esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data
## 4 Results
### HSCAT Wind Radii versus Best-Track MSW  
Similar to Figure 4, the correlation between the best-track MSW and the estimated HSCAT/ECMWF wind radii is shown in Figure 5. Compared to the maximum wind speed, the HSCAT wind radii show a significantly better correlation with best-track MSW (r = 0.54), of which the value is also larger than that found by [PERSON] and [PERSON] [23], [PERSON], and [PERSON] [24]. The main reason could be that the area, as well as the temporal duration, of this study is much smaller than that of the above mentioned literatures. Despite that the number of different cases was evaluated in those papers, the proposed algorithm of wind radii estimation is more efficient in identifying TC intensity than the maximum wind speed of HSCAT retrievals. However, the R17 value estimated from the ECMWF wind field is less effective than the ECMWF maximum wind speed in terms of representing TC intensity. Probably, that is because ECMWF has a much coarser spatial resolution than scatterometers, and in turn, it cannot resolve well the short-scale wind variance and the true TC wind structure, notably under TC conditions [6]. Nevertheless, with the linear regression model in Figure 5, the SD error of TC intensity predicted by the HSCAT and ECMWF wind radii is about 9.59 m/s and 9.72 m/s, respectively.  
Figure 4: The best-track MSW versus HSCAT maximum wind speed (**a**) and ECMWF maximum forecast wind speed (**b**). Black line indicates the linear regression result.  
Actually, previous studies [28, 31] already noted that the TC size varies with season and geographic location, so a large R17 value does not always imply an intense TC, or vice versa. Consequently, it is not realistic to develop a universal TC intensity prediction model only based on the wind-radii information. On the other hand, one may be interested in the relative changes of R17 with respect to MSW for a TC event or set of events within a short period and small region, e.g., for a few satellite overpasses of a particular TC event.  
Figure 6 illustrates the case by case correspondence between the HSCAT R17 and best-track MSW/MSLP. Note that the HSCAT acquisitions for each TC event are within 48-72 h (see Figure 1). It is obvious that the HSCAT wind radii generally correlate (anticorrelate) well with the best-track MSW (MSLP), except for the TC Halong, whose intensity rapidly increased by 23 m/s in 24 h, while the R17 value remained roughly the same, around 90 km. The correlation coefficient is calculated for the TC events with more than three HSCAT overpasses, and the results are summarized in Table 1. The correlation between the HSCAT R17 and best-track MSW/MSLP is generally larger than 0.8, better than the maximum wind speed of both HSCAT and ECMWF forecasts.  
Figure 5: The best-track MSW versus HSCAT-B R17 (**a**) and ECMWF R17 (**b**). Black line indicates the linear regression result.
## 5 Results and Findings
### Deep Learning Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Ref, Year** & **Application** & **Model** & **Images Type** & **Performance Metric** \\ \hline
4 & [22], 2019 & Detecting atherosclerotic carotid plaques on panoramic dental radiographs using deep learning. & Faster R-CNN. & Panoramic dental radiographs. & Sensitivity: 75\%; specificity: 80\%; accuracy: 83\%. \\ \hline
5 & [23], 2022 & Dental panoramic radiography dataset for benchmarking diagnostic systems using deep learning & FPN, UNet, UNet++, PSPNet, radiography. & Panoramic multi- = 95.22\%; UNet++ = 95.19\%; PSPNet = 95.55\%; DeepLabV3+. & DeeplabV3 = 94.91\%; eeplabV3+ = 95.13\%. \\ \hline
6 & [24], 2021 & Classifying gender from dental X-ray images using deep convolutional neural networks. & ResNet50. & Dental X-ray images. & Accuracy: \\ \hline
7 & [25], 2022 & Gender prediction from panoramic dental radiograph images. & DenseNet121, VGG16, ResNet50, EfficientNetB6. & Panoramic dental radiographs. & DenseNet121 = 97.25\%; VGG16 = 82.2\%; ResNet50 = 92.6\%; EfficientNetB6 = 94\%. \\ \hline
8 & [26], 2020 & Classification for early caries and dental plaque detection. & Customized convolutional neural networks (CNNs). & Dual channel dental imaging (auto-fluorescence and white light). & Accuracy: \\ \hline
9 & [27], 2022 & Automated caries screening in panoramic radiographs. & VGG16, VGG19, Resenet18 Resnet50 & Panoramic & Resnet18 = 83.02\%; Resnet50 = 86.79\%; Resnet101 = 88.68\%; \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\
10 & [28], 2022 & Dental disease detection in x-ray images. & Convolutional neural network (CNN). & Panoramic radiographs. & Accuracy: \\ \hline
11 & [29], 2021 & Classification of dental radiographs (panoramic, bitewing, periapical, cephalometric). & Baseline CNN, ResNet, Capsule Network. & Panoramic, Piewing, and cephalometric radiographs. & Accuracy: \\ \hline
12 & [30], 2022 & Automated tooth detection and numbering on pediatric panoramic radiographs. & YOLO V4. & Pediatric panoramic radiographs. & Precision 99.22\%. \\ \hline
13 & [31], 2019 & Automated tooth detection and numbering in panoramic radiographs. & Faster R-CNN. & Panoramic radiographs. & Sensitivity: 0.9941; precision: 0.9945. \\ \hline
14 & [32], 2022 & Developing a human identification system using panoramic dental radiographic (PDR-net). & Panoramic dental radiographic (PDR) images. & Accuracy 97.91\%. \\ \hline
15 & [33], 2021 & Tooth instance segmentation in 3D point cloud data of intraoral scans. & Mask-MCNet (deep neural network). & 3D point cloud data of intraoral scans. & Achieved 98\% intersection over union (IoU) score on tooth instance segmentation. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 4 Performance Metrics
### Spectral Efficiency  
The spectral efficiency (\(\eta_{s}\)) of CP-OFDM, TDS-OFDM, and eOFDM systems is compared according to the expression given in [37] as  
\[\eta_{s}=\frac{N_{\text{Data}}}{N_{\text{Data}}+N_{\text{Pilot}}}+\frac{N}{N+m} \times 100\% \tag{7}\]  
where \(N_{\text{Data}}\) and \(N_{\text{Pilot}}\) represents the number of data subcarriers and the number of pilot subcarriers, respectively, while and \(N=N_{\text{Data}}+N_{\text{Pilot}}\). It should be noted that for eOFDM, \(N_{\text{Pilot}}=G\).
# Sensor-Based Hand Gesture Detection and Recognition by Key Intervals
## 2 Related Works  
The detection of gestures from sensory data can be viewed as an object detection problem. For the computer vision applications, the detection of objects from images is a challenging and fundamental problem. State-of-the-art detection results have been achieved by various deep learning techniques [21]. A common component for some of these approaches is the employment of anchor boxes as detection candidates [22; 23]. Anchorboxes are the boxes with various sizes and aspect ratios. A large set of anchor boxes [24] may be required for accurate detection. Subsequently, high computation overhead is usually introduced for both training and inference.  
Alternatives to the anchor-based approaches are to represent each object as a single [25] or multiple keypoints [26; 27]. For the technique with a single keypoint, the keypoint of an object is the centre of the bounding box of the object. When an object is represented by a pair or a triplet of keypoints, each keypoint represents the centre or corners of the bounding box. The corresponding object detection operations are equivalent to finding the keypoints of the objects. The need for anchor boxes is then bypassed.  
Although the keypoint-based approaches have low computation complexities, they may focus only on the local features of objects for identifying keypoints. By contrast, the proposed work is based on the key intervals for the gesture detection. Global features characterizing the key intervals would then be taken into consideration by the proposed algorithm. A superior detection accuracy can be achieved with a low computation overhead. The joint training for both detection and classification as multitask operations in the proposed algorithm is also beneficial for the effective classification of gestures after detection operations. Similar schemes can also be observed in the study [28] for instance-aware human semantic parsing, where a joint training for different tasks such as keypoint detection, human-part-parsing and body-to-point project were carried out. The corresponding backbone-sharing scheme was found to be effective over its counterparts [29; 30] without multitask operations.
# Improved Bootstrap Method Based on RBF Neural Network for Reliability Assessment
## 3 Bootstrap Methodology and Its Improvement
### Bootstrap Approach
For the first problem, the exponential distribution function is used to perform the correction of the sample's empirical function, as the life of electronic products essentially obeys an exponential distribution [19]. For the second problem, based on the corrected empirical distribution function, the radial basis function (RBF) neural network is used to fit the original empirical distribution and obtain the continuous distribution characteristics of the original sample. The input set of the RBF neural network is then obtained using the neighborhood sampling method to ensure that the expanded sample is not limited by the original data. Thus, the expanded sample resembles the actual distribution of the original sample.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 2 Background
### NPTA Model and UPPAAL-SMC  
Timed automata are popular in the domain of formal methods, for its simplicity and ease-to-use features. Priced timed automata (PTA) [13] are variants of timed automata whose clocks can evolve with various rates. Owing to the different clock rates, the PTA model can be used to describe the dynamic software running environment. For example, the changing rate of user loads in the SUIS scenario can be described with cosine function, i.e., _load_ = cos(2 \(\times\) t). In addition, the PTA model supports description of stochastic behaviors of systems, and this characteristic suit to depict the uncertainty delay time within  
Figure 1: Architectural view of SUIS.  
the self-adaptation logic. The NPTA model consists of a set of correlated PTAs which communicate with each other using shared variables or broadcast channels [22].  
In order to analyze the NPTA-based probabilistic behaviors, the model-checking tool of UPPAAL-SMC [22] was created. UPPAAL-SMC is an extension of UPPAAL [23], and it is expressive enough to capture the continuous time behaviors as well as the stochastic behaviors of complex systems. In addition, UPPAAL-SMC can avoid the state-space explosion problem, as it works with the SMC technique [14].
## 3 Research Models
### Bivariate GARCH Model  
The bivariate GARCH model commonly used in the existing studies on the research of carbon-energy markets is presented below:  
\[r_{t}^{\text{EULA}}=\mu^{\text{EULA}}+\varphi^{\text{EULA}}\cdot r_{t-1}^{ \text{EULA}}+e_{t}^{\text{EULA}} \tag{1}\]  
\[r_{t}^{\text{WTT}}=\mu^{\text{WTT}}+\varphi^{\text{WTT}}\cdot r_{t-1}^{\text{ WTT}}+e_{t}^{\text{WTI}} \tag{2}\]  
\[e_{t}\Big{|}\Phi_{t-1}=\begin{bmatrix}e_{t}^{\text{EULA}}\\ e_{\text{WTI}}^{\text{EULA}}\end{bmatrix}\ \sim\ BN\ (0,\ H_{t}) \tag{3}\]  
\[H_{t}=\begin{bmatrix}h_{t}^{\text{EULA}}&h_{t}^{\text{EULA,WTI}}\\ h_{t}^{\text{EULA,WTI}}&h_{t}^{\text{WTI}}\end{bmatrix} \tag{4}\]  
where \(r_{t}^{\text{EULA}}\) and \(r_{t}^{\text{WTT}}\), respectively, represent the daily returns on EUA (carbon market) and WTI (energy market) observed on day \(t\). Since our focus is on the second moment (i.e., variances and correlations), we adopt a relatively straightforward first-order autoregressive process for our mean equations, i.e., Equations (1) and (2).  
Next, we introduce the equations for the second moment. As depicted in Equation (3), the residual returns, \(e_{t}^{\text{EULA}}\) and \(e_{t}^{\text{WTT}}\), follow a bivariate normal distribution (\(BN\)). The elements of the conditional variance-covariance matrix (\(H_{t}\)) follow the standard time-varying GARCH specifications:  
\[h_{t}^{\text{EULA}}=\omega^{\text{EULA}}+\pi^{\text{EULA}}\cdot(e_{t-1}^{ \text{EULA}})^{2}+\beta^{\text{EULA}}\cdot h_{t-1}^{\text{EULA}} \tag{5}\]  
\[h_{t}^{\text{WTT}}=\omega^{\text{WTT}}+\alpha^{\text{WTT}}\cdot(e_{t-1}^{ \text{WTT}})^{2}+\beta^{\text{WTT}}\cdot h_{t-1}^{\text{WTT}} \tag{6}\]  
\[h_{t}^{\text{EULA, WTT}}=\rho\times\left(h_{t}^{\text{EULA}}\cdot h_{t}^{\text{ WTT}}\right)^{1/2} \tag{7}\]where \(h_{t}^{ELIA}\) and \(h_{t}^{WTI}\) represent the time-varying conditional variances of EUA (energy market) and WTI (energy market) returns, respectively, and \(h_{t}^{EUA, WTI}\) is the time-varying conditional covariance of the two returns.
## 1 Introduction
Under such conditions, localisation judgments for the pink noise sources were accurate. Conversely, in the second experiment [12] each layer consisted of stereophonic loudspeakers with a base angle of 60\({}^{\circ}\) (\(\pm\)30\({}^{\circ}\)). The results of this study showed that the pink noise was perceived as being elevated with respect to the physical position of each layer. This difference in results is indicative of the phantom image elevation effect, in which stimuli are perceived as being more elevated when presented as stereophonic phantom images compared to single source only presentation [13; 14]. This has notable implications for the reduction of vertical interchannel crosstalk. If main channel images are elevated with respect to the physical position of the main channel layer as a result of the phantom image elevation effect then it could be argued that the location-based effects of vertical interchannel crosstalk would be less distracting. Consequently, the localisation threshold might be much lower under such circumstances or, alternatively, might not be necessary at all. It is therefore of interest to determine how the localisation thresholds would vary when stimuli are presented as vertically-arranged quadraphonic phantom images compared to for vertical stereophonic presentation. From the above background the following research questions were derived:  
* Does there exist a sound source dependency for localisation thresholds? * How do localisation thresholds vary for vertical quadraphonic stimulus presentation compared to vertical stereophonic? The present paper is organised as follows. An experiment is first described in which the effects of sound source, presentation method and ICTD on localisation threshold were analysed. Following this, a second experiment is presented in which the thresholds obtained in the first experiment were applied to sound sources and verified in localisation tests. The paper concludes with discussions pertaining to the results of each experiment, as well as the implications for image rendering and microphone techniques. This also includes suggestions for future work.
## 2 Materials and Methods
### Health Risk Assessment  
The health risk assessment for the heavy metals in five different vegetables for different age consumers was estimated according to the food intake data and metal concentrations in the cultivated plants [30; 31]. The food intake data were obtained from the Taiwan National Consumption Database. The average daily dose (ADD) was used to measure the amount of daily metal intake that is associated with local inhabitants' vegetable consumption. The formula used for calculating ADD for a single heavy metal through the diet was:  
\[\text{ADD}=\frac{\text{C}\times\text{IR}\times\text{AF}}{\text{BW}} \tag{2}\]  
* ADD: average daily dose via ingestion pathway (mg/kg b.w./day)
* C: concentration of metal in the vegetable (mg/kg)
* IR: daily intake of vegetable (kg/day)
* AF: absorption rate (%), set as 100%
* BW: average body weight (kg)  
It explicitly quantifies the variability and uncertainty that are given by each parameter probability function with an iteration size of 10,000 via the random sampling method (stability condition) to obtain a 95% confidence interval (CI) (MCS P95). Lognormal distribution and normal distribution models were used to determine the input parameters of Cd and Cu concentrations, food consumption data, and BW. The health risk assessment was performed using the Monte Carlo simulation with @Risk 7.5.1 (Palisade Corporation, Ithaca, NY, USA) to estimate each variable's uncertainty.  
Furthermore, the hazard quotient (HQ) was chosen to reflect a non-carcinogenic risk that was dependent on daily average exposure dose and reference dose (RfD) of the metal exposure. The total non-carcinogenic risk hazard index (HI) is the sum of the HQs exposed to the same pollutant by consuming different foods. HI < 1 indicates a non-carcinogenic risk of the substance and a less significant impact on human health. The formulas to estimate HQ and HI are:  
\[\text{HQ}=\frac{\text{ADD}}{\text{RfD}} \tag{3}\]  
\[\text{HI}=\sum\limits_{\text{HI}}\text{HQ} \tag{4}\]RfD for Cd: 0.001 mg/kg/day; for Cu: 0.04 mg/kg/day
## 4 Discussion  
In this study we have developed a 3D-printed chest phantom based on a patient's CT images with multiple lung nodules for the purpose of optimising low-dose CT protocols for lung cancer screening. The main advantage of our study over others is that we printed the comprehensive gamut of thoracic tissues, including skin, muscle, bone, subcutaneous fat, lung, vessels, airway, and nodules [22]. Beyond this, the inclusion of abdominal organs, the liver, spleen, pancreas, and stomach, adds to the complexity and realness of the phantom, whilst providing a replicable tissue environment surrounding the lung nodules for accurate and realistic HU measurements and diagnostic assessment. We evaluated various low- to ultra-low-dose CT protocols ranging from 80 to 100 kVp with 30-180 mAs to assess the detectability of lung nodules. All scans demonstrated good diagnostic confidence (subjective image quality = 4), indicating that ultra-low-dose CT (ULDCT) using 0.23 mSv (80 kVp and 30 mAs) is a potentially viable protocol for lung cancer screening, while delivering the least effective dose (0.23 mSv).
## 3 Conventional Detection Scheme
### Reduced-Complexity Joint Detection (RCJD) Scheme  
In [23], a possible simplification to reduce computational complexity is proposed by partitioning the set \(\mathcal{D}\) having \(N_{p}-2\) differential PSSS subcarriers into a number of subsets. The \(s\)-th subset with ZC root index \(u_{n}\) denoted by \(\mathcal{D}_{n}^{s}\) is computed by  
\[\mathcal{D}_{n}^{s}=\left\{k|\left|\Re\{D_{n}(k_{n}^{s})D_{n}^{*}(k)\}\right| \geq T\right\}, \tag{13}\]  
where \(\Re\{\cdot\}\) is the real part of the enclosed quantity, \(k_{n}^{s}\) is the representative subcarrier of the \(s\)-th subset, and \(T\) is the pre-determined threshold that controls the correlation between differential PSSS subcarriers in each subset. Since \(\mathcal{D}_{n}^{s}\)'s are disjoint, the union of all subsets is equal to \(\mathcal{D}\). Based on the subset partitioning, the corresponding metric is given by  
\[\Omega_{c}(v,n)=\sum_{s=1}^{N_{b,n}}\left(\sum_{k\in\mathcal{D}_{n}^{s}}B_{n}( k)\bar{Y}_{l}(k+v)\right)D_{n}^{*}(k_{n}^{s}), \tag{14}\]  
where \(N_{b,n}\) is the number of subsets corresponding to \(D_{n}(k)\), and \(B_{n}(k)\in\{1,-1\}\) is the conversion indicator that makes the phase difference between the representative PSSS subcarrier \(D_{n}(k_{n}^{s})\) and the other subcarriers \(D_{n}(k)\)'s in \(k\in\mathcal{D}_{n}^{s}\) to be equal. By finding the maximum of \(\Omega_{c}(v,n)\) with respect to \(v\) and \(n\), the IFO and SI are jointly estimated by  
\[(\hat{\epsilon},\hat{n})=\arg\max_{(v,n)}\left|\Omega_{c}(v,n)\right|. \tag{15}\]  
A main drawback of the reduced-complexity joint detection (RCJD) scheme is that a loss of autocorrelation property of the original PSSS produces similar peaks for different IFO hypotheses and deteriorates the detection performance.
## 3 Results and Discussion
### In Vitro Drug Release from ETB-NE  
The in vitro release study results (Figure 4B) showed a significant enhancement of ETB release upon formulation to ETB-NE. The ETB-NE showed a biphasic ETB release pattern. At the end of 2 h, 63.3 \(\pm\) 2.6% ETB release occurred from ETB-NE. Meanwhile, only 26.5 \(\pm\) 2.2% ETB was released from free ETB. Thus, initially, the ETB release from the aqueous dispersion was significantly lower. The ETB release from ETB-NE reached 85.3 \(\pm\) 1.8% at the end of 12 h. However, the ETB could release only a maximum of 54.8 \(\pm\) 2.6% at the end of 12 h. This type of substantial enhancement of release or dissolution  
Figure 4: (**A**) TEM image of ETB-NE. (**B**) In vitro drug release profiles from the aqueous dispersion of ETB and ETB-NE (\(f_{2}\) = 27.14).  
of poorly water-soluble drugs from NE is widely reported and explained [38]. The small droplet size of NE and the presence of a surfactant/co-surfactant system are some of the accepted reasons for this drug release enhancement. The recommended method of comparison of in vitro drug release profiles by USFDA is the use of similarity factor (f\({}_{2}\)). An f\({}_{2}\) value below 50 indicates dissimilar release profiles. Thus, the f\({}_{2}\) value of 27.14 observed in the present study indicated a significant difference between the in vitro release profiles of ETB and ETB-NE.
## 4 Conclusions
\begin{tabular}{l l} BAC & building automation control \\ BIMAX & Maximum allowed energy charged for the chosen value of battery index \\ BImin & Minimum allowed energy charge value for the chosen value of battery index \\ CHP & Combined heat and power \\ COP & Coefficient of performance \\ CSt & Storage capacity \\ DHW & Domestic hot water \\ DI & Discomfort index \\ DSM & Demand-side management \\ Ebattery & State of charge \\ ESI & Electric storage index \\ ESWH & Electrical storage water heater \\ fm,i & Monthly correction factor \\ FSOCav & Average fractional state of charge \\ GC & Grid-connected \\ HPWH & Heat pump water heater \\ HPWH + grid & Heat pump water heater combined with grid supply \\ HPWH + ST & Heat pump water heater combined with solar thermal plant \\ Imp & Current at maximum power \\ mDHW & Domestic hot water mass flowrate @ 40 \({}^{\circ}\)C \\ NPV & Net present value \\ NZEB & Net zero energy building \\ PBT & Payback time \\ PDI & Potential discomfort index \\ Pinv & Inverter deliverable power \\ Pmp & Power at maximum power \\ Ppanel & PV panel declared peak power \\ PPV & PV plant power \\ PV & Photovoltaic \\ RES & Renewable energy sources \\ SA & Stand-alone \\ SOCMAX & Maximum value of the state of charge in the battery \\ SOCmin & Minimum value of the state of charge in the battery \\ SPI & Setpoint index \\ SPT & Simple payback time \\ ST & Solar thermal \\ SWOT & Strengths, weaknesses, opportunities, and threats \\ TDHW & Outlet tank temperature of domestic hot water system \\ VDHW,max & Maximum valued of volume demand of domestic hot water \\ VDHW,min & Minimum valued of volume demand of domestic hot water \\ Vmp & Voltage at maximum power \\ \end{tabular}