esa-sceva/satcom-chunk-collection huggingface dataset:
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 2 Materials and Method
### Wind Speed Retrieval Model  
The NRCS of sea surface SAR data depends on the radar incidence angle, wind speed, and direction. The NRCS is generally described by nonlinear formulas related to the angle of incidence, wind speed, and relative wind direction (the angle between the radar-observed wind direction and the wind direction). Taking CMOD5.N as an example [6], the nonlinear formula is as follows:  
\[\sigma_{0}(v,\phi,\theta)=B_{0}(v,\theta)(1+B_{1}(V,\theta)\cos(\phi)+B_{2}(v,\theta)\cos(2\phi))^{p}, \tag{1}\]  
where the isotropic term \(B_{0}\), the upwind/downwind amplitude \(B_{1}\) and the upwind/croswind amplitude \(B_{2}\) are all the functions of wind speed \(v\) and incident angle \(\theta\). The \(\phi\) is relative wind direction and the \(p\) is a constant with a value of 1.6. Different models were used to evaluate the wind speed retrieval capability of GF3-02 SAR for VV polarization, including the CMOD_IFR2, CMOD5, CMOD5.N, CSARMOD2 and CMODH_VV models.  
For \(HH\) polarization, an additional \(PR\) model is generally required to convert HH polarization NRCS to \(VV\) polarization NRSC. PR is defined as  
\[PR=\sigma_{0}^{VV}/\sigma_{0}^{HH}, \tag{2}\]  
where \(\sigma_{0}^{VV}\) and \(\sigma_{0}^{HH}\) are the NRCS in linear units for \(VV\) and \(HH\) polarization, respectively. This paper adopts the polarization ratio formula related to the incident angle proposed by [PERSON], [PERSON], [PERSON], and [PERSON]. At the same time, the HH polarization model of CMODH_HH is used to evaluate the wind speed retrieval capability of GF3-02 SAR for HH polarization, which can directly retrieve wind speed from HH polarization NRCS without relying on the PR model.
## 3 Results and Discussions
### Models
#### 3.1.2 Calibrated Models
PGA data can be used to generate seismic hazard maps, which aid in identifying areas that are highly susceptible to liquefaction. In this study, the deterministic seismic hazard assessment (DSHA) was used to calculate peak ground acceleration (PGA), the maximum acceleration the ground experiences during an earthquake. DSHA estimates ground motion by considering the maximum magnitude of an earthquake attributable to a given seismic source and the shortest distance between the site and the fault. A limitation of DSHA is that it is site-specific and may not apply to other regions; therefore, a separate assessment must be conducted for other regions where structures are planned. Another limitation is that linear ground motion is assumed, which is not always the case during earthquakes. DSHA is still utilized despite these limitations because it is generally simpler and easier to comprehend than alternative methods. DSHA provides an estimated PGA with a model or equation that describes the relationships between PGA, earthquake magnitude, and distance from the seismic source. In this study, deployment was based on the linear model, which revealed a very strong positive relationship (R\({}^{2}\) = 0.89), represented by Equation (9). Comparing the estimated values to those of other studies revealed that the results fell within a reasonable range. However, the formula for estimating PGA is restricted to locations within a 100 km radius of the seismic sources, although the distance may exceed 100 km. \[PGA=\frac{[-335.58603+(95.62499M)-(1.31834R)]}{981} \tag{9}\]  
where PGA is the peak ground acceleration (m/s\({}^{2}\)), M is the expected magnitude of the earthquake, with a range of 5 \(\leq\) M \(\leq\) 9, and R is the distance from the fault line which may go beyond 100 km, however, the value of PGA becomes too small. Various machine learning (ML) models, such as tree, ensemble, and neural network models, demonstrate promising relationships between variables. These models are, however, highly susceptible to overfitting, which occurs when a model fits too closely to the details and noise in the training data. Due to their inability to generalize beyond the training set, overfitted models may have subpar performance when making predictions on new data. To avoid overfitting and ensure that the models can make accurate predictions on new data, care must be taken when selecting and deploying machine learning models.
# On the Efficacy of Turbulence Modelling for Sloshing
## 2 Problem Description
It is observed that, for the range of \(Fr>1\), the first 30 cycles are sufficient to pass the transition period, while for the low \(Fr\) the transition time is longer, around 60 cycles. In order to quantify the mean dissipated energy, the damping force is phase-averaged after the transition period according to  
\[\bar{F}_{s}(t)=\frac{1}{N}\sum_{n=1}^{N}F_{s}(T_{p}\times n+t), \tag{5}\]  
where \(T_{p}\) is the period of one cycle, \(n\) is the cycle number, and \(N\) is the total number of cycles considered in the averaging. \(\bar{F}_{s}(t)\) is the phase average force function of time for one cycle. The induced dissipation energy can be estimated as the work required to displace the tank against the sloshing force [7; 15]. In the case of the sinusoidal oscillation of the tank, the dissipated energy is equivalent to the enclosed area of the force-displacement hysteresis loop and can be expressed as  
\[e_{s}=\int\bar{F}_{s}(t)dz. \tag{6}\]  
This integration is estimated by the trapezoidal rule. Non-dimensional energy dissipation is used to compare with published experimental results, which were obtained by normalising \(e_{s}\) with respect to half of the maximum kinetic energy of the liquid according to  
\[E_{s}=\frac{e_{s}}{m_{w}\omega^{2}A^{2}}. \tag{7}\]
## 3 Results and Discussion
### Evaluation of the HC-LSSVM Model  
The settlement of soft soil has caused a large number of casualties and property losses. It is very important to monitor and predict the settlement of soft soil accurately for construction management. Displacement analysis and prediction is a key step in soft soil monitoring and early warning control. Previous basic prediction methods such as the LSSVM model have certain limitations in data processing prediction accuracy, so this study proposes HC-LSSVM model combined with homotopy continuation method. In order to evaluate the reliability of the HC-LSSVM model, the model was compared with previous research results [14,17] (Figure 5). For the convenience of comparison and analysis, both predicted and actual values in the research results are expressed in normalized form in Figure 5 (Equation (20)). The linear fitting of the research results shows that the slope of the line is 1.00 and the correlation coefficient \(\mathrm{R}^{2}\) is as high as 0.963, which again verifies the reliability of the research results in this study. Other research results ([PERSON] et al. -MP-LSSVM) also have the slope of the fitting line of 0.97, which is very close to the optimal value of 1.00, but the correlation coefficient \(\mathrm{R}^{2}\) is only 0.626, indicating that the data is extremely unstable. Of course, some research results ([PERSON] et al. (LSSVM)) have very high correlation coefficient and good fitting effect, but the slope of the fitting line is only 0.89, which is far from the optimal value of 1.00. In conclusion, the HC-LSSVM model established in this study can better predict soft soil settlement, its development law is in good agreement with the actual situation, and the prediction effect is better than other LSSVM models. On the basis of acquiring more learning samples, the HC-LSSVM model in this study can also predict the settlement value of soft soil for a long time,  
\[X_{n}=\frac{X_{r}-X_{\min}}{X_{\max}-X_{\min}}, \tag{20}\]  
where \(X_{n}\) is the normalized predicted value or monitored value, \(X_{r}\) is the predicted value or monitored value before the normalization, \(X_{\max}\) is the maximum predicted value or monitored value before the normalization, and \(X_{\min}\) is the minimum predicted value or monitored value before the normalization.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cumulative** & **Avalue of Cumulative** & **Predicted Value of** & **Error \(\epsilon\)** \\
**Settlement Time \(C_{st}\)** & **Settlement Amount \(A_{v}\)** & **Cumulative Settlement** & **(\%)** \\
**(Day)** & **(mm)** & **Amount \(P_{v}\) (mm)** & **(\%)** \\ \hline
618 & 187 & 180.41 & 3.52 \\
619 & 188 & 181.20 & 3.62 \\
648 & 197 & 193.02 & 2.02 \\
649 & 198 & 192.38 & 2.83 \\
679 & 203 & 202.69 & 0.15 \\
680 & 205 & 203.23 & 0.86 \\
711 & 205 & 210.31 & 2.52 \\
712 & 206 & 207.04 & 0.50 \\
741 & 208 & 210.28 & 1.08 \\
742 & 208 & 211.41 & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between predicted and actual value of soft soil settlement of test samples.
# Optimization of Chinese Chive Juice as a Functional Feed Additive
## 3 Results
### Biochemical Changes with Optimized Drying Conditions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Tentatively Identified Metabolites (1)** & **RT (2) (min)** & \multirow{2}{*}{**MW (1)**} & \multirow{2}{*}{**Measured Mass**} & \multirow{2}{*}{**MS/MS Fragments**} & \multirow{2}{*}{**Class of Compounds**} \\ \cline{5-5} \cline{8-9}  & & & & & & **Negative Mode (m/2)** & \\ \hline
1 & N-(1-Decoxy-1-fructosyl)leucine & 1.00 & 293 & 292.1377 [M \(-\) H] & 292 \(>\) 274/202/172/130 & Amino acid \\
2 & Quercetin-glucosylglacatoside-glucose or & 3.35 & 788 & 787.1852 [M \(-\) H] & 787 \(>\) 625/463/301 & Flavonol \\
3 & Kaempfer-di-glucose or & & & & & \\
3 & Kaempfer-solopyranoside & 3.59/4.05/4.37/4.50 & 610 & 609.1404 [M \(-\) H] & 609 \(>\) 447/285/489/581 & Flavonol \\
4 & Feruloyl-galactactic acid & 3.94 & 386 & 385.0739 [M \(-\) H] & 385 \(>\) 191/209/367 & Hexaric acid \\
5 & Kaempfer-solopyranoside-glucose & 3.65/4.01 & 772 & 771.1522 [M \(-\) H] & 609 \(>\) 429/447/489 & Flavonol \\
6 & Quercetin-di-glucose & 4.39 & 626 & 625.1349 [M \(-\) H] & 625 \(>\) 463/300/445/505/607 & Flavonol \\
7 & Sminmic acid-Q-glucuronide isomer & 4.55 & 400 & 390.9897 [M \(-\) H] & 399 \(>\) 381/355/223/05/187 & Phenololololol acid \\
8 & Kaempfer glycolacoste-(feruloyl)glucoside & 4.55/4.84 & 948 & 947.2362 [M \(-\) H] & 947 \(<\) 225/447/609/285 & Flavonol \\
9 & Quercetin-hexoside & 4.81 & 464 & 463.0843 [M \(-\) H] & 463 \(>\) 301 & Flavonol \\
10 & Kaempfer-glucose & 5.04 & 448 & 447.0893 [M \(-\) H] & 447 \(>\) 284/25 & Flavonol \\
11 & LyspP(E182) & 8.22/8.34 & 477 & 476.2734 [M \(-\) H] & 476 \(>\) 279/402/214 & Lipid \\
12 & LyspPC(182) & 8.28/8.40 & 519 & 564.3246 [M \(+\) A \(-\) H] & 564 \(>\) 504/279 & Lipid \\
13 & LyspPC(16.0) & 8.61 & 453 & 452.2737 [M \(-\) H] & 452 \(>\) 255/214/323/378 & Lipid \\
14 & LyspPC(16.0) & 8.68 & 495 & 540.3253 [M \(+\) A \(-\) H] & 540 \(>\) 480/255/391/224 & Lipid \\ \hline \hline \multicolumn{9}{l}{\({}^{(a)}\)Tentatively identified metabolites based on variable importance projection (VIP) analysis with cutoff value of 0.7 and a \(p\)-value \(<\)0.05 (\({}^{a}\)) RT: Retention time. (\({}^{b}\)) MW: Molecular weight. LysoPE: lysophosphatidylylethanolamine, LysoPC: lysophosphatidylcholine.} \\ \end{tabular}
\end{table} 
Table 6: Tentatively identified metabolites in CC juice based on the UHPLC-LTQ-Obritap-MS/MS analyses.
# Pediatric Endoscopy, Update 2020
## 4 Esophago-Gastro-Duodenoscopy
### Intervention
#### 4.6.3 Esophageal Stent and Endoscopic Balloon Dilatation  
The most common indications for endoscopic esophageal stent placement in pediatric population are benign diseases such as post-surgical or corrosive strictures and perforations. In the literature, the placement of fully covered metallic stent and plastic stents is described with good outcomes [79; 80]. Also, the use of biodegradable esophageal stent in pediatric case is described in one case report [81]. Pyloric stenosis due to benign disorders can be effectively and successfully treated through endoscopic balloon dilatation in suitable patients [82].
# Security of IoT Application Layer Protocols: Challenges and Findings
###### Abstract  
IoT technologies are becoming pervasive in public and private sectors and represent presently an integral part of our daily life. The advantages offered by these technologies are frequently coupled with serious security issues that are often not properly overseen or even ignored. The IoT threat landscape is extremely wide and complex and involves a wide variety of hardware and software technologies. In this framework, the security of application layer protocols is of paramount importance since these protocols are at the basis of the communications among applications and services running on different IoT devices and on cloud/edge infrastructures. This paper offers a comprehensive survey of application layer protocol security by presenting the main challenges and findings. More specifically, the paper focuses on the most popular protocols devised in IoT environments for messaging/data sharing and for service discovery. The main threats of these protocols as well as the Common Vulnerabilities and Exposures (CVE) for their products and services are analyzed and discussed in detail. Good practices and measures that can be adopted to mitigate threats and attacks are also investigated. Our findings indicate that ensuring security at the application layer is very challenging. IoT devices are exposed to numerous security risks due to lack of appropriate security services in the protocols as well as to vulnerabilities or incorrect configuration of the products and services being deployed. Moreover, the constrained capabilities of these devices affect the types of security services that can be implemented.  
IoT; security; threat; mitigation; application layer protocols; CVE; MQTT; CoAP; mDNS; SSDP; AMQP; DDS; XMPP; good practices +
Footnote †: journal:  
_Future Internet_**2020**, _12_, _55_; doi:10.3390/612030055
# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 4 Results and Discussions
### Model Evaluation Performance Metrics
The scale employed for image processing involves measuring the spatial resolutions for the red, green, and blue color channels. In Figure 7a-c the spatial resolutions are approximately 11, 10, and 11 LP/mm for the red, green, and blue channels, respectively. For Figure 7d-f, these resolutions are approximately 10, 9, and 11 LP/mm for the respective channels, while Figure 7g-i shows approximately 9 LP/mm for all three channels. Across all \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Validation**} & \multirow{2}{*}{**Loss**} & \multicolumn{2}{c}{**Validation**} & \multirow{2}{*}{**IoU**} & \multicolumn{2}{c}{**Validation**} \\  & & **Accuracy** & & & **Loss** & & **IoU** \\ \hline FCN & 78.2\% & 71.4\% & 86.5\% & 90.3\% & 65.3\% & 61.8\% \\ \hline U-Net & 82.3\% & 78.9\% & 89.6\% & 93.2\% & 71.03\% & 68.36\% \\ \hline CNN & 83.3\% & 79.4\% & 88.5\% & 92.2\% & 72.9\% & 70.2\% \\ \hline DeepLabV3 & 85.3\% & 81.5\% & 87.2\% & 91.5\% & 74.3\% & 71.9\% \\ \hline Dense + U-Net & 86.45\% & 81.76\% & 81.78 & 87.56\% & 76.90\% & 72.43\% \\ \hline
**SA-SC** & \multirow{2}{*}{**91.78\%**} & \multirow{2}{*}{**87.34\%**} & \multirow{2}{*}{**80.28\%**} & \multirow{2}{*}{**85.56\%**} & \multirow{2}{*}{**81.82\%**} & \multirow{2}{*}{**77.45\%**} \\
**U-Net (Ours)** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics SA-SC U-Net (self-attention with separable convolutions U-Net). images, the radiometric resolution remains fixed at eight bits per pixel, ensuring consistent color depth. Additionally, each image maintains a constant number of bands at three, representing the presence of red, green, and blue channels.
# Incentive Price-Based Demand Response in Active Distribution Grids
###### Abstract  
Integration of PV power generation systems at distribution grids, especially at low-voltage (LV) grids, brings in operational challenges for distribution system operators (DSOs). These challenges include grid over-voltages and overloading of cables during peak PV power production. Battery energy storage systems (BESS) are being installed alongside PV systems by customers for smart home energy management. This paper investigates the utilization of those BESS by DSOs for maintaining the grid voltages within limits. In this context, an incentive price based demand response (IDR) method is proposed for indirect control of charging/discharging power of the BESS according to the grid voltage conditions. It is shown that the proposed IDR method, which relies on a distributed computing application, is able to maintain the grid voltages within limits. The advantage of the proposed distributed implementation is that the DSOs can compute and communicate the incentive prices thereby encouraging customers to actively participate in the demand response program. An iterative distributed algorithm is used to compute the incentive prices of individual BESS to minimize the costs of net power consumption of the customer. The proposed IDR method is tested by conducting simulation studies on the model of a Danish LV grid for few study cases. The simulation results show that by using the proposed method for the control of BESS, node voltages are maintained within limits as well as the costs of net power consumption of BESS owners are minimized.  
active distribution grids; battery energy storage systems; demand response; distributed optimization; gradient method; model predictive control +
Footnote †: journal: Applied  
11
## 2 Non-Equilibrium Thermodynamic-Based Model Framework
### Thermodynamic Relationships  
The thermodynamic total differential form of unsaturated geotechnical materials is obtained as follows:  
\[\dot{w}\ =\pi_{ij}\dot{\epsilon}_{ij}^{e}+T_{g}\dot{S}_{g}+T\dot{S}+\sum_{a=S,L,G}\left(\mu_{ca}\dot{\rho}^{a}+v_{i}^{a}n_{i}^{a}\right)\text{, }a\in[S,L,G], \tag{1}\]  
where \(w\) is the total energy density. According to the Equation (1), some relationships can be obtained, such as \(\pi_{ij}=\frac{\partial w}{\partial\dot{\epsilon}_{ij}^{e}}\), \(T_{g}=\frac{\partial w}{\partial S_{g}}\).  
Since mass \(\rho\), momentum \(m\), and energy \(w\) are conserved quantities, the total mass, the total momentum, and the total energy of a unit of unsaturated geotechnical materials remain constant when there is no heat exchange with the outside world. The dissipative system of the unsaturated geotechnical materials can be obtained by \(\delta S=\)0 and \(\delta^{2}S<0\), based on the maximum entropy principle. Therefore, it can be obtained that the dissipation forces of unsaturated geotechnical materials are \(\left\{\pi_{ij},T_{g},\
abla_{i}T,\dot{\epsilon}_{ij},\psi_{ij}^{L},\psi_{ij}^ {G}\right\}\) and the corresponding dissipation flows are \(\left\{\dot{\epsilon}_{ij}^{p},I_{g},q_{k}/T,\sigma_{ij}^{\text{e}S},\sigma_{ ij}^{\text{e}L},\sigma_{ij}^{\text{e}G}\right\}\), where \(\dot{\epsilon}_{ij}\), \(\psi_{ij}^{L}\), and \(\psi_{ij}^{G}\) are the strain rate, \(\dot{\epsilon}_{ij}^{p}\) is the plastic strain rate of solid phase, \(\sigma_{ij}^{\text{e}n}\) is the viscous stress, and \(q_{k}\) is the heat conduction.  
The Onsager relationship [32] indicates that the entropy production rate \(R\) can be written as the sum of the product of dissipative forces and dissipation flows.  
\[R=\dot{\epsilon}_{ij}^{p}\pi_{ij}+I_{g}T_{g}+\frac{q_{k}}{T}\
abla_{i}T+\sigma _{ij}^{\text{e}S}\dot{\epsilon}_{ij}+\sigma_{ij}^{\text{e}L}\psi_{ij}^{L}+ \sigma_{ij}^{\text{e}G}\psi_{ij}^{G} \tag{2}\]  
Moreover, the dissipative flow can be written as a linear function of the dissipative force [33], followed by:  
\[\left[\begin{array}{c}\dot{\epsilon}_{ij}^{p}\\ I_{g}\\ q_{i}\\ \sigma_{ij}^{\text{e}L}\\ \sigma_{ij}^{\text{e}G}\end{array}\right]=\left[\begin{array}{cccccc}\eta_{ ijkl}&0&0&0&0&0&0\\ 0&\gamma&0&0&0&0\\ 0&0&\kappa_{ik}&0&0&0\\ 0&0&0&\lambda_{ijkl}^{S}&0&0\\ 0&0&0&0&\lambda_{ijkl}^{L}&0\\ 0&0&0&0&0&\lambda_{ijkl}^{G}\end{array}\right]\left[\begin{array}{c}\pi_{kl} \\ T_{g}\\ \
abla_{k}T\\ \dot{\epsilon}_{kl}\\ \psi_{kl}^{L}\\ \psi_{kl}^{G}\end{array}\right] \tag{3}\]  
The intermediate matrix is a positive definite matrix, also known as the transfer coefficient matrix, and should satisfy the reciprocity of Onsager and the Curie principle. However, the interaction between each dissipative flow in Equation (3) is not obvious, so the non-diagonal phases are considered to be 0.
# Broadband DOA Estimation by Exploiting DFT Extrapolation
## 1 Introduction  
Broadband direction-of-arrival (DOA) estimation has been actively studied to detect and estimate the origin of a signal [1, 2, 3, 4, 5, 6, 7, 8, 9]. In the lower band, the beam width of the DOA domain is widened [10], and the resolution is lowered accordingly, making it difficult to accurately estimate the DOA. Therefore, in some cases, it may not be possible to accurately estimate with the sampled signal alone. In a previous study [7], multiple signal classification (MUSIC) was used for broadband DOA estimation, which is impractical in terms of computational complexity.  
We propose discrete Fourier transform (DFT) extrapolation to estimate the DOA in a wideband, particularly in the lower bands. Previous research [11] proposed a high-resolution algorithm called DFT extrapolation to estimate the frequency of the incoming signal in the power spectrum from a limited-number dataset. By contrast, this study applies an algorithm for broadband DOA estimation.  
The remainder of this paper is organized as follows. Section 2 outlines DFT extrapolation and proposes a method for applying it to broadband DOA estimation. Section 3 describes the simulation conditions and results. In Section 4, we discuss the improvements thus achieved.
## 6 Discussion
### Limitations  
This research has some limitations. First, we did not include network traffic in the study design. This is needed to investigate security and privacy concerns introduced to app users as a result of their use. Second, analyzing HTTPS caches could cause discrepancies in the automated tool if the applications change their APIs or the way they store the caches, which means that any tool will require maintenance.
## 5 Experimental Evaluation
### Performance Metrics
#### 5.2.1 Cache Hit Rate  
The cache hit ratio is the total number of content requests which are successfully served by the local MAN server or a neighbor MAN server in a streaming period. We intend to serve user requests from the MAN servers. Any request which is unserved by the clustered servers is deemed a cache miss. Equation (2) can be simplified as  
\[\textit{Cache Hit Rate}=\frac{\textit{Total Hit Count in a time slice}}{\textit{Total Request in a time slice}}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline No. of clustered MAN servers & 5 \\ Cell coverage size & 200 m \\ No. of content files (\(f_{V}\)) & 10,000 \\ MAN to MAN delay & 20 ms \\ ROC to MAN delay & 100 ms \\ Zipf’s parameter (\(\alpha\)) & 0.55 \\ Request rate & 0.4–1.0 \\ No. of active users & 100–300 \\ Processing Power (\(P_{n}^{\mu}\)) & 1 \\ Transmission power & 46 dBm \\ Channel bandwidth & 20 MHz \\ Noise power & \(-95\ dBm\) \\ Cache size & 1–20 GB \\ Number of iterations & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.