esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Reliable long-range reservoir inflow forecast is essential to successfully manage water supply from reservoirs. This study aims to develop statistical reservoir inflow forecast models for a reservoir watershed, based on hydroclimatic teleconnection between monthly reservoir inflow and climatic variables. Predictability of such a direct relationship has not been assessed yet at the monthly time scale using the statistical ensemble approach that employs multiple data-driven models as an ensemble. For this purpose, three popular data-driven models, namely multiple linear regression (MLR), support vector machines (SVM) and artificial neural networks (ANN) were used to develop monthly reservoir inflow forecasting models. These models have been verified using leave-one-out cross-validation with expected error S as a measure of forecast skill. The S values of the MLR model ranged from 0.21 to 0.55, the ANN model ranged from 0.20 to 0.52 and the SVM from 0.21 to 0.56 for different months. When used as an ensemble, Bayesian model averaging was more accurate than simple model averaging and naive forecast for four target years tested. These were considered to be decent prediction skills, indicating that teleconnection-based models have the potential to be used as a tool to make a decision for reservoir operation in preparing for droughts.  
reservoir inflow forecasting; teleconnection indices; Bayesian model averaging +
Footnote †: journal: Applied Sciences
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 6 Conclusions and Outlook
### Challenges and Outlook  
Although many robust video watermarking algorithms have been proposed, the research on robust video watermarking algorithms still faces many challenges, which include the tradeoff between watermark capacity, invisibility, and robustness; combining with video coding standards; random detection, that is, the watermark can be detected in a small segment of video sequences at any position in the video; how to reduce the computational complexity; high real-time performance to ensure the smoothness of the video data stream, etc. Therefore, how to balance the relationship between invisibility and robustness while improving the watermark capacity and security is still the ongoing focus of researchers. Due to the high complexity of HEVC, the number of researches on the video watermarking algorithm based on HEVC is relatively low at present. Future watermarking algorithms in the compressed domain will realize watermark embedding based on in-depth analysis of the characteristics of the HEVC encoding process. Various fast algorithms and parallel algorithms will be developed and introduced into the video watermarking algorithms to shorten the operation time and meet real-time requirements. In addition, artificial intelligence schemes, like neural networks, may be introduced into video watermarking algorithms to select blocks that are more suitable for watermark embedding. What is more, H.266, also known as versatile video coding (VVC), has started the standardization process officially on 10 April 2018, and is expected to achieve formulation and publication of the standard by 2020. With the promulgation of H.266/VVC standard, robust video watermarking algorithms based on H.266 will also become the focus of researchers.  
[PERSON] and [PERSON] reviewed robust video watermarking algorithms in recent years; [PERSON] and [PERSON] classified the involved algorithms and analyzed the data; [PERSON] and [PERSON] drafted the manuscript; [PERSON] and [PERSON] revised the manuscript.  
This work was funded by the National Natural Science Foundation of China (No. 61702303) and the Shandong Provincial Natural Science Foundation, China (No. ZR2017 MF020).  
The authors declare no conflict of interest.
# Applying Topological Information for Routing Commercial Vehicles Around Traffic Congestion
## 4 Methodology and Model Development
### Diversion Model
#### 4.4.2 Traffic Conditions  
To ensure that diversion does not increase congestion on an alternative route, the available level of service on the route was determined and added to the network as a scaled cost. Roads operating near capacity or having a low level of service were assigned a cost factor of 2, which doubles the travel cost.
###### Abstract  
The present study aims at the development and validation of a quali-quantitative headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME GC-MS) method for the analysis of odorants in different Chinese and Japanese green teas. A 65 um PDMS/DVB fiber was used, and GC-MS was performed on a ZB-5 (30 m \(\times\) 0.25 mm ID, 0.25 um film) column. The main odorants in green teas samples were identified and the method was fully validated. Linearity (r\({}^{2}\) 0.981-0.999), sensitivity (LOQ 0.005-0.04 ug/mL), reproducibility (CV% 3.12-10.37), accuracy (recovery% 79.08-99.17) and matrix effect (ME% \(-\)9.5 to +4.5) were determined. Quantitation of 2,4-heptdalen, \(\beta\)-damascenone, \(\beta\)-ionone, linalool, indole, 2-ethyl-6-methyl-pyrazine, 2-ethyl-5-methyl-pyrazine, 2-ethyl-3.6-dimethylpriazine, 2-ethyl-3.5-dimethyl-pyrazine and 2-acetyl-pyrazine was carried out in the presence of isotopically labeled compounds as the internal standards. The proposed method was applied to the comparison of the profile of the volatile flavor compounds (VFCs) of green tea subjected to roasting treatment at three different temperatures (160, 180 and 200 \({}^{\circ}\)C for 30 min). In particular, the roasting process was monitored by following the quantitative variations of the selected odorant content, considered as the most important contributory components to the Hojicha or black tea (roasted tea) flavor. A temperature of 160 \({}^{\circ}\)C was found to be the best roasting temperature.  
green tea; hojicha tea; volatile flavor compounds; pyrazines; HS-SPME; GC-MS; roasting process +
Footnote †: journal: Applied Sciences
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 3 Methodology  
This section further studies the process of processing Wi-Fi data into traffic information and proposes a traffic information detection method for intersections based on Wi-Fi data. The detection process of traffic information is first presented, followed by the Wi-Fi data preprocessing process. Next, the calculation method of the spatial average speed of the intersection and the vehicle turning ratio information is introduced. Finally, the traffic data prediction model of the entrance section of the intersection is given.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 2 Problem Formulation
### Image Formation  
The range-compressed received signal \(s_{\mathrm{r}}(\xi,\tau)\) can be obtained through the matched-filtering procedure, i.e., \(g=s_{\mathrm{r}}(\xi,\tau)*s_{\mathrm{t}}^{*}(-\tau)\), where \(*\) denotes the convolution operator. Let GBP be the algorithm to be used to form the SAR image from the raw data \(g(\xi,\tau)\). Furthermore, let the slant-range plane be the image plane, into which the raw data \(g\) is backprojected. Then, a SAR image \(h(\xi,\rho)\) can be formed through the superposition of acquired data \(g(\xi,\tau)\) and its corresponding backprojection into the image plane, which is expressed mathematically as  
\[h(\xi,\rho)=\int_{-D_{\mathrm{SAR}}/2}^{D_{\mathrm{SAR}}/2}g(\xi,\tau)\,\mathrm{ d}\xi, \tag{2}\]  
where \(D_{\mathrm{SAR}}\) is the length of synthetic aperture [25]. This expression is applied to common pulse radars that use chirp signals.  
The backprojection algorithm (2) can also be applied to other types of data with small modifications. For example, for a FMCW radar, the Fourier transform in the range direction is required to be applied before the backprojection process. Radar measurements can also be based on a vector network analyzer (VNA), in which the output is the reflection coefficient \(S_{11}\). The measured data in the time domain can then be considered based on the Born approximation as a range-compressed signal from multiple point targets, which is expressed analytically as  
Figure 1: Problem setup. Representation of a UAV-based THz SAR imaging system. Here, \(R_{0}\) denotes the reference (minimal) range distance between the platform antenna and the center of the object located in the scene under illumination.  
\[g(\xi,\tau)\approx\sum_{j=1}^{K}A_{\rm tj}\,\text{sinc}[\pi(f_{\rm max}-f_{\rm min}) (\tau-\tau_{\rm tj})]\text{e}^{\text{j}2\pi f_{\rm c}(\tau-\tau_{\rm tj})}, \tag{3}\]  
where \(f_{\rm min}\) and \(f_{\rm max}\) are the lowest and the highest frequencies processed, respectively, \(f\geq 0\), \(A_{\rm tj}\) is the backscattering amplitude from the corresponding \(j\)-th target, \(f_{\rm c}=(f_{\rm max}+f_{\rm min})/2\) the center frequency, and \(\tau_{\rm tj}\) the two range time, i.e., the time required for the wave to travel from antenna to the corresponding \(j\)-th target and backwards. The reflection coefficient peaks occur at the radar ranges, where targets are present. For this reason, the backprojection process (2) can be directly applied to the output of VNA.  
The fast and fast factorized backprojection algorithms operate similarly to GBP. However, in these algorithms, the acquired data \(g(\xi,\tau)\) are divided into subsets (sub-apertures) along the azimuth axis. Each of the subsets is then superposed separately before backprojection into the corresponding sub-image plane to form the final image.
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 1 Introduction
The results show that at 800 MHz, the encoding time of maximum code length in BG1 is only 33.75 ns (27 clocks), which sufficiently meets the throughput requirement and offers a lower latency for the 5G NR standard. The rest of this paper is organized as follows. Section 2 describes the coding process of 5G LDPC codes and the structure of the parity check matrix. In Section 3, a high parallel encoding method and the corresponding encoder structure are proposed. The design details and flexible configuration are discussed in Section 4. The silicon verification and comparison results are given in Section 5. Section 6 concludes our paper.
## 4 Discussion  
In general, the study of the behavior of residuals in the case of magnetic observations is aimed at quantifying the accuracy of instrumental measurements and to quantify external influences, which are calculated differently in the Gaussian data model and in the mixed model.  
As part of the reasoning for the applicability of the aforementioned mixture model in case of SWARM data, it appears that for moderate amounts of data (\(N\sim 10^{3}\)), the KS test does not reject the model; nevertheless, as \(N\) grows to several tens of thousands measurements, the model clearly does not exhaust the complexity of inhomogeneous effects available in the SWARM magnetic data and is therefore formally rejected by the KS criterion.  
An important detail related to our model is that we assume a continuous mixture and obtain its analytic form. This is certainly an idealistic assumption, which needs not be absolutely true, especially if we have strong discrete effects in the magnetic data: for example, if we consider daytime and nighttime observations simultaneously. In this case, we are better off using a probabilistic model, which assumes that all data points appear from a mixture of a finite number of Gaussian distributions with unknown parameters. Fortunately, when the number of Gaussian distributions involved is known (at least approximately), then a well-known EM algorithm can provide (via maximum likelihood estimation) the parameters of the corresponding discrete statistical model.
## 3 Results and Discussion
### Composition of the Seed Oil
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{Comp.} & \multirow{2}{*}{**IUPAC Name**} & \multirow{2}{*}{**Common Name**} & \multirow{2}{*}{**MW** **(g/mol)**} & \multirow{2}{*}{**RT** ** **(min)**} & \multirow{2}{*}{**Percentage PMSOE (\%)**} & \multicolumn{2}{c}{**\% of**} & \multirow{2}{*}{**Similarity by**} & \multirow{2}{*}{**m/\(z\)**} \\  & & & & & & & & **NIST2.0** & \\ \hline
**CO1**\({}^{\ast}\) & 9,11,13-octadecatrienoic acid & \(\alpha\) -eleostetric acid & 278 & 16.09 & 0.19 & 0.18 & 90 & 292, 261, 232, \\
**CO2**\({}^{\ast}\) & 9,12-Octadecadienoic acid & Linoleic acid & 280 & 13.72 & 25.63 & 24.47 & 96 & 294, 263, 137, 97, 59 \\
**C13**\({}^{\ast}\) & 6,9,11-octadecatrienoic acid & X & 278 & 15.56 & 34.00 & 32.04 & 91 & 292, 177, 137, 16 \\
**C14**\({}^{\ast}\) & 9-eicosenic acid & Gadelaidic acid & 310 & 16.52 & 0.13 & 0.12 & 91 & 324, 293, 250, \\  & & & & & & & 208, 127, 85 & 282, 251, 125, \\
**C15**\({}^{\ast}\) & 9-heptadecenoic acid & Margarole acid & 268 & 12.54 & 0.04 & 0.03 & 90 & 85, 59 \\
**C16**\({}^{\ast}\) & 9-Octadecenoic acid & Oleic acid & 282 & 13.85 & 32.11 & 37.91 & 96 & 266, 265, 99, 59, 41 \\
**C17**\({}^{\ast}\) & 11-hexadecenoic acid & Palmitvaccenic acid & 254 & 11.45 & 0.17 & 0.19 & 94 & 268, 237, 83, 59, 41 \\
**C18**\({}^{\ast}\) & 9-Hexadecenoic acid & Palmitidic acid & 254 & 12.00 & 0.25 & X & 91 & 268, 237, 111, \\  & & & & & & & 71, 59, 41 & 270, 239, 57, 43 \\
**C13\({}^{\ast}\)** & Hexadecanoic acid & Palmitidic acid & 256 & 11.70 & 3.10 & 3.00 & 97 & 29 \\
**C14**\({}^{\ast}\) & Octadecanoic acid & Stearic acid & 284 & 14.14 & 1.96 & 1.45 & 97 & 298, 267, 224, \\
**C15**\({}^{\ast}\) & Eicosanoic acid & Arachid acid & 312 & 16.93 & 0.28 & 0.27 & 95 & 326, 295, 59, 57, 43 \\
**C16**\({}^{\ast}\) & Docosanoic acid & Behenic acid & 340 & 19.85 & 0.08 & 0.06 & 93 & 354, 323, 71, 99, 59, 57, 43, 29 \\
**C17**\({}^{\ast}\) & Heptadecanoic acid & Margaric acid & 270 & 12.85 & 0.04 & 0.04 & 91 & 284, 253, 59, 57, 43, 29 \\
**C18**\({}^{\ast}\) & Tetracosanoic acid & Lignoeceric acid & 368 & 22.74 & 0.06 & 0.05 & 91 & 382, 351, 71, 59, 57, 43 \\ PUFA & & & & & & 59.82 & 56.69 & & 57, 43 \\ MUFA & & & & & & 32.70 & 38.25 & & \\ SFA & & & & & & 5.62 & 4.87 & \\ \hline \hline \end{tabular}  
* m, p, and s superscripts mean MUFAs, PUFAs, and saturated fatty acids. ** PE abbreviation for petroleum ether.  
\end{table} 
Table 3: Saturated fatty acids, MUFAs, and PUFAs extracted from PMSOENH and PMSOEPE. In addition to FAs, four phthalate ester derivatives were obtained from PMSOENH: 1,2-diethyl benzene-1,2-dicarboxylate (0.26%), 1-(2-ethylhexyl) 2-methyl benzene-1,2-dicarboxylate (0.07%), 1,2-bis(2-ethylhexyl) benzene-1,2-dicarboxylate (0.43%), and 1,2-bis-2 methylpropyl benzene-1,2-dicarboxylate (0.33%), but they were not identified in the PMSOEPT analysis (Table 5), which might be due to the differences in solvent polarity.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 5 Proposed Method
### CH Selection  
CH selection and routing are done using the MOSOA. The proposed MOSOA is selected because it has a collision-avoidance property by nature, so it is mainly helpful to generate congestion-aware routing during emergencies.  
Fitness Function  
The proposed MOSOA chooses the secure optimal CHs from the clusters for obtaining secure data transmission over the network. The purpose is to choose the most optimal number of nodes such as CHs. The objective is to accomplish, proper fitness by formulating the network coverage, communication cost, residual energy, and node degree. The parameters used in the clustering optimization are as follows:  
* Network coverage  
The network coverage can be defined by Equation (13),  
\[N_{cov}=r(N_{i}) \tag{13}\]  
where \(r(N_{i})\) represents the radius covered by a node. The first objective is represented as  
\[Minimize\ f_{1}=\frac{1}{N_{T}}\sum_{i=1}^{N}N_{cov}(N_{i})\]
* Communication cost  
The cost essential for transmitting to the neighbor node is described in Equation (14).  
\[C_{com}=\frac{d_{avg}^{2}}{d_{0}^{2}} \tag{14}\]  
Figure 3: Overall block diagram of WSN-IoT.  
where \(d_{avg}^{2}\) is specified as the distance between the neighbor and node; node radius is defined as \(d_{0}^{2}\).  
The second objective is represented as \(Minimize\ f_{2}=\frac{1}{N_{T}}\sum\limits_{i=1}^{N}N_{prox}(N_{i})\), where \(N\) represents a number of nodes.  
The third objective of the residual energy is \(f_{1}\), which is reduced and represented in Equation (15).  
\[Minimize\ f_{3}=\sum\limits_{i=1}^{m}\frac{1}{E_{CHi}} \tag{15}\]  
Degree of nodes  
Node degree is defined as the quantity of non-CH participants that go to the particular mobile node. If a cluster head formerly had reduced participants, it sustains for an extensive period, due to a preference for the lesser degree of nodes. Hence, the final objective is \(f_{3}\), which is decreased in Equation (16).  
\[Minimize\ f_{4}=\sum\limits_{i=1}^{m}I_{i} \tag{16}\]  
The above-declared objectives are convert the multi-objective function into a single objective. Consequently, the normalization process (\(F(x)\)) is applied to every objective \(\alpha_{1},\alpha_{2},\alpha_{3},\alpha_{4}\).  
\[F(x)=\frac{f_{i}-f_{min}}{f_{max}-f_{min}} \tag{17}\]  
where function value is signified as \(f_{i}\), and \(f_{min}\) and \(f_{max}\) are specified as the minimum and maximum fitness values.  
\[Minimum\ fitness=\alpha_{1}f_{1}+\alpha_{2}f_{2}+\alpha_{3}f_{3}+\alpha_{4}f_{4} \tag{18}\]  
where \(\sum_{i=1}^{4}\alpha_{i}=1\); and \(\alpha_{i}\in(0,1)\).  
In the process of routing, every population dimension is identical to the quantity of cluster heads (m). Let \(P_{i}=(P_{i}^{1},P_{i}^{2},\ldots P_{i}^{m})\) be \(i\)th population, where each dimension of a population, i.e., \(P_{i}^{1}=(0,1)\), is randomly initialized. Further, a new plotting method is employed to determine the following node in the direction of BS.
## 3 Derivation of a New Finite Element
### Derivation of Load Vector Due to a Continuous Load \(q(x)\) over the Whole Element  
To complete the finite element solution for the cases where continuous loads are considered, the load vector is also required. This vector consists of nodal forces and moments that replace the actual distributed load \(q(x)\) (upwards positive) over the finite element. These terms are derived from the conditions of work-equivalency where the work \(W\) is calculated due to distributed transverse load \(q(x)\) as:  
\[W=\int\limits_{x=0}^{L_{1}}q(x)\cdot v_{1}(x)\cdot dx+\int\limits_{x=L_{1}}^{ L}q(x)\cdot v_{2}(x)\cdot dx. \tag{27}\]  
The equation is further expressed in terms of vectors of unknown nodal displacements (degrees of freedom) \(\{u\}\):  
\[W=\left\{\int\limits_{x=0}^{L_{1}}q(x)\cdot\{N_{1}\}\cdot dx+\int\limits_{x=L_ {1}}^{L}q(x)\cdot\{N_{2}\}\cdot dx\right\}\cdot\{u\}^{T} \tag{28}\]  
where the complete expressions in external curved brackets represent the substitutive load vector \(\{F_{q(x)}\}\) = \(\{F_{1},F_{2},F_{3},F_{4}\}\)T. If \(q_{L}\) and \(q_{R}\) represent values of the load at the starting and ending node, respectively, the four load vector coefficients \(F_{i}\) (\(i\) = 1, 2, 3, 4) for a linearly distributed load (upward positive) obtain the form:  
\[F_{i}=\sum\limits_{j=1}^{4}\frac{\left(L_{1}{}^{j}((1+j)Lq_{L}+jL_{1}(q_{R}-q _{L}))\right)\left(A_{i,j}-B_{i,j}\right)+L^{1+j}(q_{L}+jq_{R})B_{i,j}}{jL(1+j)}. \tag{29}\]  
This newly presented finite element solution was entitled cracked beam on 2 parametric soil Finite Element (cb2 psFE).
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 2 Signal Model and Potential Error Sources
### Signal Model  
It is important to deeply understand the signal model to assess the performance of GNSS in complex environments. In such an environment, the received GNSS signal is affected by multipath, which is the composition of direct and reflected signals [35; 36] as defined by:  
\[S_{m}=A_{d}cos\phi+A_{\xi}cos(\phi+\Delta\phi) \tag{1}\]  
where  
* \(A_{d}\) represents the magnitude of direct signals (LOS).
* \(A_{\xi}\) denotes the magnitude of indirect signals (MP, NLOS).
* \(\phi\) is the phase of the signals and \(\Delta\phi\) indicates the change in phase due to multipath and NLOS [37; 38].  
The phase change and magnitude of signals that are affected by multipath can be expressed as  
\[\Delta\phi_{m}=arctan\bigg{(}\frac{A_{\xi}sin\Delta\phi}{A_{d}+A_{\xi}cos \Delta\phi}\bigg{)} \tag{2}\]  
\[A_{m}=\sqrt{A_{d}^{2}+A_{\xi}^{2}+2A_{d}^{2}A_{\xi}^{2}cos\Delta\phi} \tag{3}\]  
In the multipath case, the estimated \(CNR\) is equivalent to the magnitude of the composite signal.
# Facial Paralysis Detection on Images Using Key Point Analysis
The authors reported their highest classification accuracy at 88.9%. [PERSON] et al. proposed a deep learning solution for the detection of facial palsy using a regular camera [10]. They formulated the facial palsy identification as an object detection problem and considered the deformation regions cause by facial palsy, or simply the palsy regions, on a patient's face as the target objects. Their proposed solution is a hierarchical network composed of three components: face detection, facial landmark detection, and local palsy region detection. The authors reported a 93% of prediction accuracy in their private database. [PERSON] et al. proposed a method to classify facial paralysis in two stages: the discrimination of healthy from unhealthy subjects, and then, facial palsy classification among unhealthy subjects [11]. They used four facial expressions (at rest, raising the eyebrows, screwing up the nose, and smiling) to measure symmetry. They built a classification model by combining a rule-based approach and a machine learning method (hybrid classifier). The authors reported an up to 98.12% sensitivity in the discrimination among healthy and unhealthy in their private database. In this work, we aim to detect facial paralysis in a set of face images, meaning that we aim to identify if the subject is healthy or a patient (binary classification). In this methodology, the evaluation of the input image is performed independently of the facial movement executed by the patient, different from other works which use a set of facial gestures to compute symmetry variations between expressions, then they detect facial paralysis. Our system measures extracted facial landmarks using simple mathematical operations that keep the implementation uncomplicated but that are still effective. Our facial measures aim to detect the level of asymmetry between the two sides of the face and also characterize facial gestures so that the learning algorithm is able to relate each asymmetry level with the expression found in the image. The proposed measures extract information from the eyebrows, eyes, nose, and mouth; they are not divided by specific regions and do not have pre-marked zones, as other authors propose. Our classification approach is based on a multi-layer perceptron, which provides a label as an output. The contributions of this research are: a set of facial measures, easily computed from facial landmarks, for the binary classification of facial paralysis independently of the facial movement performed by the subject; evaluation using two public image databases; and a classification model to detect facial paralysis. The remainder of the paper is organized as follows: Section 2 describes the proposed methodology, Section 3 introduces our findings and discussion, and finally Section 4 provides concluding remarks.