esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Evapotranspiration (ET) is an important component of the hydrologic budget because it expresses the exchange of mass and energy between the soil-water-vegetation system and the atmosphere. The prevailing weather conditions influence the potential, or reference, ET through variables such as radiation, temperature, wind, and relativity humidity. In addition to these weather variables, actual ET (ETa) is also affected by land cover type, land cover condition, and soil moisture. ETA's dependence on land cover and soil moisture, along with its direct relationship with carbon dioxide assimilation in plants, makes it an important variable to monitor and estimate crop yield and biomass for decision makers interested in food security, grain markets, water allocation, and carbon sequestration.  
Although the estimation of ETa is the ultimate goal of many researchers for hydrological and agronomical applications, it is often difficult to quantify and requires expensive instrumentation.  
However, different hydrological modeling techniques are used to estimate ETa. The two broad modeling techniques can be grouped as either based on surface energy balance (e.g., [1]; [2]; [3]; [4]; [5]) or water balance principles (e.g., [6]; [7]; [8]). More recently, [PERSON] et al. [9] developed a regression equation to estimate ETa from a combination of net radiation, vegetation index, and temperature.  
The choice of the model type depends on the availability of data and on the objective of the project. For example, the presence of cloud cover adversely impacts the surface energy balance models, but it does not impact the water balance model, which explicitly takes cloud information into consideration in the satellite-based rainfall estimation process. This feature guarantees a reliable daily ETa estimation in much of the rainfed vegetation systems regardless of cloud cover. This can be a significant advantage during the growing season in many parts of the world. On the other hand, the surface energy balance model has the advantage of estimating \"total\" ET irrespective of the water source, whereas the water balance model only estimates \"rainfed\" ET. This difference creates the opportunity to identify landscapes that meet their ET from external (e.g., stream diversion) or groundwater sources. In this study, a landscape is defined as the modeling spatial unit as determined by the resolution of the Land Surface Phenology (LSP).  
The main objective of this study is to present an improved modeling technique called **Veg**etation **ET** (VegET) that integrates commonly used water balance algorithms with remotely-sensed LSP-parameter (Figure 1) to conduct operational vegetation water balance modeling at the spatial resolution of the LSP data set using readily available global data sets.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 2 Construction of Failure Model  
The structure of the expanded anchor cable studied in the present paper is shown in Figure 1. As shown in this figure, the cable's structure could be divided into two parts (i.e., the free segment and the anchorage segment). The anchorage segment included two bearing bodies and a grouting body. In the present paper, the bearing body near the front-end surface of the anchorage segment and that near the back-end surface are referred to as the first and second bearing bodies, respectively. Each bearing body was anchored with two steel strands that were symmetrically located on both sides of the vertical plane crossing the longitudinal axis of the anchorage segment cylinder. The bearing bodies were firmly linked to the surrounding grouting body to ensure that the pullout force was transmitted to the surrounding soil through the anchorage body. To allow the steel strands to freely deform, isolating pipes were placed outside the steel strands. The pullout capacity provided by the free segment of an anchor cable is much smaller than that of the anchorage segment; hence, it was neglected in the present study. When constructing the failure model of the expanded anchor cable, its individual structures (i.e., bearing bodies and steel strands) were ignored. However, these were taken into account in the numerical simulations presented in the later sections.  
It should be noted that we only studied the ultimate pullout capacity of a single expanded anchor cable arranged in a homogeneous soil stratum. We assumed that: (1) the soil was an ideal elastic-plastic material, obeying the Mohr-Coulomb yield criterion and complying with the associated flow rule when yielding; (2) the potential slip surface in the soil area outside the pit was generated due to the construction of the foundation pit and followed [PERSON]'s active earth pressure theory. It was necessary for the anchorage segment to be buried below the potential slip surface in order to obtain the pullout capacity. Thus, the resistance provided by the soil above the potential slip surface was not considered.  
The calculated pullout capacity of the expanded anchor cable consisted of the later resistance and the end resistance, as the pullout capacity provided by the free segment was neglected in the present study. Corresponding to these two forces, the failure model of the expanded anchor cable was divided into two regions (i.e., the soil region at the front surface of the anchorage segment, and the anchorage segment and its surrounding soil, which is also referred to as the anchorage segment region). This is described in further detail in the following sections.
## 5 Conclusions  
Focused on the early detection of driver drowsiness, we attempted to classify alert and slightly drowsy states with machine learning algorithms based on hybrid measures of driving performance, behavioral features, and physiological indices. A dataset containing 10-s segments of data was created from the hybrid measures recorded during a DS experiment. The classification of alert and slightly drowsy states was performed with several machine learning algorithms. The results show that the RF algorithm can obtain 78.7% accuracy when classifying alert vs. slightly drowsy states utilizing hybrid measures and excluding physiological measures. These results demonstrate the feasibility of early detection of a driver's slightly drowsy state with high accuracy based on hybrid measures using non-contact sensors. In future work, we will further improve the reliability and applicability of the drowsiness detection system through real driving experiments.  
conceptualization, methodology, J.G., A.H. and M.S.; software, J.G.; validation, J.G.; formal analysis, J.G.; investigation, J.G., A.H. and M.S.; resources, J.G., A.H. and M.S.; data curation, J.G.; writing--original draft preparation, J.G.; writing--review and editing, J.G., A.H. and M.S.; visualization, J.G.; supervision, A.H. and M.S.; project administration, A.H. and M.S.; funding acquisition, A.H. and M.S. All authors have read and agreed to the published version of the manuscript.  
This research is supported by Nissan Motor, Co., Ltd.  
The authors declare no conflict of interest.  
Figure 7: Classification accuracy of driver drowsiness in present study (with RF algorithm) and previous studies [12; 33].
###### Abstract  
Much effort has been devoted towards the identification of brain areas recruited during during--as one of the most common motor skills of human beings. However, how driving experience impacts on the brain's intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain's intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM.  
driving; resting-state fMRI; supplementary motor area; functional connectivity; spectral Dynamic Causal Modeling; Parametric Empirical Bayes Article
## 4 Overview  
In this paper, according to the real relevant parameters in the WSN employed for monitoring wild animals in Qinling region in China, which was deployed by our lab, we establish the network topological graph and the data stream model. The specific parameters are as follows: 150 nodes deployed randomly in an area of about 1 square kilometers; the transmission distance of a single link is 80 m; the link bandwidth is 250 Kbps; the transmission success rate of a link is between 0 and 1; the number of data streams is from 0 to 30. Then, the simulations of NCR and ANCR are carried out. The simulation results show that, compared with the NCR strategy, ANCR can reduce the network delay by about 50% and increase the network throughput by about 66.67%. With the increase of network data streams, the advantage of saving energy of ANCR becomes more and more obvious.  
In order to verify the performance of ANCR, the following steps of the simulation are carried out in the paper:  
1. ANCR establish a network topological graph firstly. Then, the graph is symbolized and represented by an appropriate data structure. This step will be described in detail in Section 5.1.
2. ANCR establish a data stream model secondly. Then, the data stream model is symbolized and represented by an appropriate data structure. This step will be described in detail in Section 5.2.
3. The number of network nodes, the number of data streams and the number of packets are initialized respectively.
4. NCR is used to make routing decisions and the related parameters are calculated. The link selection criteria is defined as the smaller expected transmission count(ETX) [20]. The neighborhood search is performed on the route selected by NCR. The specific neighborhood search algorithm will be described in Section 7.
5. Calculate the related parameters about the current route and go to the next step.
6. If you want to experiment on other scenes, initialize again the number of network nodes, the number of data streams and the number of packets and back to the step (4). Otherwise, finish the experiment.  
The following sections will describe the settings of a simulation scene, the algorithm design and so on.
# SepFree NMF: A Toolbox for Analyzing the Kinetics of Sequential Spectroscopic Data
## 2 Theoretical Foundations
### SepFree NMF
These five requirements are then incorporated in the objective function \(\Psi\), defined as follows:  
\[\Psi=\alpha\bigg{(}\min_{i,j}W_{ij}\bigg{)}+\beta\bigg{(}\min_{i, j}H_{ij}\bigg{)}+\gamma\bigg{(}\max_{j}\mid 1-\sum_{i}^{r}H_{ij}\mid\bigg{)}\] \[+\delta\bigg{(}\min_{i,j}K_{ij}\bigg{)}+\mu\bigg{(}\max_{i}\mid 1 -\sum_{j}^{r}K_{ij}\mid\bigg{)}, \tag{3}\]  
where the coefficients \(\alpha,\beta,\gamma,\delta,\mu\) before each addend allow to design an objective function that fits the data characteristics. Regarding the requirements of the SepFree NMF method, the non-negativity condition of the matrix \(W\) holds or not, depending on the kind of analyzed spectrum. For example, in transition absorption spectra, the intensities in \(W\) can be also negative [18]. In this work, we compute the minimal memory effect from the kinetics matrix \(H\), obtained with the SepFree NMF algorithm. The method of the SepFree NMF has been proposed by the authors of [6]. To the notation, for any matrix \(Y,Y_{+}\) is the matrix \(Y\) without the first row and \(Y_{-}\) is the matrix \(Y\) without the last row. Let a data matrix \(M\), \(M\in\mathbb{R}^{n\times m}\) with \(\text{rank}(M)=r\) be given. Then the following steps are done in SepFree NMF:  
* Singular value decomposition (SVD) of \(M\) transposed: \(M^{T}=U\Sigma V^{T}\). * Define \(\Omega\), \(\Omega\in\mathbb{R}^{m\times r}\); the first column is the constant vector \((1,1,\dots 1)^{T}\), the other columns are the first \((r-1)\) columns of \(U\). * Use PCCA+ to find \(\tilde{H}=(\tilde{U}A)^{T}\). * Use the Penrose pseudoinverse to compute \(\tilde{W}=M\tilde{H}^{-1}\) and \(K\)= \((\tilde{H}_{-}^{-1})^{T}\tilde{H}_{+}^{T}\). * Minimize \(\Psi\) for the requirements in order to find the optimal \(A_{opt}\). * Reconstruct the concentrations and compounds intensities matrices with via \(A_{opt}\): \(H_{rec}=(\tilde{U}A_{opt})^{T}\), \(W_{rec}=MH_{rec}^{-1}\), \(K_{rec}\)=\((H_{rec,-}^{-1})^{T}\)\(H_{rec,+}^{T}\). The matrix \(K\) is computed as the autocorrelation matrix between the \(\tau\)-time-shifted concentrations \(H_{-},H_{+}\). With this relation, the matrix \(K\) has the meaning of a Markovian transition matrix since it gives information on the \(\tau\)-step development of the concentration proportions. For simplicity, we will refer in the examples to the optimized quantities \(H_{rec}\), \(W_{rec}\), and \(K_{rec}\) as \(H\), \(W\), and \(K\), respectively. This decomposition method allows to analyze experimental data with different structures because the parameters of the objective function \(\Psi\) can be adjusted to weigh more or less than a feature rather than another. Therefore, setting the parameters in one or another way influences the final results of the decomposition, or rather the algorithm finds the best decomposition for those parameters, which can be very different to another one found for other parameter sets in \(\Psi\).
# Thermo-Viscoelastic Characterization of 3D Printing Polymers
## 1 Introduction
Recent DLP printers have made the switch to using a panel of LED lights instead, which has made them cheaper and easier to maintain. In fused deposition modeling (FDM), a strand of thermoplastic material is deposited in layers to create a 3D-printed object. During printing, the plastic filament is fed through a hot extruder where the plastic becomes soft enough that it can be precisely placed by the print head and, then, the melted filament is deposited layer by layer in the print area to build the workpiece. On the other hand, HP's multi-jet fusion (MJF) and selective laser sintering (SLS) are two industrial 3D printing technologies that use the powder bed fusion technology. In both processes, parts are built by thermally fusing polymer powder particles layer by layer and the materials used in both MJF and SLS are thermoplastic polymers that come in a granular form. On the other hand, multi-jet printing (MJP) is a material jetting printing process that uses piezo printhead technology to deposit materials layer by layer and is able to achieve similar results to plastic transformation processes such as plastic injection moulding. The advantage of MJF is high accuracy of the models and highest level of detail with elaboration of fine structures, and MJF is suitable for printing molds for rapid tooling for precise products since it provides very accurate dimensional precision compared with other technologies. Multi-jet printing technology might be well-suited for printing plastic molds, as it produces a fully dense product and is capable of creating complex geometries with a high degree of dimensional accuracy. Polymer materials used in 3D printing exhibit degradation of material mechanical properties when exposed to thermal environments and thermal expansions can induce residual stresses in products or molds, which may result in dimensional instability and subsequent structural failures [8]. In addition, above the glass transition temperature (\(T_{g}\)), mechanical properties of polymers are changed significantly. It is, therefore, important to understand the thermo-mechanical properties and glass transition temperature of polymers to select the proper manufacturing temperature and provide the necessary service performance in given environments. In this study, based on linear thermo-viscoelastic principles, material degradation master curves, shift functions, and glass transition temperatures for various polymers used for 3D printing were measured by using a dynamic mechanical analyzer (DMA). The effects of temperature, frequency, and time on mechanical behaviors of polymers used for 3D printing were evaluated.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 4 Performance Evaluation
### Methods and Performance Metrics  
We compare the proposed method against the following models. Three models, which are based on the dynamic graph to vector architecture (Dyn2 vec) for embedding the dynamic graph, are constructed using the autoencoder (Dyn2 vecAE), recurrent neural network (Dyn2 vecRNN), and autoencoder-based recurrent neural network (Dyn2 vecAERNN) [36]. Dyn2 vecAE maps the dynamic graph into the embedding space by using an autoencoder. Dyn2 vecRNN inputs dynamic graphs to the recurrent neural network to calculate the embedding vectors. Dyn2 vecAERNN model utilizes the autoencoder to map the graphs into the low-dimensional space and inputs the embedding vectors into the recurrent neural network to learn the temporal features. In addition, we use the graph convolutional network (GCN) model as one of the baselines [37].  
This paper utilizes the sliding window to partition the traffic data into several time intervals. We select five slide windows with different sizes to divide the time series. The sizes of the slide windows are set as 200 s, 250 s, 300 s, 350 s, and 400 s. Given the incident period of 300 s, the reason for setting the sliding window with different sizes is that we want to observe the performance with three different cases, i.e., the size of the sliding window is smaller than, equal to, or is greater than the incident period.  
Experiments are conducted with different traffic volumes and slide windows to obtain the performance with different parameters. We construct the ground truth as follows. The time intervals are divided by using the sliding window. If the time intervals do not include the incident timestamp, the time intervals are labeled as positive. The proposed model can then be evaluated by using the Accuracy (ACC) and F1 score as  
\[Accuracy=\frac{T_{p}+T_{n}}{T_{p}+T_{n}+F_{p}+F_{n}} \tag{4}\]  
\[Precision=\frac{T_{p}}{T_{p}+F_{p}} \tag{5}\]  
\[Recall=\frac{T_{p}}{T_{p}+F_{n}} \tag{6}\]  
\[F1=\frac{2*Precision*Recall}{Recall+Precision} \tag{7}\]  
where \(T_{p}\) represents the number of anomalies detected to be anomalous, \(T_{n}\) represents the number of nonanomalies detected to be nonanomalous, \(F_{p}\) represents the number of nonanomalies detected to be anomalous, and \(F_{n}\) represents the number of anomalies detected to be nonanomalous.
# Quorum Sensing Regulation of Competence and Bacteriocins in _Streptococcus pneumoniae_ and _mutans_
## 1 Introduction  
Nearly 100 years have passed since the first hints of bacterial communication were observed in [PERSON]'s seminal descriptions of what came to be understood as horizontal gene transfer in _Streptococcus pneumoniae_[1]. However, the study of quorum sensing (QS) and the molecular mechanisms underlying coordinated processes leading to the development of competence and the production of bacteriocins (bacterially-derived antimicrobial proteins) has taken the decades since to uncover the signals and the regulatory pathways required for these processes. Unraveling the intricacies of a particular QS system is crucial for the development of methods to modify downstream bacterial behavior. Although great progress has been made in describing bacterial quorum sensing pathways, it is apparent that the complexities of each pathway are not thoroughly understood. This review compares recent findings describing the regulation of competence and the direct killing mechanisms of _Streptococcus pneumoniae_ and _Streptococcus mutans_.
## 6 Conclusions  
A numerical algorithm has been developed to obtain parameterized excitation coefficient data based on VIV model test data of a flexible riser with buoyancy elements. Two sets of excitation coefficient parameters are used to represent the bare riser parts and the buoyancy elements, respectively. The parameters are systematically varied by multiplying with modification factors and the optimal set of excitation parameters is selected when the predicted response agrees well with the model test results. The optimal datasets of excitation coefficient parameters are significantly different from the default data based on bare riser tests. A significant improvement in prediction is achieved in terms of mode, response frequency, response amplitude and RMS curvature.  
The hydrodynamic parameters are derived based on one case from one of the five staggered buoyancy element configurations. The prediction results are consistent for the other cases of the same configuration. The prediction accuracy has been significantly improved compared to the use of the default parameters. However, the prediction shows larger deviation compared to the measurements when applied to a different configuration. This is what can be expected as the interaction between the bare riser section and the buoyancy element is complicated. The hydrodynamic parameters will not be the same as the configuration or the dimension of the buoyancy element changes, as also seen in other such tests. Therefore, it can be difficult to generalize the result. However, the present approach offers one way to estimate these key hydrodynamic parameters directly from model test with elastic pipes. It can be applied to available tests to estimate and map the parameters, so that the physical basis in the hydrodynamic load modelling can be further improved. This approach can also be applied to other VIV tests.  
Figure 16: The ratio of the predicted over measured maximum RMS curvature values at different flow speeds for five configurations.  
**Author Contributions:** The author contributions are listed as following: conceptualization, J.W., M.C.O.; methodology, [PERSON], M.K.R.L, M.C.O.; software, [PERSON], M.K.R.L.; formal analysis, [PERSON], M.K.R.L., M.C.O.; investigation, [PERSON], M.K.R.L., M.C.O.; writing--original draft preparation, [PERSON], M.K.R.L.; writing--review and editing, [PERSON], M.K.R.L., M.C.O.; supervision, M.C.O., J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.