esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Investment Timing and Scale Choice of Overseas Oil Projects: A Real Option Approach
## 3 Model Description
### Value of Oil Projects
#### 3.1.1 Oilfield Production  
Oilfield production has its variation rule that directly decides investment cost, operation cost, and revenue, which is related to irreversible capital investment. In this model, the oil resource is assumed to be fully explored. Therefore, the production is determined by the volume of oilfield, the selected recovery factor, and the extraction rate. The volume of in situ oil reservoir is fixed and called _OIP_, which means the volume of oil-in-place. The selected recovery factor measures the selected portion of in situ resource that will actually be produced. This implies that the recoverable reserves is determined by the volume of oilfield (_OIP_) and the selected recovery factor. The extraction rate indicates the ratio of plateau oil production to recoverable reserves.  
Based on previous research about oilfield production [40; 41; 42], the following is known:  
Investment, build-up, plateau production, and decline phase are four production phases in an oilfield's lifetime. Firstly, oil companies mainly conduct oilfield infrastructure work, and the oil  
Figure 1: Framework for the choices of optimal investment timing and scale in overseas oil projects.  
production is 0 during this investment phase. Secondly, oil production increases in proportion to time during the build-up period. Thirdly, the production of oilfield maintains at a relatively high level and can be approximated as a constant during the plateau production period. Lastly, the production of oilfield can be approximately seen as a constant percentage decline (exponential) during the decline phase.  
In summary, we can obtain the production function of oilfield in its whole contract period.  
\[m(t)=\left\{\begin{aligned} 0,t&\in[0,T_{0})\\ a\cdot s\cdot OIP\cdot\frac{t}{T_{1}},t&\in[T_{0},T_{ 1})\\ a\cdot s\cdot OIP,t&\in[T_{1},T_{2})\\ a\cdot s\cdot OIP\cdot e^{-\omega(t-T_{2})},t&\in[ T_{2},T_{3}]\end{aligned}\right. \tag{2}\]  
where \(s\) and \(a\) denote the selected recovery factor and extraction rate of the oilfield, respectively. \(\omega\) is the oil production decline rate. \(T_{0}\), \(T_{1}\), \(T_{2}\), and \(T_{3}\) denote the termination of oilfield investment, build-up, plateau production phase, and the length of contract period, respectively.
## 3 Compensation Method
### Vibration Compensation Method  
In THz-SAR imaging, the high-frequency vibration error leads to the paired echo and reduces the image quality when the imaging algorithms are directly performed on the echo data. Here, a multi-component high-frequency vibration error compensation method based on STFT is proposed for solving the above problem.  
First, the dominant point target is selected from the coarsely focused image and the Sol is extracted for the vibration-parameters estimation. Then, the accurate vibration error parameters are estimated by the improved STFT-based fine estimation method. Finally, on the basis of the optimal estimated parameters values, the vibration error is compensated, and the THz-SAR focused image is obtained. Therefore, the flow chart of the proposed vibration error compensation method is given in Figure 2.
## 4 System Communication Implementation Plan  
### Communication between Physical Robot and ROS  
There is bidirectional communication between the physical robot and the ROS, as shown in Figure 7.  
ROS nodes in the ROS parameter server receive key information about the robot, such as the state of the robotic arm, joint positions, motion trajectories, and timestamps, by subscribing to ROS topics. These nodes can include controller nodes of the physical robot, which use protocols like Controller Bridge and Sensor Driver to publish the control data and sensor data of the robot to the corresponding ROS topics [38].  
After these data are published through the topics, other ROS nodes can subscribe to these topics to receive the data. For example, a processing node may load the robot's URDF model and obtain model information, such as joint configurations, default states, and collision detection information, and coordinate transformations based on the received data. Additionally, this processing node can create a virtual environment and use technologies like ROS Bridge to customize topic information, generating a virtual robot model and user simulation interface to achieve mapping between the physical robot and the virtual model.  
Users interact with the virtual model through the simulation interface, allowing them to monitor and control the physical robot mapped to the virtual model. The ROS uses algorithms like Cartesian path planning and forward/inverse kinematics based on topic information to plan the motion trajectories of the robot's joints, ensuring that the trajectories  
Figure 7: Bidirectional communication diagram between physical robot and ROS.  
conform to the preset thresholds of the robot model, thereby controlling the physical robot to perform actions according to the planned movements.  
The process is represented in pseudocode as follows:  
Through this method, real-time bidirectional communication is achieved between the virtual model in the ROS and the physical robot, effectively connecting the virtual and physical worlds and enhancing the flexibility and application range of the robot system.  
One of the obstacles to implementing this solution is that some robot companies do not provide corresponding URDF models for their robots, requiring users to convert CAD models into URDF models. For example, KUKA only provides CAD models.
# A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity
## Appendix A Sparsity Analysis of Audio Signal in the MDCT Domain  
Considering that the MDCT is a commonly used time-frequency transform in signal processing, the intra-object sparsity of audio signal in the MDCT domain should be investigated. Thus, a quantitative analysis for sparsity of audio signals both in the MDCT and STFT domain is given in this appendix.  
According to the \(k\)-sparsity theory interpreted in compressed sensing [32; 33], a signal/sequence is regarded as (strict) \(k\)-sparse when it contains \(k\) nonzero entries with \(k\propto K\), where \(K\) is the length of the signal or sequence. In addition, a sequence can be considered as an approximate \(k\)-sparse if \(k\) entries of the sequence occupy the majority of the total amount in magnitude, while the magnitude of other entries are remarkable small. In our previous work [19], we validated that an audio signal is not sparse in time domain, but its STFT coefficients in frequency domain fulfills the approximate \(k\)-sparsity. For this reason, STFT is selected as basic transform in our preceding designed object encoding system. The perceptual quality of the decoded signal can achieve a satisfactory level. However, STFT is not an optimum sparseness time-frequency transform. In consideration of the energy compaction property (i.e., a small number of TF instants capture the majority of the energy) of MDCT, therefore, approximate \(k\)-sparsity of audio signal in the MDCT domain will be investigated compared to that in the STFT domain by statistical analysis.
# An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection
## 5 Mode Enhancement and Defect Detection Simulation  
### Mode Enhancement Theory Based on Phase Control and Time Delay Technology  
According to the theory of [PERSON] [21], the installation interval and the excitation sequence of transducer array are set based on the expected guided wave mode, and the excited guided waves have the same phase angle. The excitation time interval is set as periodic \(T\). According to the principle of wave superposition, the synthetic amplitude of two coherent waves with the same phase is the largest when the interval of transducers is offered based on the expected mode wavelength. As shown in Figure 10, 5 transducers are installed on the rail.  
Transducer 0# is an excitation signal without time delay. The displacement function of each particle is \(\mathbf{u}_{0}(x,y,z,t)\).  
\[\mathbf{u}_{0}(x,y,z,t)=\left[\begin{array}{c}u_{x}(x,y,z,t)\\ u_{y}(x,y,z,t)\\ u_{z}(x,y,z,t)\end{array}\right]=\left[\begin{array}{c}U_{x}(y,z)\\ U_{y}(y,z)\\ U_{z}(y,z)\end{array}\right]e^{i[\zeta x-\omega t)} \tag{11}\]  
where \(\zeta\) is the wavenumber.  
The distance between transducer 1# and transducer 0# is \(\lambda\), and the time delay is \(T\). The displacement function of each particle is \(\mathbf{u}_{1}(x,y,z,t)\).  
\[\mathbf{u}_{1}(x,y,z,t)=\left[\begin{array}{c}u_{x}(x,y,z,t)\\ u_{y}(x,y,z,t)\\ u_{z}(x,y,z,t)\end{array}\right]=\left[\begin{array}{c}U_{x}(y,z)\\ U_{y}(y,z)\\ U_{z}(y,z)\end{array}\right]e^{i[\zeta(x-\lambda L)-\omega(t-\Delta t)]}=\left[ \begin{array}{c}U_{x}(y,z)\\ U_{y}(y,z)\\ U_{z}(y,z)\end{array}\right]e^{i[\zeta(x-\lambda)-\omega(t-T)]} \tag{12}\]  
As we know:  
\[\xi\lambda=2\pi \tag{13}\]  
\[\omega T=2\pi \tag{14}\]  
Substitute the Equations (13) and (14) into the Equation (12) to obtain the Equation (15).  
\[\mathbf{u}_{1}(x,y,z,t)=\left[\begin{array}{c}u_{x}(x,y,z,t)\\ u_{y}(x,y,z,t)\\ u_{z}(x,y,z,t)\end{array}\right]=\left[\begin{array}{c}U_{x}(y,z)\\ U_{y}(y,z)\\ U_{z}(y,z)\end{array}\right]e^{i[\zeta(x-\lambda)-\omega(t-T)]}=\left[\begin{array} []{c}U_{x}(y,z)\\ U_{y}(y,z)\\ U_{z}(y,z)\end{array}\right]e^{i[\zeta x-\omega t)]}=\mathbf{u}_{0}(x,y,z,t) \tag{15}\]  
Similarly, the displacement and phase of transducer array 0#, 1#, 2#, 3#, and 4# can be derived as Equation (16):  
\[\mathbf{u}_{0}(x,y,z,t)=\mathbf{u}_{1}(x,y,z,t)=\mathbf{u}_{2}(x,y,z,t)=\mathbf{u}_{3}(x,y,z,t) =\mathbf{u}_{4}(x,y,z,t) \tag{16}\]  
When the guided wave propagates along x direction, the expected mode is enhanced and the interference mode is suppressed. The transducer intervals of each mode are shown in Table 3.  
Figure 10: Phased array excitation diagram.
## 5 Conclusions  
There are relatively large uncertainties and low temporal accuracy in fragmented areas when estimating forest distribution based on either synthetic aperture radar (SAR) or optical remote sensing images alone. We proposed a straightforward and robust rule-based algorithm to map the forest based on the Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Fine Beam Dual polarization (FBD) mosaic images and the annual maximum Normalized Difference Vegetation Index of land satellite (NDVImax). The PALSAR/Landsat forest map in 2010 with the relatively high overall accuracy and lower uncertainty showed improvements against selected forest maps. This indicated that the integration of PALSAR and Landsat data is more advantageous in estimating forest area than using the Landsat or PALSAR alone in traditional fragile regions like the Loess Plateau.  
We investigated the temporal and spatial variations of forest cover after GGP in the Loess Plateau from 2007 to 2017, and found the forested areas increased about \(0.15\times 10^{5}\) km\({}^{2}\) from 2007 to 2017 in the Loess Plateau, suggesting the effectiveness and sustainability of ecological restoration projects like GGP. The resultant PALSAR/Landsat forest maps would provide a timely and accurate data source for forest management and policy formulation under the future GPP in fragile areas with complex climates.  
Conceptualization, F.X. and J.D.; Data curation, F.X. and H.Z.; Formal analysis, Z.Y. and H.Z.; Funding acquisition, H.Z. and G.Z.; Investigation, H.Z. and Z.Y.; Methodology, J.D. and J.Z.; Project administration, G.Z. and Y.Q.; Resources, Z.Y. and H.Z.; Software, Z.Y. and H.Z.; Supervision, F.X. and J.D.; Validation, H.Z.; Visualization, Z.Y. and H.Z.; Writing--original draft, H.Z. and Z.Y.; Writing--review and editing, J.D., Y.Q., and X.X.  
This study is funded by the Strategic Priority Research Program (XDA19040301) and Key Research Program of Frontier Sciences (QYZDB-SSW-DQC005) of Chinese Academy of Sciences (CAS), National Natural Science Foundation of China (61772078), Key R&D Program of Beijing (D171100001817003).  
We thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for their valuable comments during the study.  
The authors declare no conflict of interest.
# Combining Facial Expressions and Electroencephalography to Enhance Emotion Recognition
## 3 Results  
### Result Analysis  
Figure 3 provides a graphical overview of the accuracy of various detection methods among various data sets for each subject. From top to bottom, the accuracy of the MAHNOB-HCI data set, the DEAP data set, and the online experiment in both valence and arousal space are presented in order.  
The emotion recognition results of the MAHNOB-HCI data set are presented in Table 1, the results of the DEAP data set are presented in Table 2, and the results of the online experiment are presented in Table 3. Although the overall accuracy of facial expression was high, poor performance was still observed for some subjects (such as subjects 1, 3, 5, 11, and 12 for the valence space in the DEAP data set), indicating that high volatility is associated with facial expressions during emotion experiments. The performance of the modality can be improved by combining the findings with those of another modality (e.g., EEG). Except for one case (arousal space in the online experiment, where the accuracy of the first fusion method was lower than that of EEG (signal modality)), the accuracy of the fusion method was higher than that of the signal modality approach.  
Figure 3: Accuracy of various subjects among various data sets. The X-axis of each sub-figure represents the subject I.D., and the Y-axis represents the accuracy.  
Moreover, we first performed a normality test of accuracy for each of the four detectors, and the data were considered normal when the result of the normality test was below 0.05. This test was followed by a paired t-test (normal data) or Nemenyi procedure (not normal). The statistical test results are presented in Tables 4-9. The output of the paired t-test or Nemenyi procedure was considered \(p\). The results were considered significant when the \(p\) values were below 0.05. The statistical analysis based on the t-test and Nemenyi procedure indicated that in some cases (but not all), significant differences existed between the fusion and signal modalities. In the MAHNOB-HCI data set, there was a significant difference between the first fusion method and the EEG in the MAHNOB-HCI data set in the valence space (\(p=0.005\)) and the second fusion method and the EEG in the MAHNOB-HCI data set in the valence space (\(p=0.004\)). For the DEAP data set, no significant difference was observed. In the online experiment, there was a significant difference between the first fusion method and the facial expressions in the valence space (\(p=0.012\)). Additionally, for the second fusion method versus the facial expression, there was a difference in the valence  
In terms of the computational cost, the proposed CNN model contains 1,019,554 parameters, and it took 0.0647 s to conduct one forward analysis on a single sample. For the training, it took about 6 h and 30 min in the pre-training step. In the fine tuning step, it took 4-6 min for each subject. All the experiments were conducted with a GeForce GTX 950.
## 3 Results
### Switching Transient Voltages  
#### 3.2.1 Field Test Overview  
Series of switching transient voltages were measured by a portable measuring system during the commissioning of 500 kV middle Tibet power grid. In this power grid, the transmission lines are mostly longer than 150 km with an average altitude beyond 3000 m. The operations for the transient voltage measurement mainly include:  
1. switching on the no-load long overhead transmission line;
2. switching on the no-load power transformer in the substation;
3. switching off the no-load power transformer in the substation.  
For the operation (1), the sensors were arranged under the end terminal of the transmission lines and the sensors were arranged under the entrance conductors of the transformers for the operation (2) and (3).
## 3 Validation of the Proposed Method
### Analysis Results of a Specific Case Study  
#### 3.1.1 Soil Conditions and Pile Properties  
A full-scale test program realized by [PERSON] et al. [43] and set up in Taiwan, was used to evaluate the effects induced by the installation technology on the soil properties. Before installation, eight boreholes and eight SPT tests were performed, up to a depth of 80 m below the ground level (GL). Three CPT tests and two DMT tests were also conducted. The two CPT tests included the measurement of the shear wave velocity (SCPT). After installing the piles, but before the load tests, three DMT tests and three CPT tests were carried out. A comparison of the results obtained highlighted the most evident effects of the installation on soil properties occurring within the first 15 m of depth. The soil at the site, on the basis of samples and laboratory tests, was generally classified as silty sand (SM in the USCS classification) or silt (ML), with occasional layers of silty clay (CL). The water table was located at approximately 1 m below the ground level, and did not vary significantly during the tests. In [PERSON] et al. [43] are fully presented the CPT, SPT and \(G_{max}\) data profiles. The single bored pile had a diameter \(D\) equal to 1500 mm, a length, \(L\), of 34.9 m and an intact flexural rigidity, \(EI\), equal to 6.86 G\(\mathrm{N}\mathrm{m}^{2}\). Pile properties are summarized in Table 2.  
Figure 8: Comparison of measured and computed horizontal loads at the same displacement level: \(y/D=0.5\)–1.5%; \(y/D=2\)–3%; \(y/D=5\)–6%; \(y/D=9\)–10%.
## 5 Simulation and Experimental Verification
### Experimental Results
As shown in Figure 16, when the load \(R\) is changed from 50 \(\Omega\) to 45.4 \(\Omega\), the overshoot of the output voltage with the proposed VDCC method is smaller than that with the traditional constant compensation method. Figures 15 and 16 prove that the proposed VDCC method has superior transient performance against input voltage and load disturbances. Figure 14: Starting process experimental waveforms: (**a**) with the traditional constant compensation control method; (**b**) with the proposed VDCC method. Figure 13: The steady-state experimental waveforms with the proposed VDCC method: (**a**) \(U_{\text{in}}\) = 80 V, \(R\) = 50 \(\Omega\); (**b**) \(U_{\text{in}}\) = 100 V, \(R\) = 50 \(\Omega\); (**c**) \(U_{\text{in}}\) = 130 V, \(R\) = 50 \(\Omega\); (**d**) \(U_{\text{in}}\) = 160 V, \(R\) = 45.4 \(\Omega\).
# Derivation of New Staggered Compact Schemes with Application to Navier-Stokes Equations
## 5 Results  
In the following sections, two simulations, aimed at evaluating different performances of the new schemes, are presented. The first test case is an analytical solution of the Navier-Stokes equations, targeted at assessing the theoretically predicted order of accuracy of the proposed method on a Navier-Stokes solver. The second one is a double-mixing layer flow, which, although spatially periodic, presents creation of smaller scales from a smooth initial condition, and is hence targeted at evaluating the resolving capabilities of the method.
## 3 Results
### Purification of Crude Xylose Extract
#### 3.8.2 Analysis Results of Xylose Purity before and after Resin Treatment  
Table 6 shows the main components in the xylose extract after the treatments with D301 and Dowex 50 wx4. The adsorption and desorption with D301 increased the xylose content to 86.34% with a recovery over 96.8%. The arabinose and glucose contents in the treated extract were 7.77% and 1.90%, respectively, and formic acid was almost completely removed. These results suggest that D301 can be applied to the purification of xylose extract prepared by the formic acid treatment of lignocellulosic. [PERSON] et al. (2019) also reported the high-efficiency removal of acetic acid and furfural from the hydrolysate of birch for the purification of xylose that gave a xylose purity and recovery up to 90% and 99%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Resin Type**} & \multicolumn{4}{c}{**Content (mg/g)**} \\ \cline{2-5}  & **Glu** & **Ara** & **Xyl** & **Formic Acid** \\ \hline D201 & 0.13 \(\pm\) 0.00 b,c & 0.68 \(\pm\) 0.07 a & 8.13 \(\pm\) 0.06 a & 36.80 \(\pm\) 0.63 c \\ \hline D301 & 0.28 \(\pm\) 0.09 a & 0.59 \(\pm\) 0.00 b & 7.68 \(\pm  
Cation exchange resin has a good adsorption capacity for arabinose [36]. As shown in Figure 8, the extract treated with Dowex 50 wx4 resin showed a single peak of xylose. The xylose content significantly increased to 95.01% (Table 6) and the arabinose content decreased to 1.7%. The contents of glucose and other monosaccharides also significantly reduced. These results suggest the good separation efficiency of the resin for the monosaccharides, and Dowex 50 wx4 cation exchange resin can be used to remove saccharide impurities from the xylose extract obtained by the formic acid treatment of _M. Lutarioriparia_ straw.
# Lactic Acid Bacteria Co-Encapsulated with Lactobionic Acid:
## 2 Materials and Methods  
### Probiotic Microorganism, Growth Conditions and Lactic Acid Production  
_Lactobacillus paracasei_ CBA L74 (patented and provided by Heinz Italia S.p.A) was used as the probiotic microorganism. This strain is a Gram-positive homo-fermentative, and facultative anaerobic bacteria. It was stored at \(-20\)\({}^{\circ}\)C and revitalized in 10 mL of animal-free broth (AFB) (20 g/L Bacto Yeast Extract (BD Biosciences, Milan, Italy), 0.5 g/L MgSO\({}_{4}\) (Sigma-Aldrich, Milan, Italy), 50 g/L glucose (Sigma-Aldrich), 0.5 g/L citric acid (Sigma-Aldrich)) by incubation at 37 \({}^{\circ}\)C for 24 h. The bacterial load reached after revitalization was approximately 8 log\({}_{10}\) CFU/mL.  
To test whether _L. paracasei_ is able to use LBA as a substrate source, fermentation tests on three different AFB (different in terms of carbon source) were carried out. Briefly the revitalized strain was centrifuged (1600 rpm for 10 min), the supernatant discharged, and the pellet resuspended in 40 mL of fresh _AFB_ and incubated at 37 \({}^{\circ}\)C for 72 h. To verify the ability of the strain to use a carbon source other than glucose, three AFBs were tested: AFB-G, in which the carbon source was glucose; AFB-L in which the source was LBA; and AFB-GL, in which the source was a mix of the two (1:1 ratio) (all were purchased by Sigma-Aldrich). An initial bacterial load of approximately 6 log\({}_{10}\) CFU/mL was observed for all. To monitor fermentation, samples were taken at the start of fermentation (T0) and after 2 (T2), 18 (T18), 24 (T24), 48 (T48), and 72 (T72) h from inoculation. Samples were analysed for microbial growth and lactic acid production. After serial dilutions, samples were sowed on MRS agar (Sigma-Aldrich) plates, then incubated at 37 \({}^{\circ}\)C for 48 h. The lactic acid produced during fermentation was determined by high performance liquid chromatography (HPLC) (Agilent Technologies 1100), equipped with a C18 column (Agilent Zorbax C18 column--4.6 mm \(\times\) 150 mm and a pore size of 80A), with visible/UV detector. The eluent was 0.1 M NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\) aqueous solution at pH of 2.7 with a flow rate of 0.8 mL/min; the detection wavelength and temperature were set at 218 nm and 30 \({}^{\circ}\)C, respectively.