 2.80 & 1.52 & 88 & 35.34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Episode distributions relying on the “product status”.
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 8 Conclusions  
In this paper, analysis of atmospheric turbulence effects on terrestrial SISO FSOC links based on the RMS and ground wind speeds prevalent in various cities of South Africa are presented. Wind speed data provided by the SAWS were statistically processed, and the corresponding CDF, PDF, and percentage of time plots are shown for each location of interest. The \(C_{n}^{2}\) based on RMS wind speeds during clear and sunny weather are computed. The scintillation indices not exceeded 50%, 99%, 99.9%, and 99.99% of the time, based on the zero inner scale and infinite outer scale model and finite inner and finite outer scale model are calculated. Aerosol scattering losses based on visibilities not exceeded 50%, 99%, 99.9%, and 99.99% of the time for the various cities of South Africa are shown. Outage probability and BER analysis, taking into account the effect of pointing errors over weak and moderate to strong atmospheric turbulence channels, were then carried out for OOK, BPSK, and SIM-QAM SISO FSOC links deployed at the different locations of interest. All through Figures 5-9, the SISO FSOC links deployed in all the locations of interest have similar outage probability and BER performances based on the zero inner scale and infinite outer scale model and finite inner and finite outer scale model. This is because the values of \(C_{n}^{2}\) in all the investigated cities are approximately equivalent over all the time intervals (Tables 2-5) considered in this work. As part of future work, all the analytical results in this work would be verified experimentally. The \(C_{n}^{2}\) based on important meteorological parameters such as temperature, pressure, and the structure parameter for temperature, as well as three-dimensional pointing errors effects, will also be investigated for FSOC links deployed in the locations of interest.  
Conceptualization, O.O.K., T.J.O.A. and M.M.; methodology, O.O.K., T.J.O.A. and M.M.; software, O.O.K.; validation, O.O.K., T.J.O.A. and M.M.; formal analysis, O.O.K., T.J.O.A. and M.M.; investigation, O.O.K., T.J.O.A. and M.M.; resources, O.O.K., T.J.O.A. and M.M.; data curation, O.O.K.; writing--original draft preparation, O.O.K.; writing--review and editing, O.O.K., T.J.O.A. and M.M.; visualization, O.O.K., T.J.O.A. and M.M.; supervision, T.J.O.A. and M.M.; project administration, T.J.O.A. and M.M.; funding acquisition, T.J.O.A. and M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The map in in Figure 1 was generated using the data from Global Wind Atlas which can be accessed at [34] All the other results were based on the data provided by the South African Weather Service (SAWS).  
The authors of this paper wish to express their gratitude to the South African Weather Service (SAWS) for supplying the data utilized in this work.  
The authors declare no conflict of interest.
## 3 Results Analysis
### Polarization Conversion Ratio (PCR)  
The PCR value of the proposed MMA calculated for verifying the proposed design is not a polarization converter but an absorber. Figure 6a shows the co- and cross-polarization as \(Txx\) and \(Tyx\) of x-polarization of the incident wave, respectively. The unique co- and cross-polarization of the x- and y-polarization of the incident wave were also achieved due to the structural symmetricity. The PCR value was calculated by Equations (7) and (8) for y and x polarization, respectively [35]. The PCR value was calculated as near-zero because of the very small cross-polarization \(Tyx=Txy<-90\) dB. Figure 6b shows the PCR value of x- and y-polarized waves, which verify that the proposed design is an absorber, not a polarization converter.  
\[\text{Y to x polarized wave, }PCR_{y}=T_{xy}^{2}/\left(T_{yy}^{2}+T_{xy}^{2}\right) \tag{7}\]  
\[\text{X to y polarized wave }PCR_{x}=T_{yx}^{2}/\left(T_{xx}^{2}+T_{yx}^{2}\right) \tag{8}\]  
Figure 5: (a) Interference theory (IT) model, and (b) absorption plot of Interference Theory (IT) model and Computer Simulation Technology (CST).
## 4 Conclusions  
We presented an OTA using a concurrent AGC mechanism realized using a 180 nm CMOS technology, comprising a main transimpedance amplifier with an on-chip P\({}^{+}\)/NW/DNW APD and a replica TIA with another on-chip APD to render the concurrent AGC function feasible within a narrow single pulse width. The post-layout simulations revealed that the OTA can extend the dynamic range up to 74.8 dB (i.e., 1 uA\({}_{\text{PP}}\)-5.5 mA\({}_{\text{PP}}\)) and achieve a 67 dB\(\Omega\) transimpedance gain, an 830 MHz bandwidth, a \(-31\) dBm optical sensitivity for a \(10^{-12}\) BER with the assumption of 4.61 A/W APD responsivity, and a 6 mW power dissipation. Conclusively, the proposed OTA can be a highly efficient solution for application to low-power, low-cost, short-range LiDAR sensors.  
Conceptualization, S.-M.P.; methodology, S.-M.P.; validation, Y.C. and S.C.; writing--original draft preparation, Y.C., S.C. and S.-M.P.; writing--review and editing, Y.C., S.C. and S.-M.P.; visualization, Y.C. and S.C.; supervision, S.-M.P.; project administration, S.-M.P.; funding acquisition, S.-M.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Republic of Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-0-01847), supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). In addition, this work was supported by the National Research Foundation (NRF), Republic of Korea, under project BK21 FOUR.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The EDA tool was supported by the IC Design Education Center (IDEC), Republic of Korea.  
The authors declare no conflicts of interest.
## 2 System Module  
### The Layout and Working Principle of Fault Indicator Based on LoRa and NB-IoT  
Consider a power distribution system that crosses the mountainous or forest areas. In these types of areas, there are some zones with the poor or lack of coverage of communication networks. In such areas, using the traditional fault indicators is impossible. In this paper, an intelligent fault indicator based on LoRa and NB-IoT is introduced. It is composed of the acquisition unit and collection unit. The layout of the acquisition unit is shown in Figure 1. It includes clamping wire structure, indicator light, and circuit board composed of a LoRa chip, single chip microcomputer, and other components. It supplies power through the cooperation of a large-capacity dry battery and line induced current. The layout of the collection unit is shown in Figure 2. There are circuit boards composed of a NB-IoT chip, LoRa chip, single chip microcomputer, and other components inside. It is powered by solar cells and backup batteries. This new type of fault indicator cannot only indicate the fault section by flipping the card but also sends the fault information of the line to the monitoring center in time through the LoRa and NB-IoT communication technologies. As a result, the staff can know exactly which section the fault occurs on in the monitoring center, which can greatly reduce the outage time to improve the reliability and reduce the labor intensity of workers.  
Figure 1: The layout of the acquisition unit of the proposed fault indicator.  
The working principle of the intelligent fault indicator based on LoRa and NB-IoT is shown in Figure 3. The acquisition unit of fault indicator samples current waveform of the line continuously at a frequency of 4 kHz. When a fault occurs on the line, the short-circuit and grounding fault measuring module of the acquisition unit measures the fault signal on time and sends an interrupt signal to the single chip microcomputer, the single chip microcomputer responds to the interrupt, calibrates the fault occurrence time and makes the acquisition unit store the data of 10 cycles before and after the fault occurrence time. Based on this data, the fault location can be judged whether it is upstream or downstream of the fault indicator. Then the acquisition unit sends the judgment information to the nearby collection unit by LoRa communication technology. The collection unit needs to summarize the judgment information sent by the acquisition unit to realize the fault location. The collection unit has LoRa and NB-IoT communication functions and can send the fault information of line to the monitoring center by NB-IoT communication technology. Therefore, the staff can know the fault information of the entire distribution network in the monitoring center.
# Integrated Inundation Modeling of Flooded Water in Coastal Cities
### Overview of Current Researches  
In the case of two-dimensional flow analysis simulations, the United States Army Corps of Engineers conducted simulations of flood analysis in conjunction with HEC-2 and GRASS models [1]. [PERSON] et al. analyzed the occurrence of flooding in urban areas by linking a 1D sewage model with a 2D flow analysis model [2]. [PERSON] et al. analyzed 1D flood inundation using the HEC-RAS model developed by the United States Army Corps of Engineers and applied the two-dimensional finite element model 'TELEMAC-2D' and the raster-based 'LISFLOOD-FP' model to simulations of river flooding [3]. [PERSON] et al. illustrated the initial efforts of developing a modeling system configured by connecting a 1D flow routing model to a 2D-raster based model to simulate urban flooding [4]. [PERSON] et al. developed a 1D-2D hydrodynamic inundation model by connecting a previously used 1D sewer network model (SWMM) with a 2D flow analysis model [5; 6]. This model was used for small urban flooding to determine the appearance of domestic flooding occurring on land. [PERSON] and [PERSON] linked a 2D river flow model to the Sewage Model SWMM5 to simulate interaction between sewers and overland flow [7]. Moreover, combined 1D-2D models for urban flooding have been developed and applied [8; 9]. Substantial efforts have been made to evaluate flood inundation by hydrological models: the estimation of flood frequency by a continuous simulation approach for a dam site was conducted in a large catchment [10]; a distributed rainfall-runoff model was developed to approximate the horizontal flow at a point by means of a kinematic wave model [11]; an analytical distribution formulation of the peak flood and maximum annual peak flood was presented based on a simplified description of rainfall and surface runoff processes [12]; the main mechanisms of runoff generation controlling the flood frequency distribution was identified by regional analysis [13]; and targeting on a significant number of river basins belonging to a humid region, an analytical work based on a stochastic differential equation for the description of the soil water balance and runoff production, was adopted [14].
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 5 Methodology
### Convolutional Neural Networks (CNNs)
#### 5.2.1 Convolutional Layer  
The convolutional layer is identified following the convolution operation. Convolution in mathematics is defined as an operation executed on two functions that yield the third one.  
The third function is a convoluted (modified) form of one of the two previous functions. The resultant (third) function yields as an amount function that translated one of the previous functions in the case of pointwise multiplication integral of the previous functions.  
The convolutional layer is composed of neuron groups that form kernels. All kernels are often the same depth as the input and are low in size. The receptive field is a small input area that the kernel neurons are connected to. In the case of images (high dimension inputs), it is useless to connect whole neurons to whole previous outputs. For instance, if the input layer has 100 neurons and an image of 10,000 pixels (a size of \(100\times 100\)), this yields one million parameters. Thus, a neuron contains the weights of only the kernel input dimension, instead of having weights for the input full dimension. The kernels slip crosswise to input height and width for extracting high-level features and producing a 2D activation map. The kernel stride is represented as a parameter. The resultant activation maps are stacked to form the output of the convolutional layer, which will be used for defining the next layer input. For example, by considering an image of \(32\times 32\), an activation map of \(28\times 28\) will result when operating a convolutional layer over it. Applying extra convolutional layers will reduce the size further and, in turn, the size of the image will significantly be reduced. This produces a vanishing gradient problem as well as a loss of information. To overcome this problem, padding is used. Padding enlarges the input data size via packing across input data with constants. These constants have zero values; hence, this operation is called \"zero paddings\". When the spatial dimensions of the output feature are similar to the feature map of the input, then it is called \"the same padding\". This applies equally to padding right and left. Consequently, if the added columns are odd, a further column to the right is added. No padding is equivalent to \"valid padding\".  
On the other hand, a kernel passes over image pixels without including them in the output due to strides. If extra complex kernels and a larger image are utilized, strides obtain how a convolutional task operates with a kernel. More specifically, the kernel employs the stride parameter for obtaining the number of positions to be skipped when it slides the input. Usually, convolutional layers are followed by the ReLU layer, which enlarges its nonlinear properties but does not decrease the network size. It also operates the activation function max_(0, x).
## 5 Discussion  
The non-local auto-correlation on account of the infrared background and the target's sparsity has been extensively employed in the field of infrared tiny target detection. When the infrared image is homogeneous, a classical IPI effectively represents low-rank patch-background matrices using the nuclear norm. Larger solitary values really hold more information and visual detail. In other words, the complex infrared image is too complicated for the nuclear standard to handle, resulting in residual error and a blurry backdrop after reconstruction because of the rich details.  
Currently, the majority of approaches concentrate on the priori backdrop and target, but this does not effectively separate the target from the background. In order to address the residual performance issue, RIPT proposes the structure tensor. The case of a poor signal-to-noise ratio, which leads to a lack of structure information and then target loss, is ignored by RIPT in complicated scenes. The NRAM model, on account of the IPI model, introduces a tighter rank proxy.  
Based on the NRAM model, this article initially constrains the low-rank background using the tensor kernel norm rather than the rank function. The proposed MNSTLA model and other cutting-edge techniques can effectively suppress the interference caused by dynamic background and object moving on the foreground extraction and also show good performance in background suppression and object enhancement, according to qualitative and quantitative comparisons.