esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Water Circulation Structure in the Paldang Reservoir, South Korea
## 3 Results
### Analysis of Monitoring Data
#### Meteorological Data  
In this study, local meteorological observations were performed to investigate the time series of 1-h average variations in wind velocity, wind direction, temperature, humidity, pressure, and rainfall amount. Wind roses were used to classify the wind direction and velocity data into 16 compass points at 2 m/s intervals, and these results are shown in Figure 9. In general, the meteorological conditions in reservoirs are the major contributors to energy transfers such as heat exchange occurring on the water surface due to solar radiation, sensible heat transfer and evaporation, and wind-induced heat transfer [3]. The comparison of the meteorological observations for wind velocity, wind direction, temperature, humidity, pressure, and rainfall amount produced the following results. Day-night periodic variations were observed in the wind velocity, wind direction, temperature, and humidity. With regard to wind, except for the periods of sporadic wind invasion, southwesterly winds were observed in the daytime, and north-northeasterly winds were observed in the nighttime. The wind velocities gradually increased until noon and then slowly decreased in most cases. In the initial stage of observations, on May 12, 15, and 16, the daily maximum velocities exceeded 10 m/s and the average daily wind velocities exceeded 2 m/s. The average temperature increased and the diurnal temperature range decreased with time towards the later stage of observations. The average daily temperatures increased from May 19 onward, where values exceeded 20 \({}^{\circ}\)C and remained around 21-24 \({}^{\circ}\)C afterwards.  
Figure 9: Wind roses by meteorological station from 9 May to 17 June 2016.
## 2 Battery Modeling
### State-Space Model  
#### 2.3.1 State Space Model for SOC Estimation  
Taking \(X=[SOC,U_{1}]^{T}\) as the state vector, the load current \(I_{L}\) as the input and the terminal voltage \(U_{t}\) as the output, we can obtain the discrete state-space model as  
\[\left\{\begin{array}{l}X_{k+1}=f(X_{k},I_{L,k},\theta_{k})+w_{k}\\ y_{k}=U_{t,k}=h(X_{k},I_{L,k},\theta_{k})+v_{k}\end{array}\right. \tag{5}\]  
Figure 1: A first-order resistor–capacitor (RC) battery model.  
where \(\theta_{k}=[R_{0},R_{1},\tau_{1}]^{\rm T}\) represents the time-varying battery model parameter vector; \(\tau_{1}=R_{1}C_{1}\) is the time constant of the RC network; \(w_{k}\sim\mathcal{N}(0,Q_{k}^{\rm r})\) is the Gaussian process noise with covariance \(Q_{k}^{\rm r}\); \(v_{k}\sim\mathcal{N}(0,\Sigma_{k}^{\rm r})\) is the measurement noise with variance \(\Sigma_{k}^{\rm r}\). The initial state has a Gaussian prior distribution \(X_{0}\sim\mathcal{N}(\hat{X}_{0},P_{0})\). The state prior and process noise are assumed to be known, while the measurement noise variance \(\Sigma_{k}^{\rm x}\) is assumed to be unknown. In addition, the process noise, measurement noise, and initial state value are independent of each other.  
\(f(\cdot)\) and \(h(\cdot)\) represent the nonlinear functions of state vector \(X_{k}\), input \(I_{L,k}\), and battery model parameter vector \(\theta_{k}\). Their mathematical expressions are  
\[f(\cdot)=\left[\begin{array}{cc}1&0\\ 0&e^{-\frac{\Delta t}{\tau_{1}}}\end{array}\right]\left[\begin{array}{c}SOC _{k}\\ U_{1,k}\end{array}\right]+\left[\begin{array}{cc}-\frac{\eta_{c}\Delta t}{Q _{rate}}&0\\ 0&R_{1}(1-e^{-\frac{\Delta t}{\tau_{1}}})\end{array}\right]I_{L,k} \tag{6}\]  
\[h(\cdot)=U_{\rm OC}(SOC_{k})-U_{1,k}-I_{L,k}R_{0} \tag{7}\]  
where \(\Delta t\) is the sampling interval of the current.  
The state transition matrix and the input control matrix are respectively  
\[F_{k}=\left[\begin{array}{cc}1&0\\ 0&e^{-\frac{\Delta t}{\tau_{1}}}\end{array}\right] \tag{8}\]  
\[G_{k}=\left[\begin{array}{cc}-\frac{\eta_{c}\Delta t}{Q_{rate}}&0\\ 0&R_{1}(1-e^{-\frac{\Delta t}{\tau_{1}}})\end{array}\right] \tag{9}\]  
Linearizing the measurement function, we can get the Jacobian measurement matrix as  
\[H_{x,k}\!=\!\frac{\partial h(\cdot)}{\partial X_{k}}\bigg{|}_{X_{k}=\hat{X}_{ k}^{-}}=\left[\begin{array}{cc}\frac{\partial U_{\rm OC}}{\partial SOC_{k}}&-1 \end{array}\right] \tag{10}\]
# Detecting the Absence of Lung Sliding in Lung Ultrasound Using Deep Learning
## 2 Materials and Methods  
### Dataset Description  
In collaboration with physicians from the Department of Thoracic Surgery at the Jessenius Faculty of Medicine in Martin, we prepared a LUS video dataset. The dataset contains recordings of patients after thoracic surgery. Pneumothorax and pleural effusion are common postoperative complications in this context. Our dataset contains examinations for pneumothorax; during this diagnostic approach, the patient is laid on their back. The physician scans the anterior and lateral sides of the thorax using a linear probe. The probe is placed between two ribs longitudinally to the body, and both lungs are examined. The lung not affected by the surgical procedure serves as a comparison for the physician, who is looking for three signs: lung sliding, B-lines, and lung point.  
The videos were recorded in B-mode, which displays the acoustic impedance of a two-dimensional cross-section of tissue. Lung sliding motion in the video files was divided by physicians into two classes:  
1. Lung Sliding Presence (LSP)--This class represents healthy lung sliding movement (28 videos);
2. Lung Sliding Absence (LSA)--This class represents a lack of lung sliding movement (20 videos).  
The total number of available examinations is 48, from 48 patients at different stages of postoperative care. All the examinations were made with a SonoScape S2 Portable Ultrasound Machine from SonoScape Medical Corp with the linear probe SonoScape L741 working at 5.0-10.0 MHz. The recordings were captured at resolutions of either 640 \(\times\) 480 or 854 \(\times\) 480 pixels at 30 frames per second in B-mode. We cropped the frames to a 480 \(\times\) 480-pixel square image containing the middle section of the frames by cutting off black margins; this was accomplished with no information loss. Altogether, we generated 17,338 frames. Of these, 11,469 have lung sliding present and 5869 do not.  
From the frames, we generated six datasets. The overview of the class distributions and the numbers of samples in each dataset is in Figure 1. All datasets contain M-mode images created by post-processing from B-mode. M-mode images are 2D depth/time slices of B-mode images through lung tissue. Sample counts of the M-mode datasets range from 94,454 to 284,342, with a class ratio of lung sliding absence to lung sliding presence ranging from 1:1.8 to 1:3.3. The process of generating these samples has two hyperparameters. The first, step parameter, represents the number of frames between two batches of M-modeimages. The step parameter is set to 8 and 16 for 8- and 16-frame datasets, to 32 frames for 32-, 64-, and 128-frame datasets and again to 16 frames for the 256-frame dataset. The second parameter is the selection range, representing the range of x-coordinates in B-mode images. The value of the selection range is dynamically selected for each image based on the extent of lung tissue in the current image that conveys information about lung sliding.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Cognitive-Driven Gain Controller System  
In this section, we present the proposed cognitive-driven gain controller system consisting of RAAD and AGC (see Figure 4). Section 2.5.1 describes the RAAD, which generates probabilistic attention measures based on which the attended and the unattended speaker are identified. Section 2.5.2 describes the AGC, which amplifies the identified attended speaker and attenuates the identified unattended speaker based on the probabilistic attention measures.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Software/Hardware** & **Description** \\ \hline Lab Streaming Layer (LSL) software 1.12 & Handles the gUSBamp application and the Lab Recorder program \\ gUSBamp application (part of LSL) & Streams EEG responses and EEG trigger markers \\ Lab Recorder program (part of LSL) & Record EEG responses \\ OpenViBE software 2.0 & Visualizes online EEG responses \\ \hline MATLAB 11.2 & Runs RAAD algorithms, AGC algorithm, presents acoustic and visual stimuli via Fireface UC audio interface system, and generates EEG trigger marker \\ gUSBamp Research & Records EEG signals using 16 channel EEG amplifier \\ g.TRIGbox (multi-modal trigger box) & Generates EEG trigger markers synchronized \\  & with acoustic and visual stimuli \\ Fireface UC audio interface system & Presents acoustic stimuli \\ Computer & Runs MATLAB, LSL software, and OpenViBE software \\ \hline \hline \end{tabular}
\end{table}
Table 1: Software and hardware used for the proposed cognitive-driven gain controller system.  
Figure 3: Scalp map of EEG electrodes.
# Non-Face-to-Face P2P (Peer-to-Peer) Real-Time Token Payment Blockchain System
## 4 Implementation
### Token Creation  
The smart contract used in this paper is created by the smart contract by creating a transaction using the Listing 2 Array of SCT-20 input parameters and sending it to the Symverse blockchain. In other words, instead of writing smart contract code in Solidity such as Ethereum, it creates an SCT-20 contract on the Symverse blockchain using the SCT-20 contracts and transactions described in Section 2.4.2. As a result, a smart contract ID of \"0x4801e91a5068757a9484\" was created, link ([[https://scan.symverse.org/v2/sct2x_token/SCT20/0x4801e91a5068757a9484](https://scan.symverse.org/v2/sct2x_token/SCT20/0x4801e91a5068757a9484)]([https://scan.symverse.org/v2/sct2x_token/SCT20/0x4801e91a5068757a9484](https://scan.symverse.org/v2/sct2x_token/SCT20/0x4801e91a5068757a9484)), accessed on 15 May 2023).  
```
set_data=[ \"0x47,\"//SCT20 \"0x7,\"//SCT20_CREATE Command [ \"eSportsToken\", // Token Name \"EST,\"//Token Symbol \"0x204 ec5e25e20611000000\", // Token Amount (1 Billion) \"0x000358075 ec2c5410002\" // Owner's SymID ]
```  
Listing 2: Array of SCT-20 input parameters.  
The smart contract ID is a required parameter when searching for the corresponding token or inputting a transaction according to the transaction; Sct_data is encoded with RLP (Recursive Length Prefix) as shown in Listing 3.  
```
0 xc1480e98c6553706727473546f6b56e834553548c204 fce5e25026110000008a00035b875 ec2c54100
```  
Listing 3: Sct_data RLP encoding result.  
If you put this as the input value of Table 5 and sign the array of Table 5 and send it to the work node as a transaction, the block is confirmed and you can see that the token is created as shown in Listing 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environments** & **Specifications** & **Environments** & **Specifications** \\ \hline OS & Centos 7.4.17 & Database & mySQL 5.7.36 \\ \hline Language & PHP/HTML/Dart/Javascript & Webserver & Apache 2.4.52 \\ \hline \begin{tabular}{c} Webserver- \\ plugin \\ \end{tabular} & \begin{tabular}{c} Php 7.4.1 \\ Scrypt 1.4.2 \\ \end{tabular} & Blockchain & \
\begin{tabular}{c} symverse \\ \end{tabular} \\ \hline UI framework & Flutter/Bootstrap & & gsym \\ \hline Test tool & JMeter V5.5 & Browser &
\begin{tabular}{c} Chrome version \\ 111.0.5563.66 (build) \\ (64 bits) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: List of environments and libraries.  
In addition, another token can be created in the same way and the implementation system is designed to store the smart contract ID in the DB as a separator so that all token transactions can be operated.
## 3 Compact Modelling
### UTC Photodiode Compact Modeling
#### 3.2.1 Dark Current  
In forward bias conditions, the dark current of the UTC-PDs is modeled by the classical diode reverse saturation current, \(I_{S}\)[13]. In reverse bias, in addition to the saturation current and the breakdown current modeled in [13], two other contributions have been introduced to improve model accuracy: the band-to-band tunneling current, \(I_{BTB}\) (in A), and the trap-assisted tunneling current, \(I_{TAT}\) (in A). The tunneling effect is required to describe the dominant current contribution under high reverse bias (\(V<-1\)) condition. In the general case, the tunneling current takes the following form [15]:  
\[I_{BTB}=S.A_{BTB}.E_{max}{}^{2}.V_{d}.e^{-\frac{\frac{B_{BTB}}{k_{max}}}{k_{ max}}} \tag{1}\]  
where \(A_{BTB}\) (in A/V\({}^{3}\)) and \(B_{BTB}\) (in V/m) are parameters to be determined from model optimization, \(V_{d}\) is the diode voltage in V, \(S\) is the diode area in m\({}^{2}\). \(E_{max}\) is the maximum electric field in V/m at the collector input. It can be expressed as [15]  
\[E_{max}=\frac{C_{j0}(T)}{\varepsilon_{InGaAsP}.S}\frac{\left(V_{j}(T)-V_{d} \right)^{1-M}V_{j}(T)^{M}}{(1-M)} \tag{2}\]  
where \(C_{j0}\) (in F) is the zero bias junction capacitance, \(\varepsilon_{InGaAsP}\) is the collector's relative permittivity, \(V_{j}\) is the junction potential (in V) and \(M\) is the grading coefficient of the junction. The trap-assisted tunnel current is dominant at low reverse bias (\(V>-1\)) [14]. Typically, it is a Shockley-Read-Hall (SRH) generation current enhanced by tunneling effects. It can be expressed as [16]  
\[I_{TAT}=S.A_{TAT}.E_{max}.V_{d}.e^{-\frac{B_{BTB}}{k_{max}}} \tag{3}\]  
where \(A_{TAT}\) (in AV\({}^{-2}\)m\({}^{-1}\)) and \(B_{TAT}\) (V/m) are trap assisted tunneling current parameters. These parameters can be extracted from model optimization at low reverse bias. It should  
Figure 3: UTC-PD basic electrical equivalent circuit.  
be noted that the temperature dependence of the static behavior of the dark current is captured through \(I_{S}(T)\), \(C_{j0}(T)\) and \(V_{j}(T)\) and the junction temperature is recalculated dynamically via an external thermal network [13].
## 4 Calculation
### Finite Element Simulation of the Three-Point Bending Test
Table 11 shows that the finite element analysis maximum load is slightly lower than the experimentally determined average maximum load and that the maximum load of the calculation based on the tensile strength values from expressions in the _fib Model Code for Concrete Structures 2010_[17] is slightly higher than the average maximum load of the experiments. As expected for the finite element analysis, the difference is larger for higher crack mouth opening values. The finite element analysis load at CMOD = 0.5 mm exceeds the experimental load. At CMOD = 2.5 mm, it results in lower loads compared with the experiment. The load of the calculation based on the tensile strength values from the expressions in the _fib Model Code for Concrete Structures 2010_[17] (M-\(\kappa\) (fib)) at CMOD = 0.5 mm exceeds the experimental load, and at CMOD = 2.5 mm, it results in an almost equal load compared with the experiment. Note that the load values of the calculation based on the back-calculated tensile strength values match the experimental ones as the M-\(\kappa\) relation was synchronized with the experiments by varying the tensile strength and residual tensile strength values at CMOD values of 0.5 mm and 2.5 mm. The experimental and calculated displacements from the M-\(\kappa\) and M-\(\kappa\) (fib) relation are especially close at the maximum load. The finite element analysis displacements also do not significantly differ (the slightly larger difference may be attributed to the crack occurrence at a lower load due to the geometric stress concentration). In general, the displacements in the experiments, the finite element analysis, and the calculation based on the M-\(\kappa\) and M-\(\kappa\) (fib) relation differ for less than 0.1 mm for different load stages (at the maximum load and different CMOD values), which is a relatively small difference compared with 3 mm, which is the maximum displacement of interest for the three-point bending tests in the EN14651 standard [19]. An important discrepancy of the M-\(\kappa\) (fib) load-displacement results is only in the region of the crack width value of 0.5 mm, where the load is overestimated. This is a consequence of the assumed (almost) constant residual tensile strength according to the _fib Model Code for Concrete Structures 2010_[17] simplified expressions for residual tensile strength values.
# Prevalence of Root Canal Treatments among Diabetic Patients: Systematic Review and Meta-Analysis
###### Abstract  
(1) Apical periodontitis (AP) is the inflammatory response of the periapical tissue to bacterial antigens and toxins arriving from inside the root canal after pulp necrosis. To control AP, it is necessary to interrupt the passage of antigens from the root canal to the periapex, which is achieved via a root canal treatment (RCT), which is the indicated endodontic therapy in cases of AP. The prevalence of root-filled teeth (RFT) is an indicator of the frequency of endodontic infections and the degree of dental care. Diabetes is associated with AP and has been identified as the main prognostic factor in RCT. The aim of this study was to carry out a systematic review with meta-analysis answering the following question: What is the prevalence of RFT among diabetic patients? (2) This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines 2020. A literature search was undertaken without limits on time or language until 12 January 2023 in PubMed-MEDLINE, Embase and Scielo. All studies reporting the prevalence of RFT among diabetic patients via radiographic examination; both panoramic and periapical radiographs were included. Meta-analyses were calculated with Open Meta Analyst software. The main outcome variable was the prevalence of RFT, calculated as the total number of RFT divided by the total number of teeth, which is expressed as a percentage. As a secondary outcome variable, the prevalence of diabetic patients with at least one RFT, expressed as a percentage, was also calculated. The quality of evidence of the included studies was analyzed according to the guidelines provided by the Centre for Evidence-Based Medicine in Oxford. The risk of bias was assessed using the Newcastle-Ottawa Scale, which was adapted for cross-sectional studies. To estimate the variance and heterogeneity amongst the trials, the Higgings I2 test was employed. (3) Eight studies fulfilled the inclusion criteria. Four studies were classified as having a high risk of bias, and four were classified as having a moderate risk of bias. The prevalence of RFT was estimated for 37,922 teeth and 1532 diabetic patients. The overall calculated prevalence of RFT among diabetic patients was 5.5% (95% CI = 4.1-6.9%; \(p<0.001\)). The percentage of diabetics who had at least one RFT was 42.7% (95% CI = 23.9-61.4%; \(p<0.001\)). (4) This systematic review and meta-analysis concluded that the prevalence of RFT among diabetic patients is 5.5%. More than 40% of diabetics have at least one RFT. In daily clinics, dentists should suspect that patients are undiagnosed diabetics when multiple RCT failures are observed in the same patient.  
diabetes; endodontics; epidemiology; root canal treatment; root-filled teeth; prevalence; survey; population-based study 2023
## 4 Hybrid Control Based on Extension Theory
### Upper Extension Controller Design
#### 4.3.3 Measure Mode Division and Control Reasoning Mechanism  
Different parts of the extension set correspond to different states of vehicles. Three kinds of the domain in the extension set are divided to apply different control strategies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Maximum error of ride comfort under sky-hook control \(e_{10\lim}\) & 0.016 \\ Maximum error of safety under sky-hook control \(e_{20\lim}\) & 0.009 \\ Maximum error of ride comfort under sky-hook control \(e_{11\lim}\) & 1.1 \\ Maximum error of safety under sky-hook control \(e_{21\lim}\) & 2.5 \\ Factor \(k_{1}\) & 1 \\ Factor \(k_{2}\) & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of extension controller.  
Figure 8: Extension set characteristic plane.  
1. Classical domain: When correlation degree satisfies the set \(M_{1}=\{S|k(S)>0\}\), it represents \(S\) is in the positive classical domain. The system is stable under this control strategy. However, the single control strategy might deteriorate the performance of the suspension system under specific conditions. Therefore, the value of extension function \(T(S)\) should be lower than 0. The new classical set is transformed as \[M_{1}=\{S|k(S)>0,\ T(S)<0\}\] (33) 2. Extension domain: When correlation degree satisfies \(M_{2}=\{S|-1<k(S)<0\}\), which indicates that vector \(S\) is in the positive extension domain, where comprehensive performance of suspension system should be regarded. According to hybrid control, the vector \(S\) can be transformed from the extension domain to the classical domain, the transformation \(T(S)\) should be greater than 0. The new extension set is transformed as \[M_{2}=\{S|-1<k(S)<0,\ T(S)>0\}\] (34) 3. Non-domain: When the correlation degree satisfies the condition of \(M_{3}=\{S|K(S)<-1\}\), the vector \(S\) is in the non-domain, where the state is beyond the control range of the lower controller, thus the security is guaranteed first. \[M_{3}=\{S|k(S)<-1,\ T(S)>0\}\] (35)  
Different control strategies play different roles under different domains. Under the contradiction condition, in order to satisfy the comprehensive performance and eliminate the switching condition from the hybrid system, the extension control will decide suitable methods and make a transition by correlation function. The relationship between correlation function and extension function for the suspension system based on extension theory is illustrated as Figure 9:  
Based on the production rule, the control reasoning mechanism of the extensive controller is described as follows:  
IF \(k(S)\in M_{1}\); then sky-hook control  
IF \(k(S)\in M_{2}\); then hybrid control  
IF \(k(S)\in M_{3}\); then ground-hook control
## 4 Experimental Setup
### Static Characteristic Test Platform  
According to the standards of IEC 60747-8 [27] and datasheet of the device vendor [28; 29], the static characteristic test platform based on Power Device Analyzer Curve Tracer (PDACT) Agilent B1505A with the heating plate was established to measure the static characteristic of SiC MOSFET C2M0025120D (Cree, 1200 V, 90 A) and SiC SBD C4D20120A (Cree, 1200 V, 90 A), as shown in Figure 6. Considering the materials and components that make up the package are not compatible with high temperature (usually lower than 175 \({}^{\circ}\)C) [30], the testing temperature ranges from 25 to 150 \({}^{\circ}\)C. It should be noted that the case temperature \(T_{c}\) obtained by infrared thermal camera is used to approximate the junction temperature \(T_{j}\), since the latter is difficult to measure by conventional methods. The specific settings of each module are shown in Table 1.  
Figure 6: Static characteristic test platform for power device.