esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Cost Efficiency of Systems Engineering in Oil and Gas Projects
## 3 Results  
To assess the effect of SE, we used data on one of Gazpromneft's fields located on the territory of the Yamada-Nenets Autonomous regions [32]. Capital investment into the project is about $1 billion.  
The NPV for all outcomes--EMV, EMVse, and the F-effect--were calculated taking into account all stages of the field's life cycle, from concept development to retirement.  
Carrying out a series of calculations to find the maximum value of the effect made it possible to determine the optimum costs for SE typical for oil and gas projects of this scale. Time and budget deviation values corresponding to a specific SE cost were derived from Honour's correlation.  
The results of the NPV and EMV calculations for the base project and the project using systems engineering, presented in Figure 3, showed a steady positive effect of 45%.  
The set of economic efficiency indicators shown to compare two alternative options for the project implementation is presented in Table 3.  
Simulating the scenario of budget overruns from the initial volume of capital investments affects the size of lost profit due to the increase in the financial burden in the initial period and, as a consequence, the later payback. Leveling the risks of budget overruns in a project using SE allows one to save 87 million dollars and get a 3.2-times higher NPV than in the base case.  
The second failure scenario--namely, a slip in project start--reduces the NPV value because the project reaches the oil production plateau later and the revenue from the hydrocarbon sales decreases. The use of SE in this project reduces revenue loss by 34 million dollars, which leads to a 4 million dollar increase in NPV. The peculiarities of taxation and benefits explain the small magnitude of the effect in the time-failure scenario.  
Having analyzed economic indicators for projects without systems engineering and with systems engineering, we can conclude that applying the new paradigm to oil and gas projects leads to a stable  
Figure 3: The economic effect from systems engineering introduction exemplified by the Gazpromneft’s field located on the territory of the Yamada-Nenets Autonomous regions.  
positive effect due to an increase in the probability of successful project implementation and a decrease in the number of budget overruns and schedule slips.
# Sensor Observation Service API for Providing Gridded Climate Data to Agricultural Applications
## 3 Methodology
### Sos Api  
To ensure interoperability, NARO Agent provides sensor data and metadata through SOS API conforming to OGC SOS 2.0. Three core operations of SOS, getCapabilities, getObservation and describeSensor, are implemented. The list of APIs and their documents is available at [[http://133.130.90.193/static/index.html](http://133.130.90.193/static/index.html)]([http://133.130.90.193/static/index.html](http://133.130.90.193/static/index.html)). We applied the IP restriction to the API according to the NARO requirement for user registration to access data.  
As mentioned above that the structure of AmGSD is keeping data based on area, year and a climate item, while SOS is based on the observed property in a time series structure. To simultaneously provide point time series data in SOS API while maintaining grid information, the climate variable of AmGSD is mapped to the observedProperty of SOS as mentioned in Table 2. AmGSD is considered as a sensor platform, which performs the observations. AmGSD is named as urn:NARO:AgroWeatherMesh, which refers to the whole system and covers all grids. A specific grid can be referred to by the simple mesh ID as described in Table 1 or by a bounding box that is equivalent to a single coordinate.  
We implement SOS API, which supports the HTTP GET and POST methods. For the GET method, a user can request data by specifying the service endpoint of each service. For the POST method, a user has to send an XML request for processing each operation. Each operation is described separately as follows:
## Materials and Methods
### Material Characterization
#### 2.7.3 Release Measurements  
The release of GA was determined in saline medium following the procedure described by [PERSON] et al. [32]. This type of medium was selected due to both the improved water retention behavior of the electrospun PLA mats and the high release rates of GA achieved. For this, the bi- and multilayer films containing GA were initially cut in square pieces of 20\(\times\) 20 mm\({}^{2}\) in randomly selected areas. The films were weighted and submerged in 30 mL of saline medium, prepared by dissolving 9 g of NaCl in 1 L of distilled water and stored for a whole period of ten weeks, that is, 1680 h, without darkness and at room temperature, that is, 23 + 2 \({}^{\circ}\)C. At different submersion times, 1 mL of the medium solution was withdrawn (hereafter, a sample solution) and an equal amount of the fresh medium was refilled. The amount of GA in the sample solutions was determined in an Ultraviolet-Visible (UV-VIS) UV 4000 spectrophotometer (Thermo Scientific, Waltham, MA, USA) at a wavelength of 259 nm. The obtained data were calculated to determine the amount of GA released from the specimens at each submersion time point. For this, a calibration curve of GA in normal saline was previously determined, resulting in the following regression equation Y = 0.0227X + 0.0156 (R\({}^{2}\) = 0.9989), where \"Y\" is the absorbance and \"X\" the GA content, in ppm, of the sample. All measurements were carried out in triplicate.
_Article_  
**Prediction and Sound Quality Analysis of Tire Pattern Noise Based on System Identification by Utilizing an Optimal Adaptive Filter**  
**[PERSON] *(c)**, **[PERSON], [PERSON] and [PERSON]  
Acoustics and Vibration Signal Processing Lab., Department of Mechanical Engineering, Inha University, Incheon 22201, Korea; [EMAIL_ADDRESS] (K.A.); [EMAIL_ADDRESS] (H.-Y.C.); [EMAIL_ADDRESS] (S.-U.H)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +82-032-860-7305 check for updates  
Received: 18 August 2019; Accepted: 20 September 2019; Published: 24 September 2019  
**Featured Application: The proposed framework is highly suitable for designing tires with a low pattern noise.**  
**Abstract:** Identifying the cause of vehicle noise is a basic requirement for the development of low-noise vehicles. The tire pattern noise depends on the tire itself and causes complex and unpredictable sounds. In pneumatic tire pattern design, the prediction technology of the tire pattern noise according to pattern shape design is important. The conventional method of predicting tire pattern noise is to simply scan the pattern shape of tire and to analyze its spectrum. However, this method has limitations because it does not consider the transfer function and precise mechanism of tire pattern noise. In this study, adaptive filter theory was applied to identify the transfer function between the grooves of patterns and measured acoustic data. To predict the waveform of an actual pattern noise in the time domain, the impulse response of this transfer function was convolved by the scanned pattern input of tires. The predicted waveform of pattern noise was validated with the waveforms of measured noise data. Finally, a sound quality index (SQI) of tire pattern noise was developed using the measured pattern noises and was applied to estimate the sound quality of pattern noise. Eventually, using the prediction method from this study, we hope to reduce the time and cost spent on tire pattern design and verification.  
**Keywords:** adaptive filter; tire pattern noise; transfer function; sound quality index of pattern noise
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.14 Content Freshness Based  
A congestion control scheme is proposed in [30] which counters the congestion issue in network by controlling the size of congestion window in real time. The congestion window is varied based on the measured congestion ratio and freshness of congestion ratio information. Whenever a new ACK message is received by the sender node, the congestion ratio is measured. When the Congestion Ratio (CR) measured in previous ACK is greater than the current CR value, the congestion window size is reduced and when previous CR value is less than the current CR value the congestion window is increased to allow additional packet transmission. Moreover, the source node also measures the freshness of congestion ratio information by measuring the interval in addition to current CR value. This interval is the difference between reception time of current and previous ACK. If the interval is large the CR information is considered old. Finally, at the reception of the ACK message the source node also measures the RTT and distinguishes it as weak RTT (WRTT) or strong RTT (SRTT). If RTT is SRTT, the source node considers the update in current congestion window size as final congestion window size until the reception of next ACK message, whereas, if RTT is WRTT, the source divides it by 2 and determines if WRTT is greater than 2 xSRTT. In case when WRTT is less than 2 xSRTT, it is assumed that the network suffers low congestion and small reduction in congestion window is sufficient. However, in case when WRTT is greater than 2 xSTT, high congestion in network is observed and congestion window needs to be reduced sufficiently.
## Appendix B Derivation of \(A_{22}\)  
It can be rewritten \(A_{22}\) as  
\[\begin{split} A_{22}&=\Pr\left\{\left|h_{SR_{k}}\right| ^{2}>\frac{\delta}{P},\left|h_{R_{k}D_{1}}\right|^{2}<\frac{\delta\left|h_{R_ {k}P}\right|^{2}}{I},\left|h_{R_{k}P}\right|^{2}>\frac{I}{\eta\left(P\left|h_{ SR_{k}}\right|^{2}-\delta\right)}\right\}\\ &=\int_{\frac{\delta}{P}}^{\infty}f_{\left|h_{SR_{k}}\right|^{2}} \left(x\right)dx\int\limits_{\frac{I}{\eta\left(P\left(x-\delta\right)\right)} \int\limits_{\frac{I}{\eta\left(P\left(x-\delta\right)\right)}}^{\infty}f_{ \delta\left|h_{R_{k}P}\right|^{2}}\left(y\right)F_{\left|h_{R_{k}D_{1}}\right| ^{2}}\left(\frac{\delta y}{I}\right)dy\end{split} \tag{14}\]  
Then, \(A_{22}\) can be further computed by  
\[A_{22}=\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)\frac{k(-1)^{k-1}}{\Omega_{SR}}\int_{\frac{\delta}{P}}^{ \infty}\left(\mathrm{e}^{-\frac{I\Omega_{SR}}{\Omega_{SR}}}\right)dx\left( \mathrm{e}^{-\frac{I}{\Omega_{RP}\Omega\left(P\left(x-\delta\right)\right)}} -\frac{I\Omega_{RD_{1}}}{\delta\Omega_{RP}+I\Omega_{RD_{1}}}\mathrm{e}^{-\left( \frac{\delta\Omega_{RP}+I\Omega_{RD_{1}}}{\Omega_{RD_{1}}\Omega_{RP}\Omega \left(P\left(x-\delta\right)\right)}\right)}\right) \tag{15}\]  
Thus, the closed-form expression of \(A_{22}\) can be obtained as  
\[\begin{split} A_{22}&=2\sum\limits_{k=1}^{K}\left( \begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\mathrm{e}^{-\frac{k\delta}{\Omega_{SR}P}}\left( \sqrt{\frac{Ik}{\Omega_{RP}\Omega_{SR}P\eta}}K_{1}\left(2\sqrt{\frac{Ik}{ \Omega_{RP}\Omega_{SR}P\eta}}\right)\right.\\ &-\left.\frac{I\Omega_{RD_{1}}}{\delta\Omega_{RP}+I\Omega_{R_{k}D_{1}}} \sqrt{\frac{\left(\delta\Omega_{RP}+I\Omega_{RD_{1}}\right)k}{\Omega_{RD_{1}} \Omega_{RP}\Omega_{SR}P\eta}}K_{1}\left(2\sqrt{\frac{\left(\delta\Omega_{RP}+I \Omega_{RD_{1}}\right)k}{\Omega_{RD_{1}}\Omega_{RP}\Omega_{SR}P\eta}}\right) \right)\end{split} \tag{16}\]  
It completes the proof.
## 6 Applications of Chaos to Encryption  
In order to efficiently use chaos in cryptography, chaotic systems need to be implemented such that the generated entropy can produce confusion and diffusion properties. These features along with cryptographic primitives share unique characteristics, which enable chaos to be effectively applied to encryption. Herein two main topics are discussed. One issue regards the hardware implementations of encryption systems [18; 19; 20; 21; 22; 23; 24; 25; 26], whereas the other is related to the capability of chaotic systems to generate random numbers [27; 28; 29; 30; 31; 32; 33; 34; 35].
# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 4 Experimental Analysis
### Parameter Settings  
This study employed a 300-dimension pre-trained Glove as a word embedding. The hidden layer dimension of the Bi-LSTM was set to 50. The dropout rate was set to 0.3. The batch size was set to 16 for all models, and the learning rate was set to 0.002. We used Adam as the optimizer.  
To ensure the model focused on SSWs, we conducted experiments on the sentiment support weight values of SSWs. We experimented with different weight values in the SSGCN, and the specific experimental results are shown in Table 2. Herein, the accuracy rate and macro F1 were used as evaluation indices.  
We believe that the weight of the sentiment support should not be too large to highlight the sentiment support words or too small. From Table 2, it can be seen that when the sentiment support weight z was set to 2, using the accuracy and macro F1 as evaluation indicators, the sentiment analysis of the _Restaurant_ dataset obtained the best results. Therefore, in this study, we set \(z=2\).
# Value of Information Based Data Retrieval in UWSNs
## 5 Performance Measures, Simulation Setup and Results
### Simulation Setup  
To investigate our various hypotheses regarding VoI based path planning we have used a simulation approach. We assume a scenario which has two types of classes for the events; one is normal routine events while the other events are those that require an emergency response. To monitor these events a UWSN has been deployed. The nodes in the UWSN collect multimedia information through cameras. Due to a limited finite capacity, data needs to be offloaded from these nodes by an AUV. The nodes communicate infotential data using the acoustic communication medium. A path planning agent, based on the infotential data that it receives, schedules a visitation sequence of the nodes for the AUV. During its tour, the AUV off-loads multimedia data from the nodes using the optical communication medium. A node which has recorded an event that can be classified as an emergency is marked as a hot-spot.  
The simulation has 100 nodes arranged in a \(10\times 10\) mesh/grid. The horizontal/vertical inter-node distance is 1000 m while the diagonal distance is 1414.2 m. The AUV traverses the UWSN at a constant speed of 10 m/s.  
We assume that before the AUV embarks on its tour, the UWSN has been recording data for 24 h. Each node has video data reports of length 15 min each. Therefore, the reports have been recorded starting at intervals in the multiples of 15 min and with no recording overlap. The event coverage of the nodes is such that they have minimum or no-overlap, and hence, the recorded events are unique. Each node records data reports and classifies them either as a high-priority or a low-priority event. A \(\{valuation,decay\}\) tuple, corresponding to significance and damage rate, is appended to each data report. If it is a routine low-priority event, then the \(\{valuation,decay\}\) tuple \(\{A_{L},B_{L}\}\) are appended to the data report. Alternatively, if it is an emergency event, then the tuple \(\{A_{H},B_{H}\}\) are attached to it. The UWSN nodes communicate with the remote path-planning agent over the acoustic channel and transmit to it the \(\{valuation,decay\}\) and time-stamp details of the data reports that they have recorded. Based on these details, the path-planning agent determines the visitation sequence for the AUV.
## 1 Introduction
The primary contributions of this article are summarized as follows:  
* To extend the controller design method of [13] to trajectory tracking, we first show that the convex input constraints are invariant under the input transformations, i.e., the input constraints for the transformed error systems are also convex. * We formulate a convex optimization problem to characterize the minimizing input for the error systems with convex input constraints. * Based on the minimizing input, we propose a continuous constrained tracking controller by introducing an appropriate scaling function. * Through numerical simulations, we can confirm that the proposed tracking controller achieves trajectory tracking and satisfies the given input constraints. Compared to a Sontag-type (unconstrained) tracking controller [30; 31], we can also confirm that the proposed controller reduces the tracking error more efficiently. The rest of the article is organized as follows. Section 2 introduces the basic definition and results on trajectory tracking of nonlinear systems, specifically based on the Lyapunov-based approach. The problem addressed in this article is formulated in Section 3. In Section 4, we derive the minimizing input, which minimizes the time derivative of a TCLF subject to a given convex input constraint. Based on the minimizing input, we propose the constrained tracking controller in Section 5. Then, Section 6 confirms the effectiveness of the proposed controller through numerical simulations. Finally, brief conclusions are given in Section 7.
## 1 Introduction
[20] employed ANN and FEM to optimize permanent magnet flux switching generator and analyze the dynamic response of slender marine structures respectively. In these researches, ANN and FEM were still two independent tools. In contrast, in this work, a new application method of ANN and FEM was put forward based on the well-constructed ANN model. Here, the constructed ANN model for the constitutive relationship of Inconel 718 superalloy was successfully integrated with FEM by Fortran language. As is known, the stress-strain data amount and accuracy input into the finite element (FE) model have a significant influence on the simulation accuracy of the hot forming processes. Relative to in the traditional way of importing stress-strain data into the FE solver, in the way described above, the ANN model can predict abundant and accurate stress-strain data automatically for FE solver. Thus, the accuracy of FE simulation could be strongly guaranteed. Before this constitutive relationship with a new style, a continuous mapping relationship was constructed by predicting abundant stress-strain data in extensive deformation conditions. Such a constitutive relationship reflects the continuous mapping of stress to strain, strain rate and temperature. This work focused on the constitutive relationship construction of Inconel 718 superalloy using ANN model and its application in accuracy improvement of FE numerical simulation. The core of the FE model with high accuracy is the accurate constitutive relationship in wide ranges of temperature, strain and strain rate. In this research, this is guaranteed by a well-trained ANN model taking temperature (\(T\)), strain rate (\(\dot{e}\)) and true strain (\(\epsilon\)) as the input variables, and true stress (\(\sigma\)) as the output variable. Those fundamental data used for the training and test of ANN model were collected from a series of isothermal compression tests conducted by a Gleeble 3500 thermo-mechanical simulator. This work put forward an artificial neural network-based route to improve the FE numerical simulation accuracy of hot forming processes.
## 3 Results
### Icephobic Properties
#### 3.5.3 Summary of the Icephobic Properties  
Comparing the results of the ice adhesion and the freezing delay time of water droplets, a direct correlation between these parameters can be seen. The lower the ice adhesion value  
Figure 9: The ice adhesion and the freezing delay time of the epoxy–silicone samples at \(-15\,^{\circ}\)C.  
the sample showed, the higher the value of the freezing delay time. Among the samples tested, the sample MOD4 showed the highest icephobic properties. This sample displayed the lowest ice adhesion and the longest freezing delay time for water droplets.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 4 Proposed Wind Farm Control Technique in AGC
### Design Procedure of SCA-Based FOPID Controller  
#### 4.2.1 Fractional Order PID Controller  
Benefit from a high degree of freedom (by selecting suitable values for \(\lambda\) and \(\mu\)), fractional order controllers are regularly more adequate than usually used integer order models like PI and PID controllers. The general form of FOPID controller is depicted in Figure 3 and it is mathematically represented as:  
\[PI^{\lambda}D^{\mu}=K_{P}+\frac{K_{I}}{S^{\lambda}}+K_{D}S^{\mu}. \tag{6}\]  
Herein, \(\lambda\) and \(\mu\) represent the fractional order operators often adjustable in the range of (0, 1) and \(K_{P}\), \(K_{I}\) and \(K_{D}\) are the proportional, integral and differential gains of FOPID controller respectively. It can be seen from Figure 4 that, the FOPID controller can operate like simple classic controllers (P, I, PI, PID) by selecting 0 and 1 for \(\lambda\) and \(\mu\)[70].  
Figure 3: Two-area power system in the presence of wind farms.  
There are 5 parameters for the FOPID controller, which should be optimized by optimization algorithms. In this study, the SCA approach is utilized for optimization of FOPID controller. For this purpose and at the first step, the objective function and adjustable parameters of the controllers should be determined.